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AI: Leadership & Strategy
AI: Data & Governance
AI: Use cases
Team development
Misc.
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 1. India's ranking in the Stanford AI Index 2025  2. Analysis of India's relative AI strengths and weaknesses vs. USA and China India ranks 4th globally in the AI Index (figure 1) with a score of 25.54, placing it behind the US (1st, 70.06) and China (2nd, 40.17). However, a comparative analysis of India's AI strengths and weaknesses (figure 2) reveals that there are still major concerns and problems for her to solve to be able to compete with global AI leaders.
Strengths for India
Weaknesses for India
Conclusion India shows potential, particularly in leveraging its diversity, policy focus, and growing educational base for AI. However, critical gaps in infrastructure and responsible AI practices, along with translating R&D into economic gains, are major hurdles compared to global leaders like the US and China. AI Strategy & Training for Executives The gap between India's AI potential and its current infrastructural/ethical maturity requires astute leadership. The winners will be those who can strategically:
Leading effectively in the age of AI, particularly Generative AI, requires specific strategic understanding. If you would like to equip your executive team with the knowledge to make confident decisions, manage risks, and drive successful AI integration, reach out for custom AI training proposals - [email protected]. Related blogs  Introduction: From Buzzword to Bottom Line
Generative AI (GenAI) is no longer a futuristic concept whispered in tech circles; it's a powerful force reshaping industries and fundamentally altering how businesses operate. GenAI has decisively moved "from buzzword to bottom line." Early adopters are reporting significant productivity gains – customer service teams slashing response times, marketing generating months of content in days, engineering accelerating coding, and back offices becoming vastly more efficient. Some top performers even attribute over 10% of their earnings to GenAI implementations. The potential is undeniable. But harnessing this potential requires more than just plugging into the latest Large Language Model (LLM). Building sustainable, trusted, and value-generating AI capabilities within an enterprise is a complex journey. It demands a clear strategy, robust foundations, and crucially, a workforce equipped with the right skills and understanding. Without addressing the human element – the knowledge gap across all levels of the organisation – even the most sophisticated AI tools will fail to deliver on their promise. This guide, drawing insights from strategic reports and real-world experience, outlines the key stages of developing a successful enterprise GenAI strategy, emphasizing why targeted corporate training is not just beneficial, but essential at every step. The Winning Formula: A Methodical, Phased Approach The path to success is methodical: "identify high-impact use cases, build strong foundations, and scale what works." This journey typically unfolds across four key stages, underpinned by an iterative cycle of improvement. Stage 1: Develop Your AI Strategy – Laying the Foundation This initial phase (often the first 1-3 months) is about establishing the fundamental framework. Rushing this stage leads to common failure points: misaligned governance, crippling technical debt, and critical talent gaps. Success requires a three-dimensional focus: People, Process, and Technology. 1. People Executive Alignment & Sponsorship: Getting buy-in isn't enough. Leaders need a strategic vision tying AI to clear business outcomes (productivity, growth). They must understand AI's potential and limitations to provide realistic guidance. Training Need: Executive AI Briefings are crucial here, demystifying GenAI, outlining strategic opportunities/risks, and fostering informed sponsorship. Governance & Oversight: Establishing an AI review board, ethical guidelines, and transparent evaluation processes cannot be an afterthought. Trust is built on responsible foundations. Training Need: Governance teams need specialized training on AI ethics, bias detection, model evaluation principles, and regulatory compliance implications. 2. Process Pilot Selection: Avoid tackling the biggest challenges first. Identify pilots offering demonstrable value quickly, with enthusiastic sponsors, available data, and manageable compliance. Focus on addressing real friction points. Training Need: Business leaders and managers need training to identify high-potential, LLM-suitable use cases within their domains and understand the criteria for a successful pilot. Scaling Framework: Define clear "graduation criteria" (performance thresholds, operational readiness, risk management) for moving pilots to broader deployment. Training Need: Project managers and strategists need skills in defining AI-specific KPIs and operational readiness checks. 3. Technology Technical Foundation: Evaluate existing infrastructure, data architecture maturity, integration capabilities, and tooling through an "AI lens." Training Need: IT and data teams require upskilling to understand the specific infrastructural demands of AI development and deployment (e.g., GPUs, vector databases, MLOps). Data Governance: High-quality, accessible, compliant data is non-negotiable. This requires sophisticated governance and data quality management. Training Need: Data professionals need advanced training on data pipelines, quality checks, and governance frameworks specifically for AI. Stage 2: Create Business Value – Identifying and Proving Potential Once the strategy is outlined (Months 4-6, typically), the focus shifts to identifying specific use cases and demonstrating value through well-chosen pilots. Identifying Pilot Use Cases: The best initial projects leverage core LLM strengths (unstructured data processing, content classification/generation) but carry low security or operational risk. They need abundant, accessible data and measurable success metrics tied to business indicators (reduced processing time, improved accuracy, etc.). Defining Success Criteria: Move beyond vague goals. Success metrics must be Specific, Measurable, Aligned with business objectives, and Time-bound (SMART). You can find excellent examples across use cases like ticket routing, content moderation, chatbots, code generation, and data analysis. Choosing the Right Model: Consider the trade-offs between intelligence, speed, cost, and context window size based on the specific task. Training Need: Teams selecting models need foundational training on understanding these trade-offs and how different models suit different business needs and budgets. Stage 3: Build for Production – From Concept to Reality This stage involves turning the chosen use case and model into a reliable, scalable application. Prompt Engineering: It is strongly advisable to invest in prompt engineering as a key skill. Well-crafted prompts can significantly improve model capabilities, often more quickly and cost-effectively than fine-tuning. This involves structuring prompts effectively (task, role, background data, rules, examples, formatting). Training Need: Dedicated prompt engineering training is crucial for technical teams and even power users to maximize model performance without resorting to costly fine-tuning prematurely. Evaluation: Rigorous evaluation is key to iteration. It is recommended to perform detailed, specific, automatable tests (potentially using LLMs as judges), run frequently. Side-by-side comparisons, quality grading, and prompt versioning are vital. Training Need: Data scientists and ML engineers require training on robust evaluation methodologies, understanding metrics, and potentially leveraging proprietary tools Optimization: Techniques like Few-Shot examples (providing examples in the prompt) and Chain of Thought (CoT) prompting (letting the model "think step-by-step") can significantly improve output quality and accuracy. Training Need: Applying these optimization techniques effectively requires specific training for those building the AI applications. Stage 4: Deploy – Scaling and Operationalizing Once an application runs smoothly end-to-end, it's time for production deployment (Months 13+ for broad adoption). Progressive Rollout: Don't replace old systems immediately. Use progressive rollouts, A/B testing, and design user-friendly human feedback loops. LLMOps (Deploying with LLM Ops): Operationalizing LLMs requires specific practices (LLMOps), a subset of MLOps. There are five best practices: 1. Robust Monitoring & Observability: Track basic metrics (latency, errors) and LLM-specific ones (token usage, output quality). 2. Systematic Prompt Management: Version control, testing, documentation for prompts. 3. Security & Compliance by Design: Access controls, content filtering, data privacy measures from the start. 4. Scalable Infrastructure & Cost Management: Balance scalability with cost efficiency (caching, right-sizing models, token optimisation). 5. Continuous Quality Assurance: Regular testing, hallucination monitoring, user feedback loops. Training Need: Dedicated MLOps / LLMOps training* is essential for DevOps and ML engineering teams responsible for deploying and maintaining these systems reliably and cost-effectively. The Undeniable Need for Corporate AI Training Across All Levels A recurring theme throughout industry reports (like BCG citing talent shortage as the #1 challenge), is the critical need for AI competencies at every level of the organisation: 1. C-Suite Executives: Need strategic vision. They require training focused on understanding AI's potential and risks, identifying strategic opportunities, asking the right questions, and championing responsible AI governance.** Generic AI knowledge isn't enough; they need tailored insights relevant to their industry and business goals. 2. Managers & Team Leads: Need skills to guide transformation. Training should focus on identifying practical use cases within their teams, managing AI implementation projects, interpreting AI performance metrics, leading change management, and fostering collaboration between technical and non-technical staff. 3. Individual Contributors: Need practical tool proficiency. Training should equip them to use specific AI tools effectively and safely, understand basic prompt techniques, provide valuable feedback for model improvement, and be aware of ethical considerations and data privacy. 4. Technical Teams (Engineers, Data Scientists, IT): Need deep, specialized skills. This requires ongoing, in-depth training on advanced prompt engineering, fine-tuning techniques, LLMOps, model evaluation methodologies, AI security best practices, and integrating AI with existing systems. Without this multi-layered training approach, organizations risk:
Partnering for Success: Your AI Training Journey Building a successful Generative AI strategy is a marathon, not a sprint. It requires a clear roadmap, robust technology, strong governance, and, most importantly, empowered people. Generic, off-the-shelf training often falls short for the specific needs of enterprise transformation. As an expert in AI and corporate training, I help organizations navigate this complex landscape. From executive briefings that shape strategic vision to hands-on workshops that build practical skills for technical teams and business users, tailored training programs are designed to accelerate your AI adoption journey responsibly and effectively. Ready to move beyond the buzzword and build real, trusted AI capabilities? Let's discuss how targeted training can become the cornerstone of your enterprise Generative AI strategy. Please feel free to Connect to discuss your organisation's AI Training requirements.  Generative AI has exploded from a niche technological curiosity into a boardroom imperative. The hype is undeniable, but savvy CXOs across the C-suite are rapidly moving beyond fascination to practical application. They aren't just asking "What is Gen AI?" anymore; they're strategically deploying it to drive value, enhance decision-making, and reshape their organizations.
Based on recent insights from leading consultancies and publications like McKinsey, PwC, Gartner, Forbes, Harvard Business Review, and others, a clear picture emerges: CXOs view Gen AI not merely as a tool for automation, but as a powerful augmenter of strategic capabilities. It's becoming a co-pilot for leadership, helping navigate complexity and unlock new avenues for growth and efficiency. So, how specifically are top executives leveraging this transformative technology? 1. Augmenting Strategic Planning and Decision-Making This is perhaps the most significant area where CXOs are personally engaging with Gen AI. Instead of solely relying on traditional reports and human analysis, they are using Gen AI to:
2. Driving Operational Excellence and Productivity While strategic insight is key, the immediate value proposition for many lies in efficiency gains. CXOs are championing the use of Gen AI to:
3. Revolutionizing Customer Engagement and Marketing CMOs and Chief Customer Officers are leveraging Gen AI to create more personalized and effective interactions:
4. Accelerating Innovation and R&D Beyond optimizing current operations, CXOs see Gen AI's potential to fuel future breakthroughs:
The CXO's Role: Leading the Charge Responsibly Crucially, the effective use of Gen AI isn't just about deploying the technology; it's about leadership. The articles consistently emphasize several key CXO responsibilities:
Getting Started: The Imperative to Act The consensus across sources is clear: waiting is not an option. While a cautious approach is necessary regarding risks, CXOs are urged to:
Conclusion Generative AI is far more than a technological trend; it's a fundamental shift impacting how businesses operate and compete. For CXOs, it offers an unprecedented opportunity to enhance strategic thinking, boost operational efficiency, deepen customer relationships, and foster innovation. The leaders who are actively experimenting, thoughtfully integrating Gen AI into their workflows, and championing its responsible adoption are not just keeping pace – they are positioning their organizations to lead in the rapidly evolving landscape of the future. The era of the AI-augmented CXO has arrived. References
 1. Executive Summary: Indian enterprises are at the forefront of artificial intelligence (AI) adoption, demonstrating a greater inclination towards integrating this technology compared to global counterparts 1. Reports indicate that a significant majority of Indian businesses are not only aware of AI but are actively prioritizing its implementation in their strategies for 2025 1. Notably, the adoption of Generative AI (GenAI) within Indian organizations stands at an impressive 94%, positioning India as a global leader in this rapidly evolving field 3. This proactive engagement with AI signifies a strong intent among Indian enterprises to leverage its transformative potential. However, despite this enthusiastic adoption, the journey from planning to successful execution appears to encounter hurdles. The fact that India leads globally in the number of AI projects across various stages but also reports the highest number of stalled or canceled projects suggests a potential impediment in translating AI ambitions into tangible outcomes 1. This bottleneck can be attributed, in part, to a significant gap in the availability of skilled talent capable of navigating the complexities of AI development and deployment. While Indian businesses show a high level of familiarity with AI, a substantial percentage report a lack of access to the necessary talent to fully realize their AI objectives 1. To fully capitalize on the promise of AI, particularly Generative AI, and to mitigate the risks associated with stalled projects, a strategic focus on upskilling the existing workforce is paramount. Indian enterprises are primarily deploying AI-led solutions with an aim to optimize their operations and achieve their strategic goals, including boosting profitability 1. Furthermore, enhancing customer experience and improving decision-making capabilities are key objectives driving AI investments 4. Achieving these business outcomes necessitates a workforce equipped with the specialized skills to effectively leverage AI technologies. Therefore, while India demonstrates a strong initial momentum in AI adoption, the sustained success and realization of its full potential hinges on a concerted effort to bridge the AI skills gap through targeted and comprehensive upskilling initiatives, especially in the domain of Generative AI.  2. The Current Landscape of AI Adoption in Indian Enterprises:
Indian enterprises exhibit a strong inclination towards adopting artificial intelligence (AI), positioning themselves ahead of global trends. A report indicates that 79% of Indian enterprises report awareness of AI, significantly higher than the global average of 59% 1. This heightened awareness translates into action, with India leading globally in the sheer number of AI projects spanning planning, development, and implementation stages 1. This proactive engagement is further underscored by a study revealing that India leads in AI adoption, with 30% of Indian enterprises already optimizing value through its usage, surpassing the global average of 26% 6. Notably, a remarkable 100% of companies in India are actively experimenting with AI, signaling a widespread commitment to exploring its potential 6. This trend is set to continue, as evidenced by findings that 51% of Indian enterprises have confirmed plans to rapidly expand their AI adoption, with an additional 32% intending a more gradual integration 4. The commitment from leadership is also evident, with 98% of Indian business leaders considering AI adoption a top priority for 2025 2. While the initial steps in AI adoption are widespread, the fact that only 30% of Indian companies report optimizing value from AI 6 suggests that many organizations are still in the nascent stages of realizing its full benefits, potentially due to challenges in scaling beyond initial experimentation or a lack of the necessary expertise to drive meaningful impact. Several key factors are propelling AI adoption within Indian enterprises. A significant 56% of these organizations prioritize operational optimization when deploying AI-led solutions, exceeding the global average 1. Moreover, 57% of executives in India view AI as essential for achieving their strategic goals and boosting profitability 1. Beyond internal efficiencies, enhancing customer experience and improving decision-making capabilities are identified as the top three business objectives driving AI investments 4. This focus on tangible business outcomes is further supported by a survey where 78% of respondents indicated their intention to invest in AI and machine learning (ML) to improve customer experience and engagement 7. Additionally, 72% aim to leverage AI and ML for discovering useful insights to improve decision-making, and 74% plan to use these technologies for innovation or improving products and services 7. The consistent emphasis on customer experience as a primary driver suggests a strategic orientation towards using AI to better understand and serve their clientele, which in turn implies a growing need for AI skills related to customer interaction and data analysis. AI adoption in India is not confined to a single sector but is gaining momentum across a diverse range of industries. Sectors such as healthcare, financial services, manufacturing, automotive, transportation, telecom, and aviation are witnessing an acceleration in AI integration 4. Furthermore, the fintech, software, and banking industries are highlighted as rapidly utilizing AI in their operations 6. This broad-based adoption indicates a widespread recognition of AI's transformative potential in addressing sector-specific challenges and driving innovation across the Indian economy. The inclusion of sectors like healthcare and transportation points to the application of AI in solving critical real-world problems, suggesting a demand for AI professionals who possess not only core AI skills but also domain-specific knowledge within these industries. In summary, Indian enterprises are exhibiting a strong and widespread commitment to AI adoption, surpassing global averages in awareness, experimentation, and the number of projects initiated. This adoption is primarily driven by the pursuit of operational efficiencies, enhanced customer experiences, and improved decision-making, with investments spanning across various key sectors of the Indian economy. However, the disparity between adoption rates and the realization of optimal value underscores the potential need for a skilled workforce to effectively translate AI investments into tangible business results. 3. Deep Dive into Generative AI Adoption: The adoption of Generative AI (GenAI) is experiencing a significant surge within Indian enterprises, positioning the nation as a frontrunner in this cutting-edge technology. A notable finding indicates that over 74% of executives in Indian organizations consider Generative AI as one of their critical business imperatives, highlighting its strategic importance for future investments 4. This prioritization is reflected in the remarkable statistic that 94% of Indian enterprises are already utilizing GenAI in at least one function, marking the highest adoption rate across 19 countries surveyed 3. Further evidence of this strong uptake comes from a survey revealing that 36% of Indian enterprises have already allocated budgets and commenced investing in GenAI, while an additional 24% are actively experimenting with its potential applications 8. This combination of active exploration, budgetary commitment, and widespread current usage underscores a robust and enthusiastic embrace of Generative AI within the Indian business landscape. The convergence of high current usage and active exploration for future investments suggests that Indian enterprises are not merely dabbling with GenAI but are strategically integrating it into their operational frameworks and long-term planning. Accompanying this rapid adoption is a substantial financial commitment towards AI technologies, including Generative AI. While a survey focused on overall AI and ML investments indicates that a significant 37% of major Indian businesses (with turnovers over Rs 5,000 crore) planned to increase their budgets by 25-30% or more in 2024 7, the trend of increasing investment is likely to persist into 2025 given the growing recognition of AI's value. Furthermore, projections estimate that venture capital and private equity investments in AI technologies within India are expected to reach $16 billion by 2025, with a considerable portion of this funding directed towards the burgeoning field of Generative AI 9. This significant influx of capital into the Indian AI ecosystem, particularly for GenAI, points towards a thriving environment for innovation and the development of advanced AI solutions. This robust investment landscape is likely to further accelerate the adoption of GenAI by providing enterprises with access to a wider array of sophisticated tools and specialized expertise. The applications of Generative AI within Indian enterprises are diverse and continue to expand across various sectors. Beyond the general exploration of GenAI and Agentic AI as popular technologies for future investment 4, specific use cases are emerging. For instance, IndiaMART, a B2B marketplace, successfully leveraged AWS's GenAI platform to translate and transliterate over five million product listings into Hindi, significantly enhancing their reach in non-English speaking regions 10. Apollo Tyres also utilized AWS's AI to achieve a 9% improvement in operational efficiency within their heavy engineering processes 10. Across industries, customer service, operations, and sales and marketing functions are leading the way in AI adoption, with AI-powered chat, voice, and regional language tools already making a tangible impact 8. Looking ahead, Generative AI holds the potential to revolutionize various aspects of business, including generating comprehensive scenario analyses for CEOs, identifying hidden market trends, simulating complex business strategies, and providing real-time competitive intelligence 9. Major Indian IT companies like TCS are integrating GenAI into strategic planning and project management, while Infosys is developing proprietary frameworks to enhance customer experience and internal operational efficiency 9. The transformative potential extends to sectors like healthcare (faster research analysis, improved drug adherence), manufacturing (predictive maintenance, yield optimization), retail (personalized offerings, dynamic pricing), banking (personalized experiences, risk analytics), insurance (risk assessment, claims processing), and education (student enablement, personalized learning) 11. The focus on regional language tools, exemplified by IndiaMART's use case and the government-led Bhashini project aimed at creating open-source Indic language datasets 8, highlights a unique and critical application of GenAI in addressing the linguistic diversity of India. This underscores a growing demand for expertise in natural language processing for Indian languages within the context of Generative AI. In conclusion, Generative AI adoption is experiencing remarkable growth in India, characterized by high current usage, substantial planned investments, and a wide range of applications across diverse sectors. The strategic importance placed on GenAI by business leaders, coupled with the focus on addressing India's linguistic diversity, positions the country as a significant player in the global GenAI landscape. 4. The Demand for AI Skills in the Enterprise: The rapid proliferation of artificial intelligence within Indian enterprises has ignited a significant demand for a diverse range of specialized skills. Among the specific technical skills that are highly sought after is general "AI expertise" 2. This broad category encompasses a deep understanding of AI principles, methodologies, and their practical application within a business context. Beyond this overarching expertise, technical proficiency in areas like software development is also in high demand, as AI solutions often require seamless integration with existing software infrastructure 2. More granularly, specific roles such as AI Specialists, who focus on designing, testing, and optimizing AI models for real-world applications, are increasingly essential 17. Similarly, Machine Learning Engineers, responsible for building and optimizing the systems that process vast amounts of data to train AI models, are experiencing heightened demand 17. The role of the Data Scientist, tasked with analyzing and interpreting complex data to inform organizational decision-making, remains critical in the AI-driven landscape 17. Furthermore, AI Research Scientists, who pioneer new AI models and techniques, are vital for driving innovation and pushing the boundaries of AI capabilities 17. The demand for Artificial Intelligence and Machine Learning Engineers is consistently highlighted as a top technological job, requiring proficiency in programming languages like Python, deep learning frameworks such as TensorFlow and PyTorch, and Natural Language Processing (NLP) techniques 18. Cloud Computing Specialists are also in high demand, as the deployment and management of AI solutions often rely on cloud-based platforms 18. Essential skills within the AI/ML domain further include a strong foundation in machine learning basics and the ability to effectively interpret and display complex data through data visualization techniques 19. A comprehensive understanding of machine learning algorithms, deep learning frameworks, neural networks, Natural Language Processing (including pre-trained models like BERT and GPT), Computer Vision, and the principles of Data Science and Big Data (including tools like Hadoop and Spark) are all crucial skill areas in the current AI job market 20. Notably, Python programming is considered a fundamental skill, with a vast majority of AI roles in India requiring proficiency in this language 21. While technical expertise forms the bedrock of AI capabilities, the importance of complementary soft skills is increasingly recognized within Indian enterprises. Along with technical proficiencies, soft skills such as communication and problem-solving are in high demand, as AI projects often involve cross-functional teams and require the ability to articulate complex technical concepts to non-technical stakeholders 2. In fact, learning and development professionals in India overwhelmingly agree that soft skills are becoming just as critical as technical expertise in the AI domain 2. Non-technical abilities like communication, problem-solving, and creativity are essential for workplace success in the age of AI 22. Additionally, critical thinking and leadership skills are also highly valued 22. Within the specific context of AI, the ability to translate complex data into actionable insights and communicate these findings effectively through data storytelling is considered a top AI skill 21. The emphasis on these soft skills underscores the collaborative and communicative nature of successful AI implementation, where bridging the gap between technical teams and business objectives is paramount. As Generative AI adoption continues its rapid ascent within Indian enterprises, the demand for skills specifically related to this technology is also on the rise. While not always explicitly categorized as "Generative AI skills," expertise in Natural Language Processing (NLP) is inherently crucial, given the text-generative capabilities of many GenAI models 18. Similarly, familiarity with and the ability to work effectively with large language models (LLMs) are becoming increasingly important 20. Beyond the foundational understanding of these models, practical skills such as prompt engineering – the art of crafting effective prompts to elicit desired outputs from GenAI models – are gaining significance. Furthermore, the ability to critically evaluate the outputs of GenAI models, understanding their nuances and potential biases, is essential for responsible and effective application. As Generative AI continues to evolve at a rapid pace, a commitment to continuous learning and upskilling will be particularly vital for professionals in this domain to maintain their relevance and effectiveness. In summary, the demand for AI skills in Indian enterprises encompasses a broad spectrum of technical expertise, including proficiency in programming languages like Python, deep learning frameworks, NLP, and data science. Alongside these technical skills, soft skills such as communication, problem-solving, and critical thinking are increasingly valued. Specifically within the realm of Generative AI, expertise in NLP, working with large language models, prompt engineering, and a commitment to continuous learning are becoming essential for professionals seeking to contribute to this rapidly advancing field. 5. The AI Skills Gap: Challenges and Implications: The ambitious pursuit of artificial intelligence by Indian enterprises is facing a significant headwind in the form of a growing skills gap. A considerable 31% of Indian businesses report a lack of access to the necessary talent to develop AI solutions 1. This shortage of skilled AI professionals is consistently identified as one of the primary challenges hindering the widespread adoption of AI within the country 4. Despite the strong drive for AI integration across industries, finding candidates with the right mix of AI and related skills remains a substantial obstacle 2. In fact, over half of HR professionals in India indicate that only half or fewer of the job applications they receive meet all the required qualifications for AI-related roles 2. This situation is further compounded by the finding that only 42.6% of Indian graduates are deemed employable, highlighting a widening chasm between the skills possessed by the graduating workforce and the demands of employers in emerging fields like AI and data analytics 22. The scale of this skills deficit is projected to escalate, with warnings that India could face a shortfall of over a million skilled AI professionals by 2027 23. Some estimates suggest that India will need as many as 1.5 million AI professionals by 2025 just to meet its digital economy goals 21. The consistent projection of a million-plus shortfall by multiple independent reports underscores the critical nature and urgency of addressing this AI skills gap, posing a substantial threat to India's aspirations in the global AI arena. Several interconnected factors contribute to this widening AI skills gap in India. Deficiencies within the education system are a key contributor, with a noted focus on theoretical knowledge often overshadowing the development of practical, industry-relevant skills needed for AI implementation 22. The rapid pace of technological advancement in the field of AI also necessitates continuous upskilling and reskilling of the workforce, a challenge that many individuals and organizations are still grappling with 22. Furthermore, there is a perceived lack of readily available talent possessing the specific skills required for the effective deployment and scaling of AI solutions within enterprise environments 1. While organizations are actively engaging in both hiring new AI professionals and retraining their existing employees to acquire AI-related skills 26, the sheer magnitude of the projected shortfall suggests that current efforts may not be sufficient to meet the rapidly growing demand. The difficulty reported by a significant percentage of Indian businesses in rolling out developed AI solutions 1 could also be indicative of a gap in the practical implementation skills needed to translate AI models from development to real-world application. The implications of this significant AI skills gap for Indian enterprises and the nation's AI ambitions are considerable. Many organizations are already experiencing challenges in transitioning their AI projects from the planning stages to successful execution, directly attributable to the lack of necessary skills within their teams 1. The high number of stalled or canceled AI projects in India, despite the country leading in project initiation, could be a direct consequence of insufficient skilled personnel to navigate the complexities of AI development and deployment 1. The widening skills gap poses a clear obstruction to the broader adoption of AI across various industries, potentially slowing down the pace of innovation and hindering the realization of the economic benefits that AI promises 23. Perhaps more significantly, the projected shortfall of over a million skilled AI professionals by 2027 jeopardizes India's unique opportunity to position itself as a global hub for AI talent, potentially impacting its long-term competitiveness in the global technology landscape 23. The inability to cultivate a sufficiently skilled AI workforce could have a ripple effect on the national economy, limiting India's capacity to fully capitalize on the transformative power of artificial intelligence. In conclusion, India faces a critical and growing AI skills gap, with projections indicating a shortfall of over a million professionals within the next few years. This deficit, stemming from educational limitations and the rapid evolution of AI, presents a major obstacle to the successful adoption and scaling of AI within Indian enterprises, potentially impeding their growth and undermining India's aspirations to become a global leader in the field of artificial intelligence. 6. Why Upskilling in Generative AI is Crucial for Enterprise Success: In the rapidly evolving technological landscape, upskilling employees in Generative AI is no longer an optional initiative but a fundamental necessity for Indian enterprises aiming for sustained success and competitive advantage. The potential of GenAI to drive significant productivity gains across various sectors is well-documented. Reports suggest that GenAI has the capacity to boost overall productivity, impacting millions of workers and redefining the future of work 8. Specific projections indicate substantial productivity increases in key areas such as call center management, software development, content creation, customer service, and sales and marketing 15. Real-world examples further underscore this point, with companies like Apollo Tyres achieving notable productivity improvements through the strategic application of AI 10. Estimates suggest that GenAI could unlock a substantial amount of productive capacity within the Indian economy, highlighting its potential for widespread efficiency enhancements 27. This ability to automate routine tasks, augment human capabilities with advanced analytical tools, and streamline workflows empowers employees to accomplish more efficiently, leading to tangible improvements in operational efficiency and overall productivity 11. The projected percentage increases in productivity across diverse roles provide compelling quantitative evidence for the value of investing in GenAI upskilling initiatives. Beyond enhancing current operations, a workforce proficient in Generative AI is a catalyst for fostering innovation and the development of entirely new business models. As AI technologies become more accessible and cost-effective, their transformative impact is expected to redefine industries and spur innovation across the board 4. Leading Indian enterprises are already moving beyond simply using AI for productivity gains and are actively exploring its potential to reshape their core business models and invent novel approaches to value creation 6. GenAI's capabilities in areas like personalized offerings in retail and accelerated drug discovery in healthcare hint at the potential for creating entirely new products and services 11. Moreover, GenAI can unlock new revenue streams for businesses by enabling them to offer innovative solutions and cater to previously unmet market needs 13. The ability of GenAI to assist in innovative product design further underscores its role in driving creative output and market differentiation 14. This strategic shift from focusing solely on optimizing existing processes to leveraging AI for the creation of new value streams signifies a deeper understanding of its transformative potential, necessitating a workforce equipped with the skills to envision and implement these innovative applications. In an increasingly digital and AI-driven marketplace, maintaining a competitive advantage hinges on the ability to adopt and effectively utilize advanced technologies like Generative AI. Businesses that fail to upskill their workforce in this critical area risk being outpaced by competitors who are leveraging GenAI for innovation, efficiency, and enhanced customer engagement 5. The growing interest among enterprises in exploring advanced technologies like GenAI underscores their awareness of its potential to provide a crucial competitive edge 5. While outsourcing AI solutions can offer a temporary fix, cultivating in-house expertise through comprehensive upskilling programs provides a more sustainable and strategically advantageous position in the long run 1. Investing in the development of GenAI skills within the organization not only enhances its current capabilities but also future-proofs its workforce, ensuring it remains agile and competitive in the face of rapid technological advancements. Furthermore, offering employees the opportunity to acquire skills in cutting-edge technologies like Generative AI can significantly enhance an enterprise's ability to attract and retain top talent. Professionals are increasingly seeking roles that provide opportunities for growth and development in future-proof skill areas. By investing in GenAI upskilling initiatives, companies can position themselves as innovative and forward-thinking employers, thereby bolstering their reputation and making them more desirable places to work. This can lead to a more engaged and skilled workforce, further contributing to the enterprise's overall success. In conclusion, upskilling in Generative AI is not merely beneficial but absolutely essential for Indian enterprises to thrive in the current and future business environment. It serves as a powerful engine for enhanced productivity and efficiency, fosters a culture of innovation and enables the development of new business models, is crucial for maintaining a strong competitive advantage, and plays a vital role in attracting and retaining top-tier talent, collectively paving the way for long-term organizational success. 7. The Business Case for Corporate Generative AI Training: The decision for Indian enterprises to invest in corporate Generative AI training is underpinned by a compelling business case that considers both the potential gains and the significant costs associated with inaction. One of the primary costs of not upskilling in GenAI is the multitude of missed opportunities. Enterprises that fail to embrace AI risk falling behind their competitors who are leveraging it for innovation and efficiency, leading to a loss of competitive edge and missed potential for growth and improved performance 5. The failure to address the skills shortage can transform what could be a game-changing AI opportunity into a significant setback for the organization 1. Furthermore, a lack of focus on upskilling could hinder India's overall progress in becoming a global AI talent hub, with broader negative consequences for the national economy 23. The inability to adopt and effectively utilize AI technologies due to a lack of skilled personnel translates directly into missed opportunities for innovation, market expansion, and revenue generation. Beyond lost potential, the absence of a skilled workforce in Generative AI can lead to increased operational inefficiencies and costs. Companies that do not adopt AI may experience lower productivity compared to those that do 5. Moreover, organizations struggling with skills gaps often face difficulties in moving their AI projects from planning to execution, potentially resulting in wasted investments and prolonged project timelines 1. The high number of stalled AI projects in India could be indicative of such inefficiencies stemming from a lack of skilled professionals to drive them to completion 1. The difficulty in rolling out developed AI solutions due to a lack of implementation skills further highlights the inefficiencies associated with an unequipped workforce 1. Relying on external consultants to fill the skills gap can also significantly increase operational costs, making in-house upskilling a more cost-effective long-term strategy. In a market where AI adoption, particularly GenAI, is rapidly becoming a standard practice, enterprises that do not prioritize upskilling in this domain face the significant risk of falling behind their competitors 5. Organizations that are agile and innovative in their adoption of GenAI will likely gain a considerable advantage in terms of efficiency, product development, and customer engagement, leaving those who lag behind at a distinct disadvantage. Furthermore, a lack of skilled professionals can exacerbate the inherent challenges associated with implementing and scaling AI solutions. These challenges include navigating ethical concerns, mitigating bias, ensuring legal and regulatory compliance, and addressing data privacy and governance issues 4. A well-trained workforce is crucial for effectively addressing these complexities and ensuring the responsible and successful deployment of AI technologies. The difficulties faced by Indian businesses in rolling out developed AI solutions 1 and the struggles in transitioning from planning to execution due to skills gaps 1 underscore the importance of having a skilled team to manage the entire lifecycle of AI projects. In conclusion, the business case for corporate Generative AI training is compelling. The cost of neglecting this crucial area includes not only the direct expenses of missed opportunities and operational inefficiencies but also the significant risk of falling behind competitors and struggling with the complexities of AI implementation. By proactively investing in upskilling their workforce in GenAI, Indian enterprises can mitigate these risks, capitalize on the numerous benefits that GenAI offers, and secure a stronger position in the increasingly AI-driven business landscape. 8. Case Studies of Successful AI Implementation in Indian Enterprises: Several Indian enterprises have already demonstrated the transformative power of artificial intelligence, including Generative AI, by strategically implementing it across various aspects of their operations. IndiaMART, a prominent B2B marketplace, serves as a compelling example of successful GenAI adoption. By leveraging AWS's Generative AI platform, IndiaMART was able to translate and transliterate over five million product listings into Hindi 10. This initiative significantly expanded their reach to customers in Tier II cities and beyond, where English is not the primary language, highlighting the potential of GenAI to overcome language barriers and tap into new markets. Apollo Tyres is another Indian company that has effectively utilized AI to enhance its operational efficiency. By implementing AWS's AI solutions in its heavy engineering division, Apollo Tyres achieved a notable 9% improvement in productivity 10. This demonstrates the tangible impact of AI in optimizing industrial processes and driving significant gains in output. The Mahindra Group, a large Indian multinational conglomerate, has also embraced AI to gain valuable business insights. While the specific details of their implementation are not elaborated, their use of AI to uncover hidden insights underscores the technology's potential for advanced analytics and strategic decision-making within complex organizations 3. Leading Indian IT services companies, Tata Consultancy Services (TCS) and Infosys, are at the forefront of integrating Generative AI into their strategic frameworks. TCS has incorporated GenAI into its strategic planning processes to optimize global project management and enhance client engagement strategies 9. Similarly, Infosys has developed its own proprietary Generative AI frameworks aimed at improving customer experience and boosting internal operational efficiency 9. These examples showcase the strategic-level adoption of GenAI by major players in the Indian technology sector. Further examples include Reliance Jio, which utilizes AI to optimize its 5G networks, resulting in reduced downtime and significant cost savings, and Tata Motors, which has implemented AI-powered quality control measures in its manufacturing processes, leading to a reduction in defects 21. These instances illustrate the diverse applications of AI in optimizing technology infrastructure and enhancing product quality within key Indian industries. These case studies collectively demonstrate the diverse and impactful ways in which AI, including Generative AI, is being successfully implemented by Indian enterprises across various sectors. They provide concrete evidence of the tangible benefits, such as expanded market reach, improved operational efficiency, enhanced customer experience, and strategic insights, that can be realized through the strategic adoption and effective utilization of AI technologies, thereby reinforcing the importance of investing in the necessary AI skills. 9. The Role of Corporate Training in Bridging the Generative AI Skills Gap: Corporate training programs are indispensable for effectively addressing the growing Generative AI skills gap within Indian enterprises. Given the significant shortage of skilled AI professionals 4, targeted training initiatives are crucial for equipping the existing workforce with the necessary competencies to navigate the complexities of GenAI development, implementation, and management 2. By investing in upskilling programs, companies can directly tackle the talent deficit and build a strong internal foundation of GenAI expertise. The emphasis on continuous upskilling is particularly vital in the rapidly evolving field of AI, ensuring that employees remain abreast of the latest advancements and best practices 2. Effective corporate training plays a pivotal role in facilitating the successful implementation and scaling of AI solutions within organizations 1. Well-designed programs provide employees with the practical skills and in-depth knowledge required to translate AI strategies into tangible outcomes. This includes not only the technical proficiency to work with GenAI models but also a comprehensive understanding of their business applications and the strategic considerations for their deployment. Training can bridge the gap between AI planning and actual execution, empowering employees to contribute meaningfully to AI initiatives 1. Furthermore, it enables employees to better understand customer needs, enhance engagement and productivity, and make data-driven decisions, all of which are crucial for successful AI adoption 28. As Generative AI becomes more integrated into business processes, addressing the ethical concerns and potential for bias associated with this technology is paramount. Corporate training provides a crucial platform for educating employees about responsible AI development and deployment practices 4. By raising awareness about ethical considerations, bias detection and mitigation techniques, and data privacy principles, training programs can help build trust in AI systems and ensure their ethical and equitable use within the enterprise. Investing in corporate Generative AI training is also a strategic move towards building a future-ready workforce 2. As AI continues to permeate various aspects of business operations, employees equipped with GenAI skills will be better positioned to adapt to the changing demands of the AI-driven economy. Customized learning platforms offered through corporate training can foster both broad and specialized skills, supporting the professional growth and long-term employability of the workforce 28. Government initiatives like iGOT Karmayogi further underscore the national importance of upskilling the workforce for a digital future powered by technologies like AI 16. In conclusion, corporate training is an indispensable element in bridging the Generative AI skills gap in India. It directly addresses the shortage of skilled professionals, facilitates the successful implementation and scaling of AI solutions, plays a critical role in mitigating ethical risks and biases, and is essential for building a workforce that is prepared for the future of work in an AI-driven world. 10. Conclusion and Recommendations: The analysis of the current landscape reveals that Indian enterprises are at the forefront of AI and particularly Generative AI adoption globally. This proactive engagement is driven by the pursuit of operational efficiencies, enhanced customer experiences, and improved decision-making across a diverse range of industries. However, a significant and growing AI skills gap, especially in the specialized area of Generative AI, poses a considerable challenge to realizing the full potential of these technological investments. Upskilling the existing workforce in Generative AI is not merely beneficial but crucial for driving enhanced productivity, fostering innovation, maintaining a competitive advantage in the rapidly evolving market, and attracting and retaining top talent. The business case for corporate Generative AI training is compelling, highlighting the substantial costs of missed opportunities, increased operational inefficiencies, the risk of falling behind competitors, and challenges in effectively implementing and scaling AI solutions if the skills gap is not addressed. Successful case studies from Indian enterprises like IndiaMART, Apollo Tyres, TCS, and Infosys demonstrate the tangible benefits that can be achieved through strategic AI implementation, further underscoring the value of investing in the necessary skills. Corporate training emerges as a fundamental pillar in bridging the Generative AI skills gap, not only by addressing the shortage of skilled professionals but also by facilitating successful AI implementation, mitigating ethical risks, and building a future-ready workforce. Based on these findings, the following recommendations are proposed for Indian enterprises:
References 1. Indian businesses ahead of global counterparts in AI adoption https://www.financialexpress.com/business/digital-transformation-indian-businesses-ahead-of-global-counterparts-in-ai-adoption-report-3693273/ 2. 98 pc of Indian business leaders speeding up AI adoption: Report https://cio.economictimes.indiatimes.com/news/artificial-intelligence/98-pc-of-indian-business-leaders-speeding-up-ai-adoption-report/118597160 3. 94% of Indian Enterprises Using GenAI, Highest Adoption Across the World - Varindia https://www.varindia.com/news/94-of-indian-enterprises-using-genai-highest-adoption-across-the-world 4. 59% of Indian enterprises plans to adopt AI: CII-Protiviti Report, https://www.indianchemicalnews.com/digitization/59-of-indian-enterprises-plans-to-adopt-ai-cii-protiviti-report-25240 5. Over 50% of surveyed Indian enterprises set to expand AI adoption: Report - Techcircle, https://www.techcircle.in/2025/02/21/over-50-of-surveyed-indian-enterprises-set-to-expand-ai-adoption-report/ 6. India Leads in AI Adoption, Says BCG Study - IndiaAI, https://indiaai.gov.in/news/india-leads-in-ai-adoption-says-bcg-study 7. Majority of big enterprises plan to enhance spending on AI, machine learning by 10-30% this year - ET CIO, https://cio.economictimes.indiatimes.com/news/artificial-intelligence/majority-of-big-enterprises-plan-to-enhance-spending-on-ai-machine-learning-by-10-30-this-year/112557682 8. 36% of Indian enterprises started budgeting for Gen AI: E&Y report https://cfo.economictimes.indiatimes.com/news/36-of-indian-enterprises-started-budgeting-for-gen-ai-ey-report/117628004 9. Generative AI for CEOs in India - BytePlus, https://www.byteplus.com/en/topic/393037 10. AI adoption high on agenda for Indian enterprises: AWS, https://yourstory.com/enterprise-story/2025/02/ai-adoption-aws-agenda-for-indian-enterprises 11. Generative AI: Strengths, Opportunities and Future Potential - IndiaAI, https://indiaai.gov.in/article/generative-ai-strengths-opportunities-and-future-potential 12. 7 Ways Generative AI Will Steer the Indian Market in 2024 - Olibr, https://olibr.com/blog/7-ways-generative-ai-will-steer-the-indian-market/ 13. "Is Gen AI the Key to Economic Growth in India?" - Global Governance Initiative, https://www.councilonsustainabledevelopment.org/post/is-gen-ai-the-key-to-economic-growth-in-india 14. Generative AI Will Redefine Business Operations – Generative AI Use Cases - iTech India, https://itechindia.co/us/blog/generative-ai-and-future-of-business-generative-ai-usecases/ 15. AI adoption in India may impact 38 million jobs: report - CoinGeek, https://coingeek.com/ai-adoption-in-india-may-impact-38-million-jobs-report/ 16. India's path to AI autonomy - Atlantic Council, https://www.atlanticcouncil.org/in-depth-research-reports/issue-brief/indias-path-to-ai-autonomy/ 17. 5 in-demand jobs requiring AI skills - India Today, https://www.indiatoday.in/education-today/featurephilia/story/5-in-demand-jobs-requiring-ai-skills-2607282-2024-09-27 18. The Top 5 In-Demand Technology Jobs in India, https://acarasolutions.in/blog/the-top-5-in-demand-technology-jobs-in-india/ 19. Top 10 Essential Tech Skills India Employers Seek in 2025 - Nucamp, https://www.nucamp.co/blog/coding-bootcamp-india-ind-top-10-essential-tech-skills-india-employers-seek-in-2025 20. Top Most In-Demand Artificial Intelligence AI Skills In 2025 - EC-Council University, https://www.eccu.edu/blog/what-are-the-most-in-demand-skills-in-artificial-intelligence-in-2025/ 21. AI Talent Development in India & Middle East - Cognitive Today :The New World of Machine Learning and Artificial Intelligence, https://www.cognitivetoday.com/2025/03/ai-talent-development-in-india-middle-east/ 22. India faces growing job crisis: Just 42.6% of graduates are employable - Business Standard, https://www.business-standard.com/industry/news/india-job-market-graduate-skill-gap-ai-automation-employability-2025-125021800437_1.html 23. India to face AI talent gap, shortfall of more than a million workers by 2027: Report, https://timesofindia.indiatimes.com/business/india-business/india-to-face-ai-talent-gap-shortfall-of-more-than-a-million-workers-by-2027-report/articleshow/118841853.cms 24. Massive AI talent gap looms in India; report predicts shortfall of over a million workers by 2027 - HR News, https://hr.economictimes.indiatimes.com/news/trends/massive-ai-talent-gap-looms-in-india-report-predicts-shortfall-of-over-a-million-workers-by-2027/118845015 25. India may face an AI talent shortfall of over 1 million by 2027: Report - Business Standard, https://www.business-standard.com/industry/news/india-may-face-an-ai-talent-shortfall-of-over-1-million-by-2027-report-125031000484_1.html 26. The State of AI in 2025: Global survey - McKinsey & Company, https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai 27. The Economic Impact of Generative AI: - Access Partnership, https://accesspartnership.com/wp-content/uploads/2023/06/The-Economic-Impact-of-Generative-AI-The-Future-of-Work-in-the-India.pdf 28. Role of AI in Shaping Corporate Learning & Development 2025 - Disprz, https://disprz.ai/blog/ai-in-corporate-training 29. Launching a High-Accuracy Chatbot Using Generative AI Solutions on AWS with Megamedia, https://aws.amazon.com/solutions/case-studies/megamedia-case-study/ 30. The Role of AI in Corporate Training: 2025 Guide - Edstellar, https://www.edstellar.com/blog/ai-in-corporate-training 31. AI Adoption in Organizations: Unique Considerations for Change Leaders - wendy hirsch, https://wendyhirsch.com/blog/ai-adoption-challenges-for-organizations 32. Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities - MDPI, https://www.mdpi.com/2673-2688/6/1/10  The Unfortunate Reality of India’s AI efforts - #2 𝐢𝐧 𝐓𝐚𝐥𝐞𝐧𝐭 𝐛𝐮𝐭 𝐨𝐧𝐥𝐲 #68 𝐢𝐧 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞.
👉 While we should rightly celebrate our immense AI talent pool, we will undoubtedly fail to hold on to them if we do not invest in providing the appropriate infrastructure, operating environment, commercial ecosystem and a conducive culture for their professional growth in India. 👉 While US & China are the undisputed leaders in national-level AI infrastructure, it is perhaps not surprising to note that Singapore ranks #3 in AI infrastructure (and #6 in AI Talent). With a sustained long-term strategy and focus on developing its ‘people’ as their only natural resource, Singapore has consistently pioneered and led the way in harnessing its limited human resources to support its industry, society and economy. 👉 We can take a page out of Singapore’s AI playbook (e.g. AI Singapore) to scale our own AI infrastructure, R&D, commercial and government strategies and support our world-class talent in performing cutting-edge AI R&D in India. 👉 IndiaAI and other government organisations as well as private corporations, therefore, have an enormous challenge at their hands to develop India's AI capabilities at a global scale (more to come on this topic). Source of national AI rankings: The Global AI Index, 2024  What is India’s greatest asset in the global AI ecosystem? 𝐓𝐚𝐥𝐞𝐧𝐭
𝐈𝐧𝐝𝐢𝐚 𝐫𝐚𝐧𝐤𝐬 #2 𝐢𝐧 𝐭𝐞𝐫𝐦𝐬 𝐨𝐟 𝐀𝐈 𝐓𝐚𝐥𝐞𝐧𝐭, 𝐨𝐧𝐥𝐲 𝐛𝐞𝐡𝐢𝐧𝐝 𝐭𝐡𝐞 𝐔𝐒𝐀, while being ranked #10 overall (The Global AI Index, 2024). Let’s dive deeper - 1️⃣ Global optimism in India’s Talent “𝘐𝘯𝘥𝘪𝘢 𝘩𝘢𝘴 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘪𝘯𝘨𝘳𝘦𝘥𝘪𝘦𝘯𝘵𝘴 𝘵𝘰 𝘭𝘦𝘢𝘥 𝘵𝘩𝘦 𝘈𝘐 𝘳𝘦𝘷𝘰𝘭𝘶𝘵𝘪𝘰𝘯” - Jensen Huang, NVIDIA - “𝘐𝘯𝘥𝘪𝘢 𝘤𝘢𝘯 𝘭𝘦𝘢𝘥 𝘵𝘩𝘦 𝘈𝘐 𝘧𝘳𝘰𝘯𝘵𝘪𝘦𝘳” - Sundar Pichai, Google - “𝘐𝘯𝘥𝘪𝘢 𝘩𝘢𝘴 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘵𝘢𝘭𝘦𝘯𝘵𝘦𝘥 𝘱𝘦𝘰𝘱𝘭𝘦, 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘨𝘳𝘦𝘢𝘵 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴—𝘪𝘵 𝘩𝘢𝘴 𝘵𝘩𝘦 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘴 𝘵𝘰 𝘣𝘰𝘵𝘩 𝘵𝘳𝘢𝘪𝘯 𝘧𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯 𝘮𝘰𝘥𝘦𝘭𝘴 𝘢𝘯𝘥 𝘣𝘶𝘪𝘭𝘥 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴” - Andrew Ng, DeepLearning.ai India's young, capable and energetic workforce, gives us an edge that is partly due to our sheer demographic weight but also thanks to our strong network of higher education STEM institutions, and our global position as an IT outsourcing powerhouse. 2️⃣ AI Developers vs. Scientists We are particularly strong in our AI developer talent who are proficient in building generativeAI and LLM powered applications. However, in terms of highly specialised AI research scientists, India ranks only 24 (The Global AI Index, 2024). 3️⃣ AI Research Talent Churn Our AI Research Talent in particular is prone to churn. Due to the lack of a supporting infrastructure, R&D culture, commercial ecosystem, mentorship etc., a significant proportion of our talent opts out of AI research by: - Moving to industry to work on AI applications - Migrating to USA etc. for better AI research opportunities 4️⃣ Growing and Retaining India’s AI Talent In order to maintain our competitive edge in AI Talent, we need to continue investing in skill development. We not only need AI-native talent who can conduct research and build AI applications, but we also need our non-technical workforce to be adept in AI skills and tools that are critical for driving efficiency and productivity at work. This will not only result in economic gains for the country but also pave the way for future success - “𝘕𝘦𝘦𝘥 𝘵𝘰 𝘴𝘬𝘪𝘭𝘭, 𝘳𝘦-𝘴𝘬𝘪𝘭𝘭 𝘱𝘦𝘰𝘱𝘭𝘦 𝘧𝘰𝘳 𝘈𝘐-𝘥𝘳𝘪𝘷𝘦𝘯 𝘧𝘶𝘵𝘶𝘳𝘦” - 𝐏𝐌 𝐌𝐨𝐝𝐢 at AI Action Summit, Paris 2025 5️⃣ Conclusions I am personally optimistic about India’s AI potential only because of her Talent. My belief is substantiated by studies which show that India ranks 1st globally in AI skill penetration (Stanford AI Index 2024). Additionally, India also leads in AI skill penetration for Women with a penetration rate of 1.7. If we take the right steps in supporting and nurturing our talent and provide them with the necessary resources, infrastructure, ecosystem, mentorship, and foster a culture of meritocracy and research, we will not only be regarded as leaders in AI Talent but also as global leaders in AI implementation, innovation, and R&D.  What is India’s strength in AI? 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
India may be lagging behind other countries in terms of fundamental AI research but it punches above its weight when it comes to building AI applications - 1️⃣ Greater adoption of Application models vs. Foundational LLMs The number of downloads of models (on Hugging Face) focused on Indic use cases in the last month from today show up to a staggering ~90X greater adoption of smaller application models (largely developed by AI4Bhārat) vs. foundational LLMs (based on Sarvam's Sarvam-1 and Krutrim's Krutrim-2-instruct). These are the use cases for each of the Application models: - indictrans2-indic-en-1B: translation from 22 Indian languages to English - indic-bert: language model and embeddings for 12 Indian languages - indicBERtv2-MLM-only: multilingual language model for 23 languages - indictrans2-en-indic-1B: translation from English to 22 Indian languages - indic-sentence-bert-nli: sentence similarity across 10 Indian languages 👉 The application models are typically “small” models ranging from ~300M to ~1B parameters in size vs. the foundational LLMs that are 2 to 12B parameters in size. This also indicates that for solving India-specific use cases, we do not necessarily need “large” models; and the development of small, fine-tuned models on top of leading open-source LLMs from global companies is a good strategy to solve for niche domestic use cases. 2️⃣ India publishes ~2x more at Application vs. Theoretical AI Conferences Of the top 10 AI conferences, India publishes ~2 times more papers in conferences like AAAI and EMNLP that are more application focused vs. the more theory focused conferences like NeurIPS, ICML and ICLR (source: Mahajan, Bhasin & Aggarwal, 2024). 3️⃣ AI4Bharat's significant contribution to India's R&D capabilities The team at AI4Bhārat in collaboration with Microsoft India, Indian Institute of Technology, Madras, EkStep Foundation and others has done a stellar job in collecting, curating and processing local language datasets to unlock significant value for both public and private sector organisations. By using these datasets to fine-tune Transformer-based models like BERT & ALBERT, they have created models that often outperform models from global companies on niche NLP use cases. Additionally, this work has led to the formation of Sarvam as a venture-backed startup focused on the commercialisation of this research. 4️⃣ Growth of India's AI Startups The rise of generativeAI startups from India that are developing on top of the global foundational LLMs further highlights our strength in building AI applications. These startups are not only solving domestic use cases but also catering to global markets. 5️⃣ Conclusions India’s prowess in building AI applications is highly commendable. One way to make our mark on the global AI ecosystem is by standing on the shoulder of giants to build impactful products.  Can India build its own foundational LLMs? Yes
But who is using them? How much is their adoption? To find answers to these questions, I’ve sourced publicly available data from various sources as below: 1️⃣ Number of Downloads on Hugging Face Hugging Face is the de-facto platform for developers to download AI models and datasets. I’ve considered the number of downloads (as a proxy for usage and adoption) of leading, open-source LLMs from USA (from Meta), China (from DeepSeek AI & Alibaba Cloud), and India (from Sarvam & Krutrim, as the two most well capitalized Generative AI startups). The data shows that in the same time period of the last one month from today: - US: LLama’s 3.2-1B & 3.1-8B-instruct were downloaded ~11M & ~6M times - China: DeepSeek-R1 & Qwen2-VL-7B-instruct were downloaded ~4M & 1.5M times - India: Sarvam-1 & Krutrim-2-instruct (built on top of Mistral-NeMo 12B) were downloaded ~5k and ~1k times 👉 These numbers show that the adoption of our leading LLMs is 3 to 4 orders of magnitude less than the most popular LLMs from China and USA respectively. The absolute numbers might be slightly different as these LLMs are also available as APIs, on cloud platforms etc. but the overall trend may not be that different. 2️⃣ Number of forks of Github repositories Forking of Github repos represents a stronger sign of adoption by the developer community, and here also the picture is similar: - meta-llama has been forked ~9700 times - DeepSeek-v3 has been forked ~13800 times - DeepSeek-R1 has been forked ~10000 times - Qwen-VL has been forked 400 times - Krutrim-2-12B has been forked 6 times - Sarvam doesn’t have a dedicated repo for Sarvam-1 3️⃣ Listing in LLM Marketplaces Customer-centric LLM marketplaces like AWS BedRock also provide an indication of customer usage & adoption. While Meta’s LLama and DeepSeek-R1 models are supported, none of India’s LLMs are available. 4️⃣ Support from LLM inference engines LLM Inference engines like vLLM also provide signals about LLM adoption for production use cases. vllm currently supports Llama and Qwen models but again no Indian LLMs yet. 5️⃣ Conclusions Overall, the analysis indicates that Indian LLMs do not currently receive significant user interest and therefore their impact is far less than top, global LLMs. Our LLMs likely have a competitive advantage for domestic use cases focused on speech and language e.g. translation, document analysis, speech recognition etc. The market size of our domestic use cases may not be big enough to justify investment by global companies, but it clearly represents an area where indigenous LLM builders can distinguish themselves. Following my previous post on the poor trajectory of India’s AI research record at top AI conferences, these data further show that we are far from the cutting-edge of AI research and a lot of work needs to be done to raise the bar in terms of global adoption and impact.  Unfortunately No.
While India's contribution to AI papers at top AI conferences (including NeurIPS, ICLR, ICML, CVPR, EMNLP etc.) has remained flat over the last 10 years, China's contribution to the AI field, on the other hand, has dramatically increased and caught up with the USA during the same time period (Mahajan, Bhasin & Aggarwal, 2024). This period in the field of AI was marked by numerous innovations in Deep Learning for images, text, audio; Transfer Learning, Synthetic Data, Transformers to name a few. We witnessed the emergence of groundbreaking models such as BERT, GPT-1/2/3, Stable Diffusion etc., which eventually led to the development of ChatGPT and the advent of the current era of LLMs and GenerativeAI. India has missed the boat during this period and failed to proactively increase investment in R&D, infrastructure and capacity building for AI (our R&D budget is only ~0.65% of GDP vs. ~2.4% for China and ~3.5% for USA) as well as retain home-grown talent. There is no straightforward solution to India's AI R&D challenges. While are early signs of progress (e.g. AI4Bhārat, IndiaAI, BHASHINI), in order to truly turn the page and compete at the top of the global AI hierarchy, we need to execute robust AI investment, innovation and implementation strategies. (More to come on this topic) Introduction
The AI revolution is no longer a distant future—it’s reshaping industries today. By 2025, the global AI market is projected to reach $190 billion (Statista, 2023), with generative AI tools like ChatGPT and Midjourney contributing an estimated $4.4 trillion annually to the global economy (McKinsey, 2023). For tech professionals and organizations, this rapid evolution presents unparalleled opportunities but also demands strategic navigation. As an AI expert with a decade of experience working at Big Tech companies and scaling AI-first startups, I’ve witnessed firsthand the transformative power of well-executed AI strategies. This blog post distills actionable insights for:
Let’s explore how to turn AI’s potential into measurable results. Breaking into AI – A Blueprint for Early-Career Professionals The Skills That Matter in 2024 The AI job market is evolving beyond traditional coding expertise. While proficiency in Python and TensorFlow remains valuable, employers now prioritize three critical competencies: 1. Prompt Engineering: With generative AI tools like GPT4/o/o1-/o-3, Deepseek-R1, Claude Sonnet 3.5 etc., the ability to craft precise prompts is becoming a baseline skill. For example, a marketing analyst might use prompts like, “Generate 10 customer personas for a fintech app targeting Gen Z, including pain points and preferred channels.” 2. AI Literacy: 85% of hiring managers now require familiarity with responsible AI frameworks ([Deloitte, 2023](https://www2.deloitte.com)). This includes understanding bias mitigation and compliance with regulations like the EU AI Act. 3. Cross-Functional Collaboration: AI projects fail when technical teams operate in silos. Professionals who can translate business goals into technical requirements—and vice versa—are indispensable. Actionable Steps to Launch Your AI Career 1. Develop a "T-shaped" Skill Profile: Deepen expertise in machine learning (the vertical bar of the “T”) while broadening knowledge of business applications. For instance, learn how recommendation systems impact e-commerce conversion rates. 2. Build an AI Portfolio: Document projects that solve real-world problems. A compelling example: fine-tuning Meta’s Llama 2 model to summarize legal contracts, then deploying it via Hugging Face’s Inference API. 3. Leverage Micro-Credentials: Google’s [Generative AI Learning Path](https://cloud.google.com/blog/topics/training-certifications/new-generative-ai-training) and DeepLearning.AI’s short courses provide industry-recognized certifications that demonstrate proactive learning. From Individual Contributor to AI Leader – Strategies for Mid/Senior Professionals The Four Pillars of Effective AI Leadership Transitioning from technical execution to strategic leadership requires mastering these core areas: 1. Strategic Vision Alignment: Successful AI initiatives directly tie to organizational objectives. For example, a retail company might set the OKR: “Reduce supply chain forecasting errors by 40% using time-series AI models by Q3 2024.” 2. Risk Mitigation Frameworks: Generative AI models like GPT-4 can hallucinate inaccurate outputs. Leaders implement guardrails such as IBM’s [AI Ethics Toolkit](https://www.ibm.com), which includes bias detection algorithms and human-in-the-loop validation processes. 3. Stakeholder Buy-In: Use RACI matrices (Responsible, Accountable, Consulted, Informed) to clarify roles. For instance, when deploying a customer service chatbot, legal teams must be “Consulted” on compliance, while CX leads are “Accountable” for user satisfaction metrics. 4. ROI Measurement: Track metrics like inference latency (time to generate predictions) and model drift (performance degradation over time). One fintech client achieved a 41% improvement in fraud detection accuracy by combining XGBoost with transformer models, while reducing false positives by 22%. Building an AI-First Organization – A Playbook for Startups The AI Strategy Canvas 1. Problem Identification: Focus on high-impact “hair-on-fire” pain points. A logistics startup automated customs documentation—a manual 6-hour process—into a 2-minute task using GPT-4 and OCR. 2. Tool Selection Matrix: Compare open-source (e.g., Hugging Face’s LLMs) vs. enterprise solutions (Azure OpenAI). Key factors: data privacy requirements, scalability, and in-house technical maturity. 3. Implementation Phases: - Pilot (1-3 Months): Test viability with an 80/20 prototype. Example: A SaaS company used a low-code platform to build a churn prediction model with 82% accuracy using historical CRM data. - Scale (6-12 Months): Integrate models into CI/CD pipelines. One e-commerce client reduced deployment time from 14 days to 4 hours using AWS SageMaker. - Optimize (Ongoing): Conduct A/B tests between model versions. A/B testing showed that a hybrid CNN/Transformer model improved image recognition accuracy by 19% over pure CNN architectures. Generative AI in Action – Enterprise Case Studies Use Case 1: HR Transformation at a Fortune 500 Company Challenge: 45-day hiring cycles caused top candidates to accept competing offers. Solution: - GPT-4 drafted job descriptions optimized for DEI compliance - LangChain automated interview scoring using rubric-based grading - Custom embeddings matched candidates to team culture profiles Result: 33% faster hiring, 28% improvement in 12-month employee retention. Use Case 2: Supply Chain Optimization for E-Commerce Challenge: $2.3M annual loss from overstocked perishable goods. Solution: - Prophet time-series models forecasted regional demand - Fine-tuned LLMs analyzed social media trends for real-time demand sensing Result: 27% reduction in waste, 15% increase in fulfillment speed. Avoiding Common AI Adoption Pitfalls Mistake 1: Chasing Trends Without Alignment Example: A startup invested $500K in a metaverse AI chatbot despite having no metaverse strategy. Solution: Use a weighted decision matrix to evaluate tools against KPIs. Weight factors like ROI potential (30%), technical feasibility (25%), and strategic alignment (45%). Mistake 2: Ignoring Data Readiness Example: A bank’s customer churn model failed due to incomplete historical data. Solution: Conduct a data audit using frameworks like [O’Reilly’s Data Readiness Assessment](https://www.oreilly.com). Prioritize data labeling and governance. Mistake 3: Overlooking Change Management Example: A manufacturer’s warehouse staff rejected inventory robots. Solution: Apply the ADKAR framework (Awareness, Desire, Knowledge, Ability, Reinforcement). Trained “AI ambassadors” from frontline teams increased adoption by 63%. Conclusion The AI revolution rewards those who blend technical mastery with strategic execution. For professionals, this means evolving from coders to translators of business value. For organizations, success lies in treating AI as a core competency—not a buzzword. Three Principles for Sustained Success: 1. Learn Systematically: Dedicate 5 hours/week to AI upskilling through curated resources. 2. Experiment Fearlessly: Use sandbox environments to test tools like Anthropic’s Claude or Stability AI’s SDXL. 3. Collaborate Across Silos: Bridge the gap between technical teams (“What’s possible?”) and executives (“What’s profitable?”).  As artificial intelligence continues to reshape industries, the landscape of AI talent recruitment has evolved significantly. Based on my recent discussions with technical recruiters and industry leaders, I want to share comprehensive insights into the current state of AI recruitment, team structures, and what both companies and candidates should know about this rapidly evolving field.
The Modern AI Team Structure Today's AI teams are increasingly complex, organized along two primary dimensions: workflow-based and layer-based structures. This complexity reflects the maturing of AI as a field and the specialization required for different aspects of AI development and deployment. Core Team Components The modern AI team typically consists of three major divisions:
A crucial addition to this structure has been the emergence of AI-focused product managers who bridge the gap between technical capabilities and business requirements. Their role in identifying viable use cases and ensuring business alignment has become increasingly critical. Technical Interview Evolution The technical interview process for AI roles has become more sophisticated, reflecting the field's complexity. While traditional coding and system design rounds remain important, machine learning-specific assessments have become crucial:
For research positions, additional components typically include:
Engineering roles, while still requiring strong ML knowledge, place greater emphasis on deployment and optimization skills. What Drives the AI Talent Movement? Understanding what motivates AI talent is crucial for successful recruitment. The primary drivers I've observed include:
Staying Connected: Industry Networks and Resources The AI community remains highly connected through various channels: Major Conferences
Digital Platforms
The Rise of AI in Recruitment Ironically, AI itself is transforming the recruitment process. New tools and approaches include:
Effective Passive Talent Engagement Successful talent engagement strategies now include:
Portfolio Assessment and Beyond One crucial insight I've gained is the importance of looking beyond traditional metrics when assessing AI talent. While GitHub portfolios provide valuable insights, some highly capable candidates may not perform well in traditional interviews. This has led to a more holistic approach to candidate assessment, including:
Looking Ahead As the AI field continues to evolve, recruitment strategies must adapt. Companies need to focus on:
Conclusion The AI recruitment landscape continues to evolve rapidly, driven by technological advancement and changing candidate preferences. Success in this space requires a deep understanding of both technical requirements and human factors. Companies must stay agile in their recruitment approaches while maintaining high standards for technical excellence.  This image illustrates a significant trend in OpenAI's innovative work on large language models: the simultaneous reduction in costs and improvement in quality over time. This trend is crucial for AI product and business leaders to understand as it impacts strategic decision-making and competitive positioning. Key Insights:
Generative AI startups can capitalize on the trend of decreasing costs and improving quality to drive significant value for their customers. Here are some strategic approaches 1. Cost-Effective Solutions:
2. Enhanced Product Offerings:
3. Strategic Investment in R&D:
4. Operational Efficiency:
What is Agentic AI?
Agentic AI refers to a type of artificial intelligence that can independently act and make decisions in dynamic environments. AI agents go beyond traditional data processing on a wide variety of tasks that mimic humanlike intelligence. It encompasses both hardware agents, such as robots and autonomous vehicles, and software agents, such as conversational agents and chatbots. Agentic AI has recently regained prominence with the advent of [large language models](https://www.nvidia.com/en-us/glossary/data-science/large-language-models/), such as OpenAI's [GPT-4](https://openai.com/research/gpt-4) and Meta's [Llama 2](https://ai.meta.com/llama/). These powerful models are trained on massive amounts of data and can understand goals, automate work and complete complex tasks. Additionally, conversational agents, such as [ChatGPT](https://chat.openai.com/auth/login) and [Claude](https://claude.ai), which were developed using these large language models, can act on users' goals, gather relevant information from the internet (or relevant knowledge bases) and offer solutions in an iterative fashion. In this article, you'll learn more about agentic AI, its capabilities and use cases, and its future prospects and possibilities. Why You Need Agentic AI? Agentic AI is built on a few fundamental characteristics that include perceiving the environment, making decisions based on perceived data and continuously learning in order to continually improve. For instance, hardware-based agents are embedded with sensors for vision, sound, motion, heat and humidity. Through these sensors, agents can collect data and learn to make sense of their environment. A popular example is [Mars exploration rovers](https://www.enterpriseai.news/2021/04/01/how-nasa-is-using-ai-to-develop-the-next-generation-of-self-driving-planetary-rovers/) that operate autonomously and perceive their environment to achieve objectives, such as collecting soil and rock samples. To make decisions in these environments, agents process the perceptual information using intelligent models to select the most optimal course of action. This decision-making mirrors how the human brain processes information, starting from basic perception and building up to higher-level cognitive functions. Moreover, it's not enough for agents to just perceive and act if they don't continuously improve their intelligence capabilities. This self-learning ability powered by mechanisms such as [reinforcement learning](https://en.wikipedia.org/wiki/Reinforcement_learning) is an essential feature of agents. Use Cases for Agentic AI The following are a few scenarios where agentic AI really shines: Autonomous Vehicles Self-driving cars or [autonomous vehicles](https://www.embedded.com/the-role-of-artificial-intelligence-in-autonomous-vehicles) are already in production. Companies like Tesla, Uber and Waymo have pioneered their development and deployed the vehicles in real-world environments. Armed with multiple AI modules that operate on various sensors, including [LiDAR](https://en.wikipedia.org/wiki/Lidar), which helps vehicles sense and understand their surroundings, autonomous vehicles represent sophisticated AI agents that can perform complex navigational tasks without human intervention. Quality Control in Manufacturing AI agents also find multiple applications in traditional industries, like manufacturing, logistics and agriculture. For instance, modern warehouses are powered by AI agents that can sort, pick up and place items without any human supervision. In agriculture, automated fruit-picking robots can detect fruits that are ready to be picked and harvest them without any damage. Similarly, at different points in the supply chain, robots are used to identify defects and weed out poor-quality products. Contextual Conversational Agents ChatGPT has become one of the most popular conversational agents ever, gaining more than a million users in the first week of its launch. These agents can encode a very large context or sequences of words, providing personalized responses to users' queries and demonstrating human-level capabilities across several tasks and domains. [Autonomous agents, like Auto-GPT and BabyAGI](https://www.fastcompany.com/90880294/auto-gpt-and-babyagi-how-autonomous-agents-are-bringing-generative-ai-to-the-masses), represent another wave of AI agents inspired by large language models like GPT-4 that can automatically complete multiple tasks on their own. Creative Content Generation Large language models are typically based on the [transformer model architecture](https://arxiv.org/abs/1706.03762) and are usually trained to predict the next word or sequence of words using a [masked language model training objective](https://huggingface.co/docs/transformers/main/tasks/masked_language_modeling). Given a specific input or prompt, models can generate multiple sentences and paragraphs of text that are consistent with the user input. This has significantly impacted the field of writing and [creative content generation](https://zapier.com/blog/ai-content-creation). How Agentic AI Pushes the Limits of AI Conventional AI is rooted in domain-specific applications with limited ability to transfer their learnings from one task or domain to another. AI models that are adept at recognizing objects in images are not as capable in other domains like speech or language. To achieve true humanlike intelligence, AI needs to be adaptable across several tasks and domains. AI agents are a promising step in this direction as they combine perception, decision-making and self-learning abilities in dynamic environments (*eg* autonomous vehicles driving in the real world). While conventional AI may power some of the underlying capabilities, such as image segmentation and object detection, these capabilities cannot help the vehicle drive safely in real-world environments. This is where agentic AI shines. Limitations of Conventional AI Unlike agents that continually learn from interactions with their environment, conventional AI remains static. Conventional AI also struggles to operate effectively across various domains. In the following sections, you'll take a deeper look at some of the shortfalls of conventional AI and how agentic AI overcomes these challenges. Limited Learning Capacity Conventional AI models learn from data, and the knowledge that they learn during model training is confined to the specific data set and the domain. While some models are capable of doing well on related tasks with [transfer learning](https://en.wikipedia.org/wiki/Transfer_learning), the transferred knowledge is usually restricted to the same domain. For instance, models that are trained to identify sentiment in text are not capable of detecting sentiment in voices. This limited learning capacity of conventional AI models is a bottleneck in the development of humanlike intelligence. Meanwhile, agentic AI is endowed with self-learning capacity. Agents actively interact with their environment and constantly update their knowledge and capabilities. Narrow Task Focus Related to their limited learning capacity, another shortcoming of conventional AI is the narrow task or domain in which they excel. While some AI models are accurate when it comes to specific tasks and domains, they tend to significantly underperform when the nature of the data or the task changes. In comparison, AI agents can learn new tasks as needed. This makes them more versatile and adaptable than conventional AI models, which operate well in narrow task domains. Difficulty Handling Ambiguity Conventional AI also struggles to operate in ambiguous situations. For instance, a speech recognition model that is trained in American English may struggle to recognize British English accents. The difference in distribution between the data in the training set and the real-world test set evolves over time, and this results in the models being unable to handle novel and ambiguous inputs. AI agents are adept at handling ambiguity as they constantly sense their environment for changes and learn to respond to these changes. This feedback-based learning helps them adapt to novel or ambiguous features in their environment and be more robust and reliable than conventional AI models. High Maintenance and Rigidity Another downside of conventional AI is its high requirements for maintenance and the rigidity of its performance. Conventional AI models need to be constantly monitored, updated and retrained to better reflect the real-world data, or they'll struggle from [data drift](https://www.openlayer.com/blog/post/surefire-ways-to-identify-data-drift) and poor performance. The models can also be rigid in the sense that they can perform very well only in highly specific conditions and may struggle with changing environments. AI agents do not have substantial requirements for high levels of maintenance and monitoring as they're capable of monitoring their own responses to adapt to their environment. As long as the goals of the agents remain the same, agents do not need to be constantly updated, unlike conventional AI models, which require periodic retraining. Future Prospects and Possibilities of Agentic AI Agentic AI has a [bright future](https://ts2.space/en/the-rise-of-ai-agents-the-future-of-artificial-intelligence) that's bolstered by the breakneck growth and development in the field of large language models and [generative AI](https://www.mckinsey.com/featured-insights/mckinsey-explainers/whats-the-future-of-generative-ai-an-early-view-in-15-charts). Software-based agents built on large language models have boosted the efficacy of conversational intelligence, knowledge search and management, content generation and other business applications. Hardware-based agents, such as robots, also stand to benefit from agentic AI. Agentic AI can empower several fundamental aspects of robotics, such as design, generation of realistic simulations of robotic systems as well as human-robot interaction. According to [Gartner](https://www.gartner.com/en/topics/generative-ai), up to 40 percent of enterprise applications will have embedded conversational AI by 2024, and up to 100 million workers will engage agents to assist in their work by 2026. Conclusion Artificial intelligence is now ubiquitous, powering multiple consumer and enterprise applications across industries. Agentic AI represents a type of AI that can act and make decisions to accomplish specific goals in real-world environments. While robots as a form of agentic AI have been around for a while, recent advances in conventional AI based on large vision and language models are paving the way for a new breed of AI agents. In this article, you learned about the elements of agentic AI, how it's different from conventional AI, what its real-world applications are as well as what the future prospects and potential of agentic AI are. In particular, conversational agents like ChatGPT are increasingly being adopted for various use cases. These chat agents leverage a different style of technology stack that includes new elements, such as [vector databases](https://www.singlestore.com/built-in-vector-database/) that are used for storing smaller or compressed pieces of information and retrieving relevant information chunks based on a [user query in chat applications].  In the rapidly evolving landscape of artificial intelligence, understanding how to effectively monetize AI products has become crucial for businesses. This comprehensive guide delves into the economics and pricing strategies for GenAI development, offering valuable insights for companies looking to capitalize on this transformative technology.
1. The AI Monetization Challenge The primary challenges in implementing GenAI models revolve around two key factors: value and cost. While the potential value of AI solutions can be immense, quantifying and communicating this value to customers remains a significant hurdle. 1.1 Value Proposition When the value of AI is clear, the results can be staggering. For instance, Klarna's AI assistant, powered by OpenAI, demonstrated remarkable success within just one month of its global launch: - 2.3 million conversations handled, equivalent to two-thirds of Klarna's customer service chats - Workload equivalent to 700 full-time agents - Customer satisfaction scores on par with human agents - Estimated $40 million USD profit improvement for Klarna in 2024 1.2 Cost Considerations The costs associated with developing and implementing GenAI models can be substantial: - Training Llama 3.1: Approximately $1 billion - Training GPT-4: Around $100 million - Training BloombergGPT: Roughly $10 million - Custom GPT-4 model training: $2-3 million These figures highlight the significant investment required for AI development, emphasizing the need for careful cost management and strategic pricing. 2. The 5-Step Product Monetization Framework To effectively monetize AI products, a structured approach is essential. The following 5-step framework provides a comprehensive guide for pricing any software product, including AI-powered solutions: 1. Value Understanding 2. Packaging Decisions 3. Pricing Metric Decisions 4. Price Point Selection 5. Pricing Model Selection 2.1 Packaging Options When introducing a new AI product, companies must consider various packaging options along a spectrum from inflexible to highly flexible: - One-size-fits-all - Good/Better/Best - Add-ons - Usage-based The choice of packaging strategy depends on factors such as market positioning, customer needs, and product complexity. 2.2 Pricing Metric Selection Selecting the appropriate pricing metric for AI products involves considering seven key factors: 1. Customer risk perception 2. Mental anchors 3. Alignment with value 4. Consumption pattern 5. Cost patterns 6. Competitive action 7. Implementability For generative content AI products, pricing based on credit or token bundles of consumption per user is the most common metric. Enterprise SaaS with AI add-ons often employ hybrid metrics, combining per-user platform pricing with consumption-based add-ons. 3. GenAI Costs: A Deeper Dive Understanding the various cost factors associated with implementing GenAI models is crucial for effective monetization. These factors include: - Performance - Data costs - Infrastructure - Integration - Scalability - Support - Licensing - Latency - Security - Compliance - Talent 4. Implementing GenAI Models: Open vs. Closed Source When implementing GenAI models, companies have three main options: 1. Use closed-source models (e.g., GPT-4, Claude 3.5 Sonnet) 2. Leverage open-source models (e.g., Llama 3.1, Mixtral 8x22B) 3. Train their own custom model Each approach has its advantages and disadvantages: 4.1 Closed Source - Pros: Effortless integration, no infrastructure management - Cons: Potential lack of domain knowledge, customization difficulties 4.2 Open Source - Pros: Freedom to use any model and cloud, complete control over model and data - Cons: Requires specialized AI/ML talent, longer implementation time 4.3 Custom Model - Pros: Full control over training data, high data privacy and security - Cons: Most time-consuming to implement, requires significant resources 5. Recent Trends in GenAI Development Several notable trends have emerged in the GenAI landscape: 1. The performance gap between closed and open-source LLMs has decreased significantly in the past two years. 2. Custom open-source models now surpass GPT-4 across 31 use cases. 3. The speed difference between closed and open-source LLMs is now negligible. 4. The cost of tokens has reduced by 240x over two years, with inference costs dropping from ~$50 to $0.50 per 1M tokens. These trends indicate that open-source solutions are becoming increasingly competitive with closed-source options, potentially offering substantial cost savings for businesses. 6. Key Takeaways for Monetizing GenAI 1. AI product costs and value have high variance, making both development cost and pricing strategy crucial for success. 2. Packaging and pricing metric decisions are pivotal for AI products – choose wisely based on your specific use case and target market. 3. Closed-source APIs like GPT-4 offer effortless integration and faster time to market. 4. Open-source models like Llama 3.1 provide more control and can be a better long-term investment in GenAI. 5. The performance of open-source models is now comparable to closed-source APIs, with customized open-source models potentially outperforming them. 6. GenAI models will continue to become cheaper, better, smaller, faster, and easier to develop over time. By carefully considering these factors and staying informed about the latest developments in GenAI, businesses can develop effective monetization strategies that maximize the value of their AI investments while managing costs and meeting customer needs. As the AI landscape continues to evolve, companies that successfully navigate the complexities of GenAI monetization will be well-positioned to capitalize on this transformative technology and gain a competitive edge in their respective markets.  When hiring AI engineers to build Generative AI (GenAI) products during the evolution of a startup from seed-stage to PMF (Product-Market Fit) stage to Growth stage, it's important to consider strategies that align with the company's evolving needs and budget constraints. Here are some strategies to consider at each stage:
Seed Stage 1. Focus on Versatility: At this stage, hire AI engineers who are generalists and can wear multiple hats. They should have a broad understanding of AI technologies and be capable of handling various tasks, from data preprocessing to model development. 2. Leverage Freelancers and Contractors: Consider hiring freelance AI specialists or contractors for short-term projects to manage costs. This approach provides flexibility and allows you to access specialized skills without long-term commitments. 3. Upskill Existing Team Members: If you already have a technical team, consider upskilling them in AI technologies. This can be more cost-effective than hiring new talent and helps retain institutional knowledge. PMF Stage 1. Hire for Specialized Skills: As you approach product-market fit, start hiring AI engineers with specialized skills relevant to your GenAI product, such as expertise in natural language processing or computer vision. 2. Build a Strong Employer Brand: Establish a strong brand as an employer to attract top talent. Highlight your mission, values, and the impact of your GenAI product to appeal to candidates who share your vision. 3. Offer Competitive Compensation: While budget constraints are still a consideration, offering competitive salaries and benefits can help attract and retain skilled AI engineers in a competitive market. 4. Implement Knowledge-Sharing Practices: Encourage mentoring and knowledge-sharing initiatives within your team to enhance skill development and foster collaboration. Growth Stage 1. Scale the Team: As your startup grows, scale your AI team to meet increasing demands. Hire senior AI engineers and data scientists who can lead projects and mentor junior team members. 2. Invest in Continuous Learning: Provide opportunities for ongoing learning and development to keep your team updated with the latest AI advancements. This investment helps maintain a competitive edge and fosters employee satisfaction. 3. Optimize Recruitment Processes: Streamline your hiring process to efficiently identify and onboard top talent. Use AI tools to assist in candidate screening and reduce bias in hiring decisions. 4. Foster a Collaborative Culture: Create a work environment that encourages innovation, creativity, and collaboration. This helps retain talent and enhances team productivity. By adapting your hiring strategies to the specific needs and constraints of each stage, you can effectively build a strong AI team that supports the development and scaling of your GenAI products.  Vector databases have recently gained prominence with the rise of large language models and generative AI. A vector database is a data store for unstructured text in the form of vector embeddings for various AI models and applications. Embeddings are a high dimensional vector representation of text that conveys rich semantic information and represent an efficient way of capturing unstructured data like text.
The rising popularity of large language models like GPT-4, Gemini, Claude-2, Llama-2, Mixtral and others have fuelled tremendous interest in generative AI across the industry to build applications based on these models. Vector databases are specialized for handling vector data that is used to train or fine-tune these foundational models for domain and company specific use cases. Unlike traditional scalar-based databases, vector databases offer optimized storage and querying capabilities for vector embeddings. Although several vector databases are now available in the market like Pinecone, Chroma, Qdrant amongst others, deciding which vector database to choose for enterprise use cases is not a straightforward decision. In this article, you will learn how to decide which vector database to choose for your organization based on criteria like performance, reliability, scalability, cost-efficiency, developer experience, security, technical support amongst others. Key Considerations In this section, you will learn in detail about each of the key factors that should be considered to make your final selection of a vector database. These include data and use case characteristics, performance, functionality, enterprise-readiness, developer experience, and future roadmap. 1. Data and Use Case It is important to work backwards from the specific business use case that you are planning to solve by leveraging organizational data and the latest techniques from the field of generative AI. For instance, if your business objective is to build an enterprise knowledge management chatbot like McKinsey’s Lilli, you will need to organize and prepare all the in-house text data such as documents, emails, chat messages etc. The use case defines several aspects of the data, including its size, frequency, data type, growth in the volume of data over time, data freshness and consequently the nature of the underlying vector embeddings to be stored in the vector database. These vectors may be sparse, dense, and also span multiple modalities depending on the use case. Additionally, careful planning and scoping of the use case also helps you understand other crucial aspects such as the number of users, the number of queries per day, the peak number of queries at any given instant, as well as the query patterns of the users. Vector databases utilize indexing and vector search powered by k-nearest neighbors (kNN) or approximate nearest neighbor (ANN) algorithms. This empowers a vector db to perform similarity search and identify the most similar vectors in the database. This capability underlies enterprise use cases based on natural language processing such as question-answering, document analysis, recommender systems, image and voice recognition etc. 2. Performance 2.1 Query latency and query per second (QPS) The primary performance metrics of a vector db are the query latency, i.e., the time it takes to run a query and get the result and the query per second that defines the throughput in terms of the number of queries processed in a second. These parameters are critical for ensuring a seamless user experience for several applications that require real-time results such as chatbots. Typical QPS values range from ~50-300 and the average query latency from 25-100 ms depending on the underlying hardware. 2.2 Scalability Scalability measures the ability of the vector database to grow and expand further to support the requirements of its customers. The scale can be measured in terms of the number of embeddings that can be supported and in terms of horizontal scaling of existing resources and vertical scaling of additional servers. Typically, most existing vector db companies provide scale-out capabilities up to a billion vectors without any performance degradation. If the resources can scale automatically, then you can be rest assured that your application will always be up and running. 2.3 Accuracy A vector database is as good as its accuracy of retrieving the right set of results based on the user queries. Here, the choice of vector search algorithms to identify data sources with similar embeddings as the embedding of the user query is pivotal. There are several different algorithms used for powering vector search such as kNN, ANN, FAISS, NGT. These algorithms generate approximate results and the best vector databases provide a good trade-off between speed and accuracy. 3. Functionality 3.1 Filtering on metadata In practice, filtering vector search results based on the metadata helps reduce the search space, thus providing for faster and more accurate search results. Typical metadata includes information like dates, versions, tags and the ability of a vector database to store multiple metadata fields allows for a better search experience. 3.2 Integrations Integrating a vector database into the existing data and engineering infrastructure in your organization is critical to faster adoption and lesser time to value. The ability of vector databases to seamlessly integrate with essential infrastructure elements like the cloud infrastructure, underlying large language models, databases etc. is a key factor to consider. 3.3 Cost-efficiency While performance metrics and functionality are core to a technology, the cost should be reasonable and fit your budget. The pricing of vector databases is a function of the number of ‘write’ operations such as update and delete and the number of queries. Other factors that affect the cost include the dimensionality of the embedding, the number of vectors stored in the database, and the size of the metadata. Depending on your use case and requirements, it is essential to estimate the overall cost of running your application at scale on a monthly or quarterly basis and evaluate the overall costs relative to your budget and the expected revenue from running the AI applications. 4. Enterprise-readiness 4.1 Security and compliance For most enterprise companies, it is imperative that any external vendor they employ meets strict security and compliance requirements. These requirements include SOC2, GDPR, HIPAA, ISO compliance and others, depending on the domain in which the company operates. The data privacy and security standards have gone up in the light of recent cybersecurity attacks and breaches of customer data, and you should ensure that any vector db vendor meets your specific security and compliance requirements. 4.2 Cloud setup Several modern companies have undergone digital transformation and house their entire data and infrastructure in the cloud vs on-premise. You may choose to manage and maintain your infrastructure via a self-hosted setup or go for a fully managed SaaS platform. The benefit of a fully managed system is that it automates clusters with minimal requirements for you to provision and scale clusters or take care of operational issues. 4.3 Availability Availability, i.e. the ability of your vector db to run without any interruptions, issues or downtime is essential to not adversely impact user experience. Most vector database providers vouch for specific SLAs which should meet the requirements for your applications. Typical values include 99.9% for uptime SLA and a few hours to a few business days for response time SLA depending on the severity of the production issue. 4.4 Technical support More often than not, you might be stuck facing some issues with your vector db and need some hands-on support from the vendor to help troubleshoot the issue. Does the company provide you with a dedicated team who can be available at a short notice to get on a call and figure out how to solve the problem? The quality of responsiveness and customer support experience provided by a vector db company is valuable and helps you develop a stronger sense of trust in the company. 4.5 Open source vs Closed source Some vector db companies are closed source and operate under a proprietary license such as Pinecone. At the same time, there are a host of vector db companies that are open source under the Apache 2.0 license such as Qdrant or Chroma while also offering a fully managed service. This can also influence your choice of the vector db provider. 5. Developer experience 5.1 Community Software and AI engineers are the core professionals who will work on the vector db and integrate it in the company’s infrastructure and deploy your generative AI application to production. Therefore, the quality of experience that developers have with a vector db solution is integral in shaping your final decision. Having an open-source community on Slack or Discord helps build more engagement and trust with developers than commercial vendor support. It provides your developers an opportunity to learn from developers at other companies as well and discuss and solve issues by leveraging the wisdom of the community. 5.2 Onboarding Onboarding a new technology is challenging as it determines the time your developer team takes to properly understand the product, integrate it, troubleshoot any issues, and become an expert in using the vector database. The availability of APIs and SDKs as well as clear product demos and documentation goes a long way in reducing the barriers to understanding a new vector database so that your developers can build with speed and confidence. 5.3 Time to value Similar to the time to onboard a new vector db, another important factor is the time to business value. If a vector db provider vouches for a fast deployment of a production-ready application, then you can realize value sooner, and meet your business goals faster as well. A long gestation time from onboarding to business value is a deterrent for many fast-moving companies and startups especially in the current frantic race to adopt and ship generative AI applications. 5.4 Documentation The quality of the vector database’s documentation determines the time to onboard, time to value, and trust in the provider’s expertise and product. Clear instructions with tutorials, examples and case studies help your developers understand and master the vector db faster. 5.5 User education Similar to community-based offerings, expert technical content such as blogs, demos and videos focused on the existing as well as new features are helpful for your team to understand and build faster. In addition to text and video content, other offerings like user testimonials, workshops, conferences also help educate your team and build more trust in the vector db provider. 6. Future roadmap A final factor to consider is the product roadmap of the vector database provider. Vector databases are an emerging technology that will need to continuously evolve alongside the advances in generative AI models, chip design and hardware, and novel enterprise use cases across domains. Therefore, the vector db vendor should show the potential for evaluating long-term and future industry trends such as sophisticated vectorization techniques for a wider variety of data types, hybrid databases, optimized hardware accelerators for AI applications such as GPUs and TPUs, distributed vector dbs, real-time and streaming data based applications, as well as industry-specific solutions that might require advance data privacy and security. Conclusion Vector databases are an essential ingredient for modern generative AI applications built on unstructured data such as text. Their popularity has increased in parallel to the developments in the generative AI field such as large language models, large image models etc. to serve as the underlying database for handling high-dimensional data stored as vector embeddings. In this article, you learned about several important pillars to help your decision making about the choice of the vector database. These factors include data and use case considerations, performance-based requirements such as query speed and scalability, functionality requirements such integrations and cost-efficiency, enterprise-readiness including security and compliance, and developer experience including community and documentation. Several vector database companies have emerged to build this foundational infrastructure. There is no single ‘best’ vendor of vector db and the ultimate choice is highly contingent on your organization’s business goals. Therefore, a data-driven approach guided by the factors listed in this article will help you select the most optimal vector db for your organization.  Figure 1 - Mixture of Experts layer. 1. Introduction Mistral is a pioneering French AI startup that launched their own foundational large language model, called Mistral 7B in September 2023. As of the date of launch, it was the best 7 billion parameter language model, outperforming even larger language models like Llama 2 of size 13 billion parameters across multiple benchmarks. In addition to its performance, Mistral 7B is also popular as the model is open-sourced under the Apache 2.0 license with the model weights available for download. Mixtral 8x7B (hereafter, referred to as “Mixtral”) is the latest model released by Mistral in January 2024 and represents a significant extension of their prior work on Mistral 7B. It is a 7B Sparse Mixture of Experts (SMoE) language model with stronger capabilities than Mistral 7B. It uses 13B active parameters during inference out of a total of 47B parameters, and supports multiple languages, code, and 32k context window. In this blog, you will learn about the details of the Mixtral language model architecture, its performance on various standard benchmarks vis-a-vis state-of-the-art large language models like Llama 1 and 2 and GPT3.5, as well as potential use cases and applications. 2. Mixtral Mixtral is a mixture-of-experts network, similar to [GPT4]. While GPT4 is said to constitute 8 expert models of 222B parameters each, Mixtral is a mixture of 8 experts of 7B parameters each. Thus, Mixtral only requires a subset of the total parameters during decoding, thus allowing faster inference speed at low batch sizes and higher throughput at large batch sizes. 2.1 Sparse Mixture of Experts Figure 1 illustrates the Mixture of Experts (MoE) layer. Mixtral has 8 experts, and each input token is routed to two experts with different sets of weights. The final output is a weighted sum of the outputs of the expert networks, where the weights are determined by the output of the gating network. The number of experts (n) and the top K experts are hyperparameters that are set to 8 and 2 respectively. The number of experts, n determines the total or sparse parameter count while K determines the number of active parameters used for processing each input token. The MoE layer is applied independently per input token in lieu of the feed-forward sub-block of the original Transformer architecture. Each MoE layer can be run independently on a single GPU using a model parallelism distributed training strategy. 2.2 Mistral 7B Mixtral’s core architecture is similar to Mistral 7B, and therefore, a review of its architecture is relevant for a more comprehensive understanding of Mixtral. Mistral 7B is based on the Transformer architecture. In comparison to Llama, it has a few novel features that contribute to it surpassing Llama 2 (13B) on various benchmarks. 2.2.1 Grouped-Query Attention Grouped-Query Attention (GQA) is an extension of multi-query attention, which uses multiple query heads but single key and value heads. Popular language models like PaLM employ multi-query attention. GQA represents an interpolation between multi-head and multi-query attention with single key and value heads per subgroup of query heads. As shown in figure 2, GQA divides query heads into G groups, each of which shares a single key and query head. It is different to multi-query attention which shares single key and value heads across all query heads. GQA is an important feature as it significantly accelerates the speed of inference and also reduces the memory requirements during decoding. This enables the models to scale to higher batch sizes and higher throughput, which is a critical requirement for real-time AI applications. 2.2.2 Sliding Window Attention Sliding window attention (SQA), introduced in the Longformer architecture exploits the stacked layers of a Transformer to attend to information beyond the typical window size. SWA is designed to attend to a much longer sequence of tokens than vanilla attention, and also offers significant reductions in computational cost. The combination of GQA and SWA collectively enhance the performance of Mistral 7B and therefore Mixtral relative to other language models like the Llama series.  Figure 2. A comparison of the configuration of key, value and query heads for GQA vs. multi-head and multi-query attention. 3. Performance 3.1 Standard benchmarks The authors of Mixtral benchmarked the performance of the model on a range of standard benchmarks and evaluated the accuracy of Mixtral versus leading language models like Llama 1, Llama 2, and GPT3.5 as shown in figure 3, table 1, and table 2. In summary, Mixtral is better than much larger language models with up to 70B parameters like Llama 2 70B while only using 13B (~18.5%) of the active parameters during inference. Mixtral’s performance is especially superior in tasks focused on mathematics, code generation, as well as multilingual comprehension. 3.2 Multilingual understanding Table 3 shows the performance of Mixtral versus Llama models on multilingual benchmarks. As Mixtral was pretrained with a significantly higher proportion of multilingual data, it is able to outperform Llama 2 70B on multilingual tasks in French, German, Spanish, and Italian while being comparable in English. 3.3 Long-range performance As shown in figure 4, the input context length of language models has increased by several orders of magnitude in the last few years - from 512 tokens for the BERT model to 200k tokens for Claude 2. However, most large language models struggle to efficiently use the longer context. Nelson and colleagues showed that current language models do not robustly make use of information in long input contexts, and their performance is typically highest when the relevant information for tasks such as question-answering or key-value retrieval occurs at the beginning or the end of the input context, with significantly degraded performance when the the models need to access information in the middle of long contexts. Mixtral, which has a context size of 32k tokens, overcomes this deficit of large language models and shows 100% retrieval accuracy regardless of the context length or the position of the key to be retrieved in a long context. The perplexity, a metric that captures the capability of a language model to predict the next word given the context, decreases monotonically as the context length increases. Lower perplexity implies higher accuracy, and the Mixtral model is therefore capable of extremely good performance on tasks based on long context lengths as shown in figure 5.  Figure 3. Performance of Mixtral in comparison to Llama 1 and 2 models of different sizes.  Figure 4. Evolution of the context length of large language models.  Figure 5. Long range performance of Mixtral.  Table 1. Mixtral outperforms Llama 2 70B model on almost all benchmarks while using less than 1/5th of the active parameters during inference.  Table 2. Mixtral outperforms or matches the performance of Llama 2 70B and GPT-3.5 on most benchmarks.  Table 3. Mixtral’s performance on multilingual benchmarks for French, German, Spanish and Italian versus Llama 1 and 2 models. 4. Instruction Fine-tuning Instruction tuning refers to the process of further training large language models on a curated dataset containing (instruction, output) pairs of training samples. Instruction tuning is a computationally efficient method for extending the capabilities of large language models in diverse domains without extensive retraining or architectural changes. “Mixtral - Instruct” model was fine-tuned on an instruction dataset followed by Direct Preference Optimization (DPO) on a paired feedback dataset. DPO is a technique to optimize large language models to adhere to human preferences without explicit reward modeling or reinforcement learning. As of January 26, 2024, on the standard LMSys Leaderboard, Mixtral - Instruct continues to be the best performing open-source large language model. This leaderboard is a crowdsourced open platform for evaluating large language models that ranks models following the Elo ranking system in chess. Mixtral - Instruct only ranks below proprietary models like OpenAI’s GPT-4, Google’s Bard and Anthropic’s Claude models, while being a significantly small model. This extremely strong performance of Mixtral - Instruct and with an open-source friendly Apache 2.0 license opens up the possibility for tremendous adoption of Mixtral for both commercial and non-commercial applications. It represents a much more powerful alternative to Llama 2 70B that is already being used as the foundational model for extending large language models to other languages like Hindi or Tamil that are spoken widely but not adequately represented in the training dataset of these large language models.  Figure 6. Mixtral is the best performing open-source large language model on the LMSys Leaderboard. 5. Use Cases
Mixtral represents the numero uno of open-source large language models as it clearly outperforms the previous best open-source model, Llama 2 70B, by a significant margin, while providing for faster and cheaper inference. At the time of writing this article, Mixtral has been available in the open-source for less than two months and we are yet to see many examples of how it is being used in the industry. However, there are some early movers, like the Brave browser that has already incorporated Mixtral in its AI-based browser assistant, Leo. Mixtral is also incorporated by Brave for powering its [programming-related queries in Brave Search. It is only a matter of time before Mixtral witnesses widespread adoption across industry for a variety of use cases and challenges the hegemony of proprietary models like OpenAI’s GPT-4 and the likes. 6. Conclusion Mixtral is a cutting-edge, mixture-of-experts model with state-of-the-art performance among open-source models. It consistently outperforms Llama 2 70B on a variety of benchmarks while having 5x fewer active parameters during inference. It thus allows for a faster, more accurate and cost-effective performance for diverse tasks including mathematics, code generation, as well as multilingual understanding. Mixtral - Instruct also outperforms proprietary models such as Gemini-Pro, Claude-2.1, GPT-3.5 Turbo on human evaluation benchmarks. Mixtral thus represents a powerful alternative to the much larger and more compute intensive Llama 2 70B as the de facto best open-source model, and will facilitate development of new methods and applications benefitting a wide variety of domains and industries. Published by Ikigai Labs Introduction
Many types of business data are organized in time—for instance, customer purchases on an e-commerce website or frequent orders of inventory materials by companies. Making sense of this time series data is vital for data or business analytics teams to understand the future dynamics of consumption and demand for their companies' products and services. Therefore, building predictive models to forecast demand is a vital task. There's a whole range of statistical as well as machine learning (ML) models that can be leveraged to build business-critical time series forecasting applications. However, time series data can be highly variable, and no one time series forecasting model will be applicable across use cases. With recent progress in ML and deep learning, new models are being developed all the time that provide state-of-the-art forecasting performance. For instance, Amazon has been working on a series of time series forecasting models over the last decade to predict customer demand for its products, ranging from statistical models to random forests to deep learning models, and transformers. Similarly, your business can benefit immensely from leveraging time series forecasting models to make accurate predictions of customer demand. In this article, you'll learn about ARIMA, Prophet, and mSSa, three popular time series forecasting models. These models have proved to be highly robust, reliable, easy to understand and implement, and versatile for forecasting applications in industries such as e-commerce, finance, retail, and travel. By the end of this article, you'll have a better sense of which of these models might be best for your own use case. Why Do Time Series Forecasting Models Matter? Real-world time series data have several characteristic patterns that reflect the nature of consumption and demand. For instance, if you're in the business of selling electronic gadgets, it's important for you to know how much inventory to stock so that you can meet the number of customer orders. Demand for your products can also change over time due to factors such as seasonal variations, holidays, the weather, or special events like the launch of a new product. Therefore, accurately forecasting the dynamics of demand becomes a critical function for your business. Poor demand forecasts may lead to grave consequences such as a significant reduction in sales and revenue as well as losing market share to your competitors. Using time series forecasting models enables your company to predict demand for the next day, week, month, or quarter and helps you to plan and prioritize business objectives and strategy accordingly. The time series forecasting models that have emerged over the years are based on different assumptions about the nature of the underlying time series data; as such, they've been developed to suit specific applications. To determine the time series forecasting model that's right for you, you should start by conducting preliminary analytics and evaluating the statistical distribution and properties of your data. This is an important step in identifying the right set of algorithms to model your specific time series data. Getting the choice right can help make your process more efficient without the need to test out multiple models. Once you've set a good baseline in terms of your model's performance, you can further improve it by experimenting with its various parameters. Additionally, the right model allows you to place more confidence in the accuracy of its results. Therefore, defining the most relevant time series forecasting model for your specific business use case is an important decision. Choosing between ARIMA, Prophet, and mSSa As mentioned, your particular use case is a key consideration. You may have large amounts of historical data that can be leveraged to make demand predictions for the next day, week, or month. Predicting electricity demand is one example that fits this scenario. Maybe you don't have a lot of historical data but still need to make forecasts for functions like sales or viewership or usage of a particular feature or product. In this section, you'll learn about the underlying principles of the ARIMA, Prophet, and mSSa time series forecasting models and be able to decide which models would be better suited to your forecasting goals. The ARIMA Model Autoregressive integrated moving average, or ARIMA, is a forecasting algorithm based on the assumption that past time series data can be used to predict future values. The amount of past information to use for modeling is controlled by a hyperparameter, p. ARIMA also assumes that past forecast errors can also be used to improve forecasts. The most recent errors are indexed by another hyperparameter, q. ARIMA models are great for forecasting stationary time series data. This implies that the data does not contain any seasonal or temporary trends and the statistical properties of the source of the time series data, like the mean and variance, do not change over time. A time series can be made stationary through several methods, with the common technique being differencing, where each differencing value is the difference between the value at the current time period and the previous time period. The number of differences required to achieve stationarity is determined by a hyperparameter, d. ARIMA is widely used for demand forecasting use cases, such as predicting demand in food manufacturing, energy, or user demand for services like ride-hailing. The Prophet Model Prophet is an open-source time series forecasting package developed by the data science team at Facebook. It's available in both Python and R and has been widely adopted across key industries such as e-commerce, tech, and finance. The forecasting algorithm is based on an additive model that can be decomposed into three distinct components: trends, seasonality, and holidays. As the forecasting model can be decomposed into its constituent factors, it's easy to extract the model coefficients to understand the relative impact of seasonality, trends, and holidays on the forecast. Prophet is best suited for forecasting applications that are associated with:
Prophet is designed to make forecasting automated and efficient for business analysts who may not have specialized data science skills. Its default parameters often yield forecasts that are as accurate as those produced by experienced forecasters. It's easy to use by nonexperts and requires less hyperparameter tuning. The mSSa model Multivariate singular spectrum analysis, or mSS, is a novel time series forecasting method that was recently formulated by scientists at MIT; they've shown that on benchmark data sets focused on time series data from electricity grids, traffic patterns, and financial markets, mSSa performs competitively with state-of-the-art neural networks for time series, such Amazon's DeepAR and LSTM. mSSa is particularly useful for modeling multiple time series with a varying number of observations per time series; it's also highly effective at imputation, or filling in missing values. mSSa has also been used to predict real-time traffic flow in software-defined networks with high levels of accuracy. Conclusion Forecasting demand is key for businesses to respond to fluctuating customer demand for their products and services. In this article, you learned about three popular time series forecasting models that are based on different statistical foundations: ARIMA, Prophet, and mSSa. These models have been used extensively at both startup and enterprise organizations, and you're now better equipped to choose which one could be right for you. Time series forecasting models can be built from scratch using libraries in R, Python, etc. Alternatively, for some organizations, it makes more sense to leverage existing platform solutions. For example, Ikigai provides a forecasting solution that includes all available algorithms including ARIMA, Prophet, mSSa, linear regression, etc., that can be easily configured using its no-code interface. When analysts are not sure which model to use, they can easily compare different ones with a one-click interface, or rely on AutoML to help them select the best model for their specific data. Additionally, Ikigai also provides a proprietary forecasting method called DeepCast that uniquely leverages statistical models with additional layers of machine learning on top of it, resulting in 20% more accurate forecasts vis-a-vis other state-of-the-art methods. Further, DeepCast is capable of making an accurate prediction based on only three weeks of data. Published by Pachyderm MLOps refers to the practice of delivering machine-learning models through repeatable and efficient workflows. It consists of a set of practices that focuses on various aspects of the machine-learning lifecycle, from the raw data to serving the model in production.
Despite the routine nature of many of these MLOps tasks, it’s not uncommon for several steps to still be processed manually, incurring massive ongoing maintenance costs. Your organization can benefit tremendously from automating MLOps to achieve efficiency, reliability, and cost-effectiveness at scale. For example, automation could:
However, many companies lack the capabilities, talent, and infrastructure to drive machine-learning models to production reliably and efficiently. This not only means wasted time and resources but also hinders adoption and trust in AI. The sooner that companies of any size, enterprise and startups alike, invest in automating their MLOps processes to expedite delivery of machine-learning models, the sooner they can meet their business goals. So, let’s talk about six methods for automating MLOps that can help streamline the continuous delivery of machine-learning models to production. 1. Automated Data-driven Pipelines Delivering a machine-learning model involves numerous steps, from processing the raw data to serving the model to production. Machine-learning pipelines consist of several connected components that can execute automatically in an independent and modular fashion. For instance, different pipelines can focus on data processing, model training, and model deployment. When it comes to machine learning, data is as or more important than code; pipelines track changes in training data and automatically trigger pipelines for processing new or changed data. Such automated data-driven pipelines kickstart further iterations of data processing and model training based on the new datasets. Without automated pipelines, the data science team executes these steps manually. This inevitably leads to manual errors, production delays, and lack of visibility of the overall pipeline for relevant stakeholders. Manually built pipelines are harder to troubleshoot when defects creep into production, and so compound technical debt for the MLOps team. Automating pipelines can significantly reduce manual effort and free up organizational time, resources, and bandwidth so your MLOps team can focus on other challenges. 2. Automated Version Control In the realm of software engineering, version control refers to the tracking of changes in code, making it easier to monitor, troubleshoot and collaborate among large teams. In machine learning, the need for version control applies to data as well as code. Version control is especially critical for machine-learning applications in domains like healthcare and finance that have a higher burden of model explainability, data privacy, and compliance. Automating version control for machine learning ensures that the history of the different moving parts—code, data, configurations, models, pipelines—is centrally maintained and fully automated. Through automated version control, your MLOps team has a more efficient ability to trace bugs, roll back changes that didn’t work, and collaborate with greater transparency and reliability. 3. Automated Deployment Large data science organizations develop multiple models trained on structured and unstructured data for various use cases. Some of these models need to make predictions in real-time at ultra-low latencies while others may be invoked less often or serve as inputs to other models. All these models need to be periodically retrained to improve performance and mitigate challenges due to data drift. Deploying models manually in such a complex business environment is highly inefficient and time consuming. Manual deployment is cumbersome and can cause serious errors that impacts model serving and the quality of model predictions. This often leads to poor customer experience and customer churn. Deployment of models to production involves several steps. It starts with choosing multiple environments and services for staging the model, selecting appropriate servers that can handle the production traffic, and pushing the model forward to production. It then includes monitoring model performance and data drift, automating model retraining with more recent data and inputs, and ensuring the reliability of the models through better testing and security. Automating these steps yields several benefits:
4. Automated Feature Selection for Model Training Classical machine-learning models are trained on data with hundreds to thousands of features, ie, key variables in the dataset that are often correlated with model performance. Choosing a set of features that significantly account for the predictive power of the trained models is therefore essential. Feature selection by hand is cumbersome and requires significant subject matter expertise. Automating feature selection not only helps train the machine-learning model faster on a smaller dataset but also makes the model easier to interpret. Selecting fewer features but with high feature importance is critical in the preparation of training data. Automated feature selection helps reduce the size of the model to make faster predictions, or to increase the speed of training your machine learning or deep learning model. Feature selection can be automated using either unsupervised learning techniques, like principal component analysis, or supervised methods using statistical tests like f-test, t-test, or chi-squared tests. 5. Automated Data Consistency Checks A central focus of data-centric AI is the quality of data used to train machine-learning models. Data quality determines the accuracy of the models, which in turn impacts business decision-making. So the underlying data must have minimal errors, inconsistencies, or missing values. Simplify the challenge of ensuring data quality and consistency by automating unit tests that check data types, expected values, missing cells, column and row names, and counts. Consider extending your automation to the analysis and reporting of the statistical properties of relevant features. If the training dataset consists of a few thousand to millions of samples and hundreds to thousands of features, you can’t manually evaluate every row and column for data consistency. Automated routines that test for different types of data inconsistencies makes it easier to eliminate poor quality data. 6. Automated Script Shortcuts Processing data and training machine-learning models involves a lot of boilerplate code. Automate the creation of scripts for common tasks to save time and effort while providing better visibility and version control. Typically, data scientists and machine-learning engineers create their own unique automations and shortcuts, which are seldom shared among the larger team. However, having a centralized repository of script shortcuts reduces the need to improvise, and perhaps even avoids a team member reinventing the wheel. Save these shortcuts as executable bash scripts for different use cases like downloading data from data lakes or uploading model artifacts in backup folders. Automate MLOps with Pachyderm Fortunately, you don’t have to build these MLOps automation features in-house from scratch. Pachyderm is a software platform that integrates with all the major cloud providers to continuously monitor changes in data at the level of individual files. Whenever any existing file is modified or new files are added to a training dataset, Pachyderm triggers events for pipelines and launches a new iteration of data transformation, testing data quality, or model training. Pachyderm can take care of automated version control and lineage for data as well as [deployment](https://www.pachyderm.com/events/how-to-build-a-robust-ml-workflow-with-pachyderm-and-seldon/. It also enables autoscaling and parallel processing on Kubernetes, orchestrating server resources for deployment at scale. Conclusion With a lot of the machine learning lifecycle still handled manually across the industry, consider automating any of the six MLOps tasks we covered here in order to achieve efficiency and reliability at scale:
A data science organization’s level of automation across its machine-learning lifecycle indicates its maturity. The velocity of training and delivering new machine-learning models to production increases significantly with that maturity, leading to faster realization of business impact. Pachyderm, a leading enterprise-grade data science platform, helps make explainable, repeatable, and scalable machine learning systems a reality. Its automated data pipeline and versioning tools can power complex data transformations for machine learning while remaining cost effective. Introduction
Data is the cornerstone of businesses from large enterprises to small D2C brands, and huge amounts of it can be collected from websites, mobile apps, chat messages, call centers, business transactions, surveys, and social media platforms, among other channels. All this data represents a gold mine of information that can offer customer insights and lead to new ideas for features or products. However, making sense of the data is easier said than done. The information originates from various channels and in multiple formats. It can be logged erroneously and contain other errors, including missing values. Because it comes from multiple domains, it can include unstructured data like text, images, audio, and video. That is why data preparation is essential. This involves cleaning, curating, transforming, and storing data sets for downstream applications including data analytics and data visualization, as well as predictive intelligence based on machine learning and deep learning models. Data can only provide value once it has been processed from its raw form, and effective data preparation can maximize that value. This article will explain the process of data preparation, especially in terms of data labeling, and will provide a checklist for data engineers to follow. What Is Data Preparation? Data preparation is not an entirely new process in technology companies. Data-driven operations previously focused on statistical analysis of business data from structured tables. The deep learning model has grown over the past decade along with the global penetration of mobile phones, widely available internet access, and cheaper cloud storage facilities. Today an estimated 2.5 quintillion bytes of data are being generated daily. Every user interaction with online companies is recorded, from someone clicking an ad or adding a product to a shopping cart to sharing a photo on a social media app. User-generated data is generally unstructured data: images, text, audio, or video. Such data can be used to train sophisticated deep learning models to predict what users want to type in a text, which branded products are featured in an image, and what kind of customer service will be provided in a phone conversation. For deep learning models to make sense of this data, all data samples need to be labeled. Data labeling tells the machine learning models what knowledge they need to acquire via supervised learning to power smart applications. This makes labeling critical in preparing data sets for training machine learning models. However, data labeling can also represent the chief source of errors, affecting potential improvement in model performance. Machine learning models can only be as accurate as the labeled data, which represents the models’ entire knowledge for the particular use case. For example, the source image data set in a face recognition program requires a label for every face shown in every image. During the labeling process for this data set, every image is reviewed by human subject matter experts, crowdsourced labelers on platforms like Amazon Mechanical Turk, or algorithms. Labeling helps clean and prepare the data set by removing noisy or unusable data. In this case, images that don’t contain any faces, or that show unreadable faces due to poor lighting or angles, should be removed because they won’t be helpful in training a face recognition model. This step also ensures the inclusion of images that are most helpful for the desired use case. Once the data set is reviewed and annotated, it can be used for all subsequent face recognition applications instead of going back to the raw data set. This saves time and effort for data engineers, as well as data scientists who might build novel models using the same data set. Additionally, multiple labels and metadata can be applied to each image during the labeling process so that they’re available for new use cases. A tag that identifies the face as that of a man, woman, or child can be used for different computer vision applications. This can potentially give the data set more flexibility for the future. The labeling can be built upon in subsequent versions of the data set. Once the face recognition model is live in production, new images can be labeled to help the model overcome data drift and augment its performance in the face of changing data distributions. This continued labeling and organizing keeps the models more robust and consistent. Data Preparation Steps There are certain best practices to follow when preparing data sets for deep learning applications. Following is a checklist for data engineers when working with unstructured data: (1) Check data formats Samples in a data set, especially if collected via web scraping or crowdsourcing, may come in multiple data formats. For example, an image could be a JPEG, PNG, or TIFF, while an audio file could be a WAV, MP3, or FLAC. Check whether the data set samples are in different formats, so that you can standardize the format across all samples. (2) Verify data types Certain deep learning applications are based on multimodal data including text, images, audio, video, and structured metadata. For example, a model that predicts what video a user might watch next is trained using multiple data types. It verifies the type of each data sample, then indexes and stores them separately. Note that an individual data type like numbers might also belong to different types like int, float, or string. (3) Verify data dimensions It’s crucial to check the dimensionality of the samples in a data set. For example, a set of images containing faces may be gathered from different cameras, each associated with different default image dimensions. (4) Identify what data needs to be labeled Once you’ve completed the above steps, you can begin data labeling. It may not be feasible in some situations to label each data sample, because manual labeling can be prohibitively expensive and time-consuming. In this case, choose an appropriate number of data samples for labeling. For common machine learning classification use cases, you need to sample data for labeling from each category. (5) Determine what type of labeling to perform The same data sample can be labeled in multiple ways depending on the use case. For instance, an image containing people and cars may be labeled for faces, for segmenting people or cars, or for the vehicle registration plates. (6) Decide who will label the data Data labeling can be performed manually by domain experts, crowdsourced from non-experts, or done programmatically using rule-based or model-based algorithms. Determine which annotators will define what kind of data, depending on their expertise or level of training. If a data set will be labeled using software, then the required configuration parameters, protocols, and performance metrics need to be established so that labeling is consistent. (7) Review data for errors and mistakes Usually, the first round of data labeling contains errors. To improve the data quality and eradicate errors, more experienced annotators should conduct a second or third level of review. Depending on cost, time, and available resources, each data sample can also be independently labeled by multiple annotators; the most commonly provided label can be assigned as the final label. (8) Split the data set into training and testing segments Once a data set is labeled, split it into separate train and test subsets for training and evaluating the model, respectively. Depending on the use case and the amount of available data, the ratio might be 80:20, 90:10, or even 99:1. To obtain more reliable results, k-fold cross-validation is recommended. Multiple training and test sets are sampled randomly, and the final results are averaged across all the different folds. Conclusion Without the protection of systematic data preparation and labeling checks, you may find that poor quality data damages the accuracy and performance of any analysis or models based on that data. If you follow the above guide, you will be able to ensure your data is good quality and labeled accurately. Related Blogs
Introduction Metrics are widely used by data, product, strategy, and business teams to capture and summarize data about various aspects of user behavior, product performance, and the health of the business. Metrics like annual recurring revenue (ARR), gross merchandise volume (GMV), customer acquisition cost (CAC), lifetime value (LTV), and net promoter score (NPS) are common parlance in product startups and large tech companies. Technical and business stakeholders need the information collected in metrics to make sense of their product and business performance so that they can make data-driven decisions. This makes tracking metrics essential to detect potential issues, plan new business initiatives, ensure growth, and share pertinent information with regulatory bodies as well as shareholders. A change in growth metrics can deeply impact investor confidence and the perception of the company in public markets. For instance, the stock prices of Meta and Netflix recently plummeted after they reported declines in key growth metrics like daily active users (DAU) and number of subscribers, respectively. For tech companies at this scale, staying on top of metrics is critical and requires a sophisticated approach to data engineering, data governance, and data democratization. In this article, you’ll learn about how metrics are defined, used, and managed at different types of large tech companies. How Do Large Companies Define and Use Metrics? Though large companies are equally reliant on metrics to drive their decision-making, what they measure and how they measure it will vary by company. The following are examples of the metrics strategies used at Uber, Airbnb, Spotify, and Netflix. Uber Uber’s core business is a marketplace that connects riders with drivers in real time at a global scale. Its product teams rely most heavily on metrics related to trips taken and driver experience, such as “driver acceptance rate” and “completed trips.” It also uses map data to determine driver ETA and pickup and dropoff spots. Because disparate versions of the same metrics were being used across business teams, leading to ineffective and poor decision-making, Uber implemented changes to improve metric standardization. The company built a unified metric platform called uMetric to enforce a strict one-to-one mapping from business logic to metrics without any discrepancies. uMetric is built on engineering solutions that democratize data and provide a clear understanding of the entire metric lifecycle so that the data can be better used in machine learning models. The platform enables access to metrics across their entire lifecycle, from definition, discovery, and planning to computation, quality, and consumption. Clear and unambiguous definition of metrics is a key pillar of the platform, and metrics can be defined by any author without any duplication. In uMetric, a metrics definition model is designed on the following core principles:
Using this definition model is not enough to ensure metric standardization, however. Additional policies and solutions focused on data governance, data quality, and access control are necessary to scale the platform across the company. Airbnb Similar to Uber, the vacation rental marketplace Airbnb built a metrics platform called Minerva to achieve metric consistency and serve as the ground truth for data analytics, reporting, and experimentation. Airbnb built its foundation of data on lodgings and vacation rentals on tables referred to as `core_data`. As the company grew, though, teams built separate tables on top of `core_data` without any information about data lineage or correspondence between these tables. This led to conflicting results and insights, which confounded data-driven decision-makers. Minerva was designed to solve these problems. It takes facts and dimension tables as inputs, optimizes the data through denormalization, then sends the data to downstream applications. Minerva acts as the metric store for more than 30,000 metrics produced by more than 200 stakeholders across the organization. As uMetric does, Minerva supports the end-to-end lifecycle of a metric from definition to deprecation and powers the whole tech stack of Airbnb. Metrics, dimensions, and metadata are defined and stored in a central GitHub repository that is accessible by any stakeholder in the company. Once defined, metrics can be used anywhere via dashboarding tools or A/B experimentation frameworks. All the metrics defined in Minerva are indexed in Dataportal, Airbnb’s internal data catalog. A deeper dive into the metrics is facilitated by another tool called Metric Explorer, which is designed for both technical and non-technical users. Minerva powers several downstream applications:
Spotify The Spotify global audio streaming service also developed an in-house metrics catalog, but as part of a modern A/B testing experimentation platform in order to create custom metrics at scale. Spotify’s metrics catalog runs SQL pipelines to ingest metrics into a data warehouse. This enables the collected metrics to be almost instantly stored, managed, and served to the experimentation platform. A key feature of the metrics catalog is that it enables self-service. Teams can write SQL queries to define metrics, and the rest is taken care of by the managed system. To address the problem of lack of metrics standardization and metrics duplication, Spotify built a Metrics Hub. In addition to providing a single source of truth, the hub also focused on creating symmetry between offline and online use of metrics. This feature makes it easy to take any metric definition and deploy it seamlessly in different environments to power experimentation and machine learning use cases. In typical A/B testing experiments, users are split into distinct groups. Consider a hypothetical example in which Spotify wants to A/B test whether podcasts are more popular in the 30- to 39-year age group or the 20- to 29-year age group. This experiment requires a set of user-level input metrics like demographics, daily or weekly listening time, number of songs listened to, and number of podcasts listened to. Spotify’s metric pipeline integrates these metrics with the experimental group each user belongs to. This data is combined and stored in a data warehouse, then accessed with an API that allows users to query data without needing to understand the underlying storage. A metrics catalog enables multiple stakeholders to access and analyze data, which helps an organization to more efficiently and quickly improve the customer experience. Netflix As a global entertainment platform that serves real-time video content to millions of users, Netflix needs to mine numerous insights on metrics like user engagement, viewership, and video streaming quality. It uses the data it gathers to make recommendations to users based on factors like watch history and demographics. Netflix powers multiple experiments in parallel through a centralized A/B experimentation platform. Similar to Spotify, this platform has a metrics catalog at its core. A centralized metrics repository built using Python, Metrics Repo is home to diverse user-level as well as technical metrics like streaming time, play delay, and retention rate. Metrics Repo provides a unified platform for metric definitions that are typically defined and engineered differently by various business teams. In this modular architecture, data scientists can add metric definitions directly and join data tables to perform metric computations. Analytical reports can be calculated on demand without affecting the underlying metrics. Metrics Repo serves as a single source of truth for statistical analysis and causal inference based on these metrics and visualization of corresponding results and insights. This architecture provides a transparent metric lineage and definition, ensuring greater trust in the experimental results. This is critical for enabling rapid mining of insights, development of new products and strategies, and executive-level decision-making. Conclusion Metrics provide a data-driven summary of key business goals and operational performance. Product managers, data analysts, and business leaders use them to assess and track the growth of the business, as well as devise new products and strategies. Because metrics are so crucial to the health and growth of a business, stakeholders need a clearly defined way to collect and measure metrics in order to improve their decision-making. You’ve learned about how data teams define and use metrics at four top tech companies: Uber, Airbnb, Spotify, and Netflix. Uber and Airbnb built an internal metrics platform that manages the entire lifecycle of their metrics. Spotify and Netflix, meanwhile, built metrics catalogs to form a central pillar of a modular and scalable experimentation platform. These different solutions achieve the same goal of making necessary data cohesive, consistent, and actionable. Related Blogs
Published by Andela Introduction
Data culture refers to an organizational culture of using data to derive insights and make informed business decisions. Companies can build a strong data culture by arming themselves with data and the right set of people, policies, and technologies. A data culture helps companies become more competitive and resourceful by leveraging data. And data-driven companies make better, faster, and more objective business decisions. They promote greater employee engagement and retention, and drive better financial outcomes in terms of revenue, profitability, and operational efficiency. In this article, you'll learn about data culture, what its importance is for modern organizations, and how you can build a strong data culture at your company. Why You Need a Strong Data Culture? Without a solid data culture, organizations will inevitably fail to harness the power of data. As previously stated, data culture refers to a set of beliefs and practices that companies use to cultivate and drive more data-driven decisions. Traditionally, businesses relied on the instinct and gut of a select few leaders to make strategic business decisions. However, with the accumulation and collection of massive volumes of customer and business data, domain expertise and instinct can now be complemented with data-driven insights to make more informed decisions. There are several advantages to building a strong data culture. Some of these include the following:
Every business sector, from product to finance to HR, creates and collects a lot of data from external customers or internal operations. For business heads and decision-makers, it's no longer feasible to stay on top of the ever-increasing volumes of data to better understand and evaluate the current state of their organization. However, with data analysts and scientists embedded across each department, it is possible to tap business insights in real time and respond quickly to changes in business performance. A strong data culture also promotes greater employee engagement and retention. When employees see that decisions are made on the basis of data and not driven just by the highest-paid executives, they feel that they can contribute more insights to influence decision-making. In the long term, this facilitates attracting the best talent in the market who can be incentivized to have a greater say in making key business decisions using data. Moreover, there are also strong financial outcomes associated with building and promoting a data culture. Companies with data-driven cultures benefit from increased revenue, better customer services, and more operational efficiencies leading to improved profitability. How to Build a Strong Data Culture? Building a strong data culture is a long-term endeavor that requires patient support and encouragement from leadership. Companies with strong data-driven cultures have executives who lead by example and establish clear expectations that decisions will be objective and based on data. Data leaders can lead from the front by establishing clear goals and guidelines, investing in technology and training, as well as identifying and rewarding employee behaviors that embody a data-led culture. Beyond leadership setting a tone for the whole organization, let's take a look at a few other components that can help build a strong data culture. 1 Bring Business and Data Science Together One of the first steps in building a data culture is to build a strong data science team consisting of data analysts, data engineers, and data scientists. Having quality in-house data talent is a competitive advantage that reaps multiple benefits, including building a robust culture focused on data. Once a data science team is up and running, it needs to be strategically embedded across various departments of the business. This helps business professionals interact with data professionals more regularly and better understand how the power of data analytics and data science can improve business efficiencies and impact profitability and growth. At the same time, this setting enables data professionals to better understand how the business works and build intuition for developing better data and machine learning–powered tools and products. This creates a positive flywheel where both business and data science teams learn to collaborate better and benefit from their respective skill sets. By bringing business and data science together, everyone in the organization learns to appreciate the value of data and use data-driven insights to improve the quality of their decisions, products, and services. 2 Leverage Data When Creating Goals and Deadlines Driving strategic business goals and metrics by leveraging data is a key aspect of encouraging a data-led culture. When goal-setting exercises are conducted objectively and leaders regularly use data and metrics from previous business quarters or external data about competitors or the overall market, everyone in the organization will start to embrace similar data-driven approaches. Leveraging data for setting new targets also enables every stakeholder in the organization to understand and anticipate their future goals and prioritize their work accordingly. Data-led goal setting is a more democratic and fair-minded process that encourages ownership of respective goals by every employee, as opposed to arbitrary, instinct-led, unilateral decisions made by the leadership. 3 Ensure Everybody Has Access to Data A fundamental step toward attaining a data culture is to democratize access to data across the organization. Data culture is a difficult goal when employees in different parts of a business struggle to obtain data. If you don't give your employees access to your data, they won't be able to utilize it when making decisions. This disenfranchises the data analysts, engineers, and scientists disproportionately, as their day-to-day work is impacted the most. Without a motivated team of data professionals, the downstream benefits of data are unlikely to materialize across various business departments. A strong foundation of data governance and data democratization is a prerequisite to achieving the business goals associated with a robust data culture. 4 Keep Your Data Technology Up-to-Date A critical aspect of building a data culture is employing modern tools and technologies to make it easier for employees to access, analyze, and share data-driven insights. Building a modern data stack with newer components like a metrics layer simplifies data-based operations and analytics for everyone, especially nontechnical business stakeholders. Technology, like data warehouses and metrics layers; data analytics tools, like Tableau or Power BI; and customer relationship management (CRM) tools, like Salesforce, are indispensable for modern businesses. Building the data architecture in a cloud environment like Amazon Web Services further improves access to data and reduces the need for multiple tools with a steep learning curve. The right use of tools for data, collaboration, and customer service goes a long way in fostering the use of technology to drive a strong data-led culture. 5 Provide Training for Employees Having supportive leadership and access to data and technology is of little use if employees are not data literate and able to extract insights from data. This requires further investment in terms of learning and development to empower employees with the necessary skills to explore, understand, and share data-driven insights across the organization. In addition to reducing the skills gap, it also encourages people from nontechnical backgrounds to become more data savvy, collaborate better with data experts, and build more comprehensive data products and solutions to benefit the business. 6 Reward Data-Oriented Decisions and Behavior The primary challenge to becoming a data-driven organization is not technical but cultural. A strong data culture is based on a robust foundation of people, policies, and technology. However, once the initial foundation is in place, data leaders need to maintain and bolster the spirit of data-driven decision-making by incentivizing and rewarding behaviors that embody the culture. At the same time, decisions and behaviors that do not represent a holistic data-led process ought to be called out and questioned until every single employee is on board with the philosophy of using data for every decision. This includes encouraging experimentation to answer key business questions for which data does not exist yet or when the current set of data does not yield compelling evidence. Conclusion In this article, you learned about the importance of a data culture for businesses. It's a formidable task to build a strong data culture and is a top priority for a majority of CEOs. Data-driven companies are in a better position to attract and retain talent, make faster decisions with more conviction, and drive stronger growth and profitability to meet their business goals. According to research by McKinsey & Company, data-driven companies are able to achieve their goals faster and realize at least 20 percent more earnings. Related Blogs Introduction
Today, data is at the core of many companies, and it's of the highest importance for running a successful business. Companies process huge amounts of data daily, which they must store, categorize, track, and organize by cataloging, and that's where data governance comes in. Data governance is a set of processes that promote better management of business data, unlocking the true value of data by ensuring that it's more accessible, reliable, secure, and compliant. For modern data-driven organizations, a strong data governance framework is not only important but essential for the best use of data in business decisions. A strong data governance framework usually encompasses functions such as managing data access and data ownership, tracing data lineage, managing duplicate or false data, and classifying and assuring data quality. All of these are the pillars of a successful data governance process. However, implementing a robust data governance framework is no small feat. If not done systematically, it can lead to a huge loss of organizational time, resources, and effort. Companies that have made significant progress in building data governance frameworks and cultivated a strong and inclusive data culture have done so incrementally, aligning incentives and creating deep collaboration across cross-functional teams that own the data governance roadmap. Organizations are more likely to be successful if they can bring together people, processes, and technology to build their framework. In this article, you'll learn about best practices for implementing data governance in an organization. Companies can leverage existing best practices and build on them to fast-track their own data governance efforts. What Are the Challenges of Implementing Data Governance? Before you plan your data governance strategy, you need to look out for some common challenges. One major challenge for organizations is building a strong business use case for investing staff and resources in a data governance framework. Those that haven't yet embraced digital transformation and the better, faster decision-making possible with deeper data analysis might not see the long-term business value of data governance. It's important to unite relevant stakeholders across the organization to take on the challenge. Even when organizations do launch a governance framework, they may fail to achieve its true potential. Poor data leadership and ownership may be an obstacle, for example. Data governance also requires collaboration and consistent enforcement across departments to succeed. For example, the finance department could collaborate with the accountancy department to create a cross-practice team to communicate and transfer data more transparently. So, without the buy-in and blessings of the tech and collaborative data ownership that helps break down the organizational silos, the program is unlikely to come to fruition. Additionally, a good data governance framework relies on high-quality data. The primary goal of data governance is to make data more accessible, secure, and reliable for stakeholders to consume for their own use cases. However, if the quality of the data at the source is poor, implementing data governance becomes much more difficult. Data Governance Best Practices The following are best practices that have been adopted successfully by numerous organizations, such as Collibra, IBM, Informatica, Select Star, and more, in building comprehensive data governance frameworks. 1 Build a Strong Business Use Case The goal of data governance is to enable every stakeholder to use the data to make business decisions relevant to their department, whether that's sales, marketing, finance, or human resources. This means that you need the support and alignment of all users and departments right from the beginning. Without cross-functional support, building a strong business case for investing in a long-term mission like data governance is less likely to succeed. Data governance generates some significant business benefits that can make the advantages of the process clear to the leadership. It saves time and provides improved security and reliable and more accurate data, making it easier to make data-driven decisions. When these business benefits are made clear to the leadership, it's easier to get approval for needed staff, budget, and resources for the project. 2 Identify Data Stewards and Owners Clearly defined roles and owners are necessary to build the data governance framework in a structured manner. Knowing which stakeholders own certain responsibilities also helps with clear lines of communication. Exact roles may differ across organizations, but the following are common choices:
3 Start Small Creating a strong data governance framework requires the right mix of people, processes, and technology to come together. It's crucial to start small and aim for quick incremental wins rather than overpromising and underdelivering. Creating governance guidelines requires specific expertise; you could hire this expertise, but empowering and upskilling people within your existing team might be more successful as they already know your data. Those responsible for data governance then need to gradually build trust and seek alignment from various cross-functional departments before the framework policies can be enshrined as organization-wide processes. For governance-based processes to be adopted and diligently followed, your data stewards need to implement regular checks and audits and guide team members and departments that might not be familiar with good data governance practices. This guidance has two dimensions: cultural guidance and technological guidance concerning the required tools. When data stewards implement processes, they should also implement the right tools for advanced actions such as automation. Once every cross-functional team understands when and how to use governance principles in their day-to-day work with the help of the tools, you can automate some of the processes. 4 Define and Measure Metrics Data governance is a long-term investment. However, it's important to measure progress in smaller time frames to ensure that key milestones are being achieved without any delays or hurdles. Monitoring some metrics, such as the percentage of the data assets per ownership, the number of questions or errors that are reported to the data team, or the number of dashboards that are being used across the organization and their types, might help achieve those key milestones in the long term. In other words, a clear roadmap with specified deliverables, timelines, and metrics that are shared among all the owners ensures that progress can be evaluated in achievable, measurable steps. You need to be able to periodically check the progress of your governance framework to ensure that it's still on track. This image shows a detailed roadmap for establishing a data governance program over a period of two years. Individual tasks can be defined for each business quarter and for different aspects of the framework, such as data insights, data quality, data standards, and data governance and management. For example, improving data quality can be broken down into multiple milestones per business quarter. The goal for the first quarter may be hiring a data engineering team, while the next quarters may focus on establishing reference data repositories, data cleaning, and building data stores and data warehouses. This structured approach keeps cross-functional teams informed on the overall plan and ensures continued progress. 5 Establish Strong Communication Channels Frequent and effective communication is the key to aligning stakeholders and collaborating across teams. Everyone should understand the desired goals and keep others informed on their progress in implementing them. Additionally, your data stewards must be as transparent as possible to earn trust across the organization and emphasize the impact of investment in data governance to the executive leadership as well as to the downstream users of the framework. They can create a single channel for communication, which is like a linked data catalog where you can search data assets or collaborate on them. This way of communication is pivotal both during the implementation phase and after the framework is established. A single channel for communication will help drive strong adoption rates, resolve queries, and allow you to share updates to the governance policies as data and compliance requirements evolve. 6 Contextualize Data Data contextualization involves adding any relevant information to data to make it actionable. Contextualization provides users better interpretation of the data and enables organizations to make smarter decisions. This helps a data governance process work more efficiently as contextualized data has clearer meanings and allows decision makers to have enriched information regarding the actions they should take. Moreover, it can help improve how the organization handles data in its data governance environment. 7 Build a Long-Term Strategy for Data Governance Achieving a strong data governance framework can be a moving target. You need to ensure that stakeholders know this is a long-term investment. Data governance is a continuous process that consists of many smaller projects and deliverables. Ramping up speed and complexity over time helps to scale your efforts. While the overall framework may take several years, smaller milestones can be set and achieved over shorter time frames, like a business quarter. For instance, a useful set of milestones to accomplish in the first quarter of working on a data governance framework may include establishing data management policies and standards, hiring a data engineering team, and drafting a data management strategy together with all relevant stakeholders. As long as they see incremental progress, stakeholders will learn to trust the process and be invested in the success of the project. 8 Expose the Data through Documentation Knowing exactly what your data represents is a critical component of data governance. Users should have a single, centralized platform where they can find documentation related to their data. This documentation should be continuously updated, reviewed, and revised and should also be directly tied to the actual data assets. These actions will ensure that your users can trust and rely on your documentation, as it will always be up to date and accurate. Strong data governance should expose the data through process-oriented documentation that is directly connected to the data. 9 Data Lineage and Usage Knowing the source of data, where your data is flowing, and who is accessing it is important. With data governance, you have to build trust in your data, ensure the data is used properly in your organization, and troubleshoot issues when they arise. Data lineage helps automatically identify sensitive information and propagate some data governance-related policies. Data lineage also informs reports, issue logs, and audit logs, which show that the data governance requirements are met. As an example, data lineage prevents teams from using a dashboard that was supposed to be deprecated or two different business units from building a metric using different underlying data. Successful Data Governance Frameworks Several large global companies have successfully implemented data governance frameworks. The following are some examples. PwC, a global professional services company, has created a data governance framework consisting of the following components:
ING, a Dutch multinational banking and financial services corporation, leveraged IBM Cloud Pak to improve data governance for its users in a hybrid cloud environment. There are also several third-party companies that assist larger organizations with their data governance strategy and implementation, such as Collibra, Informatica, and Alation, and data catalogs that provide tools and insights required for implementing a data governance practice on your own, such as Select Star and Atlan. Outcomes of a Strong Data Governance Implementing a strong data governance strategy will inevitably lead to outcomes such as improved data quality, decreased data management costs, and better data analytics, which, in turn, leads to better decision-making throughout the organization. The following list provides an overview of the outcomes of effective data governance:
For an organization, the time it takes to achieve these outcomes is closely related to the strength of its data governance implementation processes. Over time, these all contribute to one overarching outcome: organizational success. Conclusion Data governance is an essential requirement for modern organizations to drive greater adoption of data and empower business decision-making. Organizations can find it difficult to extract the full value of their data assets, especially as the amount of data keeps growing. Data governance frameworks lay down clear policies and guidelines for improving the quality of data and democratizing its usage across a business. If you can navigate the challenges involved and follow the above best practices in creating and implementing your data governance framework, you can accelerate your organization's understanding and usage of data and deliver data-driven decision-making to your organization. Related Blogs
Introduction
Traditional machine learning is based on training models on data sets that are stored in a centralized location like an on-premise server or cloud storage. For domains like healthcare, privacy and compliance issues complicate the collection, storage, and sharing of critical patient and medical data. This poses a considerable challenge for building machine learning models for healthcare. Federated learning is a technique that enables collaborative machine learning without the need for centralized training data. A shared machine learning model is trained by keeping all the training data on a device, thereby ensuring higher levels of privacy and security compared to the traditional machine learning setup where data is stored in the cloud. This technique is especially useful in domains with high security and privacy constraints like healthcare, finance, or governance. Users benefit from the power of personalized machine learning models without compromising their sensitive data. This article describes federated learning and its various applications with a special focus on healthcare. How Does Federated Learning Work? This section discusses in detail how federated learning works for a hypothetical use case of a number of healthcare institutions working collaboratively to build a deep learning model to analyze MRI scans. In a typical federated learning setup, there’s a centralized server, for instance, in the cloud, that interacts with multiple sources of training data, such as hospitals in this example. The centralized server houses a global deep learning model for the specific use case that is copied to each hospital to train on its own data set. Each hospital in this setup trains the global deep learning model locally for a few iterations on its internal data set and sends the updated version of the model back to the centralized server. Each model update is then sent to the cloud server using encrypted communication protocols, where it’s averaged with the updates from other hospitals to improve the shared global model. The updated parameters are then shared with the participating hospitals so that they can continue local training. In this fashion, the global model can learn the intricacies of the diverse data sets stored across various partner hospitals and become more robust and accurate. At the same time, the collaborating hospitals never have to send their confidential patient data outside their premises, which helps ensure that they don’t violate strict regulatory requirements like HIPAA. The data from each hospital is secured within its own infrastructure. This unique federated learning setup is easily scalable and can accommodate new partner hospitals; it also remains unaffected if any of the existing partners decide to exit the arrangement. Use Cases for Federated Learning in Healthcare Federated learning has immense potential across many industries, including mobile applications, healthcare, and digital health. It has already been used successfully for healthcare applications, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. As an example of its use for mobile applications, Google used this technique to improve Smart Text Selection on Android mobile phones. In this use case, it enables users to select, copy, and use text quickly by predicting the desired word or sequence of words based on user input. Each time a user taps to select a piece of text and corrects the model’s suggestion, the global model receives precise feedback that’s used to improve the model. Federated learning is also relevant for autonomous vehicles to improve real-time decision-making and real-time data collection about traffic and roads. Self-driving cars require real-time updates, and the above types of information can be effectively pooled from several vehicles in real time using federated learning. Privacy and Security With increased focus on data privacy laws from governments and regulatory bodies, protecting user data is of utmost importance. Many companies store customer data, including personally identifiable information such as names, addresses, mobile numbers, email addresses, etc. Apart from these static data types, user interactions with companies such as chat, emails, and phone calls also carry sensitive details that need to be protected from hackers and malicious attacks. Privacy-enhancing technologies like differential privacy, homomorphic encryption, and secure multi-party computation have advanced significantly and are used for data management, financial transactions, and healthcare services, as well as data transfer between multiple collaborative parties. Many startups and large tech companies are investing heavily in privacy technologies like federated learning to ensure that customers have a pleasant user experience without their personal data being compromised. In the healthcare industry, federated learning is a promising technology that allows, for example, hospitals to share electronic health records (EHR) to create more accurate models. Privacy is preserved without violating strict HIPAA standards by decentralizing the data processing, which is distributed among multiple end-points instead of being managed from a central server. Simply put, federated learning allows training of machine learning models without the need to collect raw data in a central location; instead, the data used by each end-point (in this example, hospitals) remains local. By combining the above with differential privacy, hospitals can even provide a quantifiable measure of data anonymization. Federated Learning vs. Distributed Learning and Edge Computing Federated learning is often confused with distributed learning. In the context of deep learning, distributed training is used to train large, deep neural networks across a number of GPUs or machines. However, distributed learning relies on centralized training data shared across multiple nodes to increase the speed of model training. Federated learning, on the other hand, is based on decentralized data stored across a number of devices and produces a central, aggregate model. A fascinating example of the potential of this technology is using federated learning-based Person Movement Identification (PMI) through wearable devices for smart healthcare systems. Edge computing is a related concept where the data and model are centralized in the same individual device. Edge computing doesn’t train models that learn from data stored across multiple devices, as in the case of federated learning. Instead, a centrally trained model is deployed on an edge device, where it runs on data collected from that device. For example, edge computing is applied in the context of Amazon Alexa devices, where a wake word detection model is stored on the device to detect every utterance of “Alexa.” AI and Healthcare Federated machine learning has a strong appeal for healthcare applications. By design, patient and medical data is highly regulated and needs to adhere to strict security and privacy standards. By collating data from participating healthcare institutions, organizations can ensure that confidential patient data doesn’t leave their ecosystem; they can also benefit from machine learning models trained on data across a number of healthcare institutions. Large hospital networks can now work together and pool their data to build AI models for a variety of medical use cases. With federated learning, smaller community and rural hospitals with fewer resources and lower budgets can also benefit and provide better health outcomes to more of the population. This technique also helps to capture a greater variety of patient traits, including variations in age, gender, and ethnicity, which may vary significantly from one geographic region to another. Machine learning models based on such diverse data sets are likely to be less biased and more likely to produce more accurate results. In turn, the expert feedback of trained medical professionals can help to further improve the accuracy of the various AI models. Federated learning, therefore, has the potential to introduce massive innovations and discoveries in the healthcare industry and bring novel AI-driven applications to market and patients faster. Conclusion Federated learning enables secure, private, and collaborative machine learning where the training data doesn’t leave the user device or organizational infrastructure. It harnesses diverse data from various sources and produces an aggregate model that’s more accurate. This technique has introduced significant improvements in information sharing and increased the efficacy of collaborative machine learning between hospitals. It circumvents and overcomes the challenges of working with highly sensitive medical data while leveraging the power of state-of-the-art machine learning and deep learning. Related Blogs |
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