Sundeep Teki
  • Home
    • About Me
  • AI
    • Hiring
    • Speaking
    • Papers
    • Testimonials
    • Content
    • Course
    • Neuroscience >
      • Speech
      • Time
      • Memory
  • Coaching
    • Advice
    • Testimonials
  • Training
    • Testimonials
  • Consulting
  • Blog
  • Contact
    • News
    • Media

The COO’s AI Blueprint: Spearheading Operational Excellence with Generative AI

28/5/2025

Comments

 
Picture
Picture
Picture
Here's an engaging audio in the form of a conversation between two people.
I. The AI Imperative: COOs Leading the Operational Revolution

​A. Introduction: From AI Hype to Operational Reality

The rapid evolution of Artificial Intelligence, especially Generative AI (GenAI) and the emerging Agentic AI, presents both a formidable challenge and a significant opportunity for enterprise leaders. The imperative is to translate AI's vast potential into tangible operational impact and sustainable strategic advantage.1 Agentic AI, with systems capable of autonomous action, is poised to become a major trend, potentially integrating AI agents into the workforce.2

For Chief Operating Officers (COOs), the focus must be on practical application and value extraction. Many organizations are still in nascent stages; a McKinsey survey revealed only 17% of organizations derive over 10% of their Earnings Before Interest and Taxes (EBIT) from GenAI, and a mere 1% claim full GenAI maturity.1 This highlights a critical execution gap. COOs, at the nexus of strategy and execution, are pivotal in bridging this gap and moving from AI's theoretical possibilities to operational reality.

B. The Evolving COO Mandate & The Execution Gap
The COO's traditional role as an operational guardian is evolving into that of an AI-powered value architect. They are now central to driving strategic transformation by embedding intelligence into core processes and identifying new AI-fueled value streams.1 This expanded mandate requires COOs to lead the "GenAI-based rewiring" of their organizations, ensuring AI investments yield tangible returns.1 Midlevel leaders, often reporting to COOs, are instrumental in embedding AI into daily practices and cross-functional processes 3, leveraging the COO's oversight of all operational facets.4

Despite enthusiasm, a significant execution gap persists. Only 19% of US C-suite executives reported GenAI increasing revenue by over 5%, and globally, just 17% of organizations derive over 10% of EBIT from GenAI.1 Many find GenAI development too slow, and only 12% have identified revenue-generating use cases.1 This is echoed by findings that while 73% of companies invest over $1 million annually in GenAI, only a third see tangible payoffs 5, and over 80% of AI projects may fail to meet objectives.6 This gap often stems from immature data foundations, a lack of AI literacy, and ineffective change management—challenges COOs must address holistically.

II. Architecting for AI Success: Critical Foundations for COOs

A. Designing AI-Ready Operating Structures & Data Governance
To harness AI, COOs must champion AI-ready operating structures that move beyond traditional silos to foster synergy and agility. Initially, a Center of Excellence (CoE) or a "factory" model, guided by executive and operational committees, can establish standards and build foundational capabilities.1 Gartner notes organizations often evolve from communities of practice towards target operating models for scaling AI.7 As maturity grows, a federated or hub-and-spoke model, like OCBC Bank’s "internal open-source hub" 8, can empower business units while maintaining central guidance. COOs must architect these structures to balance control with empowerment, ensuring solutions are impactful yet achievable.1

Robust data governance is a non-negotiable strategic imperative. The quality, integrity, and ethical handling of data directly determine AI reliability.1 COOs, with CDOs and CIOs, must champion comprehensive data governance frameworks 1, viewing it not as a cost but as an enabler of value and a risk mitigator.10 Governance must be proactive, business-aligned, and embedded into AI workflows, moving towards automated enforcement to scale effectively.2

B. Effective Change Management: Paving the Way for AI Adoption
GenAI and Agentic AI fundamentally alter roles and processes, making effective change management critical.1 COOs must sponsor structured change management from the outset. As Forrester notes, "Whatever communication, enablement, or change management efforts you think you'll need, plan on tripling them".12
Frameworks like Gartner's multistep process (prioritizing outcomes, diverse teams, compelling narratives, "culture hacking," addressing resistance) 13 or Prosci’s ADKAR model (Awareness, Desire, Knowledge, Ability, Reinforcement) 14 offer systematic approaches. High AI project failure rates often trace back to poor adoption, a failure of change management. COOs must ensure the organization is prepared technologically, culturally, and behaviorally.

III. Driving Operational Impact: From Strategic Use Cases to Measurable ROI

A. Identifying & Prioritizing AI Use Cases for Tangible Value
COOs must guide a pragmatic approach to AI use case identification, moving beyond "pilot purgatory" to initiatives delivering tangible value aligned with business objectives.1 Gartner’s AI roadmap emphasizes starting by "prioritizing a set of initial use cases, running pilots, and tracking and demonstrating their business value".7 Focus on opportunities where AI can address "long-standing operational logjams" 1 or create new efficiencies, often starting with "narrowly defined, high-impact use cases".9 AWS highlights numerous GenAI use cases spanning customer experience, employee productivity (e.g., automated reporting, code generation), and process optimization (e.g., intelligent document processing, supply chain optimization).15 COOs should use an "impact vs. feasibility" matrix to select strategically sound and operationally achievable initiatives.

Illustrative High-Impact AI Domains:
  • Supply Chain & Logistics: Enhanced demand forecasting, autonomous procurement for improved efficiency and cost reduction.1
  • Customer Operations: Personalized communication at scale, proactive issue resolution by AI agents for increased satisfaction and agent productivity.15
  • Manufacturing & Production: Predictive maintenance, self-optimizing production lines to reduce downtime and improve quality.15
  • Finance & Risk: Anomaly detection, automated compliance monitoring for reduced losses and improved efficiency.15

B. The Ascent of Agentic AI: Autonomous Operations
Agentic AI systems "act autonomously to achieve goals without the need for constant human guidance".2 Unlike GenAI or rules-based RPA, they possess independent reasoning, decision-making, and action execution, learning from interactions (Perceive, Reason, Act, Learn).2 Their potential is immense for automating complex workflows where traditional automation falls short.16

Examples include expediting procure-to-pay approvals, resolving order-to-cash discrepancies, collating customer information in contact centers, streamlining HR onboarding, and providing immediate IT troubleshooting.16 As AI gains such autonomy, the need for robust governance, meticulous oversight, and a new trust paradigm becomes even more critical. COOs must plan for Agentic AI as a catalyst for re-imagining entire operational processes.

C. Measuring AI ROI: A Pragmatic Approach
Demonstrating AI ROI is a "business mandate" 20, yet nearly half of leaders find proving GenAI's value the biggest hurdle.20 COOs need a pragmatic approach encompassing financial metrics, operational efficiencies, and qualitative benefits.6
  • Financial Metrics: Direct cost savings and revenue uplift.6 An IDC study suggests a $3.70 return for every $1 invested in GenAI.6 A GenAI content creation tool yielded a 333% ROI, driven by labor efficiencies and reduced agency scope.14
  • Operational Efficiencies: Improvements in time-to-market, process efficiency, automation rates, and productivity.6 Chatbots can deliver 40-100% productivity gains, and intelligent document processing 500-1000%.21 LLMs for SQL code migration reduced processing time per table from one day to one hour.7
  • Qualitative Benefits: Customer satisfaction, retention, employee engagement, and decision quality.6
COOs must address "productivity leakage"—ensuring AI-driven efficiency gains translate to bottom-line savings by restructuring roles or redirecting freed-up time to higher-value activities.7

IV. The Human-Centric Transformation: Building an AI-First Culture

A. Fostering an AI-Literate Workforce & AI-First Mindset
Creating an AI-first culture requires broad AI literacy—understanding AI's capabilities, limitations, and ethics—and fostering a mindset of curiosity, experimentation, and human-AI collaboration. Forrester states, "Close The AI Literacy Gap To Unlock Real Impact," as hesitation due to lack of understanding cripples adoption.15

The journey involves "building foundational AI knowledge," "cultivating an AI-first mindset" (AI as an enhancer, not a replacer), honing "AI-specific skills," and "leading with confidence".3 Effective AI systems also need human expertise for training with "clear, labeled examples".13 COOs must champion pervasive AI literacy programs for the entire workforce.

B. Dr. Teki's Perspective: Neuroscience for Impactful AI Upskilling
Traditional corporate training often fails to align with how adults learn . Dr. Sundeep Teki's expertise in neuroscience 3 offers an advantage. Principles like spaced repetition, active learning, managing cognitive load, and leveraging emotional engagement can make AI training more effective, helping overcome the "forgetting curve" . Testimonials for Dr. Teki's training highlight its clarity and interactivity.6

Neuroscience shows that active processing, reinforcement over time, and positive emotional experiences (like achievement) enhance learning and retention . Understanding the brain's response to change is also vital for fostering psychological adaptability . Great Learning's GenAI academy, with hands-on learning and real-world case studies 4, aligns with these principles. Grounding AI upskilling in how people learn improves skill retention and workforce agility.

C. Leading Through Change: Overcoming Resistance & Building Trust
Successful AI integration is a human challenge, often met with fear of job loss, lack of trust, and resistance to new work methods.26 COOs must lead with empathy, transparency, involve employees, and build trust.14

Addressing "AI Anxiety" 9 involves visible leadership commitment, comprehensive reskilling, clear communication (AI as a supportive tool), and transparent ethical guidelines.26 Gartner emphasizes listening to understand resistance 27, while Prosci’s ADKAR model highlights building Desire and Reinforcing behaviors . Overcoming inertia may require "frame flexibility"—cognitively and emotionally reframing AI to align with organizational values . Trust is the currency of AI transformation.

D. Dr. Teki's Perspective: The Indispensable Human Element & Neuroscience of Change
The human element is indispensable. Dr. Teki's neuroscience expertise 3 provides insights into cognitive and emotional responses to change. Resistance to AI often stems from fear, anxiety, or perceived loss of status . The brain's preference for predictability means significant changes like AI adoption can trigger stress if not managed carefully .

Emotional framing—aligning change with passions and aspirations—can increase adoption . Workplace transformation impacts rational and emotional selves; applying brain science can help employees thrive . This involves fostering emotional intelligence skills like self-awareness, adaptability, empathy, and constructive interaction . Understanding these underpinnings allows COOs to deploy strategies more attuned to the human experience of change, fostering acceptance and accelerating the AI-first journey.

V. The Path Forward: The COO as Catalyst for Sustained AI-Driven Advantage

Conclusion
The COO's success in harnessing GenAI and Agentic AI hinges on integrating several strategic pillars: embracing an evolved mandate as an AI value architect; establishing AI-ready operating structures and robust data governance; pragmatically driving operational impact through strategic use cases and diligent ROI measurement; and leading a human-centric transformation by fostering AI literacy, leveraging neuroscience for upskilling, and empathetically managing change.

AI adoption is an ongoing journey of learning and continuous improvement. As AI capabilities advance, strategies and operational models must be agile.3 The pinnacle of AI maturity involves "anticipating continued disruption" and "harnessing those trends to create value".3 COOs must foster a culture of "progress over perfection" 15, valuing experimentation and institutionalizing learning.

The opportunity for COOs to redefine operational excellence with AI is immense. By spearheading these multifaceted efforts, COOs can position their organizations at the industry vanguard. Navigating this transformation requires strategic foresight, technological understanding, and a deep appreciation of human dynamics.
Explore how tailored AI strategies and corporate training can empower your organization to unlock the full, sustainable promise of Generative and Agentic AI. 


VI. References
  1. How COOs Can Use Gen AI and Agentic AI - Operations Council https://operationscouncil.org/how-coos-can-use-gen-ai-and-agentic-ai/
  2. Industry Insights: The Rise of Agentic AI – Navigating the Next Wave of Artificial Intelligence https://www.irishfunds.ie/news-knowledge/newsletter/industry-insights-the-rise-of-agentic-ai-navigating-the-next-wave-of-artificial-intelligence/
  3. AI-First Leadership: Embracing the Future of Work - Harvard ... https://www.harvardbusiness.org/ai-first-leadership-embracing-the-future-of-work/
  4. Types of Chief Operating Officers (COO) - HBR | PPT - SlideShare https://www.slideshare.net/slideshow/types-of-chief-operating-officers-coo-hbr/85690593
  5. Key Findings from the Forrester Total Economic Impact™ study on Writer https://writer.com/blog/forrester-tei-findings/
  6. The Complexities of Measuring AI ROI | Devoteam https://www.devoteam.com/expert-view/the-complexities-of-measuring-ai-roi/
  7. AI Roadmap: What It Is and How to Build One - Gartner https://www.gartner.com/en/articles/ai-roadmap
  8. OCBC's Journey To Becoming A Generative AI Pioneer - Forrester https://www.forrester.com/blogs/ocbcs-journey-to-becoming-a-generative-ai-pioneer/
  9. The Reality of Generative AI: From Buzz to Business Transformation - VKTR.com https://www.vktr.com/ai-technology/the-reality-of-generative-ai-from-buzz-to-business-transformation/
  10. How does Gartner define data governance? - Secoda https://www.secoda.co/blog/gartners-definition-of-data-governance
  11. AI & Data Strategy ant Gartner 2025 - Analytica https://www.analytica.net/blogs/gartner-2025-ai-governance-and-data-strategy/
  12. GenAI Possibilities Become Reality When Leaders Tackle The Hard Work First - Forrester https://www.forrester.com/blogs/genai-possibilities-become-reality-when-b2b-leaders-tackle-the-hard-work-first/
  13. Gartner's field guide for successful change management initiatives - DataGalaxy https://www.datagalaxy.com/en/blog/gartners-field-guide-change-management/
  14. AI Adoption: Driving Change With a People-First Approach - Prosci https://www.prosci.com/blog/ai-adoption
  15. Generative AI Use Cases and Resources - AWS https://aws.amazon.com/ai/generative-ai/use-cases/
  16. Four High-Impact Use Cases for Agentic AI in the Enterprise - Mimica https://www.mimica.ai/blog/four-high-impact-use-cases-for-agentic-ai-in-the-enterprise
  17. Why emotional intelligence training drives AI transformation | Absorb LMS Software https://www.absorblms.com/blog/emotional-upskilling-for-ai/
  18. MIT SMR Connections - The Agentic AI Shift: Strategic Imperatives for Digital Leaders https://www.mitsloanme.com/events/the-agentic-ai-shift-strategic-imperatives-for-digital-leaders/
  19. 10 Agentic AI Examples (Use Cases) for Enterprises & How To Build Them - Astera Software https://www.astera.com/type/blog/agentic-ai-examples/
  20. Proving ROI - Measuring the Business Value of Enterprise AI - Agility at Scale https://agility-at-scale.com/implementing/roi-of-enterprise-ai/
  21. Stagewise Overview of Issues Influencing Organizational Technology Adoption and Use https://pmc.ncbi.nlm.nih.gov/articles/PMC8009967/
  22. The Role of Cognitive and Emotional Framing in Innovation Adoption by Incumbent Firms - Harvard Business School https://www.hbs.edu/ris/Publication%20Files/17-091_6f7ce298-32eb-4694-abb1-384063951734.pdf
  23. Sundeep Teki - Home https://sundeepteki.org/
  24. Resistance to AI: Governance and Cultural Challenges - Allganize's AI https://www.allganize.ai/en/blog/resistance-to-ai-governance-and-cultural-challenges
  25. Why Corporate Education & Adult Learning Needs Neuroscience and Gamification (And Why It Works) | HUSPI https://huspi.com/blog-open/corporate-edication-neuroscience-gamification/
  26. 5 Case Studies of Successful AI Implementations in Financial Sectors - TAZI AI https://tazi.ai/blog/5-case-studies-of-successful-ai-implementations-in-financial-sectors/
  27. How AI drives Operational Excellence in Manufacturing Industry - Data Strategy https://www.datategy.net/2025/01/07/how-ai-drives-operational-excellence-in-manufacturing-industry



Comments
comments powered by Disqus

    Archives

    May 2025
    April 2025
    March 2025
    February 2025
    January 2025
    October 2024
    September 2024
    March 2024
    February 2024
    April 2023
    December 2022
    November 2022
    October 2022
    September 2022
    August 2022
    July 2022
    June 2022
    May 2022
    April 2022
    March 2022
    February 2022
    December 2021
    October 2021
    August 2021
    May 2021
    April 2021
    March 2021

    Categories

    All
    Ai
    Data
    Education
    Genai
    India
    Jobs
    Leadership
    NLP
    RemoteWork
    Science
    Speech
    Strategy
    Web3

    RSS Feed


    Copyright © 2025, Sundeep Teki
    All rights reserved. No part of these articles may be reproduced, distributed, or transmitted in any form or by any means, including  electronic or mechanical methods, without the prior written permission of the author. 
    Disclaimer
    This is a personal blog. Any views or opinions represented in this blog are personal and belong solely to the blog owner and do not represent those of people, institutions or organizations that the owner may or may not be associated with in professional or personal capacity, unless explicitly stated.
                                                                                                                                                                                 [email protected] 
​​  ​© 2025 | Sundeep Teki
  • Home
    • About Me
  • AI
    • Hiring
    • Speaking
    • Papers
    • Testimonials
    • Content
    • Course
    • Neuroscience >
      • Speech
      • Time
      • Memory
  • Coaching
    • Advice
    • Testimonials
  • Training
    • Testimonials
  • Consulting
  • Blog
  • Contact
    • News
    • Media