Ayesha Khanna

Why organisations work with Ayesha Khanna

• Her named “pilot purgatory” diagnostic (the finding that over 88% of AI pilots never scale) gives leadership teams a precise frame for why their programmes stall, and a structured path to production. This is a field-tested argument with a clear strategic implication.
• She brings board-level fiduciary exposure most AI advisors do not hold: Johnson Controls (smart buildings), NEOM Tonomus (national smart city infrastructure in Saudi Arabia), Mercy Healthcare (US), and L’Oréal’s Scientific Advisory Board. She has governed AI strategy, not only advised on it.
• Her advisory work spans both national AI strategy: she helped shape Singapore’s SkillsFuture programme and served on the IMDA board. Organisations get a strategist who has worked both sides.
• Her previous board position on AVEVA and her current role at Johnson Controls give her direct insight into how AI and digital transformation are reshaping critical infrastructure, highly relevant to sectors where technology adoption has long tail consequences.
• As the author of the forthcoming Gamechanger, a practical business strategy guide to generative AI, she is building the intellectual framework for the current adoption challenge while it is happening, not retrospectively.

Biography highlights

• Co-Founder and CEO, Addo AI; a global AI solutions firm whose clients have included Singtel, SMRT, Pfizer, Sompo Holdings, Habib Bank, and Smart Dubai
• Board director: Johnson Controls (NYSE: JCI); NEOM Tonomus, Saudi Arabia; previously AVEVA (FTSE100, prior to Schneider Electric acquisition)
• Scientific Advisory Board, L’Oréal; Consumer Committee, Mercy Healthcare; Singapore HTX (homeland security technology)
• Member, World Economic Forum Global Future Councils; formerly on Singapore IMDA board and Ministry of Education SkillsFuture steering committee
• Named one of Southeast Asia’s groundbreaking female entrepreneurs by Forbes; Edelman Top 50 AI Creators (2025); Salesforce 16 AI Influencers to Know (2024)
• Author of Straight Through Processing (2008) and co-author of Hybrid Reality (TED Books, 2012); forthcoming Gamechanger on generative AI business strategy
• Published and quoted in The New York Times, Harvard Business Review, Forbes, TIME, Foreign Policy, and Strategy+Business
• PhD, Information Systems (digital innovation in smart cities), London School of Economics; MS, Columbia University; BA (Honours), Harvard University

Biography

Ayesha Khanna has spent two decades at the point where AI strategy meets the decisions boards have to make. Her argument is specific: most organisations do not fail at AI because they lack ambition or budget. They fail because they build pilots without the business-outcome architecture to scale them, what she calls “pilot purgatory,” a state that traps over 88% of enterprise AI programmes.

Ayesha works with large corporations and governments on the full adoption journey, from strategy through deployment, with clients that have included Singtel, SMRT, Pfizer, Sompo Holdings, Habib Bank, and Smart Dubai.

Khanna has spent more than a decade on Wall Street developing large-scale trading, risk management, and data analytics systems. Her PhD from the London School of Economics focused on digital innovation in smart cities, grounding her practical work in a rigorous analytical framework.

What sets Khanna apart from most AI strategists is the nature of her board exposure. She currently holds directorships at Johnson Controls (smart building technology, NYSE: JCI), NEOM Tonomus (the technology infrastructure body for Saudi Arabia’s $500 billion NEOM smart city project), and Mercy Healthcare, and sits on L’Oréal’s Scientific Advisory Board.

She previously sat on the board of AVEVA when it was an FTSE100 company. These are not advisory relationships, they are governance roles in which she has been responsible for technology strategy at fiduciary level, across healthcare, infrastructure, and consumer science simultaneously.

Her forthcoming book, Gamechanger, extends this practitioner perspective into a structured business playbook for generative AI.

Her two earlier books, Straight Through Processing (2008) and the TED book Hybrid Reality (2012), co-authored with Parag Khanna, established her early argument that the human-technology relationship was shifting from coexistence to co-evolution. That argument has aged well. Khanna has been recognised by Forbes as one of Southeast Asia’s groundbreaking female entrepreneurs, named to Edelman’s Top 50 AI Creators in 2025, and quoted across The New York Times, Harvard Business Review, and Foreign Policy.

Key speaking topics

• Generative AI strategy and enterprise adoption
• AI governance and risk frameworks
• Smart cities and urban technology infrastructure
• AI in financial services and healthcare
• Future of work in an AI-driven economy
• National AI strategy and public-private collaboration
• The Hybrid Age: human-technology co-evolution

Ideal for

• CEOs and C-suite leadership teams building or scaling enterprise AI programmes
• Board directors and non-executive directors responsible for technology governance and digital risk oversight
• Government and public sector leaders developing national AI strategies or smart city initiatives
• Financial services, healthcare, and infrastructure sector organisations at advanced stages of AI deployment

Audience outcomes

• A named diagnostic for why AI pilots stall before scaling, and a framework for building the business-outcome architecture needed to move from experimentation to production
• Practical understanding of what AI governance looks like at board level: what boards need to own, what they need to delegate, and what risks require a formal framework
• Sector-specific examples of organisations generating measurable revenue and operational impact through AI, drawn from a client base across telecoms, finance, healthcare, and infrastructure
• A structured perspective on the “Hybrid Age” – the shift in how humans and AI systems operate together – and what it means for workforce strategy and organisational design
• Criteria for prioritising AI investment by business outcome rather than technology capability, applicable across industries at different stages of digital maturity