Didem Ün Ateş
Most organisations have committed to an AI strategy. Very few have built the governance architecture to make that strategy accountable at scale. The gap between an approved AI roadmap and actual enterprise-wide adoption is where initiatives stall, risk accumulates, and boards are left approving decisions they cannot yet evaluate. Closing that gap requires a different kind of expertise – one built inside organisations, not just around them.
Didem Ün Ateş, Founder and CEO of LotusAI Ltd, helps boards and investment teams close the gap between AI ambition and the implementation, governance, and scale that make it accountable, drawing on executive AI leadership at Microsoft, Accenture, and Schneider Electric and advisory roles linked to Goldman Sachs Asset Management and The Hg Foundation.
Full Profile
Why organisations work with Didem Ün Ateş
- She has operationalised responsible AI inside three of the world’s largest organisations (Microsoft, Accenture, and Schneider Electric) as an executive with direct accountability, not as an external advisor. That gives her a precise understanding of where enterprise AI governance breaks down in practice.
- Her role as Advisor and Generative AI Council Member for Goldman Sachs Value Accelerator, working with over 300 portfolio companies, gives boards and investment teams a live investor-grade view of how AI strategy translates, or fails to translate, into commercial value and risk-adjusted returns.
- LotusAI’s documented methodology, running 60 co-creation workshops across more than 200 SVPs and VPs to identify and prioritise 200 generative AI use cases inside a single Fortune 500 company, is a concrete, tested model for enterprise AI adoption, not a conceptual framework.
- As an accredited algorithm auditor and former WEF AI Governance Alliance Fellow, she connects organisations to the governance principles emerging at the regulatory and multilateral level, including the responsible-AI debates shaping how the technology is regulated globally.
- Her dual degree from the University of Pennsylvania (BS in Engineering from the Moore School and BS in Economics from the Wharton School), combined with an MBA from Columbia Business School, means she can engage credibly at both the technical architecture and commercial investment layers of an AI decision, a combination that is genuinely uncommon.
Biography highlights
- Founder and CEO of LotusAI Ltd, advising private equity firms, hedge funds, financial institutions, and Fortune 500 companies on AI strategy, responsible AI governance, and talent transformation
- Former Vice President, AI Strategy & Innovation at Schneider Electric, where she defined the company’s AI and generative AI strategy, innovation roadmaps, ecosystem partnerships, and responsible AI programme
- Former Head of Data & AI / General Manager, Data & AI at Microsoft (EMEA) and former Managing Director of Data & AI, Europe at Accenture’s Microsoft Business Group; earlier roles at Capgemini, EY, BT, and Motorola
- Advisor and Generative AI Council Member for Goldman Sachs Value Accelerator, working with the firm and over 300 portfolio companies
- Board member of the Edge AI Foundation, the Wharton AI Alumni Studio, the Columbia Business School Women’s Circle, and the Hg Foundation Technology Advisory Board
- Certified Algorithm Auditor and Executive Coach; former WEF AI Governance Alliance Fellow; Forbes Technology Council member
- TechWomen100 Champion and Trailblazer 50 honouree; 2025 recognition includes CEO of the Year Awards UK in the AI Advisory category, Responsible AI Innovation & Growth in Business Award, New Technology Consultancy of the Year, Best New Business of the Year, and British Business Excellence Award
- Alumna of the University of Pennsylvania (Management & Technology dual degree: BS in Engineering from the Moore School and BS in Economics from the Wharton School) and Columbia Business School (MBA)
Biography
Most enterprise AI strategies stall in the same place: at the gap between approved roadmap and accountable delivery. Didem Ün Ateş has worked on both sides of that gap inside organisations large enough for every failure to matter. As VP, AI Strategy & Innovation at Schneider Electric, she held direct accountability for the company’s generative AI strategy and innovation roadmap, its ecosystem partnerships, its responsible and sustainable AI programme, and the upskilling of its global workforce. Before that, she served as Head of Data & AI and General Manager, Data & AI at Microsoft EMEA, with revenue accountability of over $2.7 billion across more than 70 subsidiaries, and as Managing Director of Data & AI, Europe within Accenture’s Microsoft Business Group.
She founded LotusAI Ltd to apply that operational depth where boards and investment teams need it most. Her advisory relationship with Goldman Sachs Value Accelerator, as Advisor and Generative AI Council Member, puts her in direct conversation with over 300 portfolio companies on AI value creation, governance, and risk. LotusAI’s engagements have included guiding a generative AI opportunity scan across more than 200 senior leaders in a Fortune 500 company, delivering a three-year use-case roadmap tied to the organisation’s strategic plan and regulatory requirements.
Her work sits at the intersection of commercial strategy and institutional accountability. As an accredited algorithm auditor and former WEF AI Governance Alliance Fellow, where she has co-chaired sessions on AI and sustainability at WEF summits, she connects the boardroom conversation to the multilateral frameworks shaping how responsible AI is defined and regulated globally. She also sits on the Hg Foundation Technology Advisory Board, alongside senior leaders from ING, Google, McKinsey, and Accenture.
She holds a dual degree from the University of Pennsylvania, in Engineering from the Moore School and in Economics from the Wharton School, and an MBA from Columbia Business School. Recent recognition includes the CEO of the Year Awards UK 2025 in the AI Advisory category, the Responsible AI Innovation & Growth in Business Award 2025, and the British Business Excellence Award 2025, alongside the earlier TechWomen100 Champion and Trailblazer 50 honours.
Key speaking topics
- Enterprise AI strategy and governance
- Generative and agentic AI adoption at scale
- Responsible and sustainable AI
- AI for private equity, private credit, and financial services
- AI-driven workforce capability and talent transformation
- AI risk management and regulatory compliance
- Edge AI, physical AI, embedded AI, and tinyML
- AI leadership training and executive workshops
Ideal for
- Boards and C-suite teams (CEO, CTO, CDO, CAIO) building or pressure-testing enterprise AI strategies
- Chief Data and AI Officers designing governance frameworks for generative AI adoption
- Private equity and investment leadership teams assessing AI value creation and risk across portfolios
- Transformation and innovation leaders in financial services, industrial, and consulting sectors navigating responsible AI operationalisation
Audience outcomes
- Practical criteria for identifying, prioritising, and sequencing AI use cases, tools, platforms, and agents within a complex organisation
- A clearer model of where enterprise AI governance frameworks typically break down, and what structural conditions make them durable
- A sharper understanding of how responsible AI principles translate into investment-grade governance, not just compliance documentation
- A working language for talent transformation in the agentic AI era, including the workforce decisions board members need to be ready to make
- More confident language for board-level conversations on AI risk, sustainability, and value creation
- Awareness of inclusive AI design as both a reputational risk and a long-term commercial consideration
Talks
Translates the AI and agentic AI conversation from a technical one into a board one, equipping senior executives to lead and govern AI decisions they are accountable for but did not engineer.
Key takeaways:
- How to distinguish genuine AI value levers from hype, and what questions boards should be asking their technology leaders
- The talent and cultural conditions that determine whether an AI strategy delivers or stalls in execution
- A working model for embedding AI governance into enterprise strategy from the start, not after the regulator calls
For investment firms and their portfolio companies, AI adoption now has to clear two bars at once: accelerating returns, and surviving the governance and reputational scrutiny that LPs and regulators have started applying.
Key takeaways:
- How to assess AI readiness and value potential across a portfolio of companies at very different maturity levels
- The governance and compliance considerations that separate responsible AI adoption from regulatory exposure in financial services
- How to connect AI strategy to value creation metrics that hold up under investor scrutiny
How organisations can design and deploy responsible and inclusive AI solutions that generate commercial value alongside measurable societal outcomes.
Key takeaways:
- How to integrate AI and agentic AI into operations without losing control of the governance and compliance posture
- A model for taking responsible AI from policy statement to operating practice across different organisational contexts
- How inclusive AI design connects to workforce evolution, investment strategy, and long-term portfolio value creation