Sina Kahen
Most organisations have run AI pilots. Very few have converted them into operating performance. The gap is no longer about technical capability; it is about strategy, governance, sourcing decisions, and the readiness of the people who have to use the systems every day.
Sina Kahen is an AI strategist, author and Imperial Business School lecturer who helps organisations turn generative AI from pilot exercises into real operating advantage.
Full Profile
Why organisations work with Sina Kahen
- He has shipped a regulated, complex technology product into a sceptical market. Commercialising Celito, named publicly as the UK’s first orthopaedic robot, gives him operator credibility that very few AI speakers carry.
- He runs a generative AI company today. VAICE, where he is co-founder and CEO, builds workplace productivity software, which means his views on enterprise AI come from active build decisions, not commentary.
- His book “The Promised Land of AI” gives leaders a usable strategic playbook for AI adoption, structured around use-case discovery, data readiness, and the build, buy or partner question that most boards stall on.
- He teaches AI strategy at Imperial College Business School, so the material is tested against MBA cohorts and senior executives every term, not assembled for the stage.
- He brings a working language for ethics, philosophy and human judgement in AI, drawing on his published philosophical work, without drifting into abstraction that leaves an operating team with nothing to do on Monday.
Biography highlights
- MBA, Imperial College Business School; degree in Biomedical Sciences
- Guest Lecturer in AI, healthcare and corporate innovation, Imperial College Business School
- Co-founder and CEO, VAICE, building generative AI software for workplace productivity
- Former Innovation Business Manager at DeSoutter Medical; led commercialisation of Celito, publicly described as the UK’s first orthopaedic robot
- Author of “The Promised Land of AI: A Practical Guide to AI for Business” and “Brief Insights on Medical Robotics”
- Engagements and clients include Google, Amazon, BBC, O2, Shell, Imperial College, InterSystems, Juniper Networks, Ipsen Pharma and Novateur Ventures
Biography
Celito was the UK’s first orthopaedic robot. Sina Kahen, then Innovation Business Manager at DeSoutter Medical, was the person responsible for bringing it through commercialisation into operating theatres. That work, taking a regulated, high-stakes technology from concept into the hands of surgeons, is the experience he now applies to a different problem: enterprise adoption of generative AI.
The current vehicle for that work is VAICE, a company he co-founded and runs as CEO, building generative AI software for workplace productivity. Around it sits his book “The Promised Land of AI: A Practical Guide to AI for Business”, a Guest Lectureship at Imperial College Business School, and engagements with organisations including Google, Amazon, BBC, O2, Shell, InterSystems and Ipsen Pharma.
The argument running through all of it is operational. Most companies have proved AI can do something useful. The harder questions, which buyers are now ready to confront, are about strategy: which use cases actually compound, whether to build, buy or partner, how to make data and people ready, and how to govern decisions that cannot be fully explained. Kahen has trained as a Biomedical Scientist and holds an MBA from Imperial, which is one reason his framing of these questions feels closer to medicine than marketing: rigorous about evidence, blunt about what is not yet known.
He is also the author of several books on Jewish philosophy, and that interest shows up in the AI work as a refusal to let the human and ethical questions float free of operating practice. Boards do not need another AI ethics policy. They need a way to make defensible AI decisions inside their existing governance, and to take their workforce with them. That is the territory Kahen occupies.
Key speaking topics
- Generative AI strategy for enterprise
- AI adoption and operating performance
- AI in healthcare and life sciences
- Responsible AI and AI governance
- Build, buy or partner decisions in AI sourcing
- Innovation and intrapreneurship inside large organisations
- Philosophy, ethics and the human side of AI
Ideal for
- Boards and executive committees commissioning enterprise-wide AI strategy
- CIOs, CTOs, Chief Data Officers and Chief AI Officers translating pilots into operating systems
- Healthcare and life sciences leadership confronting AI, robotics and regulated innovation
- Heads of Innovation and transformation leads structuring AI portfolios across business units
Audience outcomes
- A working framework for moving AI from pilot into measurable operating performance
- A clearer view on when to build, buy or partner for AI capability
- Sharper questions to ask of internal teams and external vendors on data readiness, governance and use-case selection
- A practical position on AI ethics that holds up inside real operating decisions, not slide decks
- Language and reference points senior leaders can use to bring the rest of the organisation with them
Talks
A senior leadership talk on the strategic moves required to convert generative AI experiments into business performance.
Key takeaways:
- A use-case discovery method that filters for AI projects with compounding value
- A practical view on the build, buy or partner decision for AI capability
- The data, talent and governance preconditions that separate stalled pilots from operational systems
A talk on how AI and robotics are reshaping clinical workflows, drug discovery and patient care, drawing on his work commercialising the UK’s first orthopaedic robot.
Key takeaways:
- Where AI is genuinely changing healthcare economics, beyond the press cycle
- What regulated industries can teach every sector about responsible AI deployment
- How to make adoption decisions in environments where the cost of error is high
A talk on the human and philosophical questions leaders cannot delegate to technology teams.
Key takeaways:
- A working definition of “human-centric AI” that translates into product and policy
- How to design AI systems that strengthen, rather than erode, judgement and meaning at work
- A defensible position on ethics that fits inside operating governance, not alongside it
A talk on the ethical architecture organisations need as AI moves into core decisions.
Key takeaways:
- The ethical questions boards must answer before scaling AI, not after
- How to translate principles into review processes, sourcing criteria and accountability lines
- The risks of treating AI ethics as communications rather than governance