Tech Talk Interviews

Interviewer: Hello and welcome to The AI Summit London. Today we're joined by Ankit Bharadwaj, Head of Responsible AI at Liberty Specialty Markets. How did you land at Liberty Specialty Markets, and could you share a bit of your background?

Ankit: I’ve worked in AI for almost a decade in different roles. I was working with Liberty for a period, this opportunity came up at the right time, and I went for it. I’ve been here a few months and I’m really enjoying it. Working in AI is incredibly topical right now.

Interviewer: There’s a perceived value gap in AI investments. Many organisations have invested heavily, yet leaders question the return. Why is there such a gap?

Ankit: Many organisations have invested heavily in AI but still struggle to prove value. A major reason is that AI is often treated as a standalone tool rather than as part of a broader operating model and change journey. The model itself is increasingly a commodity - the value lies in everything built around it: the workflows redesigned to use it, the people who adopt it, and the processes rebuilt from the ground up.

The shift also needs to happen from measuring AI by usage alone to measuring AI by outcomes. Success depends on whether AI improves how work gets done, supports measurable business value, and is adopted by people in the flow of their roles.

The second issue is failing to reimagine workflows. You can’t just bolt AI onto broken processes or poor data and expect good outcomes. Think in an AI-native way, redesign processes end to end to unlock fundamentally different value.

Interviewer: There’s a common perception that governance slows innovation. Your work suggests otherwise. Can you shed some light on that?

Ankit: Governance is often seen as the function that says “no,” but it should enable innovation responsibly. The shift is toward being risk-aware, not risk-averse. Use a risk matrix that classifies AI use cases as low, medium, or high risk and apply proportionate controls. You don’t treat a low-risk use case the same way as a high-risk one. That means flexible review methods and accountabilities, tailored to risk. That’s the change we’re driving, governance that is adaptive and accelerates responsible adoption.

Interviewer: A big challenge is moving from promising experiments to scalable, production-ready AI. Why do so many initiatives get stuck in pilot, and how can teams bridge the gap?

Ankit: Many AI initiatives remain stuck in pilot because organisations moved quickly during the hype cycle without fully understanding the operating costs, infrastructure requirements, value case, or organisational changes needed for scale. A common pattern is dozens of experiments running in parallel, which feels like momentum but is, in practice, expensive motion. Real value appears when the second use case is cheaper to build than the first, because the organisation is reusing foundations rather than rebuilding from scratch each time.

To move beyond experimentation, organisations need to combine:

• A steady pipeline of practical AI ideas
• Flexible and proportionate governance
• Fast prototyping and iteration - Fast prototyping that replaces traditional requirements documents by building a working version first, let it become the specification, and use it to align stakeholders rather than debating a lengthy written document
• Clear value tracking
• Changes to operating model, process and adoption

Prototype quickly, deliver quickly, bring people on the journey, and keep iterating.

Interviewer: Even the most advanced tools fail if people don’t trust or adopt them. What drives adoption and builds trust?

Ankit: Trust is essential for adoption. People need to understand where AI is being used, how data is being used, and how AI outputs influence decisions.

There are several foundations for trust:

• Transparency with employees and customers
• User education and upskilling
• Strong data quality
• Clear communication
• Change management
• Human understanding of how AI outputs should be interpreted

Combine ongoing communication, change management, and data discipline to build durable trust.

Interviewer: Your session focused on how persistent memory can improve AI reliability and decision quality. How does organisational memory enhance AI trustworthiness and utility, and what roles do governance and privacy play?

Ankit: 

The central theme of the session is that memory will become a major source of advantage for enterprise AI. Memory is specifically what makes AI agents possible, systems that are capable of carrying long, complex, multi-step work across days and handoffs without losing the thread. A chatbot with a bad memory says something wrong; an agent with a bad memory does something wrong. The distinction matters because agents do not just produce text, they take actions.

As AI systems become more agentic, they increasingly depend on what they can retain, retrieve, update and apply over time. Organisational memory can help AI systems become more useful, more contextual, and more reliable, especially when feedback from outcomes is used to improve future interactions.

However, memory needs to be designed deliberately. Organisations must decide:

• What should be remembered
• How memory should be structured
• How long information should be retained
• When memory is relevant
• How memory is versioned
• How privacy, compliance and security controls apply
• How memory improves time-to-value without creating new risks

Memory is both an innovation opportunity and a governance challenge.

Interviewer: Beyond governance, what should leaders prepare for next to stay ahead?

Ankit: Technology is moving faster than organisational change. We’re seeing trends like sovereign AI, embodied or “physical” AI that understands real environments, and the mainstreaming of agentic AI. On the organisational side, the priority is operating model change and process reimagination. If you get the design and the people aspects right, you can capitalise on these technology shifts far more effectively.

Closing

Ankit Bharadwaj is Head of Responsible AI at Liberty Specialty Markets, where he leads responsible AI adoption across international insurance markets. With over 15 years’ experience across AI, automation, and technology transformation, he helps organisations move from experimentation to trusted, scalable AI. 

Prior to Liberty, he was AI Practice Lead at Gate One Consulting, advising senior leaders on AI strategy, transformation, and governance across financial services, retail, consumer health, and the public sector—enabling responsible innovation by embedding accountability and human oversight. He holds an MSc in Computing from Imperial College London and is a founding member of the university’s AI Network.

Ankit's session at The AI Summit London focused on: Memory at Scale — Building AI That Remembers, Learns & Evolves.