Zoe Webster on How to Think About AI

Zoe Webster has spent over 25 years in the field of AI, from developing systems as a computer scientist to shaping national strategy at Innovate UK, leading BT’s AI enablement centre and now working as an independent AI advisor. In this conversation with JP Doggett, she talks about the confusion around “agentic AI,” why many organisations feel burned by earlier promises and how leaders can identify where AI really fits in their supply chain.

From AI scientist to advisor

JP Doggett: Zoe, you’ve had quite a career journey across the AI landscape. Could you give us a quick overview?

Zoe Webster: I started out "classically trained" in AI - computer science, a master’s and a PhD - and worked hands-on as a scientist developing and demonstrating cutting-edge AI technology. Later I moved to Innovate UK, where I spent 13 years looking at technology innovation through a macroeconomic and societal lens, as well as through the perspective of individual companies, large through small, across a range of sectors. Then I joined BT and set up the AI Centre of Enablement. That meant recruiting and leading a team of engineers and scientists working on dozens of use cases across B2B, B2C and internal operations.

Quite often I was asked to vet suppliers and ask difficult questions to see what was really under the bonnet. The hype was sometimes greater than the substance. Just over a year ago I set up on my own to advise organisations on AI strategy, practice and governance and I also do talks on adoption, skills and where AI is real versus hype.

Agentic AI: hype or substance?

JP Doggett: The latest hype cycle seems to be around “agentic AI.” What’s your perspective?

Zoe Webster: There’s a lot of confusion. True agentic AI would mean autonomy: systems that reason across multiple goals, adapt in real time and orchestrate tasks without humans in the loop. And all within complex and dynamic environments. We’re not there yet. The nearest thing in practice is self-driving cars and even they are limited more by regulation, insurance and management systems than by the technology.

What many companies call “agents” are really programs that perform a specific task, sometimes with AI elements like machine learning or using an LLM. That’s different from agentic AI. For example, an agent that answers customer queries on your latest product or service is not the same as one that runs your entire marketing function. The difference is orchestration: if a human still decides who does what, it’s not agentic. If the system is adapting and making those calls itself, that’s closer to agentic AI.

Why leaders are sceptical

JP Doggett: In our best practice-sharing network, people talk about AI as both a paradigm shift and a disappointment. How do you help clients reconcile that?

Zoe Webster: I don’t try to brush over the scepticism, I empathise with it. AI is not magic. No technology is. If the data isn’t in good shape, AI won’t fix it. Many leaders have been sold the wrong tool for the wrong job. They’ve been told “AI will solve your problems” and then lost faith in AI entirely when it didn’t.

The first step is to put AI aside and ask: what is the organisation’s purpose, what are the goals and where are the inefficiencies? What would you love to know about your customer, suppliers or partners that you don’t know today? Once you frame it that way, you can work back to whether AI has a role and if so, what kind of AI approach might help. Sometimes the right answer isn’t AI at all...it might just be a better interface or better data management.

Avoiding the Betamax trap

JP Doggett: It's a very dated reference but one worry I hear is what I call the “Betamax problem” i.e. investing in the wrong technology and being stuck when the standards shift. Do you think that’s valid?

Zoe Webster: Absolutely. It’s the right question to ask. Technology acts as an amplifier: it can make a good process more efficient or make a bad one worse. That’s why the focus should be on what task you’re trying to achieve, not on pinning your flag to a particular vendor or product.

To future-proof, you need to be as platform-focused and vendor-agnostic as possible. That way you can plug and play as things change. Otherwise you risk embedding inefficiencies and making it harder to adapt later.

Vendor claims and what to ask

JP Doggett: Many supply chain leaders are uncertain about what questions they should be asking vendors to test their claims around AI. What’s your advice?

Zoe Webster: Always ask how their models are tested. How well do they perform in cases like yours? How is the model governed? How does it adapt when your needs change? Too often suppliers can’t answer or haven’t tested in relevant contexts. That’s a red flag.

And don’t assume big equals safe. A large vendor might sell you a general-purpose system but you may need something bespoke to handle the specifics of your data or regulatory environment. In one financial services example, a large language model couldn’t extract terms from contracts effectively so the company built a tailored solution instead.

Can mid-sized organisations do AI?

JP Doggett: For organisations without massive resources, is building in-house realistic?

Zoe Webster: It may be more realistic than people think though it really depends on what you are aiming to achieve. A skilled data scientist with some engineering knowledge can prototype quickly. It may look rough but it can prove the concept which can help build the business case to invest further. This could be someone hired on a freelance or fractional basis, not necessarily full-time.

Getting to production and putting AI into operation is where the investment is really needed and where organisations can struggle. This will depend on what you are looking to achieve and may need engineering effort to integrate with the appropriate systems. It is important also to consider, from the start, the human side of this through change management to work closely with key people, not least those likely to be impacted by the AI functionality and those responsible for monitoring in operation.

The trick is to start small, run an experiment and build trust in stages. That way you can decide whether to scale without making huge upfront commitments. It may be that investment in dedicated skills and talent would pay off as AI adoption is scaled up.

Bridging business and technical worlds

JP Doggett: From your experience, what’s most often missing for organisations struggling to use AI?

Zoe Webster: Translation. Business leaders need someone who can translate strategic goals into technical specifications and technical results back into business value.

That translation layer is critical. It’s not just about AI skills but also process expertise, analytics and communication. Without it, you either get business people buying into hype or data scientists building clever models that don’t solve the right problem.

Policy and government support

JP Doggett: With your Innovate UK background, how do you see government’s role in AI adoption?

Zoe Webster: Innovate UK runs programmes like Bridge AI to connect people with ideas and needs and they provide grants and loans to accelerate innovation. Government also has a role in showing leadership by procuring innovation solutions to problems and these could include AI-powered features, where those are genuinely helpful. Scaling AI in organisations requires time, data readiness and governance. Policy can encourage it but delivery can be slower and messier in reality.

Closing thoughts

For Zoe, the message is simple but challenging: don’t start with AI, start with the opportunity. Be wary of hype, stay vendor-agnostic where you can and invest in translation skills that connect business goals with technical execution.

As she puts it, “AI is not magic. It won’t fix your data. Focus on what you want to achieve and then decide if AI has a role that genuinely adds value.”