From early work in operational research to leading group-wide data strategies in insurance and financial services, Steve brings over 20 years of experience helping businesses make sense of their data – and using it to meet their goals.

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As Managing Director of AI, he’ll be helping enterprise customers navigate the growing complexity of AI adoption, especially in data-rich, highly regulated sectors like financial services, insurance, and energy.

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We sat down with Steve to hear about what he’s learned from two decades in data, where he sees AI heading next, and why cultural readiness – not technology – is often the biggest hurdle to success.

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Steve, tell us a bit about your journey so far.

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I started out in operational research at Lancaster University, working on things like route optimisation and aircraft scheduling – problems that today would be solved using AI. My first job was in project management, leading data engineers and analysts. Since then, I’ve spent 20 years in data and AI, mostly in consulting across industries like personal and specialty insurance, reinsurance, investment banking and utilities.

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A pivotal moment came when I became Group Head of Data & Analytics at Jardine Lloyd Thompson, the UK’s largest insurance broker at the time. I was responsible for defining the data strategy for the entire group. That role gave me a front-row seat to both the opportunities and the common blockers: a lot of information, but real challenges turning it into value.

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What have been your biggest lessons from that time?

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A few stand out. First, staying relevant is everything – technology moves fast. I've seen the evolution from on-prem to cloud, from dashboards to AI agents. Keeping pace means constant learning.

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Second, team culture is critical. Especially in consulting, a strong, close-knit team is what allows you to tackle difficult client challenges and deliver consistently.

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Third, I’ve always believed in bias for action. The people who make an impact are the ones who get stuck in, take ownership, and stay curious.

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And finally – this one’s timeless – data quality is non-negotiable. No AI system can overcome poor inputs. If your data isn’t reliable, governed, and understood, your outcomes will always be limited. AI can help with some data challenges, but you still need strong foundations.

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From a data science lens, how do you see the state of AI in the enterprise today?

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It’s a mixed picture. I’ve spoken with over 100 companies in the past few years, and the variation in maturity is striking. Interestingly, some of the largest firms – those you’d assume are furthest ahead – are actually the ones struggling the most. Meanwhile, smaller, more agile organisations are often outpacing them.

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The biggest opportunity right now is AI that boosts productivity. Whether that’s copilots, AI agents, or internal tools that support teams, I believe most enterprise employees will be using some form of AI in their day-to-day work within the next couple of years.

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But there are three major blockers:

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1. Foundational data quality – still a widespread issue. AI is only as good as the data it’s trained on.

2. Change management – there’s still anxiety about how AI will affect jobs. Communicating and supporting people through that is crucial.

3. Overhyped expectations – many leaders expect AI to solve every problem or instantly deliver value. But without clear business objectives and strong understanding, even the best tools will fall short.

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‍Agentic AI is a hot topic—how ready are enterprises for it?

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Technically, many organisations have the skills to adopt agentic AI. It’s not about building everything from scratch anymore. It’s about integrating what’s already out there.

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But culturally and operationally? That’s where most businesses are falling behind.

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The tools evolve quickly, and they can reshape processes almost overnight. If your organisation isn’t ready to absorb change that fast, adoption will stall.

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I’ve seen everything from deep caution to real appetite for experimentation. The key is understanding your risk appetite and adjusting your strategy accordingly. Early on, many businesses restricted tools like ChatGPT. Now, some are relaxing those controls and finding ways to test these tools in lower-risk environments.

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Another common challenge is at the leadership level. Senior leaders often don’t yet fully grasp what these tools can do, while junior employees are eager but feel blocked. Closing that gap, through education and empowerment, is essential.

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Industries like financial services, insurance, and energy are actually well-positioned for agentic AI because they rely on structured workflows and repeatable processes. With the right governance and buy-in, agentic AI can help streamline decision-making and unlock major efficiency gains.

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What do you think is Mesh-AI’s biggest advantage for enterprise clients?

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What attracted me to Mesh-AI is its focus at the intersection of industry, AI, and data. That’s where real transformation happens.

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We’re not just tech specialists. We understand the regulatory, operational, and strategic challenges of our customers. We help clients go beyond proof-of-concepts and actually embed solutions that deliver sustained value.

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Many organisations know the potential of AI but struggle to scale. At Mesh-AI, we bring the technical depth and the industry context to bridge that gap. That’s what sets us apart.

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What are you most excited about as you step into the role?

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What really excites me is Mesh-AI’s clarity of purpose. We’re not trying to be everything to everyone. We’re focused on a select number of industries and a few strong alliance partners. That level of specialisation is rare and powerful.

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I also know many of the team already, some for over a decade, and they’ve all spoken about the strong culture and drive for excellence here. You can feel the energy and ambition.

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Over the next 6–12 months, I’ll be focused on helping scale our tech and consulting teams, building strong internal structures, and working with clients as a true business partner and not just a service provider.

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There’s a huge opportunity in front of us, and I’m looking forward to helping Mesh-AI grow into the next phase.

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