Steve Chan

‘The biggest challenge isn’t technical, it’s belief’

Part of the AI Leadership Index – a curated list of the most inspirational figures shaping the AI revolution.

Steve Chan is Co-Founder and CTO at Stealth Labs, a technical AI studio that designs, builds, and operates software for UK Government and enterprise. With over 14 years of experience in government technology, Steve has been at the forefront of AI adoption across the UK public sector.

Before founding Stealth Labs in 2025, he spent five years at Microsoft as an AI Solutions Engineer, leading GitHub Copilot rollouts across UK Government departments and helping enterprise customers navigate their AI transformation journeys. Prior to Microsoft, he held various technical and leadership roles at BAE Systems Digital Intelligence over eight years, building deep expertise in secure, high-stakes environments.

Steve has delivered AI implementations for clients including the Home Office, HMRC, NCA, and FCDO. His practical, outcomes-focused approach has shaped Stealth Labs' mission: closing the gap between AI's potential and what actually gets built.

A Computer Science graduate from Birmingham, Steve combines technical depth with commercial pragmatism. At Stealth Labs, every person ships code, and every engagement ends in production.

Please share a case study of how you have you innovated with AI over the last year?

The innovation isn't a single project. It's building an AI studio where every person is technical, every engagement ships working software, and AI-first workflows are the operating model, not the sales pitch.

Stealth Labs was founded because we kept seeing the same pattern: smart organisations, real AI budgets, and nothing getting built. Billions spent on strategy engagements, discovery phases, and proof of concepts that never reach production. We came from consulting and big tech. We knew what was broken. So, we built something different.

We embedded AI into how the studio operates end to end. Claude and Claude Code are central to how we develop and deliver. This isn't about using AI tools occasionally. It's a fundamentally different operating model where AI amplifies what each engineer can deliver. A single developer working with the right AI tooling can now ship what previously required a much larger team. That changes the economics of software delivery entirely, and it means our clients get more output, faster, without the traditional overhead.

That model lets us move at a pace traditional consultancies can't match. We've shipped products to Azure Marketplace, delivered for government clients across some of the most security-sensitive environments in the UK, and we're growing a remote-first team of engineers across the country. We're not pitching AI transformation. We're demonstrating it through how the company itself runs.

What is the biggest challenge you have faced with AI innovation and how you have overcome it?

The biggest challenge isn't technical. It's belief.

After years of overpromising and underdelivering in tech, organisations are understandably sceptical about AI. They've seen slick demos that never reach production, proof of concepts that gather dust, and six-figure projects that fail to deliver real outcomes.

That scepticism slows adoption. Procurement adds layers of protection. Innovation stalls because no one wants to back the next expensive disappointment.

We've overcome this by leading with delivery, not decks. Every person at Stealth Labs codes. Every engagement ends in production. When clients see working software in their own systems, with their own data, scepticism gives way to advocacy. The work becomes the proof.

The gap between what AI can do today and what most decision-makers believe is possible is still huge. But that gap closes one delivered project at a time, and we're focused on stacking up those proof points across government and enterprise.

How do you balance innovation with ethical safeguards?

Ethics isn't a separate workstream for me. It's embedded in how we deliver AI.

At Microsoft, I spent five years supporting government departments adopting AI, helping shape Responsible AI frameworks, governance models, and practical guidance. When your work impacts millions of citizens, responsible implementation isn't optional. It's foundational.

That experience shapes how we operate at Stealth Labs. We align with established Responsible AI principles: fairness, reliability, privacy, inclusiveness, transparency, and accountability. These aren't pitch-deck buzzwords. They're built into our process from day one.

Working with government clients reinforces this naturally. The environments we operate in demand rigorous standards around data handling, security, and accountability. When you're delivering for organisations like the NCA and HMRC, there's no room for cutting corners.

Innovation without ethics creates long-term damage. The organisations that succeed will be those building trust through responsible delivery, not those moving fast and hoping for the best.

How do you believe the artificial intelligence space will change over the next 12-24 months?

The way organisations are built is changing. AI is enabling smaller teams to deliver at a scale that previously required much larger headcounts. At Stealth Labs we see this every day. It's about fundamentally rethinking what a team can achieve when AI is embedded into how they work. That shift is only going to accelerate.

Within 24 months, AI agents will handle tasks we currently assume require human judgement. Categories of knowledge work will be transformed. Some industries will move faster than the workforce can adapt, and that creates real questions we need to be honest about.

I've spoken about this at several meetups, deliberately sparking uncomfortable conversations, because we need to start discussing solutions now, not after the disruption hits. How do we retrain at scale? How do we make sure the benefits of AI are distributed broadly, not concentrated? These are becoming necessary policy discussions, and the AI industry has a responsibility to be part of them.

The companies and leaders who engage with these questions early, rather than ignoring them, will shape what comes next.