09 Apr 2025

EP1: How the UK Competes for Top AI & Machine Learning Talent with Chris Morrow, Digitalent

In this episode, Tim sits down with Chris Morrow, Founder and Managing Director of Digitalent, a UK-based specialist AI and machine learning recruitment agency operating across the UK, USA, and Australia. Chris shares his insider view on the global AI talent landscape, covering everything from in-demand AI engineer and machine learning engineer roles to the growing need for data scientists, data engineers, and data architects.

 

As demand for AI talent continues to outstrip supply, few people have a better view of the UK's AI recruitment landscape than Chris Morrow. In a recent episode of the UK AI podcast, host Tim spoke with Chris — founder of Digitalent, a UK-based specialist AI and machine learning recruitment agency, and a LinkedIn Top Voice — about the state of AI hiring, the UK's growing AI skills gap, and what it will take for Britain to compete in the global race for talent.

Inside Digitalent: AI and Machine Learning Recruitment

Chris built Digitalent around four core services: permanent hiring, contract recruitment, executive search for AI leadership roles, and a video-interview service called Synergy that streamlines early-stage technical interviews. With two decades of recruitment experience — the last several years focused specifically on AI and machine learning roles — Chris positions his business not just as recruiters, but as practitioners actively upskilling in AI themselves, working with clients across the UK, US, and Australia.

The Most In-Demand AI Roles

Chris outlines the key AI job roles companies are hiring for right now:

  • Data scientists — making sense of data and helping build and train models
  • Data engineers — building data pipelines, often from an analyst or software engineering background
  • Data architects — designing overall data architecture for large-scale projects
  • AI engineers — frequently retrained software engineers, often skilled in Python
  • Machine learning engineers — working in MLOps with a distinct toolset from AI engineers

He notes that demand for AI engineers and machine learning engineers is especially intense in the UK, where supply simply isn't keeping pace. Chris also highlights a shift in hiring for AI strategy roles, with professionals from business and domain backgrounds — not just technical ones — increasingly shaping company AI strategy, reflecting that AI adoption is as much a cultural and organisational challenge as a technical one.

Closing the UK's AI Skills Gap

A major theme of the conversation is the UK's AI skills shortage and what needs to change to fix it. Chris argues the UK needs a much bigger grassroots push into AI education, including:

  • Greater AI literacy taught in schools, including prompt engineering
  • Curriculum changes that integrate AI into how students learn and are assessed, rather than banning it
  • Expanded university AI courses and increased funding
  • A far more ambitious national retraining target — Chris argues the government's plan to retrain tens of thousands of people by 2030 should be scaled up to hundreds of thousands

He also points to the underrepresentation of women in AI — currently around 22% of roles globally — as a critical issue, arguing that greater diversity of thought is essential to solving the industry's biggest challenges.

Competing Globally for AI Talent

With roughly 75% of the AI talent pool concentrated in the US, Chris says the UK is engaged in a genuine global talent war. He describes the rise of "Brit-shoring" — US companies, particularly on the East Coast, hiring UK-based AI professionals remotely at salaries that UK companies often can't match.

To compete, Chris says UK businesses need to focus on:

  • Strong employer branding and a clear narrative for why candidates should join
  • Highlighting autonomy, purpose, and career freedom — advantages smaller UK companies can offer over larger, more siloed US tech firms
  • Addressing the "brain drain" of top research talent and PhDs to major US tech companies
  • Competitive compensation and equity offerings

He notes that AI professionals leaving big tech — where attrition runs 10–15% annually — are increasingly choosing smaller, mission-driven companies, such as those in health tech, where they can have greater impact and autonomy.

Why AI Adoption Requires Cultural Change

Echoing a theme common across the UK AI podcast series, Chris emphasises that AI adoption isn't purely a technology issue — it's a transformation and cultural challenge. Businesses need buy-in across the organisation, not just investment in tools, to successfully integrate AI into how they work.

Looking Ahead: A Shift Toward Whole-Team AI Hiring

Chris says he's seen a notable shift over the past six months: companies are moving from hiring one or two AI specialists to building whole AI teams at once — a sign, he says, that organisations are moving past experimentation and investing seriously in AI talent and infrastructure.

Moving Forward

  • The UK faces a significant AI skills gap, particularly for AI engineers and machine learning engineers.
  • Closing the gap requires investment in AI education, from schools to universities, and much larger national retraining targets.
  • The UK is competing in a global AI talent war, especially against well-funded US employers.
  • Employer branding, autonomy, and purpose are key differentiators UK companies can use to attract and retain top AI talent.
  • Improving diversity in AI, especially the representation of women, is essential to the industry's long-term success
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