AI doesn't scale without people

Artificial intelligence is not a trial or a trend; it is already transforming every sector across the globe. For the insurance sector - across pricing, underwriting, claims, and customer engagement, AI is already embedded into day-to-day workflows, and the race is now on to scale its applications responsibly, sustainably, and in a way that truly delivers value.

And increasingly, the answer comes down to people. Insights from Earnix's latest global trends survey of more than 400 insurance professionals – including 40 senior leaders from the UK market – show a sector that is confident, pragmatic and past the hype. UK insurers, in particular, stand out for their belief that they have the talent needed to execute on AI ambitions. Nearly half (48%) of UK respondents strongly agree their teams are ready for AI, compared with just 34% globally. A further 45% in the UK somewhat agree.

This confidence is striking. It suggests that, at least in the UK, the talent conversation has moved beyond basic skills shortages. Many insurers have already invested in data scientists, analytics leaders, and AI-literate product teams. They've built stronger partnerships with vendors, embedded analytics into business functions, and developed more mature data capabilities than they're often given credit for.

But confidence alone doesn't guarantee success. The survey also reveals a more nuanced picture of how AI is being used. Only 38% of UK insurers say AI is fully integrated across most functions, compared with 43% globally. Instead, the majority (55%) report that AI is integrated into some functions, with very few still stuck in pilots or testing phases.

This tells us two important things. First, AI in UK insurance is already operational. It's not a lab project or a future ambition; its helping teams make decisions today. Second, scaling AI across the enterprise remains hard. Moving from isolated use cases to end-to-end decisioning requires more than technical capability. It demands organisational change.

This is where talent takes on a broader meaning. AI transformation isn't just about hiring data scientists or teaching teams how to prompt a model. It's about empowering diverse, multi-skilled and cross-functional teams – product managers, underwriters, actuaries, compliance professionals and technologists – to work together around shared goals. It's about giving people the tools, governance frameworks and clarity they need to trust AI outputs and act on them.

In practice, many of the barriers to scaling AI now sit outside traditional "talent gaps". Data governance, model explainability, regulatory oversight and operating model design are often the real constraints. Teams may have the skills, but they're slowed down by fragmented data, unclear ownership, or processes that haven't evolved to match faster, AI-driven decision cycles.

That's why the next phase of AI adoption in insurance is as much about leadership and culture as it is about technology. Organisations need to shift from asking "Can we build this model?" to "How do we embed this capability safely, transparently and repeatedly across the business?"

This moment also presents an opportunity to rethink how talent is developed and supported. AI has the potential to be profoundly empowering when used as a co-pilot rather than a replacement, augmenting human judgement, accelerating analysis, and freeing people to focus on higher-value work. But that only happens when teams are trained, trusted and included in the transformation, not overwhelmed by it.

Insurance is in the same position as many industries globally in navigating the often-overlapping challenges of fast-paced regulatory scrutiny, competitive pressure and rising customer expectations. As a professional on the front line of AI-driven product development, I firmly believe that success is inextricably linked to the recognition of a simple truth: AI doesn't scale on its own. People scale it.

Moments like International Women's Day are a reminder that building the future of AI requires widening access to opportunity and investing in the full spectrum of talent.

The technology may be advancing at unprecedented speed, but progress still depends on human insight, collaboration and responsibility. Investing in AI talent, therefore, isn't just about skills for today – it's about building organisations that are resilient, inclusive and ready for whatever comes next.