You’ve got the F1 car. Now where’s your driver?

For years, the AI conversation has centered on tools — what can this technology do, and how fast can we deploy it? That chapter is closing. Nearly every organization now has access to powerful AI, and executives across industries say the bigger constraint is finding people who know how to turn those tools into outcomes. 

Think of it this way: An F1 car is fast, impressive and loaded with potential. But without a skilled driver behind the wheel, it’s just an expensive machine sitting on the track. You need someone who understands the course, who knows when to brake and when to accelerate and who can navigate risk at high speed. 

The playing field has leveled. Talent is the new edge. 

In KPMG’s Q4 2025 AI Quarterly Pulse Survey, leaders identified AI prompt engineers (71%), AI performance analysts (59%), and AI trainers/data curators as the most anticipated emerging roles for 2026 — a clear signal that the real bottleneck is talent, not technology. 

That gap is only widening. Access to AI tools is no longer a differentiator. Neither is implementation. What separates the leaders from the rest is something harder to scale — the expertise, curiosity and hands-on engagement of the people putting those tools to work every day. 

Consider a senior corporate tax planning adviser who started out skeptical about AI. A year ago, her team would manually reconcile large data sets at quarter end, spending late nights cleaning and checking numbers. Today, she and a small group of “AI hobbyists” on her team have built and refined prompts, models and workflows that automate much of that effort. Her role is less about grinding through spreadsheets and more about reviewing anomalies flagged by AI, probing the “why” behind the patterns and translating those insights into client-ready strategies. She’s still doing tax — but the value she creates now comes from judgment and interpretation, not manual effort. 

The organizations pulling ahead aren’t the ones with the biggest tech budgets — they’re the ones whose people have become hobbyists like her. They’re tinkering. They’re documenting what works and what doesn’t. Building repeatable patterns, sharing prompts and staying relentlessly curious. 

This matters because the technology isn’t standing still. Models are improving at a pace that makes static skills obsolete almost immediately. The professionals who treat AI fluency as a one-time training exercise will fall behind. The ones who approach it as a lifelong practice — fingers on keys, constantly experimenting — will compound their advantage over time. They’ll be the ones who can quickly evaluate new models, identify where they fit and redesign processes to capture value before competitors do. 

Leaders are starting to price that curiosity into the market: 76% say they would pay up to 10% more for candidates with strong AI skills, and 22% would pay 11-15% more. 

Now get out of the chat window

Those premiums don’t reward people who dabble — they reward people who go deep. And going deep means moving beyond surface-level use. It’s not enough to ask a chatbot questions and review its answers. The real value creation starts when you break out of that box — when you connect AI to your data, automate the insights that used to take days and build systems that flag anomalies before they become problems. It’s when AI moves from being a novelty in someone’s browser to being embedded in workflows, controls and decision-making. 

That’s where platform thinking comes in. The next wave of value won’t come from isolated tools; it will come from integrated systems that bring together data, AI capabilities, governance and human expertise in one place. When organizations unify their data, models and workflows on a common platform, they make it easier for people to experiment, share what works and scale good ideas quickly and safely across the enterprise. At KPMG, we’re building that kind of environment through Digital Gateway — but the principle applies universally. The platform is only as good as the people on it. 

We see the same shift inside major corporations. An early-career analyst at a large financial services firm used to spend most of his time copying data between systems, preparing slide decks and running one-off analyses requested by senior leaders. After his organization rolled out an AI-enabled platform, his day looks very different. He now configures AI agents to monitor risk indicators across portfolios, designs prompt templates that business users can reuse and works with compliance to ensure outputs meet regulatory expectations. He hasn’t left the world of analysis — he’s moved up the value chain, from producing numbers to orchestrating the system that produces and explains them. 

The bottom line 

If you’re wondering where to start, the answer is simple: Get behind the wheel. Encourage your teams to interact with every model, every tool, every capability they can access. Give them permission to experiment within guardrails and reward the behaviors that lead to better client service, sharper insights and smarter risk management. Foster a culture where curiosity isn’t a nice-to-have — it’s a performance expectation. 

Because at the end of the day, it’s not the car that wins the race. It’s the driver. And in the AI era, talent — curious, empowered and in the driver’s seat — is the only sustainable advantage. 

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