For years, AI in finance has lived in a permanent testing ground: pilot projects, proofs of concept, and internal sandboxes that never quite make it to deployment. The results look great on slides, time savings, risk reduction, better insights, but they rarely reach real workflows. That era is ending.

The regulators, investors, and clients driving financial institutions forward no longer want to hear about pilot projects; they want to see outcomes, and the firms that are still “testing” AI instead of implementing it are quickly being left behind.

The Endless Pilot Problem

The problem isn’t that AI doesn’t work, it’s that most firms never give it the chance to. In financial services, AI has spent the last five years stuck in pilot purgatory:

• Run by innovation teams, not operational leaders

• Measured in theoretical ROI instead of lived efficiency

• Blocked by outdated data systems and endless approval loops

The result has been projects that prove the case for AI again and again, but never actually change how the work gets done. Meanwhile, the same institutions run legacy processes that eat hundreds of analyst hours every week: data entry, regulatory mapping, and compliance validation that could be automated today.

2025: The Year AI Moves Out of the Lab

AI has shifted. The EU AI Act is live, regulators expect explainable, auditable automation, and the technology itself, from natural language models to real-time data integration, has matured enough to support production-scale workflows safely. Across finance, we’re seeing AI move from the lab to the floor:

• Compliance teams automating regulatory reports

• Risk teams using AI to track controversies in real time

• Compliance officers deploying models that flag disclosure gaps before deadlines

• Asset managers integrating AI insights directly into portfolio reviews

The difference is it’s no longer innovation theatre; it’s infrastructure.

Why Finance Can’t Afford Another Pilot

The “pilot mindset” is becoming a competitive liability. While one firm is still drafting governance papers for another proof of concept, its competitors are saving thousands of hours a year through real deployments.

AI pilots made sense when the technology was untested, but now, they’re excuses, ways to avoid tackling change management, data integration, and accountability. The firms that win the next phase of finance aren’t the ones that experiment the most; they’re the ones that operationalise the fastest.

From Experimentation to Execution

The shift from pilot to production isn’t about more technology; it’s about intent. To make AI stick, firms need:

• Clear ownership: AI shouldn’t live in “innovation”; it should live in workflows.

• Explainability: Every output should be traceable, logged, and defensible.

• Integration: AI must plug directly into compliance, risk, and data systems.

• Cultural shift: Analysts need to see AI as an ally, not an experiment.

In other words, stop asking if AI works and start asking why you’re not using it yet.

Final Thoughts

AI isn’t in testing anymore; it’s in production. The pilots are over. Financial institutions that keep running experiments are handing their advantage to those that have already industrialised theirs. It’s time to get the first deployment live.