Share this post
20th March 2025
AI in financial services is full of promise - smarter decisions, better customer experiences, lower costs. But for many organisations, it’s still just a promising experiment rather than a real business driver.
You’ve got proof-of-concepts (PoCs) running in different teams, but they’re not delivering tangible, scalable results. Compliance, data issues, and risk concerns mean AI projects often stall before they reach production.
At a recent 2i roundtable discussion, industry leaders agreed: the biggest AI challenge isn’t the technology - it’s moving from experimentation to execution. To make AI more than just a test-bed idea, here’s how financial institutions can break the PoC cycle and turn AI into real business impact.
-
Define clear business outcomes:
Too many AI projects start with, “What can this technology do?” instead of “What problem are we solving?”. That’s why so many PoCs get stuck in endless testing - they’re chasing technical innovation, but without a clear business case.
-
Tie AI projects to measurable business impact - if AI doesn’t improve revenue, reduce risk, or increase efficiency, it won’t make it past the testing phase. Define clear metrics for success before development begins.
-
Avoid ‘AI for AI’s sake’ - not every problem needs AI. Choose projects where automation, machine learning, or predictive analytics create real competitive advantage.
-
Make AI easy to sell internally - executives don’t want another R&D experiment. They want a roadmap to ROI. Frame AI in terms of cost savings, efficiency gains, or risk reduction, not just tech capabilities.
“The cost of maintaining data and ensuring its quality is high. For regulated financial institutions, this isn't optional—they must make these investments to meet their compliance obligations. The real question becomes how to maximise the return on this mandatory spend.”
-Dave Kelly, CEO, 2i
-
Embed AI into risk & compliance from the start – test early, test often:
AI in financial services operates under intense regulatory scrutiny - compliance can’t be an afterthought. Yet, too often, risk and compliance requirements are considered too late in the process, resulting in costly delays, rework, and compliance setbacks. The solution? Testing and governance must be embedded from the very start.
-
Make compliance a core part of your AI strategy from day one - governance should be part of the AI strategy from the start, not a box to tick just before launch.
-
Ensure AI decisions are explainable - financial regulators expect transparent, auditable AI. If you can’t explain how your model works, it’s a liability.
-
Test AI under real compliance scenarios - AI systems must handle regulatory changes, stress conditions, and bias testing before they ever touch customer data.
“If you can use AI to make compliance more efficient and drive value out of it, you can free the budget you would be spending on regulatory compliance for value-added products that retain and attract new customers.”
-Pauline Smith, COO, 2i
-
Prove value quickly with practical, scalable use cases:
If an AI project needs three years of development before it delivers value, it’s probably doomed. The best AI initiatives start small, prove impact fast, and scale easily.
-
Find ‘quick-win’ AI opportunities - use cases like AI-powered fraud detection, risk assessment, and compliance automation can show measurable value within months, not years.
-
Test with synthetic data to remove compliance delays – what is one of the biggest blockers to AI in finance? Data access and privacy concerns. AI-generated synthetic data allows organisations to train and test models without touching sensitive customer data.
-
Scale AI in controlled, low-risk environments - instead of rolling out AI across the business overnight, deploy it in controlled areas first, ensuring risk is minimal before expanding.
“You can run continuous testing through AI. I think that’s great for driving productivity, greater customer experiences, and efficiency. You can use this insight to really bolster these pillars.”
-Vikas Krishan, Chief Digital Business Officer, Altimetrik
AI in financial services doesn’t need more PoCs - it needs real-world impact. The businesses that succeed won’t be the ones with the most ambitious AI labs - they’ll be the ones that move AI from testing into production with confidence.
Want to find out how industry leaders are navigating the introduction of AI into their technology transformation? Download the full 2i roundtable report for exclusive insights.
Or, are you looking for a smarter way to test and launch AI in financial services? Get in touch to discover how 2i helps financial institutions deliver transformation without the headaches.
