You're probably sitting on more AI potential than you think
A finance team at a leading financial services provider got back more than 200 hours a month. They didn't buy anything new. They used Power Platform to automate a reporting process that had been running through spreadsheets, freeing the team to spend more time on analysis and less time on reconciliation. Sounds relatively simple, so why isn't every team doing this?
It’s not because they lack the tools. Many financial services and insurance organisations already have one or more of Microsoft 365, Dynamics 365, Power Platform, and Azure somewhere in the estate. The AI capability is often already there. But that does not mean they are using it deliberately, governing it properly, or connecting it to the processes that matter.
Adoption, not the capability, is the real challenge
Most FSI organisations already own platforms with AI functionality but turning that capability into real impact at scale is where the challenge lies. The gap isn’t lack of tools, it’s in how adoption, governance and operating models are designed, incentivised and scaled.
Tools can automate tasks, but without the right approach, those gains remain localised and inconsistent. Microsoft 365 Copilot, Dynamics 365, and Power Platform can surface insights, automate claims triage, compliance workflows, and document review. Microsoft 365 provides AI-assisted capability sitting inside the tools people use every working day.
“We were surprised by how much we could do with what we already owned. We just weren’t using the tools effectively before.” – Head of Business Applications, Major Financial Services firm
That’s one of the most consistent things we hear from teams who have made AI adoption a priority. The bottleneck wasn’t access to capability. It was knowing what to activate, where to embed it, and how to connect it to the processes that actually drive value.
Why the 40-Power-Apps problem happens to good teams
Most organisations have already started down this path. They have moved quickly, built useful things, and proved there is demand across the business.
But without the right structure, that activity can quickly become difficult to manage. In one real example, a business ended up with 40 Power Apps running across the organisation, with no central governance, limited visibility of what existed, and no clear way to stop different teams solving the same problem twice.
That is not a failure of ambition. It is what happens when adoption is not matched with governance, prioritisation, and a plan to scale.
The answer is not always to buy more. More often, it is to use what is already there more deliberately, with the governance needed to turn individual experiments into shared measurable impact.
What productive use of the same stack looks like
The organisations getting sustained value from their Microsoft investments share a few characteristics.
AI is embedded in existing workflows and not bolted on as a separate tool. A chatbot on the homepage rarely changes how a business runs; Dynamics 365 surfacing next-best-action insights during a customer conversation does. Power Platform automating claims triage can improve the process teams are already working in. The integration is the point.
Governance sits alongside deployment, not after it. A national insurer significantly reduced app sprawl by consolidating siloed applications through a Power Platform Centre of Excellence; not by stopping experimentation, but by giving it structure. Teams could still build, but there was now a way to share learnings, avoid duplication, and maintain what was live. This is especially important in regulated environments where audit, ownership and control matter as much as speed.
Finally, the budget reflects what production costs. Too many AI initiatives are funded as pilots, with nothing set aside for integration, security, training, adoption and ongoing support that production requires. The teams that get consistent returns plan for that from the start, even when they start small. None of this is complicated in principle. The gap between knowing it and doing it is almost always about discipline and governance, not technology.
From reclaimed hours to operational efficiency
The 200 hours the finance team recovered is not just a technology story. It is a discipline story. The tools were there. The outcome came from knowing what to activate, where to embed it, and how to govern it from day one.
Those hours represent more than capacity. They can be reinvested to drive operational efficiency, improving turnaround times, reducing manual rework, strengthening compliance, and enabling teams to focus on higher-value activities.
The full breakdown of what AI frontrunners in banking, insurance and capital markets are doing differently, and what is still blocking the rest, is in our eBook, The AI Disconnect in Financial Services and Insurance. Download it here.