Banking looks beyond pilot projects to turn AI into business impact

Are you leading or lagging in AI?

It’s a complicated question, and in banking and financial services, the answer is beginning to determine real competitive advantage.

In our latest NTT DATA global research, 2026 Global AI Report: A Playbook for Banking and Financial Services, we explore the divide between the frontrunners and the rest to see what sets them apart.

The findings show that success is no longer about running AI pilot projects or testing ideas. That era is over. What matters now is how deeply AI is embedded into core operations and whether it improves how banks and financial services providers generate revenue, manage risk and serve their customers.

The numbers tell the story: Alignment delivers results

The data makes it clear: Organizations that have aligned their AI strategies with their business priorities are far more likely to see measurable financial gains. In our research, 84% of them report at least a 5% increase in profit from AI. For everyone else, progress is slower and less consistent.

This has little to do with the technology itself but rather with how it is applied. AI leaders, as defined in our playbook, don’t spend years in experimentation or apply the technology thinly to multiple disconnected initiatives. They also don’t layer AI on top of existing processes. Instead, they redesign workflows end to end, with a well-defined sense of purpose, linking their AI ambitions directly to the outcomes they want to achieve.

For these organizations, this level of alignment shows up in how decisions are made. They treat AI initiatives like any other strategic investment, with clear ownership, defined outcomes and accountability tied to business performance. Rather than measuring success by how many use cases they have deployed, they focus on impact — whether it’s revenue growth, risk reduction or operational efficiency. That discipline helps them move fast in areas that deliver impact, so they can scale what works and quickly move on from what doesn’t.

It’s not just how AI is used; where it’s deployed also matters

The banking and financial services industry differs from others in that where AI is deployed is as important as how it is deployed. Decisions made in client engagement, credit, fraud and compliance directly affect revenue, risk exposure and regulatory standing. This leaves little room for low-impact experimentation.

Leading organizations focus their efforts where the stakes — and the returns — are highest. Rather than starting with back-office efficiency alone, they also prioritize front-office and revenue-driving use cases such as marketing, and client and partner engagement. This is where the impact is both immediate and measurable. In fact, 75% of AI leaders in banking and financial services focus on these areas, compared with 40% of laggards — the opposite cohort. The gap reflects a fundamental difference in strategic intent.

Speed and control: Dual drivers of success

In banking and financial services, speed is a critical differentiator. But in such a tightly regulated environment, moving quickly without control only creates risk. Organizations need to know when they can accelerate and when they can’t.

However, AI leaders aren’t ignoring risk; if anything, they’re leaning into it. They move faster but always within guardrails. Governance, oversight and accountability are included from the start, influencing how AI is deployed and how it scales.

Our data reflects this balance. Just over half of AI leaders (53%) say they aim to “move fast and lead the market,” compared with only 51% of laggards. Their willingness to act, paired with the discipline to do so responsibly, allows these leaders to scale AI beyond one-off use cases and embed it throughout the business. They move decisively in areas that support revenue growth, and maintain tight control in those that are more heavily regulated.

Once AI is built into core processes, it becomes part of the operating model. And when it starts to change how work gets done — streamlining workflows, influencing decisions and improving how the business performs day to day — the impact is measurable.

Tools versus implementation: The key differentiator

What’s striking in banking and financial services is how quickly the AI conversation has shifted from access to technology to how well it’s put to work.

Most organizations use the same AI tools. Leaders, however, are applying them differently because their AI strategies are tied directly to business goals.

Execution here goes beyond delivery; it requires focus. The key is knowing where to apply AI, how to embed it into core operations and how to move beyond pilot projects without losing control.

AI adoption is no longer an option; a competitive market demands it. But how organizations adopt it and where they apply it will define the gap between AI leaders and laggards.