From innovation to execution: Why banks are raising the bar for transformation

The bar for banking innovation has moved

Across conversations at Sibos and Money20/20 this year, one theme emerged consistently: banks remain ambitious about innovation, but they are becoming more selective about where they invest.

Interest in AI, cloud and emerging technologies remains strong, yet novelty alone is no longer enough. Institutions want a clear link between technology investment and business outcomes – whether that is greater automation, improved customer experience, faster service delivery, or the agility to launch new products and services without weakening control or adding complexity.

This reflects a more disciplined view of transformation. Demonstrating that a technology works is only the starting point. The real test is whether it can be integrated into day-to-day operations, adopted at scale and governed within the bank’s risk and control framework.

That distinction matters after more than a decade of investment in cloud platforms, digital channels and core modernisation initiatives. Many banks have made significant progress strengthening their technology foundations, but fragmented processes, disconnected data and manual intervention continue to create friction. The challenge now is less about introducing new technology and more about turning existing investments into measurable operational improvements.

McKinsey’s 2026 banking outlook highlights the growing importance of precision and speed as AI, competitive pressures and changing customer expectations continue to reshape the sector¹. In this environment, innovation and execution are no longer separate conversations. One increasingly depends on the other.

From compelling use case to operational value

For many banks, the route from use case to business value now matters more than the technology narrative itself.

The strongest transformation initiatives start with a clearly defined business challenge and a measurable objective. The goal may be to shorten decision cycles, increase straight-through processing, reduce operational exceptions or accelerate the launch of new services. What matters is that the technology delivers a clear improvement that can be demonstrated and sustained.

Achieving that outcome takes more than a successful implementation. Technology must be integrated into workflows, be supported by reliable data and align with the people and processes responsible for delivering the service. Otherwise, a capability may be live without materially changing how the bank operates.

Banks are therefore looking beyond product functionality alone. They want confidence in how a solution will be adopted, where it fits within the operating model, and how long-term value will be maintained after deployment.

AI makes the execution gap visible

AI has brought these questions into sharper focus.

Discussions during Sibos and Money20/20 highlighted that banks are moving beyond asking whether AI can create value and are increasingly focused on where that value can be realised safely and consistently within regulated environments. The challenge is no longer access to AI models; it is integrating them into everyday banking workflows in ways that deliver measurable improvements in productivity, service and operational efficiency while maintaining appropriate oversight.

That requires access to trusted, appropriately permissioned data, integration at the point of work, clear human intervention and accountability for decisions supported by AI. When these foundations are in place, AI can help automate information-intensive tasks, and accelerate decisions without compromising explainability or control.

PwC estimates that banks fully embracing AI could improve their efficiency ratio by up to 15 percentage points.² Capturing that opportunity, however, will require more than deploying individual productivity tools. It depends on redesigning processes, simplifying handoffs and applying governance that reflects the risk and significance of each use case.

Not all use cases carry the same level of risk. Activities such as summarisation, document review and exception identification may offer a relatively direct route to automation and faster service. Applications that influence credit, pricing or compliance decisions require stronger validation, explainability and human oversight. Taking a proportionate approach allows innovation to scale while maintaining appropriate controls.

Creating a credible route to value

These expectations are also reshaping what banks need from technology providers. Product capability remains essential, but so does confidence that a solution can integrate into a complex technology landscape, meet regulatory and operational requirements, and evolve without introducing further complexity.

Banks increasingly want a clear view of the path to value: how the solution will connect into existing ecosystems, which data it will rely on, how processes and controls will change, and where responsibilities sit between the bank and its partners. They also want a realistic roadmap from an initial use case to wider adoption.

Importantly, progress does not always require large-scale replacement programmes. Many institutions are looking to modernise incrementally, building on existing investments while removing friction from the processes that matter most. Starting with a defined operational challenge also creates reusable foundations, from integration patterns and data services to governance frameworks. Each implementation can then reduce the time, cost and risk of the next, helping banks bring new services to market faster.

A more meaningful measure of innovation

A stronger focus on outcomes should not be mistaken for a lack of ambition. Rather, it reflects a more mature approach to transformation, one measured by operational impact rather than technological novelty.

For banks, the priority is increasingly to direct investment toward areas where technology can make a meaningful difference to efficiency, responsiveness, customer experience or risk management. For technology providers, differentiation increasingly depends on showing not only what a solution can do, but how it will help institutions achieve and sustain those outcomes.

The next wave of transformation will not be defined by who adopts the most technology. It will be defined by who can translate innovation into measurable business outcomes most consistently and at scale.

As the conversations at Sibos and Money20/20 demonstrated, banks remain ambitious about the future. Increasingly, however, success will be measured by adoption, operational impact and the ability to demonstrate value in practice. That is the standard against which transformation programmes are now being measured.

Sources

1. McKinsey & Company. Global Banking Annual Review 2026. 2026. Available at: https://www.mckinsey.com/industries/financial-services/our-insights/global-banking-annual-review
2. PwC. How AI is reshaping banking. Available at: https://www.pwc.com/us/en/industries/financial-services/library/how-ai-is-reshaping-banking.html