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Banks are shifting their focus from broad experimentation to delivering measurable impact and return on investment from their AI strategies.
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The sector is moving away from rigid, traditional systems towards modular, composable architecture to improve agility and innovation.
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Financial institutions will increasingly utilise low-code and no-code platforms to accelerate software development and reduce their reliance on IT departments.
2025 was a unique year for banking, and 2026 will only continue this trend. This year’s banking market landscape will continue to be disruptive, marked by geopolitical crises, macroeconomic instability, changing customer expectations, increasing competition, and tighter cost scrutiny.
Revenues and profitability will continue to be under pressure, requiring banks to defend margins, diversify revenue streams, and focus on innovation to retain their competitive edge. Technology will continue to underpin banking strategies in the new year, but banks will change how and where they invest. This year will see banks shift focus from experimentation and scale to measurable impact, value, and return on investment (ROI).
AI: From hype to impact
The last few years saw banks enthusiastically adopting new technologies, especially artificial intelligence (AI). But as the hype around these technologies settles, banks are now asking tough questions. Did technology add value to organisational goals or customer experiences? Did the returns justify the investment? Did they witness an increase in profitability or revenues? Unfortunately, only around 15% of organisations can directly link AI investments to profitability, and 25% of planned AI spend is likely to be delayed into 2027.
This does not mean that the sector will slow down its technology-powered transformation agenda or reduce investments in AI. On the contrary, global spending on AI within the banking sector is expected to jump from $166 billion in 2023 to $450 billion by 2027. But unlike previous years, 2026 will see banks reshaping their technology strategies and adjusting their AI and technology investments.
Banks will consciously move away from experimentation and towards more targeted strategies that address specific challenges and execute enterprise-wide deployments. The focus will be on leveraging AI and other emerging technologies to improve decision-making, enhance customer interactions, and reduce operational friction.
Most banks have realised that point solutions do not work. To be effective, AI must be deeply integrated across the entire value chain as a connective capability that can transform customer experiences and help meet organisational goals.
Over the next year, banks will start to embed AI across core functions such as engagement, pricing, risk management, servicing, and revenue management.
At the same time, banks will change their evaluation criteria for AI in 2026. This will see them look beyond cost and efficiency gains and understand how technology impacts factors like risk accuracy, customer lifetime value, and margins.
Rigid stacks become flexible
It’s no secret that traditional monolithic technology stacks are rigid and slow down innovation. In 2026, banks will finally make the move to composable, modular architecture, which enables them to break down applications into smaller components. These components can be independently implemented, replaced, and updated without touching the rest of the platform, and even combined in different ways to create innovative offerings.
This is crucial if banks want to be able to respond quickly to market trends, customer demands, and ever-changing regulatory requirements. Organisations with mature composable architecture and comprehensive AI strategies are already trying out multi-agent environments where AI agents orchestrate complex workflows.
No code? No problem
2026 will be about value creation and delivery. The ability to configure, deploy, and update software quickly will be key to this. Over the course of this year, the banking sector will increasingly leverage low-code, no-code (LCNC) platforms to reduce dependence on IT and accelerate software development, implementation, and time to value.
Generative AI (Gen AI) will prove to be an invaluable asset for those exploring LCNC solutions, as it can help in coding, documentation, and quick prototyping. However, adoption of LCNC solutions and increasing reliance on Gen AI come with considerable risks. Banks must establish robust controls to maintain security and ensure their results remain bias-free and reliable. They need to define what can – and can’t – be automated, and back this up with comprehensive policies around version controls, compliance, audit trails, and access rights.
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All in all, 2026 will see banks focusing on aligning technology investments with strategic priorities, customer requirements, and compliance considerations.
The days of quick experimentation are gone, replaced by results-driven innovation and cautious controls. In this environment, the key to long-term success will depend on how efficiently organisations can drive measurable value across the entire ecosystem.
Banks that combine modern architecture, intelligent automation, and disciplined execution will be best placed to navigate change and drive sustainable value in 2026 and beyond.
