Banks are moving beyond isolated pilots into a new era of enterprise-wide ‘AI industrialisation’. According to a recent KPMG report, active AI use in financial services has more than doubled from 30% in 2024 to 75% in 2026, as banks increasingly embed AI into payments, compliance, fraud operations, customer servicing and software engineering.
The focus is now no longer confined to proving AI’s value, but on scaling it safely across the enterprise. Two years ago, banks were still asking whether AI was mature enough for large scale adoption. Today, most tier 1 institutions already view AI as core infrastructure rather than an optional innovation.
That shift is fundamentally changing how banks think about their technology architecture. With clear use cases emerging, the industry is now grappling with how to operationalise AI faster, safer and more effectively.
Go deeper with GlobalData
Access deeper industry intelligence
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
The operational AI boom
While customer-facing AI assistants continue to dominate headlines, the most significant transformation is happening behind the scenes across banking operations. Financial institutions are increasingly embedding AI into areas such as payments, compliance, fraud operations and software engineering; not simply to experiment with the technology, but to realise measurable gains in efficiency, productivity and operational resilience.
This includes moving into the next phase of AI implementation: agentic AI. Rather than deploying isolated tools, institutions are experimenting with coordinated AI agents capable of managing multi-step operational workflows, retrieving and synthesising information, interacting with enterprise systems and escalating decisions where necessary. This signals a shift away from standalone applications towards AI-enabled operational platforms designed to support end-to-end processes under human supervision.
In practice, this reflects an emerging operating model across banking: human-supervised automation rather than full autonomy. AI is increasingly used for triage, summarisation and operational preparation, while employees retain accountability for approvals, oversight and regulatory judgement.
As this evolves, AI is also moving beyond copilots and chat interfaces towards becoming part of the bank’s core operational control infrastructure. The most compelling applications are no longer isolated productivity tools, but systems that interpret complex inputs, reconcile data, route exceptions, document decisions and maintain auditable trails. In this sense, AI is becoming embedded into the control layer of the institution itself.
AI as core banking infrastructure
This shift is coupled with another important trend. Rather than relying on single-model or single-vendor solutions, banks are increasingly building modular architectures that combine multiple models, orchestration layers, governance controls and enterprise data foundations.
This reflects a broader recognition that the challenge is not the model itself, but the infrastructure required to operationalise AI safely in highly regulated environments. Successful outcomes depend less on model selection and more on access to high-quality, governed enterprise data and the ability to connect AI systems to it effectively.
As a result, capabilities such as retrieval-augmented generation (RAG), vector databases and semantic search are becoming central components of modern banking AI stacks, enabling institutions to ground outputs in internal policies, processes and operational data rather than relying on generic model behaviour.
At the same time, banks are prioritising architectural flexibility. With AI capabilities evolving rapidly, institutions are seeking to avoid long-term dependence on any single provider, instead maintaining the ability to switch models, combine vendors and adapt quickly as the landscape develops.
In fact, the most overhyped assumption in banking today is that the model itself is the differentiator. The real competitive advantage lies in integration, orchestration and operational redesign.
The real barriers to scale
Yet despite rapid progress, scaling AI across banking remains uneven.
This is because most banks still operate across highly fragmented legacy systems with inconsistent data models and limited real-time accessibility. AI performs best when data is clean, connected, governed and context-rich, which remains a major challenge in many institutions.
In addition, legacy core banking and payments environments were not designed for AI-native workflows. Integrating AI into production systems, particularly in real-time or mission-critical environments, is often complex, expensive and slow.
These challenges are creating a growing divergence between firms using AI tactically for productivity gains and those redesigning operational processes around AI-enabled architectures.
Governance becomes critical
As highlighted earlier, governance is also a fundamental consideration. Regulators including the FCA, Bank of England and HM Treasury have flagged the need for institutions to prepare for new categories of AI-enabled cyber and operational risk as frontier models become more capable and more widely deployed.
At the same time, research into advanced AI systems capable of identifying large volumes of software vulnerabilities has intensified concern that AI is becoming both a productivity accelerator and a systemic risk factor. As a result, banks increasingly recognise that enterprise-scale AI cannot be deployed without explainability, auditability, observability and embedded human oversight built directly into platform architecture.
The good news is that many institutions are already moving ahead of regulation, implementing governance standards that anticipate future supervisory expectations.
What AI banking looks like
Over the next three to five years, AI will continue to become deeply embedded into the operational fabric of banking rather than existing as a standalone capability. Payments, compliance, fraud operations and customer servicing will increasingly evolve into event-driven systems where AI supports real-time interpretation, exception handling and decision preparation under human supervision.
As banks shift from AI experimentation to industrialisation, the institutions best-positioned to lead this transition, and realise the benefits, will not necessarily be those deploying the newest models first. Adopting AI safely, responsibly and at scale demands strategy, strong data foundations, future-ready architecture and governance frameworks. Together, this will ensure AI delivers lasting value.
Tamsin Crossland, Principal AI Architecture, Icon Solutions
