AI Agents – a sophisticated type of software capable of planning, reasoning, and executing tasks independently – are fast becoming a serious consideration for banks looking to streamline operations and boost resilience. With organisations like the World Economic Forum (WEF) touting the transformational potential of Agentic AI, banking leaders must focus not only on the technology itself but getting it used effectively.
Will AI Agents on balance displace banking jobs, or will they become integral to a new hybrid human-machine operating model? In other words, is it ‘game over’ for bankers or is this simply the ‘endgame’?
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In the banking sector, where the cost of error is high and regulatory obligations are extensive, finding the answer hinges on more than just technological capability. Instead, it requires a clear understanding of how AI fits into existing systems, how it learns, and most crucially, how it understands the organisation it’s deployed in.
For AI Agents to be meaningfully integrated into core business operations, they need more than a generalised grasp of the world (what we might call a “Public World Model”). Agents also require a “Private World Model”, a real-time, contextual understanding of the specific business environment they serve. This Private World Model is what enables AI to move beyond basic task automation and operate with the discretion, safety, and strategic alignment necessary for use in high-stakes settings like risk, compliance, or customer operations. Building it takes more than data. It takes a structured approach that brings business context into every layer of AI deployment.
Banks seeking to move from early experimentation to strategic, at-scale adoption should follow these three key principles:
AI integration: Build around the business, not just the technology
For AI Agents to deliver value, they must be embedded into the operating model, not bolted on as isolated tools. That means defining their purpose, boundaries, and how they interact with human teams from the outset.
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By GlobalDataIn practice, this requires cross-functional alignment. That means bringing together risk, compliance, technology, and business operations to ensure governance is embedded and responsibilities are clearly allocated. It’s about answering the operational questions before the technical ones. For example:
- What will the agent do?
- What decisions can it make?
- How will performance be measured?
- How will human oversight work?
In highly regulated banking environments, this level of discipline is essential. Poorly integrated AI risks duplication, degradation of service quality, or worse, regulatory breaches and reputational harm. Successful AI programmes treat these issues as first-order design considerations, not afterthoughts.
Strategic implementation: Prioritise high-impact, low-risk use cases
The temptation to adopt AI Agents quickly across the enterprise is understandable, but rarely effective. A more sustainable approach begins with well-defined use cases that offer a high return with manageable risk.
One clear example is JPMorgan Chase’s COiN (Contract Intelligence) platform, which uses AI to review commercial agreements. It reportedly cut error rates by 80% and freed up 360,000 hours of legal review time annually. This isn’t a theoretical impact. It’s measurable operational efficiency, delivered through structured implementation and ongoing oversight.
Banks should look for similarly contained, repeatable tasks that are essential but burdensome. These create ideal environments for AI Agents to demonstrate value while allowing teams to build institutional knowledge and governance muscle before expanding into more complex areas.
Continuous learning: Maintain relevance and control over time
AI deployment is not a one-off exercise. As business needs change and regulatory frameworks evolve, AI Agents must adapt in parallel. That means embedding feedback loops and performance monitoring from day one.
Unlike static software, AI systems learn from data, and that data changes. Ensuring AI Agents remain aligned with business strategy requires structured retraining, robust monitoring, and clearly defined escalation routes when things go wrong.
Change management for the human workforce is equally important. As tasks evolve, new skills and new ways of working are needed. Supporting employees through this transition is critical to building trust in AI, ensuring adoption, and maintaining operational integrity.
What does success look like?
Retail banks must act now to embrace AI Agents before they become the industry standard, rather than a competitive edge. The prize is substantial for those who are first adopters: greater efficiency, faster decision-making, more consistent compliance, and more responsive customer operations. But the route to get there is not through a single piece of technology. It’s through a deliberate strategy grounded in business context and operational clarity.
By focusing on integration, strategic implementation, and continuous learning, banks can shift from seeing AI as a bolt-on and start treating it as a vital core capability. Rather than triggering ‘game over’ for bankers, AI’s real potential lies in shaping a more agile, resilient and scalable workforce where humans and machines complement one another.
That’s an endgame worth striving for.
David Bholat is Professional and Financial Services Director at Faculty
