Financial institutions have spent years and poured millions into automation, but with mixed results. It promises efficiency, control, speed, fewer errors, and lower costs. Yet even as agentic AI has woven its way into the tech stack, that promise still feels increasingly fragile.
Despite observability tools and human guardrails, such agentic AI approaches are suffering subtle but critical risks: inconsistent outcomes, audit failures, and the imprecise application of institutional knowledge. With the FCA, PRA and the EU AI Act tightening their grip on the need for AI explainability, the potential benefits are no use unless there is also trust baked-in by design.
Automation that can’t explain itself doesn’t reduce risk, it amplifies it. Below are four warning signs that your AI-powered decisioning systems may already be eroding the very trust and resilience they were meant to create.
1. You can’t explain how automated decisions are made
If you can’t show how a system reached its conclusion, it’s a prediction not a judgement and you can’t defend it. Regulators now expect firms to explain every automated outcome, not just what happened, but why.
Agentic AI workflows often fail this test. They deliver outcomes that may well be right, but with no causal chain of reasoning. The EU AI Act classifies opaque decisioning as “high risk,” and the FCA’s Consumer Duty demands “fair and explainable outcomes.”
With this tightening pressure, AI auditability isn’t a compliance tickbox exercise, it’s the foundation of accountability. Without it, neither customers nor regulators will – or should – trust your automation.
2. Your knowledge is scattered and inconsistent
Across banking, financial services, insurance and other regulated sectors, the logic that drives the way decisions are made is typically widely distributed: in process diagrams, tucked away in spreadsheets or old code, or simply sitting in people’s heads.
Organisational knowledge is rarely a first class citizen in the tech stack. As a result of this knowledge fragmentation, decision-making remains inconsistent and at best leads to trading underperformance. At worst it results in significant reputational and financial damage. In this AI age, institutional know-how is a differentiator, if you can scale it to machine levels. This requires a strategic approach to knowledge and how that will be leveraged in your AI strategy.
One team’s quick piece of AI innovation – like creating an AI agent to run a small process – often becomes another team’s compliance problem. When regulators come knocking asking for a single version of the truth, what happened and why, you shouldn’t have to undertake large amounts of work to try and provide answers.
Creating a knowledge layer, one that enables your institutional IP to be computed over with the same precision that Excel computes numbers, ensures that what separates one organisation from another – the expertise that makes your services special – can be scaled to machine-levels with precision, consistency and trust.
3. Your audit trail shows process steps, not reasoning
Most organisations can track what happened, who approved what and when. But they struggle to prove why. Logs that are focused only on workflow steps can only show process, not how judgment was ultimately reached.
In a world of tightening supervision, regulators want to see the logic that drove each outcome: the reasoning that was applied, the data that was considered and how uncertainty was handled. If AI-powered reasoning isn’t transparent, you’re left asserting compliance without being able to demonstrate it. A lack of auditability is an innate limitation of the LLMs that drive most agentic solutions today. They simulate a logical system without the benefit of actually being logical, and that’s a problem.
There is an answer to this. A true reasoning system can provide you with a proof trail; not just evidence of inputs and outputs, but the reasoning in the middle. There is huge value in being able to logically articulate the how – the certainty needed to satisfy both customers and regulators.
4. Accountability sits with IT, not the business
Too often, automation and AI-driven processes are expressed in code, too far from the teams who have P&L responsibility for the decision outcomes being automated. When business logic gets represented as code, it becomes slow, expensive and unwieldy to manage, lost amongst the process.
Risk, compliance and audit teams need visibility into how automated decisions are not just made but being run. When they can’t see or manage the knowledge that should be driving AI-powered decisions themselves, the business becomes stuck in a high-cost maintenance loop whenever things change.
Automation in the AI age should help experts to scale their knowledge to machine levels to deliver transformatory results. Business owners should be able to build and maintain decision logic. It has long been proven to be the only way of ensuring that agility, governance and trust can coexist to scale decision-services to the levels required to be competitive.
The root cause: automation built for efficiency, not superior, differentiated and trusted decision outcomes
These risks all come from the same flaw. Digital transformation too often focuses on making poor processes run faster, rather than building truly smart systems that encode and leverage your institutional knowledge to drive superior outcomes for customers.
The first wave of white-collar automation was driven by Robotic Process Automation (RPA) and it succeeded at automating low risk simple tasks. In the agentic AI age, we are now focused on the automation of complex decision-intensive processes. These are subject to much deeper scrutiny and require a different tech stack that brings knowledge, reasoning and auditability together.
The promise of agentic AI-like benefits but without the risks.
Today, AI auditability is the new measure of AI maturity. The next generation of agentic AI is combining human-like expert reasoning with machine scale and logical auditability. It celebrates the human expertise that distinguishes one set of experts from another, encodes this knowledge precisely into the tech stack and reasons over it at scale with compliance baked-in.
Conclusion
The automation agenda has evolved as the scope of ambition has shifted from simple task automation, for the sake of efficiency, to the automation of complex high-stakes decisions to drive new products and services that would previously have been impossible.
The acceleration of sectors like banking, financial services and insurance isn’t coming from an agentic AI approach based purely on LLM black boxes. It is coming from a hybrid approach that can bring world models of knowledge to the AI stack (usually involving knowledge graphs) and ensuring that such knowledge can be precisely computed over and applied to AI-powered decisions, at scale.
Determinism is an essential property of trust. It guarantees that the same inputs will always generate the same outputs. One of the biggest challenges with the LLM-based tech stack is a lack of determinism. Rainbird solves this by ensuring that it is formal models of knowledge that are reasoned over deterministically, guaranteeing consistency and trust.
Our platform enables regulated organisations that care about precision, determinism and auditability, to build the kind of AI systems that customers and regulators can trust. We capture and scale human expertise and policy logic in transparent, auditable models and ensure it is central to reasoning, giving you absolute visibility into how every outcome is reached.
Not every use case requires these attributes. If you can mentally insert the word “probably” before an AI-generated answer, and that is okay, you don’t need Rainbird.
But there are thousands of use cases where there is no tolerance for error. These are typically the most critical and most watched, by regulators.
If you want to enhance your agentic AI approach: ensuring that your institutional knowledge is a first-class citizen, hallucinations are avoided and outcomes are fully auditable, then reach out. Rainbird closes the gap between endless agentic AI experiments and substantive live deployments, driven by a move from a pure black-box approach to glass box one.
Contact our team to explore how explainable decision automation can strengthen compliance, reduce risk, and restore confidence in every AI decision you make.
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