AI operationalisation in financial services: moving from models to reliable systems

Why AI operationalisation matters

Most banks and insurers now have AI running in real processes such as onboarding, claims, underwriting and financial crime. Few can run it reliably at scale or show what it did and what it returned and even fewer can reconstruct a single AI decision from last month: what information it used, which version of the model was running, what instructions it followed, who approved it and what it cost. The model usually works. What fails is everything around it.

Who needs to think about AI operationalisation

Chief Operating Officers (COOs) and operational leaders moving AI from experimentation into production, alongside the data, technology and AI leaders responsible for building, governing and operating it.

Why AI operationalisation in banking and insurance raises the stakes

A poor AI decision in banking and insurance rarely stays small. Credit, Know Your Customer (KYC), pricing and claims decisions are revisited through complaints, lookbacks and past business reviews, sometimes years later, and a named executive answers for them. The same flaw applied across thousands of cases becomes a remediation programme.

Many of these decisions also rest on data the firm does not control, such as company registries, sanctions lists, credit bureaux and broker submissions, and on AI inside vendor platforms. In insurance, decisions made under delegated authority add another layer the carrier remains accountable for. AI that cannot be run and evidenced in this environment is a liability, however well the model performs.

How AI operationalisation addresses the challenge of Generative AI and agents 

With traditional AI, a firm validated a model and it behaved the same way until someone changed it. GenAI and agents behave differently from day to day, because what they are given, what they remember and what they are allowed to do keeps changing. In practice:

• The answer depends on the context. The same model gives a different answer depending on the documents and data it is given.
• They remember. Information carries across steps and cases, and so do mistakes.
• They act. Agents update records and call other systems, so what they are permitted to do needs the same thought as a new employee's access.
• A decision cannot simply be re-run to check it. Outputs vary, so the only reliable evidence is a record kept at the time.
• Every decision has a running cost. Usage-based charges scale with volume and grow when agents repeat steps.
• They change without a release. The provider updates the model, the data is refreshed and someone edits the instructions, often with no change request.

The value and the risk now sit around the model. That is the problem AI operationalisation solves.

What AI operationalisation means

AI operationalisation means turning an AI model into a working business system: one that runs reliably at scale, has a clear owner and can show what it did and what it returned. In practice, it involves:

• Data the AI can rely on, with agreed meaning, data contracts and a named owner.
• A redesigned workflow, where AI handles routine cases and people handle exceptions.
• A business owner for each AI system.
• A record of every decision, so it can be reconstructed.
• Ongoing checks on quality, drift and cost.
• Controls built into daily running, including who can switch it off.

Behind that sits a system with more parts than most firms track. Take an AI agent carrying out KYC checks on a new corporate client. The model is one component. Around it sits everything below.

How can leaders get there

Firms that get this right can answer a challenge on any AI decision with evidence, instead of rebuilding it from memory. They know what each system costs per decision, so money moves from pilots that do not pay back to those that do. Each new use case also becomes easier to approve, because the records and controls already exist.

What is next in your AI operationalisation process?

How Forvis Mazars can help

Our AI Operationalisation Framework brings these practices together across four areas: operational maturity assessment, intelligence accounting, workflow and operating model transformation, and governance and assurance.

We can help you review a production or production-ready AI system, identify the operational gaps around it and define practical actions to strengthen control, evidence and performance.