It is easy to make an AI demonstration look impressive. Proving that it improves a banking operation is harder.
A tool can summarize a document in seconds and still leave the overall process largely unchanged. Someone has to find the right documents, confirm that they are current, check the output, resolve conflicting information, and record the decision. If those activities consume most of the effort, faster summarization solves only a small part of the problem.
I think this is where the conversation about AI needs to start. What work are we trying to improve, and what would better performance actually look like?
Start with the work
Our Enterprise Process Intelligence Framework, EPIF, begins by establishing how the operation performs today. That includes volumes, handling time, waiting time, error rates, control outcomes, and the cost of delivering an acceptable result.
Take a credit review. Before selecting an AI tool, follow a review from the initial request through the final decision. Where does it wait? Which information is collected twice? How often does the reviewer send it back? Which decisions require experience, and which follow a clear rule?
That evidence determines the intervention. Eliminate unnecessary work. Simplify the remaining steps. Standardize the inputs and decision rules. Integrate systems where fragmentation creates effort. Then assess where automation or AI would add value.
The sequence matters. A better checklist might solve one problem. A system connection might solve another. AI might be the right answer for extracting information from varied documents or preparing an evidence-based draft for a reviewer. Each choice needs its own case.
Design the operating model around the capability
An AI deployment needs clear boundaries. Specify the information it can access, the sources it must reference, the actions it can take, and the circumstances that require human review. Define what happens when a source is missing, evidence conflicts, or the service becomes unavailable.
EPIF governs the operating decision and value case. Security, AI governance, and model-risk processes provide the separate controls needed to assess and manage the technology. Those responsibilities have to come together before deployment.
Evaluation should include difficult cases and actual workflow conditions. A strong average score can conceal errors in the cases that carry the most risk.
Count the value that reaches the business
Finance and Operations should agree the measurement approach before implementation. Include integration, licensing, review effort, monitoring, maintenance, and the cost of handling exceptions.
Time saved becomes financial value only when the organization uses it productively. It may reduce overtime, avoid additional hiring, increase throughput, or improve service. Those are different outcomes and should be reported separately.
For me, a successful AI program has a straightforward test: the operation performs better, the controls remain effective, and the value holds after the project team leaves. That is what deserves to scale.
Where to start
Start with one workflow where the AI opportunity looks promising. An EPIF assessment can establish what would make it worth funding.
Related
EPIF for AI deployments: use cases, evidence and oversight
AI adoption moves at the speed of trust
Documented result: 67% fewer unproductive alerts