A sensible first use case has repeated work, a visible business outcome, usable information, clear ownership and risk that can be bounded. The workflow should be narrow enough to test properly and important enough for the result to matter.
The first useful AI project inside a financial-services firm could be something as ordinary as preparing a monthly report.
Someone pulls information from several places. They compare it with the previous month, chase missing items and prepare a draft for review. Much of the work is familiar. The odd cases still need a person who understands the client, the records and what a reasonable answer looks like.
This kind of workflow can be a good place to begin because the work already exists. The team can show where it slows down, which inputs cause trouble and what has to be checked before anything leaves the firm.
Begin with work you can observe
A use case is easier to assess when the current process can be watched from beginning to end. That means looking at the actual source documents, systems, handoffs, approvals and exceptions involved.
Process maps help, but they rarely contain the whole story. A short working session with the people who run the process usually reveals more. They know which spreadsheet arrives late, which client still uses an old template and which figure gets checked twice because experience has taught them to be careful.
Those details shape the implementation. They tell us where AI may assist, where a rule-based automation could be sufficient and where human judgment needs to remain visible.
Establish what the workflow costs today
A firm needs a starting point if it wants to judge whether the project helped. The baseline can be simple. How many people touch the work? How long does a normal run take? How often do items come back for correction? Where do deadlines slip?
The relevant measure depends on the workflow. A reporting process may be judged by preparation time, review effort and the number of missing items. An onboarding workflow may care more about turnaround time, avoidable follow-up and the quality of the file when it reaches approval.
A useful baseline also keeps the conversation grounded. The firm can compare the likely benefit with the effort, controls and change required to build the capability.
Draw the control boundary early
Each step should have a clear role. AI may collect, extract, compare, classify, flag or draft. People may need to review exceptions, approve material judgments and remain accountable for anything sent to a client or used in a regulated decision.
The boundary will vary by firm and workflow. It should still be written down before the pilot grows. The team needs to know what information the system can access, how sources are checked, what happens when confidence is low and who owns the final output.
A five-part screen for the first use case
Is the outcome worth improving?
Name the service, capacity, risk, cost or revenue effect leadership cares about.
Can the work be observed?
The team should be able to show the current inputs, steps, exceptions and approvals.
Is the information usable?
Confirm that the required records can be accessed lawfully and are reliable enough for the intended task.
Can risk be bounded?
Keep the scope narrow, define the human review and establish what the system must do when something is unclear.
Will somebody own it?
A process owner and an engaged group of users are needed to test, correct and operate the capability.
Choose the smallest credible test
The first version should be large enough to produce meaningful evidence and small enough for the team to inspect closely. That may mean one document type, one client segment or one stage of a longer process.
A narrow test allows the firm to learn about permissions, source quality, exception handling, review time and user behavior before wider deployment. It also makes it easier to stop or revise the idea if the evidence is weak.
The business case may hold up. It may reveal that the data or process needs attention first. Finding either answer early can save the firm from committing to a larger project on enthusiasm alone.
The first project also sets a precedent. A careful choice shows the team that AI work will be tied to real operating outcomes, examined with the people involved and introduced within clear boundaries.
Further reading
Founder of Halyard, an AI implementation company helping financial services firms find and implement practical AI safely.