Where to Use AI in Your Business: Start With the Work
To decide where AI could help your business, look closely at recurring work that takes too much time or delays an important decision. Choose a process someone owns, then examine the information it needs, the judgments people make, and what a dependable result looks like.
A monthly report is a good place to see the problem. Imagine a finance team spending days gathering figures, checking discrepancies and chasing explanations. By the time the report reaches the person who needs it, the decision is already late.
Giving that team a tool that writes a summary in seconds might help. It might also leave the real delay untouched.
Before buying anything, follow one report from the first request to the decision it supports. Find out where it sits and why.
Find the work that waits
Ask the person doing the work to walk you through the last completed example. Open the spreadsheet. Read the email asking for a missing number. Look at what happened when two departments supplied different answers.
You are looking for the point where progress depends on someone finding, checking or explaining something. It could be a queue of customer requests that needs sorting, or an approval package that comes back every week with the same missing information.
Be specific about the consequence. A report taking too long matters because someone is waiting to decide where to spend money, what to change or which problem needs attention.
That consequence gives the proposed change a purpose. It also gives you a way to judge whether the work improved.
Choose a process someone owns
A useful test needs someone who can say what a good result looks like and decide whether the test met that standard.
For the monthly report, that person needs to know which sources can be trusted, what requires an explanation, and who can approve the finished work. If those decisions are unclear, settle them before building.
Use these questions to narrow the choice:
- Does this work recur often enough for an improvement to matter?
- Can the team access the information needed to complete it?
- Can someone check the result and catch a mistake before it causes a problem?
- Who will own the process after the test?
Adding AI to a process with no clear owner can leave the team with another tool people use only when they have time. Choose work that already has someone accountable for getting it done.
Give AI a defined part of the job
A useful test should examine the information coming in, the task AI will perform, and the review needed before the output can be used. Include incomplete or conflicting inputs so you can see where the process needs human judgment.
In the report example, AI might prepare a first draft of the commentary from approved figures and supporting notes. The calculations still need explicit rules. The finance lead still needs to review the explanation and approve what goes forward.
Keep the first test narrow enough that you can inspect the result. If a required input is missing, decide how the process should stop and who should supply it. If the draft makes a claim the source material does not support, the reviewer needs a way to catch it.
Some problems will turn out to need a clearer handoff or a simple rule. Fix those directly. There is no reason to put AI between two steps that already have a dependable answer.
Measure the finished work
Record how long the process takes today, from the first input to an accepted result. Include the time people spend checking and correcting it.
Then test the proposed change against that baseline. A summary produced in seconds has little value if a manager spends the afternoon repairing it. The useful measure is whether the team gets to a dependable decision sooner, with an acceptable amount of review.
Keep track of the exceptions, too. A process that works on the cleanest example may fall apart when a department misses a deadline or the figures do not agree. Those are part of the job the team has to do every month.
You should be able to describe the proposed change in a few sentences: the work that is waiting, the part AI will handle, who will check it, and what would count as an improvement. If that description is still vague, spend more time with the people doing the work.
Start with a recurring part of the business where an improvement would matter. Test whether AI helps the team complete that work more reliably, with less time spent checking and correcting it.
Want to put this into practice? HVL Blueprint works with your team to assess where AI can help and test it in the work you do every day.
