Before You Buy an AI Tool, Find the Work That Gets Stuck

A business owner says, “We should be using more AI.” It sounds like a plan, but it does not tell anyone what work should change.

Start with a more useful question: What does your team do repeatedly, and where does that work get stuck?

That might be meeting follow-up, preparing similar quotes, sorting incoming requests, or moving information between email and another system. Once you can describe the work, you can decide whether AI, ordinary automation, an existing application, or a clearer process is the right response.

Follow one task from start to finish

Imagine a small professional services firm whose team holds several client meetings each week. After each meeting, someone gathers notes, identifies decisions, assigns follow-up work, and writes a client summary. The summary often goes out later than intended.

“Use AI to write meeting summaries” is a tempting solution. It may help, but first watch one meeting move through the entire process. Are the notes hard to organize? Is nobody clearly responsible for the follow-up? Are decisions left open during the meeting? Does the draft sit waiting for approval?

These are different problems. If the delay is turning good notes into a first draft, an approved AI tool might help. If the delay is deciding who owns each action, the team needs a better meeting close and handoff. If approvals take three days, faster drafting will not make the client response arrive sooner.

Choose a task that is suitable for a first test

A promising first task happens often enough to measure, takes meaningful staff time, and has an outcome someone can check. It should also have a clear owner. You do not need to automate an entire department to learn something useful.

Ask the person doing the work to show you two or three recent examples. Note when each task started, where it waited, what information it required, and how much rework followed. Include the time spent checking and correcting the result. Saving ten minutes on a draft is not a win if review takes fifteen more.

Match the solution to the problem

If people repeatedly write similar but judgment-heavy text, AI may help produce a draft for review. If the same fields are copied between systems, an integration or conventional automation may be more dependable. If work stalls because nobody knows who approves it, define the decision and owner first.

You may already have useful capabilities in Microsoft 365. Before buying additional licenses or connecting a new tool to company information, review the specific use case, existing permissions, and who will check the output. PCC’s Microsoft Copilot readiness guide explains those considerations, and the Microsoft 365 resource center includes a use-case worksheet and pilot planning template.

Run a small pilot with a real stopping point

For the meeting follow-up example, choose one team member and a limited number of routine meetings. Agree on what the tool may receive, what it may draft, and what must stay with a person. Have the owner verify decisions, dates, names, commitments, and client-facing wording before anything is sent.

Record the current time from meeting end to approved follow-up. Then compare several pilot examples against that baseline. Look at total staff time, turnaround, corrections, and whether the client receives a clearer response. Ask the employee doing the work whether the new method actually reduces friction.

At the end, make an explicit decision: expand the approach, revise it, try a simpler process change, or stop. A pilot that shows a tool is the wrong fit still prevents a larger, more expensive mistake.

Protect the information and the decision

Before using real business information, decide which tool is approved and what information employees may enter. Client records, confidential financial details, and sensitive personnel information should not be casually pasted into an unapproved service. Someone also needs responsibility for the final result, especially when it affects a client, a payment, or a business commitment.

This becomes more important when software can do more than draft. A tool that sends messages, changes records, or acts across applications needs narrower access and stronger oversight than one that proposes text for a person to review. PCC’s AI governance resources cover practical questions about approved tools, data, and accountability.

The first win may have nothing to do with AI

A clearer meeting template, a named follow-up owner, or one approval deadline might solve much of the problem. AI could then help with the remaining drafting work. Or it might add little value after the process improves.

That is a useful result. The goal is not to prove that AI belongs everywhere. It is to make recurring work easier, faster, and reliable enough that the team wants to keep the change.