How to Identify AI Opportunities in Your Business

To identify worthwhile AI opportunities, start with business goals and a real walkthrough of the work. Look for repeated effort, information gaps, and constraints on growth. Then compare the potential benefit with the implementation effort, risk, and changes required from the people using the result.

You may know AI could be useful without knowing where it belongs in your business. That is exactly the uncertainty discovery should help resolve.

That is enough of a starting point.

You do not need to select a platform, identify the perfect use case, or write a technical brief before asking for help. Those decisions should follow a better understanding of the business.

The first thing I would want to do is learn how your work actually happens.

Begin with what the business is trying to achieve

Are you trying to serve more customers, improve follow-through, reduce repeated administration, or introduce a new service?

Perhaps the goal is not yet clear. You may simply know that people are busy, information is scattered, and it takes too much effort to keep everything moving.

Either situation gives us something to investigate.

Growth strategy matters here because an efficiency improvement is only one kind of opportunity. The business may need a better way to generate demand, maintain relationships, or deliver something customers value.

AI is relevant when it can help put the right change into practice. It should not define the goal before we understand it.

Walk through a real working day

A process chart can be useful. So can watching how a recent assignment moved through the business.

I would ask where the request arrived, who handled it, what information they needed, and which tools they used. What happened next? Where did the work wait? What had to be checked or repeated?

Talk to the people doing the work as well as the person responsible for the result. A necessary step may be invisible to someone looking only at the final output.

This does not require exposing confidential records through a website form. Appropriate access and examples should be arranged as part of the engagement.

Look for opportunities, not just obvious pain

Some opportunities are easy to describe: repeated data entry, slow research, missing updates, or time-consuming reporting.

Others may be less visible. Information collected for one job could become useful on the next. A clearer customer process might reduce avoidable questions. Better organization of existing knowledge might help a team take on work it currently struggles to support.

Industry research can add context, but it should not replace learning how this particular business works.

A tool that helps another company may still be the wrong fit here.

Compare the options before choosing technology

For each promising opportunity, I would want to understand the expected benefit, the information required, the implementation effort, and the practical risks.

Who will use the result? What happens if it is wrong? What would the business need to change? Is there a simpler approach?

Microsoft’s planning guidance evaluates AI opportunities against business impact, technical feasibility, and user desirability.¹ That distinction helps keep the conversation connected to the work, rather than an impressive demonstration alone.

The recommendation may involve existing software, an integration, a clearer process, custom development, or a combination.

Deciding not to use AI in a particular step is not the same as ignoring AI’s potential.

Make the recommendation understandable

The client should be able to understand what is worth investigating next and why.

A useful recommendation would explain the current situation, the proposed change, the expected benefit, and the assumptions that still need testing. It should make clear where people remain responsible and what implementation would involve.

At Fidelis, that direction becomes a tailored growth and systems plan: the opportunities worth pursuing, recommended changes, and how implementation could proceed. The detail depends on the engagement.

A simple opportunity record can capture the discussion:

Question	What to write down
What are we trying to improve?	The business goal and who benefits
What happens today?	A real example of the task and its handoffs
What might change?	The proposed process or system change
What remains uncertain?	Access, accuracy, effort, risk, and user needs
What would justify proceeding?	A useful result and a way to test it

This is a conversation aid, not an automated readiness score.

The point is not to create paperwork for its own sake. It is to support an informed decision.

Start with enough scope to learn something useful

A broad discovery can reveal several connected improvements. Implementation does not have to attempt all of them at once.

Choose an initial step that can test an important assumption or deliver useful change without concealing the larger goal. Include representative work, future users, and a clear way to evaluate the result.

The first build should not become a fixed commitment to a tool regardless of what is learned.

Anthropic’s engineering guidance recommends starting simply and increasing complexity when the task warrants it.² That is a helpful discipline when deciding how much system to build.