Once you’ve established that AI needs to justify itself financially, the next step is deciding where it actually belongs.

Instead of beginning with software demos or vendor calls, try a simple exercise. Use AI itself as a tool for clarity. Describe your business and how it operates — without sharing any sensitive or confidential information — and ask the system what practical applications of AI might make sense for a company like yours.

In most cases, the model will generate a list of five to ten potential use cases. Some may be obvious. Others may highlight inefficiencies you hadn’t fully considered. The goal isn’t to accept every suggestion, but to surface possibilities in a structured way.

From there, resist the urge to do everything at once. Choose one solution. Test it. Measure the impact. If it consistently saves time, reduces errors, or improves visibility, keep it. If it doesn’t, move on.

It’s also important to remember that not all AI tools are built for the same purpose. Some excel at summarizing information. Others are designed for workflow automation, forecasting, or data analysis. Selecting the wrong tool for a specific task often creates frustration rather than efficiency.

That’s why ownership matters. Someone inside the organization should be responsible for researching options, testing them carefully, and evaluating results. This might be a technically inclined team member or a small internal working group. In some cases, consulting with a trusted technical advisor can provide guardrails and help avoid unnecessary costs or security risks.

The businesses that benefit most from AI approach it as ongoing improvement, not transformation. They don’t try to overhaul operations overnight. They identify friction, apply a focused solution, and build from there.

When the use case drives the decision, AI becomes practical. When the tool drives the decision, it rarely does.