Before you spend another dollar on AI tooling, answer these five questions. If you can’t answer them clearly, that’s the problem you should solve first.
1. Which three workflows in your business cost the most time and produce the most errors?
If you don’t know this, your AI investments are guesses. You’re buying solutions before you’ve diagnosed the problem.
2. Who would own an AI system if you deployed one?
Not “the team” — a named person. If ownership is unclear before deployment, it won’t clarify after. Systems without owners degrade.
3. What does your current data quality look like?
AI systems run on data. If your data is siloed, inconsistent, or lives in spreadsheets that only one person knows how to use, your AI system will produce inconsistent results at scale. This isn’t the AI’s fault — it’s a data architecture problem that has to be solved upstream.
4. What does success look like in six months, and how will you measure it?
“More efficient” is not a success metric. “The intake process takes 40% less time per case and produces zero missing fields” is a success metric. If you can’t define the measurement, you can’t run the project.
5. What’s the highest-stakes thing that could go wrong?
Every AI deployment has a tail risk. For a law firm, it might be a client service failure. For a healthcare company, it might be a compliance issue. For a B2B SaaS company, it might be bad data reaching customers. Naming the tail risk lets you design for it. Ignoring it means you’ll encounter it in production.
What to do if you can’t answer these
Most teams can answer three or four. Almost none can answer all five cleanly.
That’s not a failure — it’s diagnostic. The questions you can’t answer clearly are the work that needs to happen before the AI project starts, not after.
The audit as a deliverable
We built the Edge Audit around these five questions because we found, over and over, that clients who arrived with all five answered clearly got dramatically better outcomes from their implementations. Not because we were smarter. Because they were better positioned.
A two-week audit that produces clear answers to these questions is worth more than any individual AI tool. The tools are commodities. The clarity about where to apply them, and how to measure the result, is the actual scarce resource.