Most AI initiatives begin the same way. Someone sees a demo, gets excited, and the conversation jumps straight to tools and vendors. Six months later the company has a pilot, a monthly bill, and no clear answer to the question that should have come first: was this worth doing?
If a capital request came to you with no return estimate, you'd send it back. AI projects deserve the same treatment.
Start with the outcome, not the technology
Every AI initiative should name which of four outcomes it's supposed to create:
- More revenue: sell more, convert better, expand accounts.
- Better margin: lower labor, rework or cost to serve.
- More speed: shorter cycle times and faster decisions.
- Less risk: better accuracy, control or consistency.
If the team can't pick one, the project isn't ready. "We should be using AI" is a feeling, not a business case.
Force the economics into the conversation
You don't need precise numbers. You need rough ones that everyone can argue about. What's the annual value if this works? What will the first year cost, including people's time? How confident are you that it will work, and how proven is the workflow today?
That last question matters more than people expect. Automating a process that already works well is a much safer bet than using AI to fix a process nobody has figured out yet. If the workflow scores low, the first investment isn't AI. It's getting the process right by hand.
Decide where your edge lives
Ask one hard question: if a competitor could buy the same thing tomorrow, where would your advantage be?
If the answer is proprietary data, or a workflow that only works with your company's context, there may be a case for building. If the honest answer is "nowhere, it's just useful," rent it. Buying a commodity capability is fine. Paying to build one is how companies end up with expensive software that gives them no edge.
And if the answer is "we don't know yet," the decision is neither build nor buy. It's to run a small, cheap test that proves the value first.
What to do this week
- Pick your biggest AI initiative and write the one outcome it's meant to create.
- Put rough numbers on value, cost and confidence.
- Score how proven the workflow is today from 1 to 10.
- Answer the competitor question in one sentence.
- Decide: build, buy, prove first, or stop. Write it down and share it.
AI Investment Decision
The AI Investment Decision tool walks a real initiative through the outcome, the economics, where your edge lives, and how proven the workflow is, then gives you a recommendation on what to own, what to rent and what to prove first, written up as a memo you can bring to your leadership team.
About five minutes. Ends with a memo, not a vibe.
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Danny Feldman is a 3× founder and the advisor behind Feldman Advisory. He founded and scaled Stellar to $100M in annual revenue across 15 states, raised $55M+ as a founder, and used AI to double revenue before exiting to private equity. Earlier, he recruited and trained 55,000+ sales reps at Cutco and grew a division from 1 to 58 locations. Operator to operator, he now helps founders of $1M–$20M companies get out of the middle of the business, build the leadership team, and raise capital. Book a 30-minute call →