The Hardest Truth I Learned Selling AI to the Enterprise

When I first started selling AI/ML into the enterprise, I thought the hardest part was proving the tech worked. I was wrong.

If you were to ask me what the real challenge was, I would tell you that it's understanding how people inside large organizations make decisions, and how those decisions ripple up to budgets, priorities, and approvals.

If I could talk to my younger self, here’s what I’d tell them:

1. Your champion isn’t enough unless the boardroom buys in.
Early in my career, I pinned my hopes on a CTO who was all in; only to watch the CFO shut it down.
Lesson: map the goals of every key decision-maker from day one, and connect your story to their scoreboard.

2. A pilot doesn’t mean you’ve won.
I once treated a successful POC like it was game over.
It’s not.
A pilot is just the opening move in a political process.
Now, I use it to build a coalition of supporters so multiple departments are invested in the win.

3. Vague “success” will cost you later.
Some painful renewals taught me this.
If no one agrees on what success looks like before you sign, you’re inviting friction later.
Today, I define explicit, measurable criteria up front. It’s the fastest way to protect trust and get to renewal.

4. Outcomes close deals. Features don't.
I’ve seen brilliant AI architectures lose momentum because the story stayed stuck in technical jargon.
If you can’t connect it to time saved, costs reduced, or revenue unlocked, it won’t land.

What no one told me until I learned it the hard way:
Enterprise AI deals aren’t won because your algorithm is better.
They’re won because you can translate that advantage into something the business cares about right now.