I’ve sold AI in two very different contexts:
- At a start-up that was AI-native from day one
- Now at a publicly traded company (where I finished Q1 as #2 globally in AI sales)
And no matter the environment, the same core challenges keep coming up:
1. Data readiness
- At least half of prospects simply don’t have the data in place (LLMs aside, domain specific AI lives and dies on customer data quality)
- Without structured, usable data, no POV will ever work.
- Pre-qualification on data maturity is the first gate in AI sales.
2. Use case focus
- AI can, in theory, do thousands of things.
- In practice, you need to lock in on 2-3 lighthouse use cases that hurt the most.
- That’s where urgency and budget come from, not from “AI can do everything.”
3. Post-sales complexity
- AI is, in most cases, not SaaS where you just “switch it on.”
- Every use case needs iterations, tweaking, sometimes complete rebuilds.
- Customers underestimate this. If you don’t set expectations, adoption will stall (sell a professional service package on top!).
My current playbook:
- Qualify hard for data.
- Anchor on 2-3 high-value use cases.
- Build a power-user group inside the customer to drive adoption.
- Over-communicate: AI will not solve everything, but it can transform specific workflows.
Now curious about you:
- What’s your experience with selling AI so far?
- Have you ever sold AI solutions yourself?
- Anyone here working on the LLM side? OpenAI is pushing insane revenue right now, but what do those enterprise contracts actually look like?
- Is this just printing money at the moment, or are there similar challenges under the hood?
Would love to hear your perspective, whether you’re in enterprise, start-ups, or anywhere in between.
PS: This post actually came out of this week’s newsletter. If you want future editions, you can check it out here: Tech Sales Temple Newsletter.