"Selling AI": Moving from Persuasion to Proof

In my last post I touched on how AI is changing the nature of work itself. In this post, I want to specifically focus on the sales process changes enabled by these tools...

… none of this is theoretical, it’s all lessons learned in the field based on actually selling and shipping an enterprise healthcare solution.

First I want to talk about the discovery process. How do you even “sell AI” in the enterprise? An approach to take is to find a workflow to improve that has real business impact. One of the sharpest sales minds I’ve ever encountered, Michael Page, framed it the best: “Our job is not to sell. Our job is to solve problems. And if we solve a big enough customer problem, we get outsized rewards.” But this has to be humanized. How do we figure out what problem to solve? Without a doubt this requires industry domain expertise, but that is not enough. You must get deep IN that customer. Deeper than ever before. I found a very simple way to start this discovery process, in this context:

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1- Identify someone who is in the middle of these workflows

2- Ask them one simple question: “What do you hate most about your job?”

In healthcare the the goal is always to optimize patient care. Yes, healthcare organizations need to make money, but it should not be thought of as a pure revenue optimization exercise. But what’s interesting is that in healthcare these two things are often times tightly coupled. If you find a way to make them more capital efficient, you will also improve the patient care.

So I started this process by interviewing clinicians at the prospect. After I gathered some data points, I grouped them into cohorts and then did some light modeling on patient care / revenue impact, and then sorted these by the top 3. Now I had the top 3 things to target sorted by which would make the most impact in the organization. I did this modeling with AI tools as well. If there is interest I can discuss what I learned about how sellers should be leveraging AI for their own internal sales processes.

In this case what was identified was some extremely tedious and manual work on new patient lab data which was taking upwards of 10/hr/week per clinician. If we can improve this process, it will allow for clinicians to spend more time with patients and see more patients while also improving their job satisfaction. I want you to internalize this: see more patients (higher revenue) and also give them better care. This is the tight coupling of patient care optimization and revenue.

From there we went right to a prototype. No slide ware, no drawn out proposals, no weeks of planning, no canned demos/accelerators, no hand-wavy POC (which is different than a prototype). Just a 48-96 hour build cycle with reusable scaffolds and tight guardrails, running the same week. This was the iPhone moment for me. I could not believe how quickly we could get to the working proof.

Hence the title of this post: the shift in AI from a sales perspective is moving from persuasion (slide ware, meetings, calls, meetings) to proof (working prototype running quickly). Because building anything was expensive (both capital and human) the sales process optimized around persuasion (slides) before proof (software). In this new world, the sales process needs to optimize for proof-first selling. You need to quickly deliver a working slice of the customer solution, and it needs to be built fast enough that it becomes the next sales meeting. Figma wire-frame is a conversation starter but a working prototype is a decision accelerator. It changes the customer conversation from “do we like the idea?” to “how do we make this real in production?”

From there it was a question of sitting down with the buyer stakeholders and rapidly iterating on the workflow specifics and clinical preferences and other technical details to get it production ready. I cannot share much here because this part is proprietary and confidential to this particular customer. The business metrics here made it a straight forward “sell” after the prototype: the solution will pay for itself in 6months, with a 20%+ revenue impact for this group.

This is prototype-to-production selling, enabled by AI tooling.

I want to keep this post mostly focused on the sales process so I’ll follow up on the solution details in the a follow up post, but here is a quick teaser: 100% built with Claude Code. Multi-Cloud support (AWS, GCP, Azure) with runtime provider switching. HIPAA-compliant audit logging with 7-year retention. Containerized and running in production on AWS. Integrated with the customer’s EHR (eCW in this case). It’s as real as it gets in enterprise healthcare. Built in weeks not months/years.

Key takeaways for me:

1- Sales economics will completely shift. Credibility used to come from brand + slides + references. Now credibility comes from speed-to-proof and iteration velocity + quality.

2- The buyer psychology shifts from slides which invite debate, to working software which invite decisions.

3- When the buyer can “touch/taste/feel the solution“ (hat tip to an ex-team member Brian Farrell for this great tagline & concept which he introduced to me years ago) the conversation quickly jumps from “Do we believe you?” to “What would it take to run this in production?“

4- As I stated in my previous post, AI is a compression algorithm. In this case, it compresses the sales cycle.

5- The key artifacts are not slides, they are a working slice of the customer solution built in days not months.

6- Most clinicians have an anti-AI perspective. You need to be prepared for this. The right framing is “what if you could just do the parts of the job you love.” Again, not metrics, but humanizing.

7- Even with the sales cycle acceleration, it doesn’t take away ANY of the sales twists and turns that occur when moving a deal through the sales stages. You still need a sales professional leading the effort, but their focus shifts considerably. You still still have to deal with politics. You will still get last minute surprises. None of that is changing anytime soon IMO. If there is interest, I can dive more into this as well.

This type of selling will require the entire org (marketing, sales, engineering) to completely re-think their approach. You cannot just “use an AI tool” to sell into 2026+. That is not enough. And you cannot just make this change in the sales org. This is the compression of marketing, sales, pre-sales, delivery, etc that I spoke of in my last post and they all have to think differently and move together in a way that is more tightly coupled than ever before.

If you are selling in this way, I would love to hear from you to compare notes & share. We are very early in all of this and I don’t have all the right answers. What I do know is that change is in the air and its super exciting to be thinking through these changes. An ex-colleague Robert Swinkin captured my feelings on this the best in a post he made on his new exciting journey: “As my remit has continued to grow into the billions, I found my mind drifting back to those breathless early years — at the coal face, where everything was a first, every trail blazed was virgin, and the business moved faster than any rule or process could contain it.“

Happy Selling!