7 Go-to-Market Lessons from a Former Health Plan CEO Turned Healthcare AI Founder

7 Go-to-Market Lessons from a Former Health Plan CEO Turned Healthcare AI Founder

The graveyard of healthcare technology startups is filled with brilliant algorithms that never found buyers. The common autopsy report: great technology, wrong go-to-market approach.

In a recent episode of Category Visionaries, Trey Sutten, CEO and Co-Founder of Siftwell Analytics, a healthcare technology company that’s raised over 5 million in funding, shared how his years as a Medicaid CFO and health plan CEO shaped a radically different playbook for bringing healthcare AI to market. These aren’t theoretical frameworks—they’re lessons learned from the operator’s chair, then applied to building and scaling a healthcare AI company.

Lesson 1: Prove Technology with Real Data Before You Sell Anything

Most founders race to market with MVPs and iterate based on customer feedback. Trey took the opposite approach: prove the technology works before making sales promises.

After collecting C-suite executives from his previous health plan to identify market gaps, Siftwell didn’t immediately start selling. Instead, they acquired sample data sets, ran competitive analyses with data scientists, and then took the critical step: convincing a health plan to provide real, live data for pro bono analysis.

“Those early days were really about like, you know, is the advancements that are being made in machine learning and artificial intelligence, can they truly be applied in the context of healthcare?” Trey explained. “And the answer resoundingly came back yes.”

That pro bono client became a paying customer—proof the technology delivered results, not just impressive demos. This methodical validation gave Trey credible proof points before scaling sales, eliminating the dangerous gap between what’s promised and what’s delivered.

Lesson 2: Sell Outcomes, Not Algorithm Accuracy

Here’s the uncomfortable truth about healthcare AI sales: your buyers don’t care about your model’s AUC score. They care whether emergency department utilization goes down.

Trey watched countless technologists enter healthcare armed with impressive accuracy metrics and wondered why they struggled to close deals. His operator experience gave him the answer: “I think a lot of people, a lot of technologists have entered the healthcare industry and thought that, you know, it was a better model, a better mousetrap that was going to do it. They wanted to talk about accuracy measures like area under the curve or something.”

The shift in positioning was fundamental: “What way more important to me is getting the results that I’m trying to get. This isn’t about how accurate your model is, it’s about your model telling me that an individual needs an intervention and so that their use of an emergency department or their readmission rates go down.”

For founders, this means restructuring your entire sales narrative. Lead with the business outcome, support it with proof of results, and save the technical accuracy discussion for the data science teams doing due diligence.

Lesson 3: Domain Expertise Is Your Unfair Competitive Advantage

At Digital Health New York, Trey experienced a moment that crystallized why Siftwell wins deals. During a panel discussion with a client health plan CEO, founders in the audience stopped the conversation to ask for explanations of basic healthcare acronyms.

“The acronyms were so fundamental to healthcare and so basic to healthcare that I was actually surprised that somebody was pausing us to get an explanation on our particular acronym,” Trey recalled.

His advice to healthcare founders was unequivocal: “Learn the space or learn one space and really focus on it. I think the healthcare industry is plagued by technologists that have really cool mousetraps but don’t understand the complexity of some of the problems that healthcare, particularly managed care organizations, are facing. So my recommendation would first and foremost find a partner that knows the space or learn it yourself.”

This domain expertise became Siftwell’s sales weapon: “What gets us into the door oftentimes is the fact that we are, you know, a number of the members on the team are former managed care operators, and we speak the language, we know the problems and the know, quote, unquote use cases.”

Lesson 4: Position at the Intersection of Categories

Siftwell operates in predictive analytics but deliberately positions itself as something different. This nuanced positioning captures demand while differentiating on dimensions that matter to operators.

“We broadly fall under the category of predictive analytics, but we’re an entirely different animal, Brett,” Trey explained. The distinction isn’t semantic—it’s strategic. Traditional predictive analytics solutions deliver lists of high-risk patients. Siftwell delivers lists plus context plus recommended actions.

“We’re taking really deep and battle tested managed care experience, combining it with these advanced Technologies to not only point out who they should focus on, but why they should focus on and even what they should do in order to better engage them,” Trey said.

This positioning allows Siftwell to appear in category searches while standing apart on evaluation criteria. When buyers compare predictive analytics vendors, Siftwell competes on a different playing field entirely.

Lesson 5: Master the Referential Market Playbook

Healthcare enterprise sales don’t follow SaaS playbooks. Digital marketing funnels and SEO strategies take a back seat to relationships and face-to-face education.

“For us it’s a highly referential market, you know, so it’s getting out there, it’s relying on long standing relationships that you had, it’s being face to face,” Trey explained. “I spent a lot of my time on the road interacting with our existing customers, making connections with new customers, panels, speaking opportunities, or just, you know, direct meetings with individuals or teams.”

But these interactions serve a specific purpose beyond relationship building. Trey uses face-to-face conversations to dismantle the misconceptions preventing buyers from acting: “There’s a lot of misconceptions around some obstacles that people think that they’ve got. And so, you know, I’ve used some of my job responsibilities. Getting out there, educating people on this isn’t really big and scary tech, but rather you can get started today and in fact you need to.”

The lesson: in referential markets, education-based selling beats feature-based selling every time.

Lesson 6: Question the Timing of Your Market Entry

AI hype creates both opportunity and noise. Trey observed three distinct levels of AI adoption in healthcare, each with different implications for when and how to sell.

First, ad hoc usage: marketing and HR teams using large language models for content and job descriptions. Second, middle and back office applications: financial management, revenue cycle, fraud detection—industry-agnostic functions where AI has gained traction. Third, the patient-provider setting: clinical decision-making where “people have been really trigger shy.”

Understanding these adoption layers helped Siftwell position strategically. They focused on proving value in areas where health plans were ready to adopt, avoiding the minefield of clinical utilization management where “some folks have gotten in trouble and I think there’s a bit of a snapback or trigger shyness.”

Lesson 7: Do Due Diligence on Your Investors

Fundraising due diligence runs both ways. Trey learned this through observation and applied it rigorously to Siftwell’s fundraising process.

“How important it is to do your due diligence as a Founder,” Trey emphasized. “I have often heard about founders getting funding. That was the exciting piece. But then there was a mismatch between what the company actually needed and who ended up on their board.”

For Siftwell’s next funding round, capital is only the starting point: “That’s something that we’re really double clicking on and making sure that there’s the experience match, there’s the industry expertise match, that there’s a cultural match, all the rest of it that we eventually are going to be working with, understand what we’re building, how we’re building it and why we’re building it.”

This discipline prevents the common founder trap: taking money from investors who don’t understand your market, can’t open the right doors, or push for growth strategies that don’t fit your business model.

The Compounding Effect of Operator Experience

These seven lessons share a common thread: they’re all informed by Trey’s experience sitting in the buyer’s chair. He knows what health plan CFOs and CMOs care about because he was one. He understands the procurement process because he ran it. He speaks the language because he lived in that world for years.

For healthcare technology founders without that background, Trey’s advice is simple but non-negotiable: “Find a partner that knows the space or learn it yourself.”

The healthcare AI market will continue attracting brilliant technologists with powerful algorithms. The ones who win will be those who understand that in healthcare, the best technology doesn’t always win—the best go-to-market strategy does.