In Health Tech, AI Doesn’t Win Deals – Outcomes Do

Every healthcare service and technology company now claims to be AI-powered. What once signaled innovation now reads as category shorthand. According to a recent McKinsey survey, 85 percent of healthcare leaders are now exploring or have adopted generative AI capabilities—making “AI-powered” closer to table stakes than a point of distinction. Meanwhile, buyer skepticism is rising in parallel: a national survey from Ohio State University and SSRS found that public openness to AI in care dropped from 52 percent to 42 percent in just two years. The presence of AI alone no longer earns attention. Buyers want to know what it actually does, where it matters, and why they should believe it will work in the environments in which they operate. 

The market is saturated with undifferentiated AI claims. That shift has created a new messaging challenge. In many health-tech companies, AI is described at one of two unhelpful extremes: either as a broad aspiration that could apply to almost anyone, or as a technical capability that only product teams can decode. The result is a value gap. Companies may be making real investments in data, models, and intelligent workflows, but their market story still fails to answer the most important buyer question: why should this matter to me? 

The Three Places Companies Tend to Tell Their AI Story

From our work across healthcare, data and technology businesses, we see a consistent pattern. AI messaging tends to land in three places, but only one of them creates real differentiation.

• First, there is AI in the product: copilots, smart features, clinical suggestions, intelligent routing. These capabilities are increasingly expected, but rarely distinctive. Nearly every competitor either has them or claims to. Framed this way, AI becomes a feature label, not a market advantage. 
• Second, there is AI in the business: internal efficiency, lower cost-to-serve, faster processing, better staffing. This can be an important part of the investor story. But it is usually the wrong lead message for customers. Buyers care about whether those gains translate into better service, better economics, or better outcomes for them. 
• Third, there is AI as a driver of customer value. This is where differentiation begins. The message shifts from what the technology is to what it changes: which workflow improves, which decision gets smarter, which friction point is removed. In this mode, AI is not the headline. The headline is the benefit it generates. 

The hardest claims to copy are not capability claims. They are claims rooted in proprietary data, embedded workflows, measurable results, and a trust model that holds up in practice.

A Simple Test for Whether Your Message is Working

We saw this clearly in a recent messaging engagement with a major virtual care platform. Like many companies in the category, it faced a familiar risk: its AI language sounded too broad to be credible and too similar to what others were already saying. Terms such as personalization, insights and integration were directionally right, but too generic to carry the story. What sharpened the narrative was greater specificity — which data made the system smarter, which moments in the care journey improved, how the technology helped care teams act sooner and engage the right people. Just as important, the company needed to show where clinician oversight remained essential and where governance was built into the system. In healthcare, trust is not supporting detail. It is part of the value proposition. 

This points to a simple pressure test: if you remove the phrase “AI” from your message and it no longer says anything meaningful, you are describing the technology, not the advantage. If the story remains compelling without the term — because it communicates workflow impact, outcomes, proof, and trust — then the message is doing real strategic work. 

In Healthcare, Credibility is the Differentiator

The companies standing out today follow the same discipline. They name the user and the workflow. They quantify the effect. They make clear why their data or delivery model gives them an edge. And they treat governance and responsible use as visible parts of the story, not footnotes for legal review. In healthcare, where the standard for credibility is structurally higher, vague AI rhetoric does more than blur differentiation. It can actively weaken trust.