Healthcare technology companies face a paradox: the market has never been more bullish on the sector — recent research showed that AI-enabled companies now capture 55% of all health tech funding and command a 19% premium on deal size — yet public health tech companies still trade at a meaningful discount to cloud peers, despite roughly 2x the revenue growth and free cash flow margin. 

Strong businesses still lose value when the story is unclear: The gap isn’t just a business problem; it’s a narrative problem. Most health-tech companies have a brand platform in one deck, a product story in another, an investor narrative from the CFO’s team, and a sales pitch from field marketing. Individually, each is internally logical, but collectively incoherent and hard for teams to articulate. Too often, companies speak in higher-order benefits without ever making clear what they actually do, where they play, or why they should win an organization’s business. 

We recently sat in a room where a health-tech company pitched its full suite of services. After they finished, our client simply asked at the vendor: “So what is it that you actually do?” 

A Story of Value connects the story across audiences: What closes this gap for clients is a Story of Value: a coherent narrative spine that captures who the company is, the tension it resolves, how it creates value, and why that story justifies premium valuation. When the narrative is properly modulated for priority audience groups, it retains its core value while ensuring resonance for each audience.  

Five Moves That Make a Value Story Credible

Health-tech companies that get this right will: 

• Define a clear frame of reference first. Before reaching for elevated positioning, companies need to answer a basic question: what do they actually do? Name the competitive set, the outcomes, and where they play. In our work with a major healthcare financial services company, the challenge was moving beyond its best-known role in facilitating payments transactions toward a broader frame: bridging gaps in the healthcare financial system, improving the financial experience of healthcare, and aligning the interests of payers, providers, and members. That shift created a stronger platform for growth. 
• Name where AI creates defensible economic value. Being AI-enabled may now be table stakes; it cannot be the whole story. The real question is where AI creates advantage that compounds over time and is difficult to copy. In our work with a major virtual care platform, we saw how quickly AI language can flatten into sameness: personalization, insights, integration. What created credibility was not broader AI rhetoric, but specificity — which data, improving which workflow, producing which measurable result. 
• Ensure the human touch is inextricably linked to AI. In healthcare, meticulous care around data is paramount, especially when it comes to AI use. In helping shape the story for that same virtual care platform, one important choice was to frame AI as an enabler of people, not a replacement for them, with clinician oversight built into the moments that matter most. The important move was not just having that governance mindset internally but making visible to the market where automation stops and expert judgment begins. 
• Thread a single narrative across every audience. Investors, buyers, clinicians, and patients should all recognize themselves in the same core story, with the emphasis adjusted for each audience. We saw this in our previously mentioned work for a healthcare financial services company: by anchoring the company in bridging gaps across the healthcare financial system, the story could resonate across payers, providers, and members without splintering into disconnected messages. One framework, many expressions. 
• Build a system of proof — and earn your claims over time. Sophisticated healthcare buyers are not rewarding ambition alone; they are asking for evidence. Large employers and health systems want proof before integrating new tools and systems, especially when AI claims are involved. In our work with a patient financial engagement platform, the strongest story was not AI for AI’s sake, but AI tied to operating outcomes: higher collections, lower cost-to-collect, faster cash flow, and fewer billing calls through intelligent support. That kind of results-backed proof makes innovation more credible because it connects technology directly to measurable value. 

In the age of AI, the margin for narrative incoherence is zero. The companies that answer the market’s implicit question, “why this company?”, with one credible, evidence-grounded Story of Value will earn the multiples, the deals, and the trust, outpacing competitors without a clear articulation of what they do and why it matters.

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.

AI offers significant promise to help solve long-standing challenges in the U.S. healthcare system. Some gains are already well documented, from diagnostic tools and curriculums to GenAI-powered transcription and coding solutions.  

But the U.S. healthcare ecosystem is also one of the most fragmented, complex and data-sensitive industries within which to consider effective AI implementation on a broader scale. As we step into 2026, amid the rapid evolution of AI capabilities, continued public concerns and a capricious regulatory climate, it’s necessary for healthcare leaders across systems, payers and technology solutions to identify how to use AI for lasting value, and identify the greatest areas of untapped potential in ways that make sense for patients and caregivers. 

To address this challenge, Prophet’s Healthcare team took a consumer-centered lens, starting with our global AI survey, to understand where people see opportunities for AI to add value to their healthcare experience. We then engaged AI-focused healthcare leaders to react to these consumer views and share their own perspectives on opportunities across the patient journey. 

Our research explored two key questions: 

1. Where can AI unlock value in the consumer experience? 
2. What must be true within organizations to realize that value ethically and effectively? 

Opportunities and Organizational Imperatives 

While there are countless potential areas for adding value, our study found that consumers across generations and demographics want AI tools in healthcare to help them personalize their experience (63% of consumers agree or strongly agree that Gen AI will help with health monitoring and proactive advice, and also save them time and money). This was balanced with clear preferences to maintain the human element of healthcare, with consumers pointing out the AI should not be the final decision maker in place of a doctor, nor should it be so intrusive that it’s always monitoring them without their control (“I really see AI as just helping us, but it’s not the final say [in medical decisions].”). Our leader interviews also revealed similar opportunities to meaningfully enhance care beyond the patient visit, improve navigation, and streamline the way people experience healthcare. These findings point to three key areas of opportunity for healthcare leaders across the ecosystem to capitalize on AI while balancing patient autonomy and dignity, which are:  

• Guiding Care: Navigation tools that reduce system complexity
• Personalizing Care: Personalization that respects autonomy 
• Extending Care: Coaching models that scale support out of acute care facilities 

We also recognize that identifying the opportunity area won’t spell success without the organizational environment to succeed. Our interviews validated how leaders must understand how to translate opportunities in ways that will be most relevant for the unique populations they serve and operationalize AI tools with governance and foresight. This all means that there are critical organizational and cultural components to successful AI adoption that go beyond the data backbone and infrastructure, namely:  

• Setting a strategic vision  
• Implementing a governance model that can adapt 
• Addressing change management & cultural adoption 

We explored both the consumer-focused opportunity and the organizational requirements for healthcare companies to succeed with AI. 

Opportunity 1: Guiding Care – Navigation Tools That Reduce Complexity 

Navigating U.S. healthcare is notoriously difficult, and often the most complained about pain point in healthcare, from finding care and resources to demystifying pricing and payments (Tufts). It’s also an area where patients typically don’t have the benefit of reaching a human to help them, which costs them significant time. In 2024, nearly two-thirds of physicians used AI for documentation, diagnosis, and care planning (AMA), but on the patient end, there’s a need for AI tools to similarly save time and effort. Given the capabilities for AI tools to synthesize data and summarize disparate information, this is one, if not the biggest, area for AI to enhance the patient experience, particularly in micro-moments where patients feel most burdened.  

There are good reasons why care navigation remains a clear opportunity. Patients are engaging with a deeply fragmented ecosystem that no single player in the healthcare ecosystem can solve. Healthcare leaders across providers and payers might start small, through “narrow applications to alleviate specific pain points across the journey” as one leader whom we spoke with pointed out, but over time these AI-driven solutions can serve the incredible value of empowering with options and enabling patients to make clear decisions about their health providers, treatments and costs. 

Opportunity 2: Personalizing the Experience – Personalization That Respects Autonomy 

Personalization is the cornerstone to humanizing the healthcare experience, but the U.S. healthcare system isn’t delivering (Harvard Business Review) despite consumer preferences (Human Centered AI: Culture as the Catalyst for AI-enabled Growth). With the computing power of AI there’s clear opportunity to enhance how patients feel known, heard and understood to add to moments of care, but also to add value across the entire healthcare journey in ways that have never been done before.  

When integrated into care delivery, AI-driven personalization can help redefine patient engagement and amplify the patient-provider connection, equipping providers with comprehensive patient health reports, patients’ ingoing questions and personalized therapeutic options so that patients feel known and understood. As noted by the leaders we interviewed, when AI is deployed transparently in the care setting and decision-making stays with the provider, it’s a win-win in terms of value and trust building. Outside of the doctor’s office, AI-powered personalized platforms can enable real-time personalization (and assistants) that give patients more peace of mind and control of their health management, such as we’re starting to see with Twin Health, Televox, Luma Health, Klara, and others. Capitalizing on AI-driven personalization can also extend beyond care, affording patients greater access and options to suit unique preferences, language needs and lifestyles. The opportunities for AI-driven personalization that enhance the patient experience are rich, and while much has been discussed about the limitations of data and privacy, with the right design, there’s a wealth of value in even the earliest steps forward. 

Opportunity 3: Extend It – Engagement Tools Augment Remote Care

Extending care delivery without compromising quality is an ongoing, major challenge where patients are often left without the support they need, particularly within the context of chronic care needs. Here, AI tools can provide significant value that patients feel immediately. This can include AI tools for prospective care (monitoring and anticipating risks based on patients’ lifestyle choices, adherence and activity levels), to responsive care that enables more orchestrated, complete care across the patient journey. Remote care companies are leading the charge with new AI platforms, such as Teladoc Health’s intervention-focused AI-model, and Verily’s Onduo for coordinated virtual care of chronic diseases. These platforms bring care out of the clinic in ways that go far beyond the remote models of the past decade, and there’s a significant opportunity to capitalize on this opportunity across the healthcare ecosystem.  

What It Will Take to Deliver  

As we’ve noted above, adopting AI tools for the patient experience requires a host of careful considerations about patients, their privacy and your organization, as well as examining emerging regulations and ethical guidance. The leaders we spoke with emphasized not only the opportunities, but also the challenges with organizational silos, data readiness, and cultural burnout or skepticism. As we think beyond the opportunity and start to address the organizational component to power effective AI in healthcare, most leaders are immediately focused on the infrastructure and workflow integration, which is essential. But any AI driven transformation should be focused on adding value for people so they are guided, equipped and empowered to be successful with new AI tools, particularly along the patient experience. 

At Prophet, we help organizations embed AI into their DNA, mind, body, and soul, aligning purpose, scaling skills, redesigning workflows, and deepening human connection.  

DNA: A Consumer-Backed Strategic Vision  

A successful consumer-oriented AI strategy begins with a clear vision for how AI will enhance consumers’ patient experience, which should include defined goals and targeted use cases based on clear patient and provider needs, particularly as organizations seek to balance adding sustainable value without breaching confidentiality or trust. We’ve identified three broad needs, but any AI-driven strategy will need a depth of understanding for how these needs can best be addressed in context. 

Body: Governance That Champions Transparency and Security  

Strong AI and data governance is essential to unify accountability, transparency, and security across the organization. In the context of an AI-enhanced patient experience, leaders also emphasized how governance and human oversight need to extend to the caregivers themselves, to ensure there are clear systems for active oversight. Plus, as AI tools become more broadly used, governance needs to include ongoing assessments to identify gaps in underserved populations and to monitor AI model behavior for fairness and accuracy. Clear liability structures must also be established to protect clinicians and patients, while ensuring compliance with regulatory standards and ethical guidelines. Multidisciplinary teams beyond the care setting, including data scientists and IT professionals, should be formed to support implementation and maintenance.

Soul: Employee Engagement & Cultural Adoption 

Effective employee engagement is critical to drive adoption and minimize resistance. This involves crafting a comprehensive plan that fosters engagement and collaboration across all levels of the organization. Bridging the gap between executives and frontline staff by involving both in planning and decision-making helps build trust and accelerate cultural adoption of AI technologies. 

For more read our research report, Human-Centered AI: Culture as the Catalyst for AI-enabled Growth.