The Contrarian Mandate in HealthTech: The Power of a Human Centric Narrative in an AI-First World

Lloyd Price
6 minutes ago
13 min read

Executive Summary

The health technology sector is experiencing a period of intense focus on artificial intelligence, driven by an "AI-first" narrative that has captivated investors and dominated industry discourse. This report posits that this concentrated market behaviour, characterised by an investment "gold rush," presents a profound opportunity for a contrarian business strategy. The prevailing AI narrative, much like other market frenzies, has led to a collective overvaluation of a single technology and its perceived benefits, while simultaneously undervaluing more fundamental, human-centric solutions. The true market need is not for more AI, but for proven, trustworthy and seamlessly integrated solutions that prioritise tangible human outcomes.

A successful and durable healthtech business can be built by explicitly embracing a "non-AI story." This strategy leverages the inherent vulnerabilities of the AI-first model, including its unproven return on investment, ethical and regulatory risks, and deep-seated human distrust, to create a defensible, problem-first, and trust-centric value proposition.

This is not a rejection of technological progress but a strategic re-framing of it as a powerful enabler, not the core promise. By focusing on a nuanced, human-in-the-loop approach and building competitive moats in integration and distribution, a company can carve out a sustainable path to success in a market intoxicated by hype.

The Current HealthTech Landscape: An Analysis of the AI-First Narrative

The Investment "AI Gold Rush" of H1 2025

The digital health sector has emerged as a rare bright spot within a broader healthcare investment landscape that has seen a significant slowdown. In the first half of 2025, digital health startups in the United States and Europe collectively raised $6.4 Billion, representing an increase from the $6 Billion raised in the first half of 2024.However, a closer look at this funding activity reveals a highly concentrated and potentially imbalanced market dynamic.

While the overall amount of capital increased, the number of fundraising deals decreased to 245, a notable drop from 273 in the first half of the previous year. This trend could lead to the lowest overall deal count since 2020, suggesting that capital is being funnelled into a smaller number of larger, later-stage rounds. This pattern is almost entirely attributable to the overwhelming investor excitement surrounding artificial intelligence.

Startups that identify as AI-enabled captured a disproportionate share of this funding, securing 62% of all digital health venture capital, or $3.95 Billion dollars. The funding disparity is particularly striking at the individual deal level, where AI-enabled startups raised an average of $34.4 Million dollars per round, an 83% premium over the $18.8 Million dollars raised by their non-AI counterparts. The consolidation of capital is further underscored by the fact that nine of the 11 "mega-deals," defined as rounds over 100 million dollars, were awarded to AI-enabled companies.

This data indicates that venture capitalists are not merely pro-AI; they are exhibiting a herd-like behaviour where a fear of missing out on the next big AI winner drives investment decisions. As a result, non-AI companies, regardless of their intrinsic value, may find themselves systematically starved of capital, making a compelling and differentiated narrative a matter of survival. This creates an environment where the market is overpricing "hot" stocks and underestimating the value of companies that do not fit the dominant narrative.

The Prevailing Value Proposition: A Focus on Efficiency and Automation

The dominant "AI-first" narrative is built on the promise of solving some of healthcare's most pressing systemic challenges, such as rising costs, persistent workforce shortages, and operational inefficiencies.The primary value proposition centres on using technology to automate and augment human tasks. This is a story of efficiency, not of empathy or human connection.

Key AI use cases that embody this value proposition include:

Clinical and Non-Clinical Workflow: Tools like ambient listening and machine vision are being deployed to reduce the burden of clinical documentation and free up providers' time. In the non-clinical domain, AI is being leveraged to automate administrative tasks such as medical record-keeping, billing, and patient scheduling, which are seen as "convoluted" and "ripe for disruption".
Diagnostics and Personalisation: AI's data-processing capabilities are used to analyse vast datasets for earlier disease detection, more accurate diagnoses, and the creation of personalised treatment plans.Platforms that analyse medical imaging are positioned as tools that augment a clinician's ability to spot abnormalities and improve accuracy.

The narrative behind these applications is straightforward: AI can perform tasks that are typically done by humans, but in less time and at a fraction of the cost.This message resonates strongly with hospital administrators, IT teams and payers, as it offers a clear path to cost savings and streamlined operations.However, this singular focus on efficiency leaves a significant gap in the market. It neglects the profound importance of trust, empathy, and the human connection that are central to the patient and clinician experience. While AI can improve patient experience by making care easier to find and schedule, the core story remains centred on output and cost reduction, creating an emotional and psychological opening for a contrarian narrative built on human-centric care.

The Foundational Flaws and Inherent Vulnerabilities of the AI-First Model

The Unproven Promises: Hype vs. Reality

The intense hype surrounding AI in healthtech masks a harsh reality: a significant number of these projects are failing to deliver meaningful results. A recent MIT study found that a staggering 95% of generative AI business projects have failed to provide measurable productivity gains or revenue acceleration, despite the substantial investments they have attracted. This dramatic disconnect between market perception and real-world outcomes suggests that the sector may be facing a bubble reminiscent of the dot-com crash.

The failures are not merely a result of poor execution; they are often rooted in fundamental technical and operational challenges. AI models in healthcare are highly dependent on high-quality data, but the industry's data is notoriously fragmented, unstructured, and lacks standardisation. Furthermore, training these models requires extensive data annotation, a time-consuming and expensive process that can only be performed by medical professionals. Even if a model performs well in one facility, it may fail in another due to differences in medical protocols and equipment, making the creation of universal solutions a complex endeavour. This high rate of failure and the difficult path to implementation are creating a form of "tech trauma" among healthcare providers, who may become increasingly risk-averse and skeptical of unproven technological claims.

The Ethical and Regulatory Minefield

Beyond technical challenges, the AI-first model is fraught with ethical and regulatory risks that are beginning to attract scrutiny. These are not mere side effects of the technology but fundamental vulnerabilities that cannot be easily mitigated.

One of the most pressing concerns is data bias. AI models trained on flawed or historically biased datasets can perpetuate and even amplify systemic inequalities. Examples include a widely used risk prediction algorithm that underestimated the care needs of Black patients because it was trained to predict healthcare costs rather than illness severity. Similarly, AI systems trained predominantly on images of lighter skin tones are more likely to under-diagnose skin cancers in people with darker skin, a significant issue with broad implications for health equity. These examples illustrate that when AI is a core part of the solution, the risks of misdiagnosis, inappropriate treatments, or denied access to care are very real.

Another major vulnerability is the "black box" problem. The decision-making process of many AI models is opaque, making it difficult for clinicians to understand how conclusions are reached. In a high-stakes clinical environment, this lack of transparency is a major barrier to adoption and trust, as it creates a lack of accountability if an AI makes a harmful decision. The legal and regulatory landscape is also struggling to keep pace, with frameworks like the EU AI Act emerging to impose strict requirements for fairness and transparency. A company that bases its value on a fragile, unproven AI foundation is entering a legal and ethical minefield that could lead to significant fines and reputational damage.

The Human and Clinical Distrust

The AI-first narrative often overlooks a critical stakeholder: the patient and the clinician. In a high-stakes industry like healthcare, the need for human connection, empathy, and trust is paramount. A recent study found that a majority of patients, 60%, are uncomfortable with a doctor relying on AI, and a third believe it could worsen their treatment. This skepticism is not unfounded; only 29% of people in a UK study would trust AI for basic health advice.

The core value proposition of an AI-first company, which focuses on cost reduction and automation, is in direct conflict with the deeply personal and emotional nature of the patient-provider relationship. While AI can offer efficiency, it lacks the human touch and empathetic understanding that clinicians provide. When technology is presented as a replacement for human judgment and interaction, it can create a sense of unease and disconnect. This fundamental tension between a technology-centric narrative and the human need for care presents a clear strategic opening for a company that can credibly offer a different story—one that prioritizes and celebrates human involvement.

The Contrarian Advantage: Building a "Non-AI Story"

Defining a Contrarian Strategy in HealthTech

A contrarian strategy is an investment or branding approach that "bucks against existing market trends" to find an opportunity in an opinion that is "unexpected or contrary to what is widely believed". In the context of healthtech, this means consciously moving against the current of AI-first narratives and venture capital flows. The goal is to create a "polar" brand association that sets a company "apart from all other competitors in a radical way" by highlighting the over-saturation and potential vulnerabilities of the consensus.

This is a strategy of deliberate differentiation. When the market is saturated with the "same regurgitated narratives" about efficiency and automation, a company that offers a different, more compelling story is more likely to be heard and to expand its reach.

The value in going against the consensus is that what you are saying gets "diluted and competed away" when you go with the crowd. The contrarian advantage is not about rejecting technology, but about refusing to make it the central character of the story. The central character must be the human problem being solved, with technology as a powerful, but supporting, force.

Case Studies in Human-Centricity

The viability of a "non-AI story" is not a theoretical concept; it is a proven business model demonstrated by successful companies that have built strong, defensible value propositions. These companies' success validates that investors and customers will back solutions that solve painful, specific problems, irrespective of whether they are AI-first.

Calm: This company's success is rooted in its focus on mental well-being, stress, and sleep. Its value proposition is built on human-guided meditation, soundscapes, and expert-led programs. The brand is built around emotional connection and personal relief, providing a trusted source of support rather than a technological solution to a human problem.
Maven Clinic: As a virtual clinic for women's and family health, Maven positions itself around specific patient populations and their unique needs. Its value proposition is "inclusive care access" and personalised support for families, with technology acting as the platform that enables these services.The story is about people helping people, using technology as a facilitator.
Force Therapeutics: This platform for musculoskeletal conditions focuses on patient-provider communication and video-based education. Its value is in empowering patients and clinicians with tools that improve communication and care coordination, rather than a narrative of replacing human interaction with an algorithm.

These companies demonstrate that a problem-first, human-centric approach can lead to a powerful, scalable business model. Their success proves that a "non-AI story" is not a defensive position but a potent strategic choice. The core of their message is a tangible human outcome, feeling better, having access to care, or a better care experience. The technology is an enabler, not the story itself, a crucial distinction that directly counters the prevailing AI funding trend.

The Strategic Playbook for a Human-Centric HealthTech Business

Crafting the Value Proposition: From Tech to Trust

To build a successful contrarian brand, a company must shift its value proposition from a focus on technological features to one that is "human needs-driven" and sparks an "emotional connection". This requires a conscious effort to move beyond product-centric messaging, such as "our software is smarter and faster," and reframe it around a tangible, human-centric promise.

For example, instead of claiming an AI solution "can interpret brain scans," a human-centric company would say, "our solution helps doctors find more bone fractures than humans can, reducing missed diagnoses and improving patient outcomes". The former is a technical claim, while the latter addresses a human problem and a human benefit. This reframing is not just about marketing; it is about fundamentally defining the business around the most critical, often overlooked, aspects of healthcare, trust and outcomes. A brand that leads with a story of reducing physician burnout so they can focus on patient care is more durable and emotionally resonant than one that simply touts automation and cost savings.

Establishing Trust and Credibility: The New Competitive Moat

In an industry where data breaches and ethical failures are constant threats, credibility is a powerful and defensible competitive moat. A "non-AI story" can be explicitly built around a company's commitment to trust and transparency.

This requires a multi-pronged approach:

Compliance and Transparency: A company must implement robust data protection policies from the outset, ensuring full compliance with complex regulations like HIPAA and GDPR. Non-compliance can result in massive fines and significant reputational damage.
Demonstrating Expertise: Building authority is essential. This can be achieved through thought leadership via content marketing, such as publishing case studies and e-books, and leveraging partnerships with reputable organisations.
Engagement and Delivery: A company must be proactive in listening to customer feedback and consistently delivering on its promises. This builds confidence and fosters long-term relationships, distinguishing the company from those that fail to deliver on over-hyped promises.

The "AI Gold Rush" has created a low-trust environment due to ethical concerns, data bias, and a high failure rate of projects. The contrarian company, by contrast, can build its brand on the antithesis of these flaws: a focus on human oversight, proven outcomes, and a genuine commitment to ethical and responsible practices. This is not a technical feature but a fundamental business practice that becomes a core, difficult-to-replicate part of the "non-AI story."

The "Human-in-the-Loop" Model: A Superior and More Mature Approach

The most sophisticated healthtech business model is not "AI-first" but a "human-in-the-loop" model that strategically blends automation with human oversight. This approach directly addresses the primary flaws of an AI-only solution while retaining the benefits of efficiency.

The philosophy behind this model is not to replace people, but to "evolve how people work". It uses technology to automate routine, repetitive tasks, such as administrative work, data gathering, or first-pass alert triage, while reserving critical, high-impact decisions for human experts.

This hybrid model directly mitigates the risks inherent in "AI-first" solutions. The "black box" problem is addressed because a human is always in the loop to review and contextualise AI recommendations. The model adds the "empathetic understanding" that AI lacks and ensures accountability by making a human ultimately responsible for the decision.

The strategic superiority of this approach is clear. It leverages AI's power to drive efficiency while mitigating the high risks of misdiagnosis, bias, and lack of accountability. This positions the company as a responsible and outcome-focused innovator, not a high-risk tech speculator. The "human-in-the-loop" model is a more mature and durable business strategy that is well-suited for a high-stakes industry where trust and safety are paramount.

Strategic Pillars: AI-First vs. Human-Centric Models

Strategic Dimension	AI-First Model	Human-Centric (Non-AI) Model
Core Philosophy	Technology as the solution.	The human as the solution, technology as an enabler.
Primary Value Driver	Efficiency, cost reduction, and scale.	Trust, safety, empathy, and improved outcomes.
Primary Audience	Hospital administrators, IT teams, payers.	Clinicians and patients.
Competitive Moat	Technological innovation, data exclusivity.	Seamless integration, human expertise, brand trust.
Key Risks	High failure rate, data bias, regulatory uncertainty, lack of trust.	Perceived as "outdated," slower to attract investment.
Investor Pitch	"We are using advanced AI to transform healthcare."	"We solve a specific, painful problem for people."

Competitive Moats Beyond AI

For a contrarian company, the ultimate defense is building moats that are not dependent on AI. As AI tools become increasingly commoditised, true differentiation will come from factors like "integration, distribution and documented workflows".

A key vulnerability for many AI startups is their inability to integrate seamlessly with outdated legacy systems like EHRs. A "non-AI" company can build a powerful competitive advantage by focusing on creating solutions that "fit with people where they live, work, and receive care" and seamlessly integrate into existing systems and protocols. This is a more mature approach that recognises the operational complexities of the healthcare industry.

Furthermore, a "non-AI" company can build a more sustainable business model by focusing on a "clear path to profitability" and a "durable return on investment". The high cost and lack of proven unit economics for many generative AI models pose a significant financial risk. By focusing on a problem with a demonstrable and sustainable business model, a contrarian company can position itself for long-term growth and profitability, while overfunded, undifferentiated AI-first companies risk becoming superfluous or contributing to a bubble.The strategic choice to be a "non-AI" company is not a regression to "outdated technology" but a sophisticated focus on the foundational aspects of business health.

Conclusion: A Nuanced Path to Sustainable Value

The healthtech industry is at a pivotal juncture. While the "AI-first" narrative has captured the imagination of investors and the media, its foundations are more fragile than the hype suggests. The high failure rate of generative AI projects, combined with the significant ethical, regulatory, and trust-based vulnerabilities, presents a compelling opportunity for a contrarian strategy.

A successful healthtech company of the future will be one that embraces a "non-AI story" not as a compromise, but as a strategic advantage. This is a sophisticated choice to lead with a problem-first, human-centric value proposition, to build a brand around trust and transparency, and to establish competitive moats in seamless integration and superior distribution rather than in technology alone.

This approach is not a rejection of progress, but a recognition that technology, including AI, is most powerful when it is a thoughtfully integrated tool in service of a human-first mission. The ultimate contrarian move is to bet on the enduring value of trust and proven outcomes in a market intoxicated by hype.

Nelson Advisors > HealthTech and MedTech M&A

Nelson Advisors specialise in mergers, acquisitions and partnerships for Digital Health, HealthTech, Health IT, Consumer HealthTech, Healthcare Cybersecurity, Healthcare AI companies based in the UK, Europe and North America. www.nelsonadvisors.co.uk

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