The AI Valuation Gap at Exit: Why Medtech Companies Should Not Ignore AI

A clear pattern is emerging among small and mid-sized medtech companies engaged in M&A activity—yet most remain unaware of its recent evolution. Two companies with nearly identical revenue, margins, and market positions can command very different exit valuations. The differentiating factor is not the enterprises’ product pipelines or manufacturing footprints, but whether they have meaningfully embedded artificial intelligence (AI) into their operations, products, or data infrastructure.

Welcome to the AI valuation gap, and for medtech company owners considering an exit in the next two to five years, this chasm is widening by the quarter. Organizations navigating this pathway must consider several factors to successfully close this gap. Some suggestions follow. 

AI Moves from Buzzword to Gating Criterion

Not long ago, medtech companies could include AI initiatives in their investor materials and expect a favorable reaction—an innovation halo, so to speak, that signaled forward-thinking leadership. But those days are over. Today’s strategic acquirers and private equity sponsors are far more sophisticated in their AI evaluations. They are no longer impressed by intention; they instead want demonstrated, validated, and regulator-ready AI capabilities that can show clear clinical or operational impact.

PwC’s 2026 M&A outlook indicates that buyers are now allocating capital with greater precision, prioritizing growth opportunities enabled by digital innovation, AI adoption, and differentiated technology platforms. PwC also noted that AI is increasingly being treated as a core driver of margin expansion and top-line growth rather than a bolt-on enhancement, with valuation premiums shifting toward platforms with proven operations leveraging real data. AI’s new growth-driving role is now evident in M&A projects.

This shift is visible in deal data, too. Medtech deal value surged to $97.6 billion in 2025—the highest level in more than a decade—with strategic acquirers targeting areas like diagnostics, surgical robotics, cardiovascular devices, and connected care platforms, where AI-enabled capabilities are most prevalent. The message to smaller companies is clear: AI readiness is no longer the admission ticket to these conversations. Rather, organizations must demonstrate AI implementations, even if only in pilot projects that validate a pathway to scalability.

Desires of Acquirers

Companies are increasingly asking for help in articulating their AI story—particularly their execution of the technology, as well as an established foundation an acquirer can scale. Buyers are asking such pointed questions as:

1. What proprietary data assets exist? AI is only as powerful as the data that trains it. Companies that have accumulated structured, curated, and clinically relevant datasets—even basic collections without AI models—are more attractive to buyers than those with no databanks.

2. Is AI embedded in the product or workflow? There is a significant difference between a company that has added an AI dashboard as a reporting tool and one that has integrated AI into device performance, quality monitoring, or clinical decision support. The latter commands a meaningfully higher multiple.

3. Has the regulatory pathway been considered? The U.S. Food and Drug Administration has steadily increased AI-enabled medical device approvals—recording a 39% jump in authorizations between 2022 and 2023 alone—and acquirers want assurance that a company’s AI use either has or can achieve regulatory clarity. Companies that have proactively mapped their AI applications to pertinent FDA guidance are generally considered lower-risk targets.

4. Can AI drive recurring revenue? The medtech industry is gradually shifting from capital equipment sales models to outcome-based and recurring revenue models. AI is driving this transition, and acquirers are paying premium multiples for companies that have either begun or completed this shift (as previously stated, a pilot project demonstrates competency in this area).

Numbers Behind the Gap 

Financial data reinforces what is occurring in practice. In the first half of 2025, AI-enabled healthcare startups captured 62% of all U.S. digital health venture funding, raising an average $34.4 million per round—an 83% premium over non-AI startups. While these figures reflect the venture market more broadly, the M&A sector is exhibiting the same premium logic. Acquirers are not just paying more for AI-native companies; they are actively deprioritizing firms that cannot articulate an AI strategy.

GlobalData forecasts that medical device companies’ AI spending will grow from $2.4 billion in 2024 to $11.9 billion by 2029. Additionally, more than 2,600 additional AI-driven medical device solutions are expected to receive FDA approval through 2035. The competitive landscape is being reshaped in real time. Consequently, companies that don’t invest in AI today will face a structural disadvantage in M&A transactions—not only due to missing capabilities, but because the gap between them and AI-enabled competitors will become clearly visible and measurable during due diligence.

A Practical Framework for SMEs 

AI readiness can be achieved through the following formula: Value = Strategic Fit + Timing. Strategic fit is now inseparable from AI, as the company’s AI capability must align with the acquirer’s future plans. The timing question carries new urgency because the window is narrowing for building a credible AI narrative before entering the sales process.

Small and mid-market medtech companies do not have to be pure-play AI firms to benefit from an AI valuation premium, but they must employ a deliberate, defensible strategy. Some good starting points include:

• Auditing data assets—Before building anything, companies must understand the data they have. Clinical outcomes data, manufacturing quality data, device performance data, and customer usage data are all potentially valuable inputs to AI systems. Many companies are sitting on data assets they have not yet recognized as strategic.
• Identifying one high-impact AI use case—Rather than launching a broad AI initiative that risks becoming a costly distraction, medtech firms should identify one area where AI can drive a measurable outcome—whether it be predictive quality control on the manufacturing floor, AI-assisted image analysis, or intelligent supply chain optimization. Build from there.
• Engaging regulatory and legal teams early—If an AI application can be considered Software as a Medical Device (SaMD), a regulatory pathway should be mapped before entering the sale process. Acquirers will likely ask about regulatory preparedness, and an unprepared answer is a red flag.
• Documenting and communicating the AI roadmap—Even in cases of early-stage AI implementation, companies with a clearly articulated, board-approved roadmap convey organizational seriousness. It gives acquirers a credible vision to underwrite, and that alone can influence valuation conversations.

The Cost of Inaction Is Compounding

The AI valuation gap is compounding, not static. Every quarter that a company delays meaningful AI investment is a quarter in which competitors are building proprietary datasets, refining AI models, establishing regulatory precedents, and deepening relationships with the strategic buyers that will eventually drive their valuation.

AI valuation is currently manifesting itself in tariff-driven supply chain disruption. AI-enabled supply chain optimization is emerging as one of the most practical and immediately valuable applications for medtech manufacturers. Companies that deploy AI in this area are not only reducing operational costs, they are also building a capability that is directly relevant to the strategic concerns of any potential acquirer operating in today’s volatile trade environment.

Medtech executives or board members anticipating a future liquidity event—whether in two years or five—should begin building their organization’s AI story now. The buyers who will set valuations are already asking AI-specific questions during initial conversations, and medtech firms better have compelling answers if they want to remain attractive M&A targets.  

The medtech industry has always rewarded companies that invest ahead of the market. The entities that began investing in quality systems before they were required, in supply chain resilience before the disruptions arrived, and in digital infrastructure before competitors understood its importance are those that will command premium multiples at exit.

AI is the next version of that same inflection point. The gap is real, measurable, and widening, prompting immediate action to prevent the rift from spreading. AI is not a fad and is not going away. It’s a permanent part of the medtech landscape, and certainly an integral part of future growth. Don’t be left behind the valuation gap—stay ahead of it for the benefit of all stakeholders.

MORE FROM THESE AUTHORS—Tariffs and the Medtech Industry: Disruption, Realignment, and Strategic Consequences 

Florence Joffroy-Black, CM&AA, and Dave Sheppard, CM&AA, are managing partners at MedWorld Advisors, a global M&A advisory firm serving the medical technology and life science industries.

Joffroy-Black can be reached at [email protected], and Sheppard can be reached at [email protected].