Why 2026 Will Force Structural Change Across Healthcare

By Sara Mallatt

The healthcare industry in 2025 was largely defined by defensive maneuvering. Having faced federal drug pricing reforms, new transparency rules for pharmacy benefit managers, and a shifting regulatory landscape, many organizations focused on protecting existing revenue streams and adjusting contracts to minimize disruption.

As the industry moves further into 2026, that posture is no longer sufficient. Healthcare companies are now entering a period of regulatory volatility, compounded by economic pressure and rapid technological advancement. Rather than reacting to individual policy changes, organizations are forced to rethink their strategies more holistically. This convergence presents a challenging but powerful window for healthcare leaders to realign priorities and enact forward-looking roadmaps.

Three factors in particular are influencing strategic decision-making across the industry: The biopharma patent cliff, the evolution of AI into core organizational infrastructure, and sustained policy and pricing pressures. Together, these dynamics are redefining how healthcare organizations allocate capital, leverage technology, and redesign operating models in what may be one of the most transformative periods the sector has faced in decades.

The patent cliff turns M&A into a necessity

The biopharma patent cliff will likely continue to be a primary catalyst for healthcare mergers and acquisitions (M&A) in 2026. This is due to a significant wave of branded drugs losing market exclusivity in the next decade, exposing once-protected revenue streams to an onslaught of generic and biosimilar competition. For products that previously generated billions of dollars annually, revenue erosion can occur within months once exclusivity expires.

Organizations can’t afford to take this lightly given that total lost exclusivity represents more than $300 billion in annual biopharma revenue through 2037. The scale and timing of these losses highlight a fundamental challenge: internal research and development pipelines alone cannot replenish portfolios fast enough to offset the decline. As a result, M&A has shifted from being a discretionary growth lever to a core survival strategy.

This shift is already visible in deal activity. By the end of 2025, biopharma M&A had already surpassed 2024 totals, reaching 129 deals valued at $138 billion, including nine transactions exceeding $5 billion, according to J.P. Morgan’s Q4 2025 Biopharma Licensing and Venture Report. One of the most illustrative examples came at the end of last year, when Pfizer emerged victorious from an intense bidding war with Novo Nordisk for Metsera, a maker of GLP-1 weight loss drugs. The competition and the resulting $10B valuation underscore how critical it has become for large pharmaceutical companies to acquire late-stage assets with large addressable markets to shore up future revenue.

Looking ahead, M&A activity is expected to concentrate in a few key areas: Precision oncology, cell and gene therapy platforms, and metabolic health assets tied to next-generation GLP-1s. These categories not only serve large and ever-growing patient populations but also offer stronger pricing power, an increasingly important factor as other parts of the industry face margin compression.

AI becomes core infrastructure across healthcare

The last year was dominated by a year of AI experimentation across healthcare. This year, we can expect a more consequential shift: The transition from piloted tools to AI embedded directly into core infrastructure. What was once viewed as a productivity enhancer is rapidly becoming a prerequisite for operating at scale in an increasingly constrained environment.

On one hand, the cost and complexity of developing new therapies continues to rise, while regulatory scrutiny and pricing pressure limits also increase. On the other hand, competitive timelines are compressing. Companies that move from discovery to approval faster, and with greater confidence, gain an increasingly durable advantage. AI has emerged as one of the few strategies capable of meaningfully bending that curve.

Early adopters are already realizing measurable gains. In some cases, AI has reduced some drug development process costs by up to 50%. These improvements are not incremental. By accelerating target identification, streamlining clinical trial design, and improving signal detection in real-world data, AI is collapsing development cycles that once spanned years into months, sometimes even weeks depending on the workflow.

Crucially, AI’s role is no longer confined to R&D. It is becoming a strategic layer across the enterprise. In M&A, AI-driven analysis helps teams assess asset viability, competitive positioning, and long-term revenue durability with greater speed and precision. In commercialization, AI is being used to refine launch sequencing, optimize field force deployment, and model demand elasticity in a more volatile pricing environment. In pricing and contracting, advanced models are enabling more dynamic, data-informed decisions as rebate-driven structures erode. Further, AI capabilities are also proving valuable at the clinical level. Amid the persistent labor shortages in today’s healthcare systems, AI is increasingly filling gaps where human capacity is constrained, working to augment—rather than replace—expertise in functions that are time-sensitive and resource-intensive.

That said, healthcare faces a challenge unique among industries adopting AI: The cost of failure. Errors in healthcare carry real-world, grave consequences. Hallucinations, biased outputs, or data breaches are not merely operational risks; they represent threats to patient safety and confidentiality. As Hinge Health noted during its Q3 earnings call, “The stakes are simply much higher in healthcare than in your average industry where AI is being adopted.”

This reality is forcing organizations to rethink not just whether to deploy AI, but how. As AI moves deeper into mission-critical workflows, governance, validation, and data integrity become as important as performance. Leading organizations will focus on building AI into their operating models with the same rigor applied to clinical or regulatory infrastructure. It’s no longer a race to deploy as many AI tools as possible but to select and implement the right tools.

That distinction is what separates AI as a standalone initiative from AI as a foundation. For healthcare organizations to remain competitive, relevant, and financially resilient, AI must be embedded across decision-making, execution, and long-term planning, especially to endure the complex and sometimes unforgiving market.

Policy and pricing pressure force business model reinvention

At the same time, public policy is reshaping the economic environment for healthcare. Drug pricing reform is accelerating as manufacturers confront the Inflation Reduction Act’s Part D redesign, state-level PBM reforms, and a broader shift away from rebate-driven models. These changes are dissolving long-standing commercialization strategies and forcing companies to rethink how value is created and captured.

In response, manufacturers are moving away from volume-based approaches and toward business models more closely tied to value and outcomes. Patient-centricity is no longer just a strategic aspiration but an economic necessity.

Many organizations are also exploring direct-to-consumer channels as a way to preserve margins, maintain pricing control, and insulate portions of their business from tariff exposure. At the same time, expanding U.S.-based manufacturing has emerged as both a supply-chain safeguard and a lever for potential tariff relief.

Political turbulence is unlikely to subside anytime soon. The companies best positioned for stability will be those that proactively adapt to policy changes rather than reacting after the fact.

Preparing for structural change

Healthcare organizations must be equipped to navigate the economic and regulatory shifts that lie ahead. Implementing structural changes now will better position them to absorb future shocks, whether from policy, technology, or market dynamics.

For healthcare leaders, today’s pressures also represent an opportunity: to build more resilient, adaptable operating models capable of supporting the industry through the decades ahead. The healthcare frontrunners of this year will be those that treat AI investment and business model redesign as integrated priorities. While the coming year will be transformative for healthcare, it marks only the beginning of a longer period of growth, innovation, and recalibration.

Sara Mallatt is Director of Healthcare Research at AlphaSense.