To better understand what 2026 might bring, we asked three MMIT market access experts to share their perspectives on upcoming market shifts. Read the first of this two-part series for key insights into the year ahead.
1. What’s the biggest challenge for manufacturers launching new products this year?
Steve Callahan, Senior Director, Advisory & Insights: We’ve observed a tightening of payer access which will continue into 2026. New-to-market blocks are one of the key controls payers use to manage access for newly launched products.
As these blocks occur in the critical window when pharma is focused on spreading awareness about the new treatment option to physicians, they can lead to a loss of momentum as physicians experience coverage hurdles with the drug. According to our policy and restrictions (PAR) data, a few years ago, new-to-market blocks impacted about half of covered lives in the U.S.; by 2025, that figure had grown to almost two-thirds of covered lives.
Madeline Verbeke, Supervisor, Clinical Advisor: Securing optimal market access is becoming increasingly more difficult, requiring manufacturers to integrate market access planning much earlier in the development cycle. With the Inflation Reduction Act, manufacturers must now plan their launch strategy to maximize returns, knowing their drug will be eligible for Medicare price negotiation after a fixed number of years.
Payers are increasingly demanding that new therapies demonstrate value not only through clinical efficacy (safety and effectiveness), but also through quantifiable value for the entire healthcare network, leading to escalating utilization management (UM) strategies that further complicate patient access. Success in the current market hinges on replacing traditional launch planning with a data-driven, integrated commercial strategy that begins at the earliest stages of development.
Carolyn Zele, Advisor, Solution Consulting: As payers become more restrictive, providers are becoming less likely to fight for patient access. The burden of having to justify treatment decisions and spend time appealing prior authorization (PA) denials becomes too much for prescribers. Instead, they are more likely to select covered alternatives.
Many payers are now leveraging AI to write policies, evaluate trends, understand clinical trial outcomes, and even design formularies and UM systems. While relying on AI can reduce the costs of UM and policy development, policies may be written with wording that is even more restrictive than the payer intended, because AI used the inclusion criteria from the clinical trials to create the policy.
As policies may be written by AI prior to a drug’s launch, manufacturers may not be able to meet with payers and PBMs in time to ensure policy language is appropriate. It will be imperative for manufacturers to determine their most impactful payers (those who make decisions for most of their patients) and meet with them as soon as possible before their drug is approved.
Manufacturers should come to these meetings armed with value-based or outcomes-based contracting options. They should also provide real-world evidence that helps to prove two points: that there is a patient population who needs this drug, and that this drug could lower the total cost of care for these patients. Even PBMs are beginning to be concerned with the total cost of care for a patient.
2. Are there any new trends in pharma/payer engagement and contracting?
Steve Callahan: The use of AI will make innovative contracting approaches less burdensome for payers. Traditionally, value- or outcomes-based contracting agreements required payers to dedicate administrative resources to conduct in-depth analysis across a variety of data sets, resulting in the preference for straightforward, upfront rebates. AI simplifies the process for enacting more complicated rebate approaches which focus more on patient outcomes.
Madeline Verbeke: We will continue to see value-based contracting gain traction, not only for million-dollar treatments like gene therapy, but also for high-volume medications. While less expensive individually, drugs like GLP-1s for obesity present a massive total cost to the healthcare system that these contracts would help manage. I also expect pharmaceutical manufacturers to increasingly utilize AI to engage payers with data-backed evidence and models that help demonstrate a drug’s efficacy and cost-effectiveness.
Carolyn Zele: Contracting will always be about controlling the payers’ overall drug spend, no matter how creative we get with contracting types. However, there are many ways to control spend other than just lowering the price of the drug. Manufacturers should be thinking of ways to control the overall cost of care, including the education of downstream physicians and pharmacy networks as well as patients. Value-based and outcomes-based contracting mechanisms offer a way to provide payers a safety net, because the manufacturer takes on some of the risk of the cost of patient care. These types of contracting mechanisms will grow and become even more creative in 2026 and beyond.
3. How do you expect the use of generative AI and/or real-world data to grow?
Madeline Verbeke: AI is expected to experience significant growth across the pharmaceutical industry this year, from pipeline to patient. GenAI is being used for a wide variety of tasks, from creating new molecules quicker than the traditional methods to optimizing clinical trials, making them more efficient and successful. We also expect AI to be consistently integrated into pharmacy and health plan operations, such as decreasing administrative burden and streamlining PA reviews.
Steve Callahan: AI allows its users to conduct complex analysis across a variety of datasets. We’ve seen a trend where access planning has transitioned to earlier in a product’s development, starting as early as Phase 1 or 2. AI, powered by RWD, can optimize pharma’s strategy early on with predictive analytics. We can expect to see clinical trial design and the primary/secondary endpoints being influenced by AI and RWD. Closer to launch, we expect to see AI used for pricing and contracting modelling, as well as payer and HCP segmentation.
Carolyn Zele: AI will help manufacturers meet the cost-cutting and consolidation challenges they’ve faced in the years post-COVID. AI will help manufacturers do more with less, not only by reducing human error in the manufacturing line, but also in procurement, distribution, clinical trial design and control, FDA submission documentation and RWE generation. We may also see commercial teams utilizing AI to create the PIE story, optimize contract scenarios, and predict future UM in the markets into which they are launching.
We will see the same cost-controlling AI mechanisms tried by payers and PBMs, as well as pharmacies and hospital systems, to control drug spend, optimize supply, determine treatment paradigms, and even understand comorbidity evolution. AI will never replace the treating physician in front of the patient, but AI can provide some focus through symptom collection and interpretation based on huge amounts of patient data, which will provide a foundation for earlier diagnosing and treatment.
4. What should pharma companies focus on to prepare for the future?
Madeline Verbeke: Pharmaceutical companies should focus on technology integration and value demonstration. This requires embedding technology and data across the organization to accelerate drug discovery, proactively shape launch strategies, and demonstrate both the clinical and economic value of every product. Importantly, real-world data will be essential not only for enhancing clinical trial enrollment and design, but also for creating the robust evidence needed to prove that clinical and economic value and secure favorable coverage.
Steve Callahan: Pharma should focus on building their AI infrastructure to ensure decision-making is being driven by their data. Manufacturers should anticipate that they will start to experience increased scrutiny when launching a new drug, as AI will be used by payers to assess new products. Pharma should steer away from all-purpose launch strategies and adopt highly tailored strategies based on payer and HCP segmentation.
Carolyn Zele: Pharma should be learning to scale AI use across discovery, clinical development, manufacturing and commercialization. This will require a robust, transparent and integrated data infrastructure and an upskilling of technical talent. Manufacturers need a deep understanding of their supply chain to create end-to-end risk assessments to identify potential shortfalls in both supply and distribution. Shortages of drug supplies will no longer be tolerated in this competitive industry.
Commercialization efforts should be closely linked to patient outcomes via DTC platforms and services, plus educational assistance to support diagnosis, adherence, and decision-making. Vigilance in understanding government regulatory changes is a must, as things are changing quickly via the Inflation Reduction Act and global pricing and regulatory reforms. Pharma companies should think about collaborating with technology partners, payers, and providers to co-develop digital health solutions focused on the patient. There are more players than ever along within U.S. health care continuum, and manufacturers need to be communicating with all of them to ultimately improve patient outcomes.
For help improving your product positioning in an evolving landscape, learn more about MMIT’s market access solutions.
