5 forces reshaping pharma commercialisation in 2026
As infrastructure bottlenecks ease and regulatory frameworks mature, pharma's AI adoption is shifting from proof-of-concept to production-grade deployment
Developments in 2025 brought contradictory signals. R&D funding hit a 10-year high of $102 billion, up from $71 billion in 2023. Yet, European price freezes, reimbursement cuts, and tariffs triggered manufacturing investment shifts to the US While field medical teams doubled down on digital platforms and RWE integration, only 34% of consumers felt "cared for" by the healthcare system.
The FDA's growing acceptance of real-world data for regulatory submissions opened new pathways, while Lilly's LillyDirect and Pfizer's PfizerForAll demonstrated the evolution of direct-to-consumer beyond traditional distribution.
The year revealed pharma's central tension: massive investment in innovation while execution gaps persisted, particularly in AI deployment, where only 11% of organisations achieved enterprise-wide implementation despite widespread adoption efforts.
As we traverse 2026, here are the five forces reshaping pharma commercialisation.
1. Data infrastructure becomes the unlock
Most pharma AI projects stall during data engineering, not model development. Commercial, R&D, manufacturing, and clinical data exist in incompatible silos with inconsistent governance. By the time teams wrangle data into usable form, 60% of project timelines have elapsed.
Data fabric architectures solve this by creating logical connection layers that unify access without mass migration. This enables cross-functional AI applications while preserving domain-specific controls, such as "last mile patient" identification that combines EHR mining with claims analysis and genomic databases.
In 2026, expect infrastructure investments to accelerate. Organisations will shift budgets from building more AI models to building the data foundations those models require. Companies with mature data fabric implementations can operationalise AI use cases in weeks, and those without will spend months on data preparation for each initiative.
2. AI focus shifts from pilots to enterprise governance
AI deployment now requires formal operational frameworks that span observability, compliance, and multi-agent orchestration. The 40% project failure rate stems from organisations treating AI as isolated experiments rather than as enterprise infrastructure.
Agentic AI is compressing commercial timelines from months to days. Launch planning that took 12-18 months now completes in weeks. Territory alignment that would take weeks of analysis takes minutes. Multi-agent frameworks coordinate specialised agents that monitor formulary changes, analyse prescribing patterns, and model contract optimisation to generate real-time recommendations.
But speed without governance creates risk. Organisations are implementing LLMOps for prompt versioning and output monitoring, deploying observability platforms tracking model drift and bias, establishing human-in-the-loop requirements for high-stakes decisions, and maintaining audit-ready logs for regulatory scrutiny.
The execution gap persists because technology alone isn't sufficient. Success in 2026 requires positioning AI as augmentation: automating administrative burden while preserving human expertise, combined with robust change management and explicit ROI frameworks.
3. DTC evolves into care orchestration platforms
The launches of LillyDirect, PfizerForAll, and several other DTC platforms in 2025 signalled a strategic shift. Direct patient relationships are becoming strategic assets, with services replacing promotion as the primary value proposition.
These platforms orchestrate end-to-end care: diagnosis support through virtual consultations, access navigation, management of prior authorisations and copay assistance, and adherence tools that integrate reminders with refill automation. The model inverts traditional pharma distribution. Instead of convincing physicians and negotiating with PBMs, companies are enabling patients to access therapy while reducing friction points.
In 2026, expect this model to expand beyond GLP-1s and chronic disease management. While DTC distribution will not be applicable to all therapeutic areas, manufacturers will invest in the infrastructure to expand patient reach. Similarly, specialty pharma will integrate home infusion coordination, and Medtech companies will add consumables subscription models.
The competitive advantage here isn't technology, it's the patient relationship data. Companies owning direct patient engagement can identify drop-off patterns before they occur and personalise intervention strategies. They will capture real-world effectiveness evidence that traditional channels miss. This transforms patient support from a cost centre to a strategic differentiator.
4. Patient identification becomes the commercial imperative
Traditional commercial models are collapsing for rare disease and specialty therapies. When addressable populations number in the thousands rather than the millions, the core challenge shifts from convincing physicians to prescribe to identifying patients who need therapy.
The "last mile patient" problem is acute. Individuals with hard-to-diagnose conditions remain unidentified, buried in fragmented health system data. GenAI is emerging as a solution – mining unstructured EHR notes for disease indicators, analysing claims patterns for diagnostic odyssey signatures, and linking genomic databases to phenotypic presentations.
This represents a fundamental strategy shift. Patient finding replaces provider targeting as the core launch activity. Marketing budgets reallocate from physician detailing to patient identification infrastructure.
Real-world data integration amplifies this. Continuous monitoring of treatment patterns, adherence behaviours, and brand switching provides the feedback loop that sharpens patient identification algorithms. When RWD reveals which patient subgroups achieve optimal outcomes, commercial teams can focus identification efforts on similar profiles. Integration with agentic AI creates predictive commercial intelligence that identifies patients likely to benefit before they experience treatment failure on current therapy.
And to compound the success, organisations that master patient identification can demonstrate superior real-world effectiveness (by reaching appropriate patients faster), command better payer positioning (because outcomes data validate value), and build defensible market positions in crowded therapeutic areas.
5. Biosimilar commercialisation finally breaks through
The 118 biologics losing patent protection between 2025 and 2034 represent a $232 billion opportunity. Still, the biosimilar market share remains stuck below 20% despite regulatory streamlining that eliminated switching studies and eased interchangeability requirements.
That means the bottleneck is commercial, not regulatory. Provider and patient education deficits remain. Despite pharmacists now having easier substitution authority, prescribing behaviour hasn't shifted proportionally. The gap between regulatory enablement and market adoption reveals a fundamental commercialisation challenge.
In 2026, expect biosimilar strategies to shift toward confidence-building rather than relying on price-based competition alone. Organisations will invest in clinical evidence demonstrating real-world equivalence, develop comprehensive HCP education programmes to address lingering safety concerns, and build patient support infrastructure that rivals that of originator brands.
The winners will be those who recognise that biosimilars require the same commercial rigour as novel launches: dedicated sales teams, robust medical affairs support, and integrated patient services that go beyond mere price lowering and automatic substitution to drive uptake.
The execution imperative
IRA Medicare negotiation timelines are compressing profitable commercial lifespans, with $350 billion in revenue at risk by 2030 from patent cliffs. Speed to market has shifted from a competitive advantage to a survival requirement.
Infrastructure deficits are blocking acceleration. Cloud adoption remains insufficient to deploy Agentic AI, and data architecture has become the primary bottleneck to AI adoption at scale. Organisations are underestimating the unified data platforms required to operationalise multi-agent systems.
What separates winners from laggards in 2026 won't be who has the most sophisticated AI models. It will be who can perform. Who can build the cloud-native infrastructure that enables AI to scale? Who can implement the data fabric architectures that provide unified access across silos? Who can establish the governance frameworks that satisfy regulatory requirements? And who can maintain focus on patient identification and access as the ultimate commercial metric?
The companies solving these challenges will find that their AI systems improve faster because data flows seamlessly, their launches accelerate because infrastructure is in place, their patient identification improves because RWD integration is operational, and their market positions strengthen because they reach the right patients faster than competitors.
2026 is the year pharma's AI investments deliver enterprise value, but success will rely heavily on execution.
About Axtria
Axtria is a global provider of AI-native cloud software and data analytics to the life sciences industry. We help transform the clinical-to-commercialisation journey to drive sales growth and deliver the right treatments to the right patients at the right time.
About the author
Jaswinder ('Jassi') Chadha is a pioneer in the field of data analytics and decision support software. He currently serves as president & CEO of Axtria. Since its founding in 2010, Axtria has grown rapidly to become a global leader in the life sciences industry, with customers in over 135 countries. Previously, Chadha was co-founder and CEO of marketRx, acquired by Cognizant Technology Solutions. Chadha and his companies have been featured in some of the most aspirational lists – INC 500, Deloitte FAST 50, NJBiz FAST 50, SmartCEO Future 50, Red Herring, and several other growth, product, culture, and technology awards.
