Digital Innovation in Pharma: What Will 2026 Bring?

The rise of AI has powered a wave of interest in digital innovation within pharma. But to have a lasting impact, AI — like any innovation in digital health — must be able to deliver value by being easily absorbed into pharma’s everyday workflow.

I believe that 2026 will be the turning point from AI “hype” to the adoption of meaningful AI and digital health solutions in pharma. Here are my predictions:

1. Pharma leans into greater efficiency 

Pharma’s focus in 2026 will be on efficiency. Facing uncertainty about the future of pricing and regulation, pharma executives are intently focused on bringing down costs and getting drugs to market more quickly.

Increasing drug discovery efficiency has been a long-standing goal of pharma. Today, however, there are many AI models available to assist pharma with drug discovery and translational research. We are moving toward a world where pharma needs help managing the myriad of discovery and translational AI models. Technology to take the legal, infrastructure, and administrative burden off pharma and allow access to hundreds of AI tools in parallel is becoming a necessity. I predict that tools that manage AI for drug development will become mainstream in 2026.

Pharma’s longstanding desire to speed up clinical trials will also intensify, with a focus on faster and more accurate identification of patients who can participate in trials. Traditionally, patient recruitment for and engagement in clinical trials were major operational bottlenecks. I predict that pharma will be leaning into new digital health and AI tools that streamline patient identification and retention. 

2. Agentic AI is selectively applied in pharma

Like many industries, pharma has broadly adopted generative AI for back-office activities. But in 2026, select applications of agentic AI (AI that reasons and executes tasks) will be adopted, particularly to manage the collection and analysis of pharma data. Agentic AI tools for drug discovery, regulated content review and production, clinical trial design, management and interpretation of clinical trial data, and data cleaning are among the most exciting and immediate opportunities.

We are at a tipping point. Startups are now offering effective agentic AI tools for pharma that will make a real difference, saving time and money in drug development. 2026 will be the year we start to see progress beyond pilots. Pharma will gain real value and measurable savings from agentic AI tools at scale. However, because of the variability and associated risk, the application of agentic AI at the patient-pharma interface may still be farther off.

3. Real world data is more important than ever

Pharma’s thirst for the collection and use of real-world data — data collected in everyday settings outside of structured clinical trials — will increase in 2026. Real-world data (RWD) has been, and continues to be, needed by pharma to plan trials, predict and understand the impact of therapies, and support contracting and regulatory activities. 

With the rise and use of AI tools, particularly agentic AI, there’s even more that can be done with RWD. In 2026, pharma will embrace AI tools that use RWD to de-identify and tokenize patient data. Tokenized patient data is vital to pharma for clinical trial modeling, conducting longitudinal studies, and recruiting patients. Pharma’s appetite for RWD will also increase as it embraces the use of digital twins. Digital twins provide pharma with models of patients’ individualized responses to therapy.

We will see growing pharma demand for RWD from new geographies as well as from entirely new sources. Pharma is eager for patients’ symptom data and physicians’ query data. These novel types of RWD will expand pharma’s understanding of the clinical ecosystem.

4. Digital biomarkers’ broad adoption begins

My last prediction is that in 2026, digital biomarkers — the objective, quantifiable digital measurements of health, drug levels, or disease states — will begin to see broad adoption in pharma. These tools will also contribute to greater efficiency.

Historically, pharma’s adoption of biomarkers has been plagued by the “chicken-and-egg” nature of their use and validation. Digital biomarkers must be validated to get used, but they must be used to get validated. In 2026, we’ll begin to see a new level of commitment by pharma to the validation and use of these powerful tools. 2026 will usher in the golden age of digital biomarkers for pharma.

2026 marks the year pharma’s digital health adoption accelerates.  

Pharma has been experimenting with digital tools, RWD, digital biomarkers, and generative AI for some time. But now, as they double down on their focus on drug development efficiency, the adoption of these innovations will gather speed. In 2026, agentic AI and innovative uses of RWD and digital biomarkers will help pharma discover and develop drugs more quickly. These technologies will move from small-scale pilots to become part of the fabric of drug development from discovery through clinical trials. When pharma broadly adopts these promising digital technologies, they will see time and cost savings, helping them bring exciting, new, much-needed therapies to patients around the world.

Photo: rudall30, Getty Images

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