Momentum and Automation Driving Pharma Innovation and Digitalization Through 2026

PharmTech connected recently with Dr. Eva-Maria Hempe, Executive Director Public Sector EMEA, NVIDIA, to look back at 2025 and ahead into 2026. Hempe discusses the significant impact of AI, digital technologies, and agentic systems on the pharmaceutical value chain. A central theme of the interview is the versatility of these technologies, especially agent-based platforms, which have moved beyond hype to practical use in areas like scientific literature search and automating internal business processes. The discussion highlights dramatic improvements in preclinical research, where AI reduces drug discovery timelines from years to months by serving as a "flashlight" to survey the extensive chemical space of potential compounds. Looking ahead, while fully achieving the seamless "lab-in-the-loop" integration of physical and digital environments remains a barrier, Hempe expects the momentum behind AI adoption to continue growing through 2026, driving increased automation and efficiency across research and operational processes.

See a visual summary of our discussion in the infographic; the interview continues below.

PharmTech: Which 2025 trends impacted the pharmaceutical industry the most?

Hempe: During the past year, agentic, or agent-based, technologies moved beyond the hype to being used in practical, versatile ways across the industry. Agentic platforms have been especially important for research assistance, making it much easier to search through scientific literature to find relevant findings from past studies and from novel research hypotheses. They’re also moving into areas like finding the right patients for clinical trials and automating internal business processes. Overall, it’s the versatility of these agent-based systems that’s made the biggest difference along the entire pharma value chain.

What role do AI and digital technologies play in accelerating drug discovery and manufacturing efficiency?

AI and digital technologies are increasingly impacting the whole pharmaceutical value chain. One of the most dramatic improvements has been in preclinical research, where timelines can be reduced from years to months by using AI to survey the huge chemical space of possible drug compounds. AI serves as a sort of flashlight in that space, helping researchers orient themselves and explore new possibilities that would have been missed with just experience or intuition.

In clinical development, AI helps identify patient care gaps, which means finding untreated or undiagnosed patients much faster, something that benefits both patients and the companies running trials.

In commercial and manufacturing areas, AI is helping optimize how factories are planned, modeled virtually, and managed, reducing costly mistakes before anything is built. There’s also a lot of work being done to automate quality checks, like using machine vision to spot faulty vials on the production line instead of relying on manual inspection.

One barrier that remains is the full integration of physical and digital environments. While progress is happening, achieving that seamless "lab-in-the-loop" workflow, where digital and real-world systems continuously interact and update each other, is still an evolving capability.

What strategies are most effective in attracting and retaining top talent as tech advancements shift roles?

Investing in upskilling and reskilling has become key, especially as technology changes what’s required of frontline and technical staff. It's important to help people develop expertise at the intersection of the physical and digital so they can function in both realms. Creating a culture that prioritizes innovation and lifelong learning, and actively helping employees build these new skills, is what’s really working to attract and retain talent. And of course, you have to provide state-of-the-art AI tools and environments that allow employees to rapidly prototype their ideas and scale what works.

Which recent innovations have the broadest impacts in manufacturing operations?

The use of AI in preclinical work stands out for shrinking drug discovery timelines by mapping the chemical space more efficiently. Automating patient recruitment with digital tools has also significantly impacted speed and access in clinical trials, which is critical as recruiting patients can consume a substantial share of the overall development timeline and is becoming increasingly time-consuming. In manufacturing, the shift toward virtual factory planning and automating product quality checks, like identifying faulty vials with machine learning, has made the biggest difference in cost, speed, and quality.

Which trends do you see driving innovation and drug development in 2026?

The momentum behind AI adoption will keep growing across the industry. There will be more virtual and digital approaches to both research and manufacturing and increasing levels of automation in clinical and operational processes, particularly around the automated and, at some point autonomous lab. The patient-centric approach, with digital tools improving diversity and inclusivity in clinical trials, will continue to gain importance. As supply chains and manufacturing play a bigger strategic role, digitization and the use of smart systems will keep driving change in 2026, leading to efficiency gains and lower costs.