Pharma Digitalisation in 2026: AI, Automation, GxP Challenges and What’s Really Driving Change

Across Europe, pharma digitalisation is moving from isolated pilots to operational priority. Companies are no longer looking for tools that are impressive in a presentation, they are looking for solutions that can work at scale in regulated environments. In this context, AUTOMA+ 2026 stands out as a place where industry experts exchange practical experience and discuss what is really shaping the future of pharma.

Why Pharma Automation and Digitalisation Are Now Business Priorities

Not long ago, “digital pharma” often meant pilots: a dashboard built by one team, a chatbot tested by another, a computer-vision demo that looked promising but never made it through validation. In 2026, that phase is clearly fading. Companies are still investing in innovation, but the focus has shifted to something harder: making digital solutions work inside real pharmaceutical operations, with documentation, governance, change control and security built in from the start.

For instance, AstraZeneca agreed to acquire Modella AI to strengthen oncology R&D with AI foundation models and agents for biomarker work and clinical development, while Eli Lilly and Nvidia announced a five-year $1 billion joint lab to accelerate AI-driven drug discovery and development. Digital is no longer being treated as a side experiment. For major players, it is becoming part of core operating infrastructure.

From Pilots to Scale: What Companies Need Right Now

When leaders talk about what is slowing digital adoption, the same barriers keep coming up. Rahul Prajapati, Automation Project Engineer at Terumo Pharmaceutical Solutions Division, described the first one directly: “legacy systems and integration”. Many sites still run on older platforms, and connecting them to modern digital infrastructure remains harder than people like to admit. The second challenge is data security and cyber security, especially as more information is generated and patient-related data becomes increasingly central to digital processes.

If you want to hear these topics discussed in the voices of pharma leaders themselves, watch special video on the barriers to pharma digitalisation on our YouTube channel.

This combination of infrastructure and trust explains a lot. The issue is not a shortage of tools, but whether companies can connect what they already have, protect what they already know and scale without introducing new risks.

There is also a less technical obstacle that comes up just as often – the gap between teams. If quality, IT, manufacturing and data all “speak” in different terms, even a strong technology stack can turn into friction. As Joachim Bär, Director DevOps CellCulture at Boehringer Ingelheim Pharma GmbH & Co. KG, put it, 

“From my perspective, it's mainly breaking the barriers between different people… so that we are not lost in translation”.

Companies are responding more pragmatically now. Instead of waiting for one massive system replacement, many are moving towards modular roll-outs. At the same time, the European Health Data Space implementation roadmap is pushing companies to prepare earlier for stronger interoperability, trusted reuse of data and clearer governance across borders.

AI in Pharmaceutical Workflows, R&D and Manufacturing

AI is still the biggest digital theme in pharma, but its role is becoming more specific. In 2026, it is moving beyond experiments and into workflows across research, clinical operations, safety, quality, manufacturing and commercial functions. The shift is simple: AI is no longer something that sits outside the process – it is increasingly becoming part of how work gets done.

Once AI touches regulated operations, leaders want to know who owns model performance over time, how outputs are documented, what happens when edge cases appear and how a system will stand up in a GxP environment.

At the same time, companies are launching more targeted AI projects instead of speaking in generalities. Medicus Pharma, for example, partnered with Reliant AI to build a clinical data analytics platform aimed at dynamic site selection, patient stratification and enrolment forecasting for a Teverelix study planned for 2026. The already mentioned AstraZeneca–Modella AI deal points in the same direction: companies are buying specialised AI capability not just for discovery, but for more precise biomarker research and better clinical development decisions.

Data Integrity, Big Data and GxP Compliance: The Foundation for Safe Pharma Digitalisation

Fragmented data remains one of the biggest practical obstacles. Different systems, definitions, formats and owners mean companies can generate outputs, but not always trust them. This is why data integrity, lineage, governance and clear ownership have become central to pharma digitalisation. Without that foundation, there is no safe scale. AI, analytics, automation and predictive capabilities all depend on data that is consistent and traceable.

Cybersecurity adds another layer. In pharma, security questions quickly turn into availability questions, and availability questions can turn into patient-impact ones. Trust in data and in the systems around it has therefore become a core business concern.

Regulators are also giving the market more structure. The FDA’s final guidance on real-world evidence for medical devices, issued in December 2025 and operationalised through a February 2026 town hall, clarified more explicitly how sponsors should demonstrate the quality and provenance of real-world data in submissions. In Europe, the EHDS timeline is creating similar pressure by forcing organisations to think earlier about trusted reuse, interoperability and the rules around health data exchange.

How CDMOs and Drug Manufacturing Companies Are Applying AI in GxP Environments

A lot of public discussions around AI in pharma still focus on R&D or customer-facing applications. Those areas matter, but manufacturing is increasingly stepping forward with its own digital priorities. And here, the challenge is sharper: companies do not get to experiment their way around GxP.

Bastian Baur, Global Head of Digitalization & IT at Adragos Pharma GmbH, pointed to the key unresolved question: how regulators and auditors will work with AI, and how companies will implement these tools in a GxP environment, because for now, “this is still very challenging”. That line captures the state of the industry well. AI is moving into manufacturing, but only the companies that can align it with GxP, documentation and process discipline will be able to use it confidently at scale.

This is why automation, smarter digital tools and robotics are becoming less about headline innovation and more about robust execution. Aizon’s 2026 manufacturing proposition is a good example: a lightweight execution layer to replace slow digital roll-outs, a unifying data backbone across manufacturing systems and predictive analytics aimed at reducing deviations and improving yield in GxP settings.

Pharma Digital Trends with Real Impact

Another important shift in 2026 is the way companies talk about impact. Digital projects are no longer expected to prove only that they save time or reduce costs. More and more, leaders want to understand what those technologies change for patients, for access and for the wider delivery of care.

Harald Schnidar, CEO at SCARLETRED Holding GmbH, framed this especially well. Technology should improve procedures and create efficiency, but it should also have social impact. He pointed to rare diseases and remote populations as an example. In those situations, AI can “cover a digital bridge” and provide support regardless of geography.

The market is also putting more weight behind projects that connect digitalisation with patient relevance. The EU-backed DREAMS programme is building an AI-and-iPSC platform for drug discovery in rare muscular disorders and developing new clinical trial designs around those diseases. At the same time, companies such as Phesi are arguing that digital patient profiles and digital twins are becoming practical enough to support protocol optimisation and better use of small, hard-to-reach rare-disease populations.

Why AUTOMA+ 2026 Is the Right Place to Compare Notes

As digitalisation projects grow more complex and the gap between innovation, compliance, manufacturing and data governance becomes harder to bridge, companies increasingly need to learn not only from their own experience, but from each other’s as well. AUTOMA+ is a platform where specialists compare what happens after the announcement, when companies try to deploy digital solutions across sites, across teams and under regulatory scrutiny. The Congress brings together quality leaders, digital transformation teams, pharmaceutical manufacturers, CDMOs, technology providers, licensors and other pharmaceutical experts. That mix makes the discussion more practical and more honest.

Join AUTOMA+ 2026 on 16-17 November in Zurich to exchange experience with industry peers, explore best practices and take part in the conversations shaping production-grade digital pharma.

FAQ

What is AUTOMA+ 2026?

AUTOMA+ 2026 is the Pharmaceutical Automation and Digitalisation Congress, bringing together senior decision-makers and specialists from pharmaceutical manufacturers, CMOs, CDMOs, equipment suppliers and technology providers to address the practical challenges of automation, digitalisation and manufacturing excellence in pharma. The programme covers MES, SCADA, LIMS, AI in manufacturing, GMP compliance, quality systems and digital infrastructure for regulated environments.

When and where does AUTOMA+ 2026 take place?

AUTOMA+ 2026 takes place on 16-17 November 2026 in Zurich, Switzerland, across two days of sessions, roundtables, an exhibition and structured B2B meetings with participants from the pharmaceutical value chain.

Who attends AUTOMA+ 2026?

AUTOMA+ 2026 is attended by C-level executives, heads of automation, digitalisation leads, manufacturing directors, quality and engineering specialists from pharmaceutical operators, CMOs and CDMOs, alongside equipment manufacturers, system integrators and technology providers serving the regulated pharma environment. The congress operates on a closed-door model to ensure a focused professional environment of end-users, licensors and solution providers.

How do companies participate in AUTOMA+ 2026?

Companies participate in AUTOMA+ 2026 as delegates, sponsors, exhibitors or speakers. Participation details are available on request.

What are the biggest barriers to pharma digitalisation in 2026?

The biggest barriers to pharma digitalisation in 2026 are legacy system integration, data security and the organisational gap between teams. Many sites still run on older platforms that are difficult to connect to modern digital infrastructure. As more patient-related data enters digital processes, cybersecurity and data integrity become critical concerns - in pharma, a security failure can become a patient-impact failure. The third barrier is cross-functional: when quality, IT, manufacturing and data teams operate in different languages and frameworks, even a capable technology stack creates friction rather than value. Companies are responding with modular roll-outs rather than large-scale replacements.

How is AI being used in pharmaceutical manufacturing and GxP environments?

AI in pharmaceutical manufacturing is moving from R&D experiments into regulated production workflows, but implementation in GxP environments remains challenging. Companies are deploying AI for predictive analytics, deviation reduction, yield improvement and real-time batch monitoring, but must align every application with GxP documentation requirements, change control and validation. The unresolved question for most organisations is how regulators and auditors will assess AI systems in GxP settings, and how to demonstrate model performance, output traceability and governance over time. Companies such as Boehringer Ingelheim and Aizon are among those building practical frameworks for GxP-compliant AI deployment in manufacturing.