Artificial intelligence is moving quickly from experimentation into practical use across the pharmaceutical industry. Regulatory teams are already exploring how AI can support regulatory intelligence, document review, submission preparation, data analysis, safety activities, and other parts of the product lifecycle. But in regulatory affairs, the question has never been simply whether a technology can make work faster. The more important question is whether it can be used without compromising regulatory confidence, scientific judgment, or patient safety.
Regulatory affairs have always involved large amounts of information. Professionals need to monitor regulatory changes, interpret guidance, review scientific and clinical information, prepare submissions, assess requirements across markets, and make decisions within increasingly complex regulatory environments. Similarly, AI can assist with document review by identifying inconsistencies, organising information, summarising content, and helping regulatory professionals navigate large volumes of material. Used well, AI can give regulatory professionals more time to focus on the activities where experience and judgment matter most.
But there is an important boundary, AI can support regulatory work; it does not remove regulatory accountability. An AI-generated output still needs to be understood, challenged, and appropriately reviewed before it influences an important regulatory activity or decision and that brings us to one of the more important aspects of AI adoption which is data.
Why 2026 matters for AI in regulatory affairs
The regulatory conversation around AI is becoming more concrete. In January 2026, the European Medicines Agency and the U.S. FDA jointly published ten guiding principles for good AI practice in drug development. The principles are intended to support the use of AI across the medicines lifecycle, including evidence generation and monitoring, and emphasise the need for careful management of AI technologies as they are developed, deployed, used, and maintained.
EMA is also continuing to develop its approach to AI across the medicines lifecycle, including work on terminology, responsible AI, guidance, and AI literacy.
At the same time, the European Union’s AI Act reached another important implementation milestone in August 2026, with most of its provisions becoming applicable and enforcement beginning for the provisions already in scope. The requirements for certain high-risk AI systems will follow later, with the current timeline extending into 2027 and 2028 depending on the category of system.
For pharmaceutical companies operating internationally, this creates an important reality: There is no single global AI rulebook; instead, organisations need to understand a developing combination of pharmaceutical regulatory expectations, broader AI legislation, quality requirements, data governance, and internal controls. That makes 2026 less about waiting for one definitive set of AI regulations and more about building the organisational capability to manage AI responsibly as expectations continue to develop.
Five priorities for AI-enabled regulatory affairs in 2026
- Know what the AI is being used for
There is a significant difference between using AI to search regulatory information, using it to review submission content, and using it to support an activity that could influence a regulatory or safety decision. The potential impact of the use case should determine the level of oversight and control required.
This context-of-use thinking is becoming increasingly important in the regulatory discussion around AI. The more consequential the activity, the stronger the evidence and controls should be.
- Treat data governance as part of AI governance
AI cannot be separated from the data it uses. Regulatory information may come from multiple systems, markets, documents, databases, and business processes. If that information is inconsistent or poorly controlled, the resulting AI output may be equally unreliable.
For pharmaceutical organisations, this means AI implementation should go hand in hand with questions about:
- Data quality
- Data provenance
- Access and controls
- Version management
- Information consistency
- Traceability
- Validate AI for its intended purpose
An AI system used to help organise regulatory intelligence may present a very different risk profile from one used to support a higher-impact regulatory activity, that is why a risk-based, context-specific approach matters.
Organisations need to understand what the system is intended to do, what could go wrong, how its performance will be assessed, and what evidence is needed to demonstrate that it is fit for purpose. Validation should not be treated as something that happens once at implementation and is then forgotten.
- Keep human judgement at the centre
This may be the most important principle of all. Regulatory decision-making is not simply an information-processing exercise. It involves scientific interpretation, regulatory experience, understanding of context, assessment of uncertainty, and consideration of potential impact on patients.
AI can help professionals process information faster and identify things they may want to investigate further. But the responsibility to understand the significance of that information remains human. The future is therefore unlikely to be about AI versus regulatory professionals. It is much more likely to be about regulatory professionals working with AI.
- Monitor AI throughout its lifecycle
An AI system does not exist in isolation. Its performance can be affected by changes in data, processes, system configuration, intended use, or the wider regulatory environment, that means organisations need to think beyond implementation.
They need processes for ongoing monitoring, change management, issue escalation, documentation, and periodic reassessment. This is particularly important as AI technologies evolve rapidly. The control framework around an AI system needs to evolve with it.
From automation to augmentation
There is a lot of discussion about whether AI will eventually replace parts of regulatory work. AI can reduce the time spent searching, sorting, comparing, summarising, and processing information.
That can allow regulatory professionals to spend more time on higher-value activities:
- Interpreting evidence
- Assessing regulatory implications
- Challenging assumptions
- Communicating with stakeholders
- Managing uncertainty
- Making informed professional judgments
The organisations that benefit most from AI may therefore not be those that automate the greatest number of tasks. They may be the organisations that are best at deciding where automation makes sense—and where it does not.
Building an AI-ready regulatory organisation
AI adoption is often presented as a technology project. For pharmaceutical companies, it is much more than that. It is an organisational readiness exercise. An AI-ready regulatory function needs reliable information, clear governance, appropriate validation, defined accountability, effective change management, and professionals who understand both regulatory requirements and the limitations of AI.
AI literacy: Regulatory professionals do not necessarily need to become AI engineers, but they do need to understand enough about AI to ask the right questions, recognise limitations, challenge inappropriate outputs, and know when additional review is required. Technology is only one part of the equation. The real capability comes from combining technology with people, processes, data, and governance.
What should pharmaceutical companies be asking in 2026?
For pharmaceutical companies, the question in 2026 is no longer simply whether AI can be introduced into regulatory affairs. The more important issue is whether it can be used in a way that is controlled, reliable, and appropriate for the decisions it supports. Before implementing an AI-enabled process, organisations should consider five fundamental questions:
- What role will AI play?
Clearly define the intended use of the system and understand the potential impact if its output is incomplete, inaccurate, or misleading. - Can we trust the data behind it?
AI is only as dependable as the information it uses. Organisations should consider whether the underlying data is accurate, current, appropriately controlled, and traceable. - What evidence supports its use?
AI should not be adopted simply because it appears to perform well. There should be appropriate evidence that the system is suitable for its intended purpose and performs consistently within its defined context. - Where does human accountability sit?
AI may support analysis, drafting, monitoring, or decision-making, but responsibility cannot simply be transferred to the technology. Organisations need clear ownership, defined review points, and appropriate human oversight. - What happens when the system changes?
AI-enabled processes need ongoing oversight. Changes to models, data, systems, workflows, or intended use can affect performance and should be assessed and managed throughout the system’s lifecycle.
What this means for multinational pharmaceutical companies
For companies operating across multiple markets, the challenge is even greater. AI expectations are developing alongside existing pharmaceutical regulations and broader technology legislation. Different jurisdictions may move at different speeds and use different terminology or approaches. The answer is unlikely to be a collection of disconnected local solutions. Organisations will need a coherent AI governance approach that can adapt to different regulatory environments while maintaining consistent internal standards.
Understanding not only what regulators have published, but also where expectations are heading, can help organisations make better decisions about technology adoption before a regulatory requirement becomes a business constraint.
How DDReg can support AI-enabled regulatory transformation
The successful use of AI in regulatory affairs solution requires more than access to technology. It requires an understanding of regulatory processes, data, governance, compliance expectations, and the practical realities of working within a highly regulated environment.
DDReg supports pharmaceutical organisations in strengthening regulatory intelligence, improving regulatory workflows, enabling data-driven processes, and preparing for an increasingly digital regulatory environment. The objective is not simply to introduce AI into existing processes, it is to identify where AI can create genuine value, understand the associated risks, establish appropriate controls, and ensure that regulatory expertise remains central to the process.
Conclusion
AI will become a more important part of pharmaceutical regulatory affairs, but the organisations that benefit most will not necessarily be those that adopt AI the fastest. They will be the ones that understand where AI belongs, where it needs stronger controls, and where human judgment must remain in charge. The next phase of regulatory decision-making will not be defined by replacing regulatory professionals with machines. It will be defined by how effectively organisations combine artificial intelligence with regulatory intelligence, that means better technology, better data, stronger governance, and—most importantly—people who know when to trust an AI output and when to question it. The goal is not to automate accountability, but to make regulatory work smarter, more efficient, and more reliable—without losing the human judgment that ultimately matters.
Frequently Asked Questions
Key considerations include AI governance, data quality, risk assessment, validation, transparency, documentation, human oversight, cybersecurity, change management, and ongoing performance monitoring. The level of control should be proportionate to the intended use and potential impact of the AI system.
AI is more likely to augment regulatory professionals than replace them. Regulatory work requires scientific understanding, interpretation of requirements, contextual judgment, and accountability. AI can support these activities, but human expertise remains essential.
The key priorities include clear AI governance, reliable data, risk-based validation, transparency and traceability, meaningful human oversight, AI literacy, and lifecycle monitoring. For multinational organisations, understanding how expectations are developing across different jurisdictions will also be an important part of AI strategy.