Why AI and Regulations Will Drive New Drug Development Strategies in 2026

Large biopharmas and biotechs have advanced drug development operations by connecting data and processes across the product lifecycle to support AI use cases. In 2026, life sciences operational focus will shift towards creating better data flow across stakeholders through connected execution. To get there, organizations will establish a technology foundation that delivers better transparency, traceability, and compliance. This is especially important to keep up with evolving regulations, like EU Clinical Trial Regulation (EU CTR) and ICH E6 (R3). 

At the same time, AI adoption will shift from early use cases that complement capabilities toward embedding AI compliantly within systems. Below are four predictions that detail where the industry will make progress in the year ahead.

Better clinical data flow will improve patient recruitment, access, and experience for all stakeholders

The flow of clinical trial data between research sites and sponsors will enable speed and efficiency in studies. Trial information will flow directly to physicians, allowing them to connect patients with available studies options. New embedded AI will connect trial data between sponsors and sites, enabling physicians to search for potential treatment or trial options based on a patient’s needs. Taking this direct-to-physician approach will reduce life sciences’ reliance on sites to identify trial participants, helping to meet recruitment goals sooner while improving patient access to clinical research.

With a reduced burden from patient recruitment requirements and advanced solutions, sites will see the promise of eliminating paper and manual source data verification (SDV) for clinical research associates (CRAs) become real. eSource tools will improve the connection between upstream and downstream clinical data sources, starting with electronic health records (EHRs) so patient health data can merge more efficiently with trial data. 

Once connected with electronic data capture systems (EDC), source forms will be defined by study definition so data can flow quicker, and with more clarity, to the sponsor. This improved data flow will streamline trial visits for patients and advance studies for sites and sponsors.

Industry regulations will shift teams toward inspection-ready execution by design

Europe regulatory changes will begin to feel less like a series of single milestones and more like a stable way to execute. Study teams will be firmly in a CTIS-first world under EU CTR, which is continuously increasing expectations for consistency across countries, speed of coordination, and complete and traceable documentation. This also includes the broader move toward more structured submissions, such as eCTD 4.0. With companies adjusting to this new reality, the pressure will shift from “working to get it done” toward “doing it right, every time” with less exceptions and no local workarounds.

At the same time, ICH E6(R3) will advance the industry to a clear risk-based approach to good clinical practice (GCP). More often, sponsors will be expected to show how quality is designed into a trial and how oversight is being conducted across study partners, data sources, and technology. The changes will shorten the gap between trial operations and compliance. It also will redefine “inspection readiness” and how it is applied on the day-to-day. The process to maintain compliance shouldn’t be a scramble in the end, it should be a continuous state rooted in clear process ownership, documentation, and a reliable trail of decisions.

Lastly, structured data requirements will continue to advance. IDMP is a sign of where regulators are headed, prioritizing standardized product and substance data that can be reused and reconciled throughout a products lifecycle. In practice, the regulatory changes this year will reward organizations that lower the need for manual handoffs across functions like clinical, regulatory, and patient safety. Those that operate on shared data and standard processes for audit-readiness will reap the benefits.

Companies will prioritize data, process, and AI agents

Companies will begin to move past the novelty of AI as they evaluate the benefits or shortcomings of early initiatives for specific areas, such as summarization or draft generation. These applications of AI also showcased a consistent limitation: the output is only as dependable as the data, processes, and governance it is designed on. As expectations grow and the EU AI Act redefines how regulated industries approach responsible AI, more biopharmas and biotechs will treat AI-readiness as an operational capability, not a series of AI pilots.

The conversations will move from “Can AI help?” to “Can AI help in ways that are reliable, understandable, and scalable?” To get there, organizations will need:

• Harmonized data and metadata: ensures AI outputs are grounded and consistent
• Standard workflows: enables tasks to be executed with clear control points
• Governance: drives explicit responsibility, validation, and monitoring
• Audit-friendly traceability: shows background so decisions can be understood and defended

These are the foundations that can make agentic AI possible. In 2026, more companies will start to operationalize controlled, task-based agents that can start workflows, check for completeness, identify exceptions, and route work to the right person or functional area. Organizations will also need to pair AI with disciplined processes and connected data to make this achievable. Taking a measured approach ensures that AI agents improve cycle times and quality, not introduce unnecessary risk.

Agentic AI lab assistants will drive faster, connected quality control execution

Labs will begin to move past chatbots and embed agentic lab assistants that connect highly specific tasks into its regulated environment. Quality control (QC) labs are turning their attention to AI agents’ potential to drive efficiency and steering effort toward activating them across people and process. But since QC lab technology ecosystems are fragmented, staff heavily rely on paper-based processes. Organizations will take action to modernize and consolidate systems in the QC lab, standardize data and workflows, and integrate quality assurance to reap the productivity benefits of QC-specific AI.

Lab analysts will work hand-in-hand with AI agents capable of starting workflows, summarizing outcomes, and observing and analyzing trends. This will advance proactive risk management by identifying issues earlier and driving right first-time execution. The changes will deliver a highly effective and efficient QC lab where people and agents work together to cut batch cycle times.

The impact on life sciences in 2026

The throughline across these predictions is connected execution. Europe’s regulatory momentum is increasing expectations for visibility, traceability, and partner oversight. Simultaneously, AI is leading organizations to take a deep look into operational foundations since agents cannot scale on siloed data and inconsistent processes. 

In 2026, the companies that move quickest will be able to build data flow across clinical, regulatory, safety, and quality with an inspection-ready technology foundation. This will help them apply AI in efficient and trusted ways. The results are practical and measurable: less handoffs, improved compliance, and accelerated development and delivery of new therapies.