Clinical Researcher—June 2026 (Volume 40, Issue 3)
PRESCRIPTIONS FOR BUSINESS
Jim Reilly
Life sciences organizations have made progress in connecting data and processes across the product lifecycle, in part for preparation to launch artificial intelligence (AI) use cases. The focus on streamlining operations is quickly shifting to create even greater data flow through connected execution across stakeholders. This will be supported by a technology foundation that delivers better visibility, traceability, and inspection readiness as regulations, including the European Union’s Clinical Trial Regulation (EU CTR) and the International Council for Harmonization’s ICH E6(R3) guideline for Good Clinical Practice (GCP), keep evolving.
In parallel, AI is shifting from early use cases that augment capabilities toward more practical AI that is embedded within systems and AI agents, like agentic labor, that can reduce cost, increase speed, and maintain compliance. Below are key areas where life sciences are continuing to advance and prioritize these goals.
Clinical data flow will advance patient recruitment, improving access and experience
The flow of clinical trial data between research sites and sponsors will deliver faster, more effective trials. Study information will reach physicians directly to connect their patients with relevant research. 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 conditions or test results. This direct-to-physician approach will lower life sciences’ reliance on sites to identify trial participants, meeting recruitment goals sooner and improving patient access to studies.
With a reduced burden from patient recruitment requirements and modern solutions, sites will see the promise of eliminating paper and manual source data verification for clinical research associates become a reality. eSource tools will improve the connection upstream and downstream to clinical data sources, first with electronic health records so that patient health data can merge more efficiently with trial data.
When connected with electronic data capture systems, source forms will be defined by study definition so data can flow quicker, and with more clarity, to the sponsor. This data flow will simplify study visits for patients and advance trials for sites and sponsors.
Regulations will move teams toward inspection-ready execution by design
Regulatory change in Europe will feel less like a series of single milestones and more like a stable way to operate. Study teams will be firmly in a Clinical Trials Information System (CTIS)–first world under EU CTR, which continues to increase expectations for consistency across countries, faster coordination, and more complete and traceable documentation. This also includes the broader shift for more structured submissions, such as the U.S. Food and Drug Administration’s electronic Common Technical Document (eCTD) 4.0. As companies adapt to this new reality, the pressure will move from “working to get it done” toward “doing it right, every time,” with fewer exceptions and no local workarounds.
Simultaneously, ICH E6(R3) will advance the industry to a clear risk-based approach to GCP. More frequently, sponsors will be expected to show how quality is designed into a trial and how oversight is executed across study partners, data sources, and systems. The changes narrow the gap between trial operations and compliance. It also changes the definition of “inspection readiness” and what it means on a day-to-day basis. The process isn’t a scramble in the end. It is a continuous state that depends on clear process ownership, documentation, and a reliable trail of decisions.
Lastly, structured data requirements will continue to advance. The International Organization for Standardization’s Identification of Medicinal Products (IDMP) standards represent a signal of where regulators are heading, prioritizing standardized product and substance data that can be reused and reconciled throughout a products lifecycle. In practice, the regulatory changes in 2026 will reward companies that lower the need for manual handoffs across clinical, regulatory, safety, and quality, and instead, operate on shared data and standard processes for audit-readiness.
Data, process, and AI agents will be the priority
Many organizations will be moving past the novelty of AI as they analyze the benefits of early initiatives in specific areas, such as summarization, classification, and draft generation. These applications of AI also surface a consistent limitation: the outcome is only as dependable as the data, processes, and governance it is built on. As expectations rise and the EU AI Act shapes how regulated industries think about responsible AI, more sponsors will treat AI-readiness as an operational capability, not a series of pilots.
The conversations will shift from “Can AI help?” to “Can AI help in ways that are reliable, clear, and scalable?” To get there, companies will need:
- Harmonized data and metadata so AI outputs are grounded and consistent
- Standardized workflows so tasks can be executed with clear control points
- Strong governance so responsibility, validation, and monitoring are explicit
- Audit-friendly traceability so decisions can be understood and defended
These are the foundations that will make agentic AI, and agentic labor, possible. More companies will start to operationalize controlled, task-based agents that can start workflows, check for completeness, review and summarize outcomes, identify exceptions, and route work to the correct person or team. For this to be achievable, organizations will need to pair AI with disciplined processes and connected data. This ensures that AI agents improve cycle times and quality, not introduce risk of non-compliance.
Agentic AI lab assistants will drive connected, faster execution
Labs will move beyond chatbots to embed agentic lab assistants that bring together highly specific tasks in a regulated environment. Quality control (QC) labs are turning their attention to the efficiency potential of AI agents and prioritizing activating them across people and processes. However, QC lab ecosystems are fragmented and heavily rely on paper-based processes. To realize the productivity gains of QC-specific AI, organizations will modernize and simplify their system infrastructure, standardize data and workflows, and integrate with quality assurance.
Agents capable of starting workflows, summarizing outcomes, and determining trends will work alongside lab analysts. This change in the way labs work will enable proactive risk management by pinpointing issues early on and improving right first-time execution. The outcome will be people and agents working together in an effective and efficient QC lab that can shorten batch cycle times.
What these shifts mean for life sciences
The throughline in how the industry is advancing operations is connected execution. Europe’s regulatory evolution is raising expectations for transparency, traceability, and consistent oversight. At the same time, organizations are taking a deep look at operations since AI agents cannot scale on fragmented data and inconsistent processes.
In the months ahead, the organizations that take steps to enable data flow across product development with an inspection-ready foundation will be better positioned to keep up with regulatory changes and benefit from AI. The outcome is less handoffs, better compliance, and faster execution.
Jim Reilly, President of Veeva Development Cloud, has more than 20 years of experience in life sciences industry software, strategy, and consulting. Today, he leads the strategy, execution, and growth of Veeva Development Cloud and is a Board Member for The Association of Clinical Research Organizations (ACRO).
