Artificial intelligence (AI) is moving from experimentation to practical application across the pharmaceutical and biotechnology industry. Imagining when all the pilot projects move to mainstream adoption, AI will reshape pharma and biotech workflows. The important question is no longer whether AI will affect clinical development. It is how AI will change the operating model through which clinical development is planned, executed, governed, and optimized.
For the pharmaceutical and biotech sectors, this distinction matters.
AI can help analyze data faster, identify potential patients, improve trial design, automate documentation, identify operational risks, and support regulatory activities. However, simply adding AI tools to existing processes will not necessarily make clinical development faster or more productive. And if the clinical operations talent is not ready, it can create new issues that can impact speed.
The larger opportunity is to redesign how clinical development works.
The future clinical development organization will increasingly combine human scientific and strategic judgment with AI-enabled analysis, automation, prediction, and decision support. The companies that will benefit the most will be those that understand where AI belongs within the operating model—and where human expertise remains essential.
Clinical Research Organizations (CROs), which are responsible for a significant percentage of clinical operations and delivery in the industry, have a huge opportunity to lead the change in their operating models, specifically in the full-service delivery model.
A clinical development operating model defines how an organization translates its development strategy into execution.
It encompasses much more than the clinical operations department. A modern operating model connects:
Historically, many of these functions have been operating through collaboration and sequential handoffs. A development strategy is created, clinical operations translates it into a study, vendors execute activities, data is generated and analyzed, and leadership reviews results at defined milestones.
AI creates the possibility of making this model substantially more connected.
Instead of viewing clinical development as a series of functional activities, organizations can increasingly manage it as an integrated, data-driven decision system.
The FDA has recognized the increasing use of AI across the drug product lifecycle, including nonclinical development, clinical development, manufacturing, post-market activities, digital health technologies, and real-world data analytics. The agency has also developed guiding principles for responsible AI use in drug development with the European Medicines Agency.
Potential applications include:
AI can support the analysis of historical clinical trial data, patient populations, eligibility criteria, geographic distributions, and operational assumptions.
This creates an opportunity to challenge traditional protocol development.
Rather than asking only, “Can we execute this protocol?”, development teams can increasingly ask:
“What protocol design is most likely to generate the necessary evidence while minimizing unnecessary burden on patients and sites?”
That is an important shift.
A protocol that is scientifically elegant but operationally unrealistic usually creates downstream delays. AI-enabled analysis can help identify potential operational challenges before the study begins. It will depend on how clinical development experts embrace the input during protocol development versus previous standard ways of operating. For decades operational experts were providing feedback on feasibility, which did not always result in the initial protocol design being modified. AI can provide insights, and clinical development experts will have to determine those insights in the strategic context for a development program. Perhaps, AI will better influence protocol designs versus traditional feasibility methods early in the design process. Time will tell us if this approach becomes accepted and has impact on speeding development in the broader industry.
Patient recruitment remains one of the most persistent challenges in clinical development.
AI may help sponsors identify patient populations, predict enrollment patterns, prioritize sites, and analyze real-world data to understand where eligible populations may be concentrated. This may require new ways to reach patients outside of traditional clinical sites and therefore new operational solutions.
The objective should not simply be to find more patients.
The objective should be to create a more predictable enrollment engine that supports the timelines that are important for the product’s overall development plan.
That means connecting feasibility, site selection, patient identification, recruitment forecasting, and enrollment monitoring rather than managing each as a separate activity.
AI can potentially help sponsors evaluate site characteristics, historical performance, investigator experience, patient availability, enrollment patterns, and operational risk.
This can move site selection from a relatively static assessment toward a dynamic model.
A sponsor could increasingly ask:
The value comes from turning data into earlier decisions.
Clinical trials generate enormous amounts of operational information.
AI can help identify patterns across:
The opportunity is to move from retrospective reporting toward predictive program management.
In clinical operations, predictive program management is essentially the shift from managing what has already happened to using AI and data to anticipate what is likely to happen next—and intervene before it affects the trial.
What it means in practice
Traditional program management often looks like:
Monitor → Identify issue → Escalate → Correct → Recover
Predictive program management aims for:
Sense → Predict → Prioritize → Intervene → Prevent
AI becomes the intelligence layer across the clinical-operating environment.
One of the most important misconceptions about AI in clinical development is that automation will replace experienced clinical and program leaders.
The opposite is more likely.
As AI generates more information, organizations will need strong leaders to determine what information matters, what decisions should be made, and what risks are acceptable.
AI can identify patterns.
It cannot independently establish the strategic context for a development program.
Clinical and program leaders remain responsible for interpreting scientific evidence, balancing risk and opportunity, understanding regulatory expectations, aligning stakeholders, and making decisions when the data is incomplete or contradictory.
This makes program leadership increasingly important—not less important.
Celeris Consulting’s approach to strategic program leadership emphasizes integrated planning, governance, risk management, cross-functional coordination, and KPI visibility across development programs. Those capabilities become even more important as AI introduces additional data and decision inputs into the development environment.
Organizations often approach AI by asking:
“What AI tool should we buy?”
A better question is:
“What decisions or processes are limiting the performance of our development program, and where could AI improve them?”
This leads to a fundamentally different implementation strategy.
Not every clinical development activity requires AI.
Companies should identify decisions where better information, faster analysis, or improved prediction could materially affect:
AI is only as useful as the data and processes supporting it.
Organizations need to understand:
The FDA’s current AI framework emphasizes defining a model’s context of use and assessing credibility based on the risks associated with that use. The agency recommends a risk-based approach rather than treating all AI applications identically.
This is particularly important in regulated drug development.
An AI model used to summarize internal meeting notes presents a very different risk profile from an AI model whose output contributes to evidence supporting a regulatory decision.
This is where many organizations will struggle.
If an AI system produces a prediction but nobody owns the resulting decision, the organization has not actually improved its operating model.
Every AI-enabled workflow needs:
The most effective clinical development organizations are unlikely to be fully automated.
They will be augmented organizations.
AI will increasingly handle tasks involving:
Human teams will increasingly focus on:
This division of labor has the potential to make development organizations both faster and more strategic.
AI adoption cannot be separated from regulatory strategy.
The FDA has explicitly encouraged sponsors to engage with the agency when AI is being used in connection with specific development programs.
The implication for sponsors is clear: AI should be incorporated into development strategy with appropriate consideration of regulatory expectations rather than treated exclusively as an internal technology initiative.
Celeris’s regulatory advisory and project management approach emphasizes early planning, regulatory risk assessment, cross-functional coordination, and alignment between development strategy and regulatory objectives.
Companies do not need to transform their entire clinical development organization overnight.
A pragmatic approach is to begin with several high-value use cases.
Ask:
The goal should be measurable improvement—not AI adoption for its own sake.
AI has the potential to transform clinical development, but the greatest opportunity is not simply automation.
It is operating model reinvention.
The pharmaceutical companies that gain the greatest advantage will connect AI with strong governance, high-quality data, regulatory strategy, program leadership, and disciplined execution.
A redesigned operating model determines whether speed actually creates value.
Celeris Consulting helps biotech and pharmaceutical companies connect strategy, regulatory considerations, project execution, and program leadership across the drug development lifecycle. Learn more about Celeris Consulting’s strategic approach to drug development.