The clinical development model is structurally incompatible with the industry’s direction. A recent Bain & Company survey of 120 trial sponsors, contract research organizations (CROs), and trial site professionals reveals that 53% report delays in current phase 2 and 3 trials. And 76% expect complexity to rise over the next three years. Only 2% foresee relief.
Despite a decade of investment, patient identification, recruitment, and site activation remain bottlenecks. AI is shortening discovery timelines, but without a redesigned model, those gains are evaporating.
A fundamentally different model is possible today, but it takes more than using AI to optimize trials at the margins. Leaders will build a connected evidence-generation system and reimagine clinical development from first principles: the science, the patients, and the evidence needed for regulatory and commercial success.
Why is the current clinical trial model unsustainable?
Three forces are testing the limits of pharma’s development infrastructure:
- Expanding pipeline, intensifying competition. The number of drugs in phase 2 and 3 is up roughly 9% year over year, nimble biotechs are running more phase 3 trials, and fast early-stage trials in China are shrinking the time from proof of concept to competitive threat.
- A widening gap between trial demand and eligible patients. Biomarker-driven therapies are narrowing eligible patient populations, and growing requirements for diversity and local evidence further complicate recruitment.
- A fragmented evidence-generation system. Siloed teams mean insights rarely carry from one stage to the next, costing time and lowering the probability of success.
What an AI-fluent R&D system looks like
Clinical development is ripe for AI at scale. It has rich structured data, repeatable processes, and direct bottom-line payoff from even modest cycle-time cuts. Success requires moving beyond disconnected tools to specialized agents across every development, regulatory, and commercial function. Working from a shared evidence base, they exchange information, challenge assumptions, flag inconsistencies, and escalate consequential decisions to accountable experts.
Imagine AI replacing today’s trial-by-trial framework by connecting five activities:
- Choosing the right asset, indication, dose, and patient population. AI can prioritize assets and indications with the best odds of success, optimize dose and regimen, and build responder profiles for smaller, faster trials.
- Designing an integrated clinical, regulatory, and market-access evidence strategy. Synthesizing evidence as it arrives means fewer downstream amendments and the right data for broad reimbursement and adoption.
- Ensuring chemistry, manufacturing, and controls; bioanalytics; and supply. AI can surface manufacturing or analytical constraints before they cause delay.
- Executing an adaptive, patient- and site-centered trial. AI-powered decisions can shorten time to first patient enrolled. Prespecified adaptive designs, with modifications based on interim evidence, cut waste without compromising statistical validity.
- Turning evidence into decisions, submissions, and portfolio learning. AI shifts decision cadence from milestone-based to event-based. Every trial event updates the models for the next trials.
How can pharma leaders redesign clinical development?
Nearly half of sponsors and CROs are piloting AI in select functions, but only 16% are redesigning roles and workflows around it. Pilots without restructuring yield only local efficiency gains. First movers are taking three steps:
- Building a connected evidence-generation system. Start with bounded projects, such as quantitative systems pharmacology-based dose selection, then add cross-functional teams, governance with decision gates, and interoperable platforms.
- Redesigning clinical development from first principles. Ask, “What evidence do we need to make a confident decision?” Leaders who work backward from the answer invest faster with stronger evidence. Stage gates let companies end lower-probability programs early to redirect capital in real time. Leading companies will compress the typical 20 to 30 months between phases by reexamining internal decision making, adopting adaptive phase 2 and 3 designs, removing lags in development timelines, and, eventually, piloting real-time regulatory engagement.
- Designing the workforce and partnership model deliberately. Decide which core competitive capabilities to own in-house and which to source through AI-native CROs, specialized technology providers, and platform partners. Rebuild roles, incentives, and structures around small cross-functional teams working alongside agents that track enrollment and site performance, reconcile operational and clinical data, flag safety or compliance signals, and recommend interventions.
This doesn’t mean unsupervised automation. Decisions on patient selection, dose, safety, protocol, and regulatory claims demand validated models, auditable data, human accountability, and monitoring for bias and model drift. Winners will pair automation with stronger scientific, regulatory, and quality governance.
What will separate leaders from followers?
If a competitor moves from early signal to registrational-quality evidence by compressing or replacing a conventional intermediate trial, latecomers will find the market already claimed. That competitor will lock in formulary position, physician habit, and payer relationships for the life of the asset. Companies that haven’t started redesigning will have to catch up, with less flexibility and at higher cost.
To read the full article, please click here