Faster clinical development requires more than AI tools

AI could accelerate clinical development by changing how pharmaceutical companies make decisions

Bain & Company’s April 2026 survey found that 53% of current phase 2 and 3 clinical trials were experiencing delays. Another 76% of respondents expected trials to become more complex over the following three years. Yet pharmaceutical companies are investing in artificial intelligence while leaving much of their development process unchanged. Only 16% of surveyed sponsors and contract research organizations (CROs), which conduct research on behalf of drug developers, were redesigning roles and workflows around AI.

The gap matters because clinical development depends on decisions across several specialized functions. AI can accelerate protocol development, improve patient identification, and automate monitoring. But those gains may still leave months of waiting for regulatory alignment, manufacturing readiness, investment approval, and cross-functional reviews. A faster task shortens the development timeline only when it changes an activity or decision that determines when the program can advance.

An alternative is to organize development around evidence that is continuously updated and accessible to the people who need it. AI can connect research findings, clinical observations, operational information, and commercial requirements so teams can recognize when new evidence warrants action. Making this work requires pharmaceutical companies to redesign their decision processes while keeping human responsibility for consequential clinical judgments clear.

Clinical development is becoming harder to execute

The pressure on clinical development comes from several directions. Bain & Company’s April 2026 Clinical Trials Voice of Customer Survey covered 120 sponsors, CROs, and trial site professionals. Most respondents said trials had become more complex over the preceding three years, and they expected that trend to continue.

The findings show how operational pressure is growing while organizational readiness lags.

The AI adoption findings come from the 80 sponsor and CRO respondents within the overall sample. They describe how organizations are working rather than measured development outcomes.

Increasing complexity reflects changes in both the medicines being developed and the resources available to test them. The number of drugs in phase 2 and 3 development is growing by approximately 9% year over year, while agile biotechnology companies are conducting more phase 3 trials. Rapid early-stage development in China adds competitive pressure because sponsors have less time to respond when promising clinical results emerge.

As more programs compete for investigators, patients, and trial sites, study requirements are also becoming more demanding. Targeted therapies increasingly depend on biomarkers, measurable biological characteristics used to identify patients or assess treatment effects. These requirements can narrow eligible populations and make recruitment harder, even when a medicine has a compelling scientific rationale.

Patient identification, enrollment, and site activation have therefore remained persistent challenges despite roughly a decade of investment in clinical development capabilities. Sponsors are responding partly by recruiting more patients outside the US, where additional sites may improve access, reduce costs, and accelerate enrollment.

International recruitment brings another planning challenge. Regulators are placing greater emphasis on locally representative patient populations and evidence relevant to specific geographic markets. A trial may enroll patients efficiently across countries, but its results must still apply to the populations who will receive the treatment.

Recruitment strategy therefore shapes regulatory planning and commercial market selection. Expanding internationally may help a sponsor move faster, but the resulting evidence must support its intended registration and market-access strategy.

The consequences go beyond trial execution. When a development program falls behind competing therapies, its eventual commercial value may change. Earlier entrants can establish prescribing patterns, reimbursement relationships, and familiarity among physicians, leaving later products under greater pressure to demonstrate meaningful differences.

These pressures explain why sponsors are turning to AI. More complex studies, limited patient access, and stronger competition create demand for better forecasting, faster analysis, and more efficient operations. The question is how those capabilities can change the decisions that determine when a medicine reaches patients.

The AI adoption gap reflects how development organizations operate

Bain’s adoption findings reveal the difference between introducing technology and changing how development work gets done. Sponsors can deploy AI within individual departments while keeping the same reviews, approvals, and handoffs that determine the overall schedule.

Consider AI-assisted protocol drafting. Medical writers and clinical teams may produce an initial protocol faster, reducing the time needed to prepare a study. But if the document then passes through the same sequential reviews and approvals, much of the time saved during drafting may disappear before the trial begins.

Operational applications face a similar constraint. One system might identify enrollment problems earlier, another might improve safety reporting, and a third might automate data cleaning. Each improves a specific activity, but resolving an enrollment problem may still require coordinated changes to site allocation, eligibility criteria, manufacturing supply, and regulatory plans.

These coordination problems follow from how clinical development responsibilities are divided. Clinical operations manages trial execution, medical and scientific teams interpret evidence, regulatory affairs handles agency requirements, and commercial groups evaluate market opportunities. Each contributes necessary expertise, but information often moves between functions through meetings, documents, and formal handoffs.

Those handoffs matter most when new evidence suggests a change in strategy. A clinical team may identify a promising patient subgroup, for example, but changing the development plan could require input from biostatistics, regulatory affairs, manufacturing, and commercial planning. Even when everyone has the relevant information, the decision may wait until the next scheduled governance review.

Milestone-based governance creates waiting periods

Clinical development has traditionally concentrated major decisions around predefined milestones, including phase completion, database lock, and formal governance meetings. Database lock occurs when trial data have been cleaned and finalized for analysis. These milestones provide discipline, but they can also hold up decisions when sufficiently reliable evidence becomes available between reviews.

Commercial involvement shows the cost of that sequence. Sponsors often select endpoints, comparators, and patient populations years before launch, while market-access and commercial teams may contribute their perspectives after important protocol decisions have been made. A study can therefore meet immediate clinical and regulatory objectives while generating limited evidence about comparative effectiveness, patient experience, or economic value.

Correcting those gaps later may require protocol amendments, additional studies, or a more restricted commercial strategy. Bringing teams together earlier would help them consider the evidence needed for regulatory approval and reimbursement while the study is still being designed.

Fragmented information creates a related problem. Large pharmaceutical companies have accumulated decades of proprietary research findings, clinical data, operational experience, and regulatory interactions. These assets could improve new programs involving similar biological pathways, patient populations, or therapeutic approaches.

Yet that knowledge often remains scattered across databases, documents, functions, and individual experts. Preclinical findings may have little influence on trial design, while lessons from previous protocol amendments or manufacturing deviations may never reach the next development team.

Independent departmental AI deployments can preserve these boundaries. They make information easier to process within each function while leaving the organization dependent on manual coordination between functions.

This changes how AI investment should be evaluated. Faster documentation, cleaner data, and better operational reporting can improve quality and reduce costs. Their effect on total development time depends on whether they remove a dependency, shorten a critical activity, or allow an important decision to happen earlier.

Connected evidence changes the unit of improvement

A connected development organization starts with the decisions its teams must make and identifies the evidence each decision requires. Scientific, clinical, regulatory, manufacturing, and commercial experts can then work from shared information that is continuously updated.

AI supports this process by bringing evidence together, testing assumptions, identifying inconsistencies, and alerting teams when predefined conditions have been met. Accountable experts retain authority over decisions, while automated systems reduce the work needed to assemble and interpret the information.

This approach gives established pharmaceutical companies a potentially important advantage. Their accumulated proprietary data can improve decisions across multiple programs, provided the organization makes that information accessible and useful beyond the function that originally generated it.

Connected evidence can improve five development decisions

Building development around connected evidence changes how programs are planned and executed. Instead of starting with activities that lead toward phase completion, teams identify the decisions needed to advance an asset and determine what evidence will support them.

Five activities are particularly important: selecting development opportunities, designing studies, preparing manufacturing and analytical capabilities, managing trials as evidence accumulates, and making investment and development decisions when predefined conditions are met.

1. Select assets, indications, doses, and patient populations

The first opportunity comes before a sponsor commits substantial resources to a clinical program. Decisions about the asset, indication, dose, and intended patient population establish assumptions that shape every subsequent study.

Weak assumptions can lead to expensive trials with limited prospects for demonstrating meaningful efficacy, safety, or differentiation. Connected evidence can help teams examine those assumptions using preclinical findings, previous clinical results, real-world evidence, and competitive information.

AI-supported models can compare development strategies using information that would otherwise demand extensive manual analysis across scientific and operational teams. This is especially useful when decisions depend on relationships between biological mechanisms, drug exposure, patient characteristics, and clinical outcomes.

Pharmacokinetics describes how a drug is absorbed, distributed, metabolized, and removed by the body. Pharmacodynamics examines how the drug affects biological systems. Together, pharmacokinetic and pharmacodynamic modeling can help explain how different doses may produce different treatment effects.

Exposure-response analysis adds another perspective by examining how drug exposure relates to efficacy and safety. By incorporating emerging clinical observations and safety data, these analyses help development teams make better-informed choices about dose levels, treatment schedules, and the populations most likely to benefit.

A model may indicate that a particular regimen offers a better balance between treatment effect and adverse events. It may also suggest that meaningful benefit is concentrated among patients with specific biological characteristics.

These findings can influence trial design before large numbers of patients receive treatment. They can also support decisions to stop or redirect programs whose probability of demonstrating meaningful benefit has declined.

Patient selection offers another application. Traditional eligibility criteria often rely on disease classifications, clinical characteristics, or individual biomarkers. AI-supported analysis can combine genomic information, medical imaging, digital health measurements, clinical history, and real-world treatment data to build more detailed profiles of likely responders.

These multidimensional profiles may identify patient subgroups in which a treatment produces a clearer effect. A stronger treatment effect can improve statistical efficiency, potentially allowing a smaller trial to answer the relevant clinical question.

The models need prospective validation before their predictions can reliably determine enrollment or support regulatory claims. Historical data may contain selection bias, incomplete measurements, or relationships that fail to hold in the intended trial population. Scientific validation and accountable clinical judgment therefore remain essential when turning model-generated hypotheses into eligibility criteria.

Better patient selection also informs portfolio allocation. When several indications appear plausible for an asset, connected modeling can compare their biological rationale, development requirements, competitive position, and commercial potential before the sponsor commits to one path.

2. Design studies around regulatory and commercial evidence needs

Once a development team selects an asset and target population, it must decide what the studies need to demonstrate. That choice affects regulatory approval, reimbursement, physician adoption, and patient acceptance.

These outcomes depend on evidence requirements that overlap but differ in important ways. A trial may establish sufficient efficacy and safety for regulatory authorization while providing limited information about the treatment’s value compared with available alternatives.

For example, a comparator may satisfy a regulatory objective while offering little help to payers assessing a medicine against current clinical practice. An endpoint may demonstrate biological or clinical activity while leaving uncertainty about outcomes that matter to patients, clinicians, or health technology assessment bodies.

Health technology assessment, or HTA, evaluates the clinical and economic value of healthcare interventions to inform coverage and reimbursement decisions. Its evidence requirements can differ from those of regulators, particularly when payers need to understand a treatment’s comparative benefits and costs.

These differences make early coordination valuable. Clinical, regulatory, commercial, and market-access teams can evaluate proposed endpoints, comparators, treatment settings, follow-up periods, and patient-reported outcomes together while protocols are still relatively easy to change.

AI-supported evidence synthesis can help teams compare requirements across markets and revisit their choices as new information emerges. It can also bring health-economic measures into clinical protocol development earlier.

Consider a treatment expected to improve patient outcomes while reducing hospital use or other healthcare costs. Demonstrating those benefits may require specific resource-use measurements, follow-up periods, or patient-reported outcomes. Including them prospectively gives sponsors more useful evidence when reimbursement decisions begin.

Discovering those requirements after pivotal studies have finished can create demand for additional research. Early commercial involvement therefore affects the value of the evidence generated throughout development.

Connected systems can also track changes in the external environment. New competitor results, evolving standards of care, or changing regulatory expectations may affect whether a proposed endpoint or comparator remains appropriate.

Spotting those changes before a study begins can prevent avoidable amendments. It also helps sponsors ensure that a technically successful trial will answer the clinical and commercial questions needed to support the medicine’s intended use.

3. Connect clinical plans with manufacturing and analytical readiness

Even a well-designed clinical program can encounter delays when manufacturing or analytical capabilities are unprepared. Clinical progress must therefore be assessed alongside the sponsor’s ability to produce, test, and supply the treatment.

Chemistry, manufacturing, and controls, commonly called CMC, covers the production processes and quality requirements used to manufacture a medicine consistently. Bioanalytical capabilities provide measurements of drug concentrations, biological activity, and related clinical evidence.

Both can determine when a program is ready to advance. A sponsor might complete an important clinical milestone but remain unable to begin the next study because production capacity, analytical methods, or treatment supply is insufficient.

For complex therapeutic modalities, these constraints may matter more to the schedule than enrollment performance. Waiting until clinical results are finalized to resolve manufacturing dependencies can delay the next development stage.

Connected planning brings manufacturing forecasts, analytical requirements, trial timelines, and supply assumptions into the same decision process. AI can identify constraints earlier, helping teams assess whether a clinically attractive strategy is operationally feasible.

Manufacturing and analytical specialists can then address capacity, validation, and readiness requirements while clinical evidence is still accumulating. Their involvement also helps sponsors distinguish between a promising development timeline and one they can realistically execute.

4. Use adaptive trials and continuous monitoring to improve execution

Once a study begins, connected evidence can improve recruitment, site management, safety oversight, and responses to emerging results.

AI-supported site selection can combine patient availability, historical enrollment performance, investigator capacity, and operational feasibility. This broader evidence base may help sponsors identify locations better positioned to recruit eligible patients.

Patient-matching systems can also connect suitable individuals with available studies. These applications address recruitment delays directly by improving patient identification and helping sponsors select sites likely to reach enrollment targets.

More substantial changes become possible when a trial is designed to respond to interim evidence. Adaptive clinical trials allow prespecified modifications based on information collected during the study. Depending on the design, these changes may affect sample size, treatment allocation, or other predefined elements.

Adaptation rules must be established prospectively and preserve the trial’s statistical validity. AI can support simulation and analysis, while biostatisticians, clinicians, and regulators determine whether the proposed design and adaptations are scientifically acceptable.

For suitable programs, seamless phase 2/3 designs can combine objectives that would otherwise require separate studies. This may reduce transitions between development phases, provided the design supports reliable interpretation and meets regulatory expectations.

Alternative control strategies offer another possibility. External control arms use information from patients treated outside the randomized study. Synthetic controls construct comparison groups using suitable existing patient data.

With regulatory agreement, these approaches may supplement or reduce conventional control-group enrollment in appropriate circumstances. Their suitability depends on evidence quality and comparability, especially whether differences between patient populations could bias estimates of treatment effect.

Simulation helps sponsors assess these design choices before exposing patients to the proposed study. Patient-level simulation and digital-twin methods use computational models to represent possible clinical trajectories. They can test assumptions about disease progression, treatment response, and trial performance, potentially revealing weaknesses in eligibility criteria, dosing strategies, or endpoints.

Simulated outcomes remain hypotheses until supported by appropriate prospective evidence. Their practical value lies in improving the questions a study asks and how it is designed to answer them.

Continuous monitoring offers a more immediate operational application. Connected systems can examine adverse-event reports and laboratory results as information arrives. When a pattern suggests a safety concern, the system can alert responsible clinical and safety experts while there is still time to investigate and intervene.

Earlier identification may protect patients and reduce delays caused by discovering important problems late in a study. Automated site queries can likewise flag missing, inconsistent, or potentially erroneous trial data while sites are still collecting information.

Resolving those issues throughout the trial reduces the cleaning required after the final patient visit. It may also shorten the interval between completing data collection and database lock.

These operational benefits grow when findings reach the functions responsible for acting on them. Enrollment forecasts can inform site investment and supply planning. Safety signals can trigger medical review and possible protocol changes. Emerging efficacy results can guide regulatory discussions and portfolio decisions.

5. Make development decisions when evidence becomes actionable

The final activity connects the preceding work to formal development governance. In conventional processes, teams prepare evidence packages for scheduled milestone reviews. Committees then decide whether programs should advance, change, or stop.

A connected model preserves those decision rights while allowing reviews to begin when relevant evidence becomes available. Development teams establish predefined thresholds for decisions involving efficacy, safety, enrollment performance, manufacturing readiness, and continued investment. AI systems can monitor the supporting information and flag when the agreed conditions have been met.

For example, a program may have a prospectively defined criterion for whether an emerging efficacy signal justifies further investment. When the evidence meets that criterion, specialized AI agents can assemble analyses, identify conflicting findings, and alert the accountable team.

An AI agent is a system designed to complete a sequence of related tasks using information sources and software tools. In clinical development, agents could retrieve evidence, reconcile discrepancies, coordinate analysis across functions, and prepare recommendations for expert review.

Medical directors, statisticians, regulatory specialists, and portfolio leaders can then examine the information together. Formal authority remains with qualified decision-makers, who determine whether the evidence supports the proposed action.

Evidence-triggered governance needs carefully defined thresholds. Continuously updated analyses can produce more frequent signals, but those signals must remain scientifically reliable, auditable, and consistent with regulatory requirements.

When these conditions are met, the organization can act sooner on decisions to continue, modify, or terminate development. Sponsors may identify unsuccessful programs earlier and redirect investment toward stronger candidates.

The same process can improve learning across the portfolio. A protocol amendment may reveal that an eligibility criterion was unnecessarily restrictive. A manufacturing deviation may expose an underestimated production risk. A completed trial may show that a particular patient characteristic predicts response more effectively than an earlier biomarker hypothesis.

In a fragmented organization, those lessons often remain buried in project records or individual experience. Connected systems can incorporate them into subsequent models, planning assumptions, and development decisions.

Each program then contributes evidence that improves future choices. The benefit reaches beyond the individual study because later programs can use knowledge generated by earlier ones.

AI-native biotechnology companies are testing integrated development models

The potential of connected development is becoming visible in biotechnology companies that have built their processes around integrated technology systems.

Formation Bio provides one example. The AI-native biotechnology company has organized clinical development around a platform that connects activities traditionally managed by separate specialist teams.

Formation Bio says its platform can generate and assess thousands of potential trial design scenarios, draft study builds in minutes, and identify important signals as studies progress. The company has a commercial interest in demonstrating that its AI-centered approach can improve development performance, so these capabilities are company-reported examples of its operating model.

The capabilities illustrate a different planning process. Rather than evaluating a small number of trial designs through separate analytical and operational activities, a development team can explore more possibilities before choosing its strategy.

The platform also connects design choices with trial execution and emerging information. Teams can revisit assumptions as evidence develops, instead of treating the initial protocol as a decision isolated from subsequent operations.

Formation Bio reported reaching first-participant dosing in a phase 1 trial five months after closing the relevant deal. That milestone shows the timeline achieved for the reported program, while the contribution of individual activities, including AI, remains distinct from the overall elapsed time.

The example matters because integrated technology may change the resources smaller biotechnology companies need. Advanced modeling, simulation, operational planning, and evidence monitoring have historically required substantial specialist capacity.

AI platforms may give smaller organizations access to similar capabilities with fewer internal functions and less coordination overhead. This creates a competitive challenge for established pharmaceutical companies, whose scale brings both advantages and organizational complexity.

Large sponsors possess extensive proprietary clinical and scientific data, accumulated regulatory experience, and substantial development infrastructure. These assets are particularly valuable for complex global programs requiring significant capital and operational capacity.

Smaller AI-native competitors may have less historical evidence, but they can establish connected workflows without rebuilding decades of existing structures. Competitive performance will depend on how effectively each organization combines its evidence, expertise, resources, and decision processes.

Regulators are exploring more continuous evidence review

Changes in regulatory practice could extend the benefits of connected development beyond decisions made within pharmaceutical companies.

The US Food and Drug Administration is piloting technology-enabled collaboration with AstraZeneca and Amgen using a platform from Paradigm Health. The initiative explores more continuous sponsor-regulator review and dialogue, reducing reliance on the conventional sequence of document preparation, submission, feedback, and revision.

AstraZeneca and Amgen participate as pharmaceutical companies seeking to improve development interactions. Paradigm Health has a commercial interest in the use of its technology platform. Their involvement shows how sponsors and technology providers are testing new approaches to regulatory collaboration.

The potential benefit comes from shortening the wait between identifying a scientific question and receiving the feedback needed to proceed. Earlier regulatory interaction may help sponsors resolve uncertainties about study design, evidence requirements, and development plans before those issues delay subsequent work.

FDA officials have suggested that AI-enabled sponsor-regulator systems could potentially reduce overall clinical trial timelines by 20% to 40%. This is a projected improvement associated with the proposed approach, rather than an outcome established by the pilot.

The approach could complement existing regulatory pathways. For programs seeking FDA Breakthrough Therapy designation, earlier clinical planning may help sponsors establish the basis for more frequent engagement with the agency.

Other initiatives are addressing the scientific methods used to generate evidence. Operation TrialBlazer, associated with the US Department of Health and Human Services, reflects growing interest in modernizing clinical development. Related FDA initiatives include draft guidance concerning quantitative systems pharmacology-based dose selection for first-in-human trials and updated master protocol frameworks that support more adaptive development strategies.

Quantitative systems pharmacology, or QSP, uses computational models of biological systems and drug effects to inform development decisions. Applied to first-in-human dose selection, it helps teams combine biological knowledge and experimental findings when evaluating potential starting doses.

Master protocols provide a common study framework for evaluating multiple treatments, populations, or research questions. Updated frameworks can support more flexible designs that incorporate accumulating evidence while maintaining appropriate scientific controls.

These developments create opportunities to improve how studies are organized and how sponsors interact with regulators. A development program may be able to answer several related questions more efficiently, use suitable adaptive methods, and resolve regulatory uncertainties earlier.

Earlier evidence can also affect commercial competition. When a sponsor reaches convincing clinical results and moves toward registration ahead of competing programs, it gains opportunities to establish formulary positioning, physician familiarity, and payer relationships.

Those advantages can persist throughout an asset’s commercial life, particularly when later entrants offer limited additional clinical value. The commercial impact of faster development can therefore exceed the immediate operational savings from shortening individual studies.

Pharmaceutical companies need to redesign processes, teams, and decision rights

For established pharmaceutical organizations, the challenge is to introduce connected AI capabilities while changing the decisions and responsibilities those capabilities support.

The work involves three linked priorities: integrating evidence, redesigning development around explicit decision requirements, and changing workforce responsibilities and partnership models.

Build connected evidence systems incrementally

Large pharmaceutical companies already possess extensive proprietary datasets that can improve future development decisions. Connecting those assets across scientific research, clinical operations, regulatory affairs, and commercial planning can make existing knowledge more useful.

Trying to transform every development process at once would introduce considerable technical and organizational complexity. A practical approach starts with bounded applications that support identifiable decisions and can later connect to a broader evidence system.

QSP-based dose modeling offers one starting point by bringing biological and clinical information into dosing decisions. Evidence-informed protocol development can incorporate regulatory and market-access requirements earlier. Real-time data cleaning can improve the quality and availability of trial information throughout execution.

Each application needs a defined owner, a specific decision it supports, and a way to evaluate its contribution to the underlying process. This allows organizations to improve a limited activity while establishing the standards needed for wider integration.

The next step is to connect applications through common data definitions and interoperable systems. Information generated in one activity should be available to authorized teams in other functions, with appropriate access controls and traceable records.

Cross-functional teams must take responsibility for evidence quality as information moves between systems. Differences in definitions, measurement methods, or assumptions can otherwise produce inconsistent conclusions even when the technology is connected.

Shared standards also need organizational ownership. Some centralization may be necessary to establish data governance, privacy requirements, interoperability rules, and consistent methods for recording decisions.

With these foundations in place, specialized multistep AI agents can coordinate defined activities across connected systems. An agent might retrieve relevant evidence, compare it with predefined requirements, flag inconsistencies, and prepare an analysis for expert review.

Successful applications can then become components of a progressively integrated development environment. Each addition expands the decisions teams can support using shared evidence.

Design development programs around the evidence needed for decisions

Technology integration becomes more valuable when sponsors reconsider how programs are planned.

The starting point is to define the evidence required by regulators, payers, clinicians, patients, and internal investment committees. Teams can then work backward to determine which studies, measurements, patient populations, and analytical methods will generate that evidence efficiently.

This approach can reduce unnecessary burdens on patients and trial sites. Simpler procedures, practical visit schedules, and eligibility criteria aligned with the study’s objectives may improve recruitment and retention while supporting more complete and representative information.

Earlier scientific evaluation offers another opportunity. AI-enabled biological modeling, human-relevant experimental systems, and predictive toxicology can help identify potential efficacy and safety concerns before sponsors commit substantial resources to clinical development.

These methods can inform whether a candidate is promising enough to advance and which uncertainties need further investigation. They can also help teams compare possible indications before committing to a single development path.

Sponsors may evaluate multiple indications in parallel using shared development infrastructure. Prospectively defined biomarker thresholds can then help determine which opportunities show enough biological or clinical promise to justify further investment.

Explicit stage gates establish criteria for advancing, modifying, or terminating programs. When a candidate fails to meet an agreed threshold, leaders can redirect capital and specialist resources rather than continuing development primarily because the organization has already invested in it.

The value of this discipline becomes clearer when examining the intervals between clinical phases. The typical time between the start of one clinical phase and the start of the next is approximately 20 to 30 months. Some of this period reflects necessary study execution, analysis, and preparation. Other delays come from decisions and dependencies that organizations may be able to manage more effectively.

Four approaches can address those intervals.

First, sponsors can improve internal go/no-go decisions by examining where evidence reviews, governance meetings, and investment approvals create waiting periods. Clear thresholds and continuously prepared evidence packages allow leaders to act when the relevant information becomes available.

Second, suitable adaptive or seamless phase 2/3 designs can reduce transitions between separate studies. These approaches require prospective planning, appropriate statistical methods, and regulatory alignment.

Third, cross-functional planning can reduce delays along the development critical path. The critical path is the sequence of dependent activities that determines the earliest possible completion date.

Manufacturing readiness, analytical validation, protocol preparation, site activation, and regulatory work can proceed in parallel where their dependencies allow. Coordinating these activities early can prevent a completed clinical milestone from being followed by avoidable waiting for another function.

Fourth, technology-supported regulatory engagement may shorten the time between identifying a development question and receiving the feedback needed to proceed. More continuous interaction can help sponsors resolve issues while related clinical and operational work is still underway.

Together, these approaches require leaders to examine where delays accumulate across the program. Accelerating an individual clinical activity has limited scheduling value when investment approval, manufacturing capacity, or regulatory preparation prevents the next stage from beginning.

Rebuild workforce responsibilities and partnership models

Connected development also changes which capabilities pharmaceutical companies need to own and how their employees spend time.

Organizations must decide where internal expertise and proprietary technology create lasting advantages. Other activities may be delivered more efficiently through AI-native CROs, specialized technology providers, or integrated platform partners.

The choice depends on the sponsor’s strategic priorities. A company with distinctive biological models, proprietary clinical datasets, or specialized development expertise may benefit from keeping those capabilities internally.

More standardized technology and operational services may be suitable for external partnerships. In those arrangements, the sponsor still needs control over evidence quality, interoperability, and consequential development decisions.

The make-versus-partner decision also shapes team structure. Traditional development organizations often rely on large specialist groups that coordinate through meetings, reports, and functional handoffs. Connected systems could support smaller cross-functional teams that supervise automated monitoring and analysis across defined areas of development.

Study managers, for example, may spend less time manually coordinating routine activities. AI systems could monitor enrollment and site performance, reconcile operational and clinical information, identify possible protocol-compliance issues, and recommend interventions.

Managers would then spend more time supervising those processes, investigating exceptions, and making operational decisions.

Clinical research associates could see a similar division of responsibilities. Monitoring agents may perform appropriate data reviews and signal detection, while site-facing professionals concentrate on investigator engagement, coordinator relationships, and insights gained through direct interaction.

Those relationships remain especially important in therapeutic areas where multiple studies compete for the same investigators, sites, and patients. Automation can make operational information more accessible, while experienced professionals use it to manage the relationships on which enrollment depends.

Medical directors could likewise devote more attention to clinical strategy, study design, and interpretation as AI systems take on suitable administrative and analytical responsibilities.

Changing job descriptions alone will have limited effect unless organizations also redesign decision rights, reporting structures, and incentives. A department measured mainly on its own productivity may have little reason to prioritize work that improves the broader development program. Leaders therefore need performance measures that recognize cross-functional outcomes and timely resolution of shared dependencies.

Executive sponsorship is essential to establishing those priorities. An executive-level champion can resolve conflicts between functions, support common standards, and authorize the initial centralization needed for effective governance.

That authority must also cover the relationship between automated recommendations and clinical decisions.

Keep clinical accountability explicit

AI-generated analyses can influence patient selection, dosing, safety assessment, protocol modifications, and regulatory strategy. These activities carry scientific, ethical, and legal responsibilities, so organizations need controls suited to the consequences of each decision.

Models must be validated for their intended purposes. Their assumptions and supporting evidence must remain documented and auditable so responsible experts can examine how recommendations were produced.

Ongoing monitoring is also necessary because model performance can change over time. Model drift occurs when changes in underlying data, patient populations, or clinical practices make previously reliable predictions less accurate.

Bias monitoring is equally important when historical clinical datasets underrepresent particular populations. A model trained on those datasets may perform differently across patient groups, potentially affecting eligibility assessments or treatment recommendations.

Privacy and information security bring additional requirements. External AI systems must protect proprietary research, confidential development information, and patient data while complying with applicable regional privacy obligations.

These safeguards need to be part of connected development processes from the beginning. Any system that influences clinical decisions must preserve traceability, reliable evidence, and explicit human authority throughout its operation.

Physicians and qualified development experts remain responsible for consequential judgments about patient eligibility, dosing, safety, protocol changes, and regulatory claims. AI can accelerate the preparation and delivery of relevant information, while accountable professionals determine whether the evidence is sufficient to act.

The same responsibility applies when a system identifies a decision threshold that has been reached. The alert can trigger an earlier review, but the authorized team must examine the scientific basis, evaluate conflicting information, and decide how to respond.

This creates a concrete governance requirement for pharmaceutical leaders: every automated workflow that can influence a consequential development decision needs an identified decision owner, an auditable evidence record, and a defined process for expert review.

Recap

AI’s potential to accelerate clinical development depends less on how quickly pharmaceutical companies adopt new tools and more on how effectively they change the decisions those tools support. Automating individual activities can improve productivity, but the greatest opportunities lie in reducing delays between functions, aligning evidence requirements earlier, and acting on reliable information as it becomes available.

For pharmaceutical executives, this shifts the focus from technology deployment to operating model design. Leaders need to identify where development programs lose time, which decisions depend on fragmented information, and how connected AI systems could improve coordination across clinical, regulatory, manufacturing, and commercial teams. Investment priorities should reflect their potential to shorten the overall development timeline rather than simply accelerate individual tasks.

The transition also requires changes in governance, workforce responsibilities, and accountability. AI can continuously monitor evidence, identify emerging risks, and prepare recommendations, but qualified experts must retain authority over consequential clinical decisions. Organizations that establish clear decision rights, shared data standards, and measurable development outcomes will be better positioned to scale these capabilities.

The competitive implications extend beyond operational efficiency. Companies that generate stronger evidence earlier, make informed investment decisions faster, and reduce avoidable delays may reach regulatory and commercial milestones ahead of competitors. Established pharmaceutical companies have substantial advantages in proprietary data, scientific expertise, and development infrastructure, but realizing their value requires connecting assets that have traditionally operated in separate functions.

The strategic opportunity is to make clinical development more responsive to evidence without compromising scientific rigor or patient safety. For business leaders, success will depend on building organizations that can turn new information into coordinated decisions, allowing promising medicines to advance more efficiently while directing resources away from programs less likely to succeed.