Pharma and biotech companies have access to an unprecedented variety of health data, from electronic health records and medical or pharmacy claims to genomic, behavioral and sensor data. Yet it can be difficult to determine which data sources are fit-for-purpose, so few firms are maximizing the return on their data investments. Even the most sophisticated organizations often face barriers when transforming raw data into meaningful real-world evidence (RWE), while smaller biotechs may still be building the capabilities needed to keep pace.
As analytic tools mature and datasets become richer and more connected, AI is finally giving teams the clarity and speed they’ve been waiting for. These new capabilities can help both large and emerging organizations unlock new insights, act with greater clarity and close long-standing evidence gaps.
“For a long time, data volume grew faster than our ability to learn from it,” says Holly Henderson, Global Head of Real World Late Phase and RWE at Syneos Health. “AI changes that by helping teams re-imagine how evidence is generated and applied. It brings forward insights that allow people to work with far more confidence and precision than they could in the past.”
By processing large, messy datasets and uncovering patterns that are time prohibitive in traditional analytics, AI is changing what’s possible and helping teams make faster, more confident decisions across four stages of the drug lifecycle: early development, clinical development, launch and market access, and ongoing commercialization.
Here’s what that process looks like:
Applications in early development: de-risking discovery and design
The earliest stages of development demand some of the most consequential choices, such as which indication to pursue, how to select translational models and biomarkers, how to design initial trials and when to advance or halt an asset. Using AI that analyzes high-quality RWD allows teams to examine patient experiences, condition prevalence, genomic drivers, comorbidity patterns and treatment gaps before assets move into the clinic.
This deeper context helps companies:
- Identify the most responsive or highest-need patient populations
- Predict early safety signals based on real-world tolerability patterns
- Prioritize viable indications with stronger biological or clinical rationale
- Understand disease heterogeneity at a granular, subpopulation level
“When we combine real-world genomic data with mechanistic insights, we can identify the patient subpopulations most likely to benefit or be harmed,” says Pierre Meyer, Director, Real World Evidence Strategy at Syneos Health. “It’s one of the clearest examples of AI and RWE directly improving patient targeting.”
These insights even extend beyond traditional RWD into linked genomic, proteomic and other molecular datasets. Teams can explore mechanistic pathways, disease progression signatures and predicted drug sensitivities across real-world cohorts.
For emerging companies, this earlier visibility strengthens investor confidence and helps avoid costly late-stage pivots. As Henderson observes, “If you can use data to understand safety signals or identify the right patient population before a trial even begins, that fundamentally changes investor confidence and development strategy. We’re seeing real-world evidence become essential to that calculus.”
Applications in clinical development: driving smarter, faster trials
Once an asset reaches Phase II or III, every operational choice like site selection, patient enrollment tactics, eligibility criteria and protocol design can influence timelines and cost. AI-enabled RWE provides a more informed view of how a trial may perform in practice, giving teams the information they need to plan with greater accuracy.
By integrating real-world patient prevalence, referral routes and treatment patterns, AI helps identify sites with the strongest potential to enroll the intended population. Predictive models can forecast enrollment with greater precision by accounting for dropout trends, competing studies and barriers that differ across regions.
Automated monitoring also flags anomalies in safety data or protocol adherence earlier, proactively allowing teams to correct issues before they compromise timelines or data quality.
As Henderson explains, “Operationally, evidence allows us to model what will actually happen at a site, not what we assume will happen. Knowing how patients are truly treated, where protocols may conflict with real practice, or where competition may cannibalize enrollment can save months and millions in research budgets.”
AI also strengthens the design process itself. Teams can compare protocol assumptions to real-world practice, ensuring eligibility criteria and visit schedules align with how care is delivered. This reduces the risk of designing a trial that is theoretically strong but impractical in execution.
Meyer highlights why AI improves these decisions: “Machine learning allows teams to consider far more variables than a person can manage at once. When the reality of clinical practice does not match intuition, these models help uncover patterns that support stronger choices.”
Applications in launch and market access: connecting clinical insight to market performance
As products approach approval and launch, the evidence questions shift from clinical performance to market dynamics. Teams need to understand how real patients move through the healthcare system, how clinicians make decisions in practice and how payers evaluate value and access. AI-enabled RWE provides a clearer picture of these dynamics at the exact moment they matter most.
By analyzing treatment patterns, comorbidity trends, geographic variation and referral behaviors, AI helps organizations identify where early adoption is most likely to occur and where barriers may emerge. These insights can guide payer strategy, refine field deployment and strengthen the value story by translating trial results into real-world context.
“Once you move outside the controlled environment of a trial, the way patients receive care can vary significantly,” says Ashley Brenton, Vice President, RWE at Syneos Health. “AI-powered RWE helps teams understand those variations and shape launch strategy with a level of precision that is difficult to achieve through traditional analytics.”
This evidence also supports key market access activities, from anticipating formulary decisions to strengthening cost-effectiveness arguments for health technology assessments (HTAs). By linking real-world utilization, adherence patterns and safety outcomes, companies can continuously refine value communication, identify priority populations and ensure resources flow to the accounts where adoption potential is highest.
Meyer notes that at this stage, continuous data integration is the key to launch success: “Continuous learning is much harder in healthcare than in industries like streaming media or retail, because our data is fragmented and collected for different purposes. To make AI successful, we need partnerships that expand and connect datasets. That foundation is what enables real continuous improvement.”
Applications in continuous learning: building a flywheel of evidence
Perhaps the greatest promise of AI-enabled RWE lies in its ability to connect insights across the entire lifecycle. Each new data input, like clinical data, safety outcomes, payer behavior or real-world utilization, strengthens the models used to guide decisions.
With unified evidence, teams can adjust portfolio strategy based on real-time performance, identify new subpopulations for label expansion and refine clinical assumptions using post-market behavior. Market access, commercial and pharmacovigilance teams can respond more quickly to payer shifts, emerging safety patterns, or regional differences in adoption.
Over time, teams can benefit from a flywheel effect where evidence informs strategy, and strategy guides what evidence is generated next. Sustaining that momentum requires alignment across teams and clarity about how insights flow from one stage to another.
“Integrating evidence across the continuum requires education, because each stage has different owners and priorities,” remarks Henderson. “Our role is helping teams see how insights generated at launch can inform early development, or how clinical insights should shape market access. Connecting those dots is what turns RWE into a continuous learning system.”
When organizations embrace continuous learning, evidence generation becomes an always-on capability rather than a series of isolated analyses. AI plays the central role in enabling this system, helping teams move from static, point-in-time studies to a responsive insight engine that can evolve alongside the product, the market, and the patients it serves.
Building an evidence engine for the future
AI is redefining how pharmaceutical companies learn from the real world, enabling faster, more confident decisions from discovery through commercialization. By transforming raw data into evidence and evidence into action, organizations can move with greater precision and agility at every stage of development.
“Teams have always had access to large amounts of data, but the real challenge has been turning that information into something meaningful,” explains Henderson. “We are finally at a point where analytic methods and data quality allow us to see what was previously hidden.”
The companies that succeed will be those that build continuous, AI-enabled evidence systems rather than rely on one-off analytics, and those that invest early in the data foundations required for trustworthy insight. To do so effectively, pharma and biotech organizations should engage experienced partners who can help design and implement sustainable real-world evidence engines by integrating fit-for-purpose data, governance frameworks, and analytical capabilities that evolve with the portfolio and regulatory landscape.
To learn more about the Real World & Late Phase Research capabilities at Syneos Health, visit www.syneoshealth.com/real-world-evidence.
