Scimitar, Inc.
Tufts Center for the Study of Drug Development, Tufts University School of Medicine
sk any clinical operations leader what they need most when it comes to artificial intelligence (AI) and machine learning (ML), and you will hear a consistent answer: clarity. Not more solutions. Not more vendor pitches. Clarity.
In early 2026, the Partnership for Advancing Clinical Trials (PACT) consortium—facilitated by the Tufts Center for the Study of Drug Development (Tufts CSDD)—convened senior clinical operations leaders to recognize and address this challenge. What emerged was not just a framework for evaluating solutions, but a call for a fundamentally different way of thinking about AI/ML investment. The message was simple: Take a step back, apply structured analytical rigor, and start where the return on investment is clear. The taxonomy and value pyramid described in this article are grounded in that collaborative research: empirical frameworks derived from the structured analysis of real-world AI/ML investment patterns across PACT member organizations and validated against Tufts CSDD’s longitudinal benchmarking data spanning over 255 pharmaceutical and biotechnology companies.
The Decline of Monolithic SaaS—and What Comes Next
For decades, the Software-as-a-Service (SaaS) model shaped how biopharma companies bought and deployed technology. A handful of enterprise platforms—validated, compliant, and expensive—defined the landscape. That model is eroding rapidly, and the clinical space is no exception.
Coding time has collapsed exponentially. Fit-for-purpose, customized AI solutions that would have taken years and millions of dollars to build five years ago can now be prototyped in weeks. We are entering an era of use-case-led adoption where the question is no longer “Which enterprise platform do we license?” but “Which specific problem do we solve first, and how do we solve it well?”
This shift is not merely technological; it is organizational. As Tufts CSDD research has consistently shown, the adoption of transformative innovations in clinical research is painfully slow and highly inefficient. A 2023 study among 255 pharmaceutical and biotechnology companies found that recent innovations take each company an average of six to seven years to fully implement. Nearly all companies (90%) reported relying on a decentralized approach to promote adoption—meaning individual business units and functional teams independently pursued AI/ML uptake on their own timelines, without a coordinating enterprise governance structure or centralized AI Center of Excellence providing oversight and direction—and nearly eight out of 10 (78%) felt the process took much longer than expected.
A Taxonomy for Navigation
The most valuable contribution clinical operations leaders can make right now is not to identify the “best” AI solution but rather to apply a clear taxonomy for categorizing and making sense out of what is available. Through the PACT consortium’s analysis, a layered clinical AI stack has emerged that provides this structure. These five categories span the full trial lifecycle:
Protocol Intelligence and Design Optimization encompasses generative and predictive models that simulate trials before enrollment, optimize eligibility criteria, and reduce amendment rates. Companies have deployed AI feasibility engines to rank countries, sites, and principal investigators, with some reporting up to a 60% reduction in site identification cycle time.
Patient Recruitment and Enrollment covers patient matching, site forecasting, and diversity analytics. EHR-based patient matching, AI-driven outreach, and screen-fail prediction tools represent the most commercially mature investments in this category, with adoption accelerating rapidly in response to FDA diversity requirements.
Intelligent Trial Operations—ClinOps 2.0—encompasses study monitoring with agentic AI, TMF automation, risk-based monitoring, and automated medical coding. Risk-based monitoring tools are now widely deployed, with documented reductions in onsite visits of 40% to 50%. NLP-based medical coding has demonstrated 60% to 80% reductions in coding cycle time.
Data Science and Evidence Generation includes synthetic control arms, automated database lock, and radiomics. Synthetic controls carry perhaps the greatest long-term transformative potential—meaningful reductions in required patient enrollment—but remain under high regulatory scrutiny and are most viable today in rare-disease and oncology settings.
Pharmacovigilance and Regulatory Submissions is the final category, and it is moving fast. Real-time adverse event detection and generative AI for dossier drafting are gaining traction. Early adopters of LLM-assisted CSR and IND drafting report 40% to 60% time savings on first drafts, though validation frameworks remain nascent and regulatory acceptance continues to evolve.
Think in Layers, Not Platforms: The Value Pyramid
One of the most important reframes for clinical AI investment is recognizing that not all solutions operate at the same strategic level. A value pyramid model offers a practical organizing principle for sequencing and prioritizing investment.
Key benefits per layer: Infrastructure: data readiness and AI-ready workflows; Transactional: immediate ROI via cost reduction and throughput; Transformational: process reinvention and resource optimization; Strategic Data: competitive intelligence and decision support; Data Governance: regulatory compliance, audit trails, and durable enterprise value.
Above that is the Transactional Layer, where AI earns its most immediate and reliable return on investment: cost reduction and throughput improvement. Automated coding, protocol digitization, data cleaning automation, and database lock acceleration live here. These are “deploy now” investments: high readiness, high impact, with ROI that can be articulated clearly in any budget conversation.
As AI-enabled solutions become even more advanced, they move beyond processes where labor- and time-intensive activities are augmented. Solutions in the next level—the Transformational Layer—include content creation, such as documents, images, and programming that recreates and transforms legacy processes and operating practices. The ROI is even greater from these solutions as they generate streamlined processes and the reduction and replacement of capacity and resources.
The fourth tier is the Strategic Data Layer, where AI begins to enable a qualitatively different kind of intelligence: competitive benchmarks, CRO performance KPIs, delivery timelines, innovation signals, and the data that informs portfolio strategy. This is where AI moves from efficiency tool to decision-support capability.
At the apex is the Data Governance Layer: the hardest to build and the most durable in value. Cycle time monitoring, audit trail management, regulatory compliance tracking, and information control define this tier. Organizations with robust governance foundations will be better positioned as the FDA and EMA continue to develop and publish evolving guidance on AI/ML use in clinical trials.
Where Maturity and Investment Should Align
Not all solution categories are equally ready for deployment, and calibrating investment to maturity is one of the clearest differentiators between high-performing and struggling organizations in the AI/ML space.
Site performance prediction and risk-based monitoring are the most mature applications in the clinical AI landscape; in 2026, they are essentially standard industry practices. Sponsors not already leveraging these tools face a competitive disadvantage in cycle time and site performance. Automated medical coding and EHR patient matching follow closely, with meaningful adoption and documented business value.
Protocol simulation, digital endpoints, and protocol and clinical study report authoring are gaining ground but require more organizational readiness. Synthetic control arms and radiomics remain in an emerging maturity category despite their transformative potential; regulatory acceptance is the primary bottleneck, and organizations investing now are doing so as deliberate strategic bets, building expertise and audit trails in advance of broader acceptance.
A Roadmap for Cutting Through the Fog
The experience of building AI Centers of Excellence across the biopharma sector points toward a consistent set of success factors: a practical roadmap for moving from ambiguity to effective action in a way that respects both urgency and the principles of sound change management.
Start with governance before glamour. Establish a clear operating model with defined intake, prioritization, and portfolio management processes before selecting solutions. A standing AI Center of Excellence—with a leadership office for strategic oversight and a dedicated implementation team for delivery—provides the institutional backbone that individual pilots cannot. Without governance, accountability becomes diffuse, and proof-of-concept tools fail to convert into production-grade deployments.
Invest in the base of the pyramid. Data standardization, infrastructure integration, and workforce capability development are not optional prerequisites; they are the rate-limiting step. The most sophisticated AI algorithm is only as valuable as the data pipeline it relies on. Broad staff up-leveling, not just for technical teams but for clinical operations functions, dramatically improves adoption rates and the durability of AI as an enterprise capability.
Adopt a “rapid pilot” posture and resist the temptation of the “big bet.” Four-to-six-week pilots demonstrate ROI 40% faster than large-scale rollouts, and they surface integration requirements and governance needs before commitments become expensive. The clinical AI market is moving quickly enough that flexibility is itself a competitive advantage. Be especially wary of large, enterprise-wide investment decisions before pilot data exists.
Build human-in-the-loop design into every generative AI application, particularly those touching regulatory submissions. Maintaining meaningful human oversight is not a concession to risk aversion; it is a competitive differentiator. Companies building explainability and audit trails into their AI workflows now will be first movers as regulatory expectations formalize.
Finally, treat adoption—not deployment—as the primary goal. As Tufts CSDD research has made clear, the phrase “if you build it, they will come” does not hold in clinical research. Change management—communications, incentive alignment, grassroots buy-in at the function level—must be embedded in every AI/ML initiative from day one, not retrofitted after utilization falls short. Decentralization of ownership to those who bear executional risk is not a failure of central strategy; it is a necessary stage of mature deployment.
The Right Investments, Not the Most Investments
The fog of AI/ML support in clinical research will not lift on its own. It clears when leaders apply the discipline to categorize, prioritize, and sequence investments with the same rigor they bring to their clinical programs. The taxonomy exists. The maturity signals are visible. The pyramid provides a practical organizing principle that converts strategic ambiguity into a structured and effective plan.
The companies that will look back on this period as a turning point are not those that invested most aggressively. They are those that invested most thoughtfully: starting at the base, capturing near-term wins in the transactional layer, building toward strategic intelligence, and governing it all with clarity and accountability.
The fog clears one layer at a time.
References available upon request.