CRO Selection in the AI Era

Why the Old CRO Scorecard No Longer Works

For most of the last two decades, sponsors have selected CROs on a fairly consistent scorecard: therapeutic area depth, prior experience with the indication, site network in target geographies, project management discipline, quality track record, pricing, and cultural fit. Those criteria still matter. They are also increasingly insufficient. The reason is that clinical operations at large CROs is now a technology-mediated activity, and the technology is moving fast.

The Association of Clinical Research Organizations reported that its 2026 survey of member companies found AI deployment across feasibility assessments, site selection, protocol optimization, clinical research associate support, data management, and content authoring, with adoption for data management optimization reported at 71% and AI-enabled site workflows at 64%.¹ That penetration means the choice of CRO is no longer just a choice about people and process. It is a choice about which AI systems will be running in the background of your study, which vendors those systems depend on, how models were trained, and what happens to your data when it flows through them.

The economics reinforce the point. Tufts Center for the Study of Drug Development analysis has estimated that a single day of clinical trial delay represents somewhere between $500,000 and $800,000 in lost prescription revenue for a program with meaningful commercial potential, and that AI-enabled work across 36 distinct clinical development activities delivered an average 18% cycle time reduction in sponsor reports, with the single highest-impact use case (identifying targeted patient communities) showing time savings of 68%.² A CRO that materially accelerates enrollment, data lock, or medical writing is not simply nicer to work with. It is measurably worth more per study.

The counter-argument sponsors sometimes raise is that AI in clinical operations is still early, still noisy, and still overpromised, and that basing selection decisions on it risks paying for capability that does not yet reliably deliver. That argument has real merit at the tool level. Individual GenAI drafting tools are uneven, individual model outputs need human review, and CRO marketing claims still outrun operational reality in many pockets of the industry. But that is the wrong altitude at which to make a selection decision. At the enterprise level, whether a CRO is investing in a unified data platform, a governed model inventory, and integration standards is not speculative. Those investments either exist or they do not, and their existence is a strong predictor of how quickly the CRO will convert the next wave of tool-level advances into operational advantage on your study.

The other pattern to notice is the shape of the industry consolidation that AI is accelerating. As Clinical Leader has documented in its CRO industry outlook, the leaders are re-positioning as strategic co-developers rather than transactional vendors, integrating technology, therapeutic expertise, regulatory intelligence, and global operations into a single offering.¹² Sponsors that continue to treat CRO selection as a commoditized bid process are systematically pricing themselves out of the top tier of that market. Not because those CROs are more expensive on paper, but because they are prioritizing sponsors that can engage upstream and use the capability well.

The competitive dynamic among CROs has shifted accordingly. As one industry analysis has noted, AI-enabled CROs win by engaging sponsors during protocol planning rather than at the RFP stage, using early data-driven feasibility work to shape trial design and convert to preferred provider status.⁴ Sponsors that continue to run classic RFP-driven selection processes without room for those upstream conversations are systematically de-selecting the CROs that have invested most in AI.

The New Selection Criteria for 2026

The traditional CRO scorecard does not need to be thrown out. It needs to be extended. In our experience working with pharma and biotech sponsors on operational decisions, the criteria below now belong alongside therapeutic area experience and site network in every selection conversation.

AI-Assisted Operations

The concrete question is what a CRO actually uses AI for on your study, not what it claims to use AI for on its marketing site. Four use cases now have enough traction to be table-stakes questions: protocol optimization (analyzing historical enrollment and eligibility data to identify criteria that will slow recruitment), site selection AI (using site performance data, patient populations, and operational trends to rank candidate sites), risk-based monitoring analytics (real-time detection of anomalies, high-risk sites, and data quality signals), and generative AI for medical writing (informed consent forms, clinical study reports, patient narratives, and literature reviews).

IQVIA has publicly described a Clinical Data Review Agent that reduces data review timelines from roughly seven weeks to roughly two weeks and a Literature AI capability that reduces literature review times by over 75%.⁵ Those specific numbers matter less than the operational pattern they represent. If your candidate CROs cannot describe what they use AI for, at what step, and with what measured impact, they are not yet operating at the same altitude as the market leaders.

Data Platform Capabilities

AI is only as useful as the data plumbing beneath it. A meaningful platform capability includes unified data models across EDC, eSource, safety, and operational systems; a defined layer for AI features and inference; documented data retention and lineage; and clean APIs that sponsors can actually use. Suvoda has noted that CROs often manage clinical trials with twenty-five or more distinct solutions across planning, startup, conduct, closeout, and insights, and that a shared data layer is what turns that ecosystem from an integration burden into a strategic asset.⁶

Integration Architecture with Sponsor Systems

The old model was to accept whatever data cut a CRO could deliver at study end. The new model requires ongoing, secure data flow between CRO platforms and sponsor safety, regulatory, medical, and analytics systems throughout the study. That means the CRO needs to speak the sponsor’s integration language: CDISC standards, USDM for machine-readable protocols, event-driven APIs where appropriate, and clear guidance on what identifiers and metadata will accompany each data element.

AI Governance Transparency

Sponsors remain responsible for trial conduct under Good Clinical Practice even when a CRO or its vendors deploy AI-enabled systems on their behalf.⁷ That accountability does not delegate cleanly. If your CRO cannot show you a model inventory, describe how model performance is monitored in production, document who reviews model outputs before regulatory-relevant decisions, and describe how change control works when a model is retrained, you inherit that missing governance the moment you sign the contract.

IP Protection for Sponsor Data

Perhaps the most under-negotiated issue in 2026 is how sponsor data flows through CRO AI systems and third-party AI vendors. Clinical trials involve investigational product data, interim results, and proprietary research methods, and the introduction of AI tooling can create confidentiality risks if that information is fed into shared models without clear contractual restrictions.⁸ Sponsors need explicit language about whether their data is used for model training, whether other sponsors will benefit from what a model learns on their study, and what happens to embeddings and derived features when the contract ends.

AI Validation Posture

Under ICH E6(R3), which becomes a UK legal requirement in April 2026 and is being adopted by the FDA, sponsors must implement a documented quality management system, oversee third-party providers, and ensure that computerized systems (including cloud databases, randomization tools, and eConsent platforms) go through rigorous validation.⁹ AI systems used in trial conduct are computerized systems. A credible CRO should be able to describe its AI validation framework in the same breath it describes its EDC validation.

A useful lens for putting all six criteria together is to ask what the CRO’s “AI resume” looks like across a full study lifecycle. That resume should read at four moments: at protocol design, where AI shapes eligibility and endpoint choices against enrollment feasibility; at startup, where site selection AI drives site rank and activation sequencing; during conduct, where RBM analytics and central data review surface signals to operations and quality; and at closeout, where GenAI accelerates medical writing, narrative generation, and regulatory dossier assembly. A CRO that has a coherent story at each of those four moments, with named tools, measurable outcomes, and clear human oversight, is meaningfully different from one that has strong marketing on AI in the abstract.

A Modernized CRO Evaluation Matrix

The evaluation matrix below is a starting template, not a final scorecard. Weights should be adjusted to reflect study complexity, therapeutic area, and portfolio strategy. What matters is that AI-era criteria are represented as first-class dimensions rather than tucked into a “technology” catch-all worth 5%.

Sponsors sometimes push back that adding AI-era dimensions dilutes therapeutic area weight. In practice, elevating these dimensions makes the therapeutic area weight more meaningful, because it separates CROs that talk about therapeutic experience from CROs that operationalize that experience through data and models. A vendor with three prior studies in the indication and a credible AI-assisted feasibility engine is a materially different partner than a vendor with three prior studies and a spreadsheet.

An AI-Focused RFP Question Bank

The question bank below is intended to be inserted into an existing RFP structure. It is deliberately concrete. Vague questions produce vague answers, and vague answers protect vendors that have not yet done the work.

Protocol Design and Feasibility

• Describe the AI or advanced analytics tools you use to assess protocol feasibility. What data sources feed those tools? What percentage of eligibility criteria and endpoints do you review through them?
• Provide two anonymized case examples where AI-informed feasibility work materially changed inclusion or exclusion criteria, and quantify the enrollment impact.
• Which USDM-aligned or CDISC-aligned digital protocol capabilities do you support?

Site Selection and Startup

• Describe the model or platform used to rank candidate sites. What features are used? How often is the model retrained, and on what data?
• How do you validate model recommendations against on-the-ground site knowledge? Who has final say?
• What measured impact have you observed on time to first patient in and enrollment velocity from AI-enabled site selection?

Trial Conduct and Risk-Based Monitoring

• Which RBM analytics platform do you use? What KRIs and QTLs are monitored via AI or ML models? Who reviews signals, and on what cadence?
• Describe how anomaly detection outputs are triaged and closed out. What happens when the model disagrees with the CRA in the field?
• How is model performance monitored in production, and how are drift or degradation events handled?

Medical Writing and Content Generation

• Which GenAI tools do you use for medical writing (informed consent, protocol amendments, CSR sections, patient narratives, literature reviews)?
• Describe your human-in-the-loop review process for AI-generated content. Who signs off, and against what checklist?
• How are hallucinations, fabricated citations, and inconsistencies with source documents detected before content leaves your organization?

Data, IP, and Model Governance

• List every third-party AI or ML vendor whose systems will process sponsor data on this study. Include the data categories, the purpose, and the geographic locations of processing.
• Will sponsor data be used to train models that are then used to serve other sponsors? If so, describe the isolation, anonymization, and opt-out terms available.
• Describe your model inventory. For AI systems in the critical path of this study, provide the name, purpose, data inputs, training data source, validation approach, and monitoring approach.
• Describe your validation approach for AI-enabled computerized systems relative to ICH E6(R3), 21 CFR Part 11, and the FDA’s 2025 draft guidance on AI in regulatory decision-making.

A CRO AI Capability Maturity Model

Not every study needs the highest level of CRO AI maturity, and not every sponsor should pay for it. A rare-disease Phase 1 dose-escalation study has very different needs than a global Phase 3 registration trial or an oncology basket study. The maturity model below is intended to help sponsors quickly locate a candidate CRO on a spectrum and match maturity to study needs.

Sponsor Data, IP, and the AI Reuse Problem

The single most avoidable failure mode in CRO selection today is signing a master service agreement that does not clearly address what happens to sponsor data inside AI systems. Traditional clinical trial agreements have decades of case law and practice around IP, publication rights, and data ownership. AI-era data flows create genuinely new questions that many older contract templates do not answer.

Legal commentary on AI in clinical trials has identified a set of contractual issues that need explicit treatment: whether AI tools are permitted to process sensitive investigational product data, whether outputs of AI processing are owned by sponsor or vendor, whether models trained on sponsor data can be used for other sponsors, and how confidential information persists (or does not) in model weights, embeddings, and vector stores.¹⁰

The most important consequence of getting these terms right is not litigation protection. It is that the negotiation forces both sides to make the underlying data architecture explicit. Many CROs discover during MSA negotiations that they cannot answer the sub-processor question or the model persistence question because those answers do not yet exist internally. That is useful information. It tells the sponsor what maturity level the CRO is actually at, regardless of what the RFP said.

There is also a subtler dynamic that emerges over time. A CRO that operates a shared AI infrastructure across many sponsors accumulates something valuable: a body of operational data on what works, at which sites, with which populations, under which protocols. That accumulated intelligence is precisely what makes AI-enabled CROs faster and better at each subsequent study. It also means that every sponsor is, in effect, contributing to a shared learning system whose benefits are unevenly distributed. Sponsors do not necessarily need to prevent this dynamic (it is often net positive), but they do need to be conscious of it. The right question is not whether the CRO learns from your data; it is which parts of that learning should stay confined to your program and which parts you are comfortable letting flow into the shared system, on what terms, and at what point in the study.

A practical middle path that several sophisticated sponsors have adopted is a tiered data-use framework in the MSA. Aggregate operational telemetry (site enrollment velocity, query cycle times, monitoring visit efficiency) is contributed to the CRO’s shared learning system by default, since the value of that pooling flows back to every sponsor over time. Study-specific clinical data (subject-level EDC contents, adverse events, endpoint measurements) is walled off by default and cannot be used to train or fine-tune models that serve other sponsors. Anything in between (site quality signals, protocol amendment patterns, safety trends by therapeutic area) is negotiated explicitly. That framework is simple enough to hold up under audit and flexible enough to work across a portfolio.

Governance, Validation, and the Regulatory Envelope

The regulatory envelope around AI in drug development is finally clarifying. The FDA’s January 2025 draft guidance on AI to support regulatory decision-making introduced a seven-step, risk-based credibility assessment framework, and in January 2026 the FDA and EMA jointly published aligned principles for the use of AI in medicine development.¹¹ That is a meaningful signal to sponsors and CROs: the regulators want to see model context of use, model risk, validation activities, and human oversight documented in a form they can evaluate.

At the same time, ICH E6(R3) sharpens the sponsor’s oversight obligations for CROs and other third parties, expects a documented quality management system, and expects computerized systems (including AI-enabled ones) to be validated with cybersecurity considerations addressed.⁹ These two threads converge on a single practical requirement: sponsors need to know what their CRO will do to make its AI systems inspectable, both by the sponsor and by regulators, throughout the study.

The concrete governance artifacts sponsors should ask to see include a model inventory scoped to the study, model risk classification with a stated context of use, validation evidence proportionate to risk, a monitoring plan with defined performance thresholds, a change control procedure for model updates, and a documented human oversight model that names the roles that review model output before regulator-relevant decisions.

The FDA’s own AI credibility framework asks a structured set of questions about model context of use, model risk, credibility evidence, and residual uncertainty.¹⁴ Sponsors can borrow that structure directly for CRO oversight. For each AI system in the critical path of a study, the sponsor should be able to state the model’s context of use in one paragraph, its risk classification, the evidence supporting its use, the residual uncertainty, and the human oversight applied. If the CRO cannot provide the inputs and the sponsor cannot produce that one-paragraph summary, that gap is worth closing before the study opens rather than during an inspection. The good news is that CROs at maturity Level 3 and above already produce most of these artifacts as a matter of course; the effort for the sponsor is mostly a matter of asking for them and reading them, not standing up new infrastructure.

Operationalizing the New Selection Process

Adopting AI-era selection criteria is straightforward. Operationalizing the selection process to actually use them is the harder work, because it touches how sponsors run RFPs, who sits on evaluation panels, and how vendor management is structured after signing.

Move Evaluation Earlier

The competitive dynamic has already shifted upstream. CROs that invest in AI-driven feasibility engines are engaging sponsors during protocol conception, not at RFP release.⁴ Sponsors who want to see the best a CRO can do should structure early strategic conversations before formal solicitation, using those conversations to inform protocol design and to observe CRO thinking under real conditions.

Expand the Evaluation Panel

An evaluation panel drawn only from clinical operations will systematically underweight the criteria that matter most in the AI era. Adding permanent seats for data management, clinical IT or digital operations, quality, and privacy or information security legal counsel improves both the selection outcome and the CRO’s understanding that these dimensions actually matter.

Build in AI-Specific Diligence

Alongside financial and quality diligence, an AI diligence workstream is warranted for material studies. That workstream reviews model inventories, validation packages, sub-processor lists, and the CRO’s incident and near-miss history for AI-related events. Some of this can be structured as a workshop with the CRO’s AI leadership rather than a document request, which surfaces cultural signal that documents miss.

Make Governance a Post-Signature Discipline

The selection process is only the front end. Ongoing CRO governance should include a scheduled review of AI-related changes on the study, updates to sub-processor lists, model performance monitoring outputs relevant to the study, and any regulatory correspondence involving AI-enabled activities. Adding these as standing agenda items in existing sponsor-CRO joint operating meetings costs little and prevents surprise.

Right-Size the Effort

Not every study deserves the full treatment above. For small early-phase studies with a well-understood CRO, the modernized criteria may inform a lightweight decision without a full RFP. The point is not to add process; it is to add altitude. A sponsor that reflexively runs the same procurement process it used five years ago is systematically underinformed about what is now being bought.

Rewire Vendor Management After Selection

Selection sets the direction; vendor management determines whether the direction is actually followed. In an AI-enabled trial, vendor management needs three additions that most sponsor operating models do not yet have. First, someone on the sponsor side needs to own the AI relationship, with authority to ask model-level questions and read the CRO’s AI governance artifacts. Second, the joint operating committee needs a standing agenda item on AI-related change (new sub-processors, retrained models, updated GenAI tools in medical writing) so that changes are surfaced rather than discovered. Third, escalation paths need to include AI-specific failure modes: material model drift, sub-processor incidents, and regulator inquiries about AI-enabled activities. None of this is heavy. All of it is missing from most current vendor management playbooks.

Treat the First Study as a Diagnostic

Every new CRO relationship is also an early-warning system for that CRO’s AI maturity. The first six months tell you a great deal about whether the AI capabilities described in the RFP are real, whether the model outputs are actually reviewed, whether integrations work when data volumes increase, and whether the AI governance narrative holds up under real conditions. Sponsors that build a lightweight “AI readback” into the first quarterly business review (What did your AI systems do on our study? What did they miss? What changed?) convert that early period into a genuine diagnostic rather than a warm-glow relationship-building phase. Studies that go badly rarely go badly because sponsors did not ask hard questions on day one. They go badly because sponsors stopped asking them after day 90.

Conclusion

The core insight for sponsors is that CRO selection in 2026 is no longer a choice about people and process alone. It is a choice about which data architecture, which models, which vendors, and which governance posture will run in the background of a study, often for years. The traditional criteria still apply. But they are wrapped inside a new set of decisions that determine speed, quality, cost, and risk in ways that a therapeutic area track record alone cannot predict. A modernized evaluation matrix, a concrete RFP question bank, and a maturity model that matches CRO capability to study need are the practical instruments for making those decisions well.

Sakara Digital works with pharma and biotech organizations that are rethinking how they evaluate, govern, and operate with contract research partners in an AI-first environment. If you are updating your CRO selection framework, negotiating a new master service agreement, or building the internal governance capability to oversee AI-enabled trial conduct, and you want an independent perspective grounded in data quality, AI governance, and clinical operations, we are happy to have that conversation.