Estimated reading time: 8 minutes
In early June 2025, the Food and Drug Administration (FDA) launched Elsa, an internal generative AI tool designed to support scientific review, inspection readiness, and core regulatory workflows. Elsa represents a significant step in the agency’s broader effort to modernize how it processes, analyzes, and assesses regulatory information.
For the FDA, Elsa enables its reviewers to process information more quickly and surface inconsistencies more readily, which means sponsors, contract research organizations (CROs), and research sites should expect faster-paced regulatory interactions, increased scrutiny of documentation quality, and a greater emphasis on clarity, consistency, and data traceability across all submission materials.
By understanding how Elsa fits into the FDA’s modernization strategy and the practical steps organizations can take now, sponsors, sites, and CROs can better prepare for AI-enabled regulatory workflows and align with evolving agency expectations.
What Elsa is and how it works
Elsa is a secure, FDA-only generative AI tool designed to summarize, analyze, and categorize regulatory documents. The FDA built Elsa in a protected environment that prevents exposure to, or training on, confidential sponsor data. Elsa supports FDA reviewers by accelerating time-intensive tasks without replacing human judgment and decision-making authority:
- Elsa enables reviewers to quickly synthesize large volumes of submission content, allowing earlier identification of unclear narratives across related documents.
- It supports more consistent internal review workflows across divisions and review teams.
- The tool reinforces the FDA’s focus on efficiency without altering regulatory standards, increasing the importance of well-organized, clearly articulated submissions.
As we begin to understand what Elsa does, it is equally important to clarify its boundaries and limitations to avoid misconceptions about how AI fits into regulatory decision-making.
What Elsa is not
Elsa does not serve as a regulatory decision-maker. It does not evaluate product efficacy, safety, or risk. The system is not trained on proprietary industry submissions and does not replace human scientific review or regulatory oversight. For example, Elsa does not:
- Make approval, rejection, or labeling decisions independent of FDA reviewers.
- Substitute for clinical judgment, benefit-risk assessment, or expert interpretation of data.
- Eliminate the need for complete, compliant, and well-supported regulatory submissions.
Understanding these limitations will help organizations focus their preparation efforts on improving documentation quality and review readiness rather than attempting to optimize for automation alone.
Early implications for sponsors, sites, and CROs
Because Elsa can rapidly summarize documents and highlight inconsistencies, sponsors, sites, and CROs may face more targeted regulatory questions, quicker turnaround expectations, and increased focus on documentation accuracy and alignment across protocols, reports, and submission components:
- Regulatory feedback may become more specific and data-driven, with questions tied directly to discrepancies or unclear rationale surfaced during AI-assisted review.
- Review timelines may compress as reviewers spend less time manually locating information and more time evaluating content quality and coherence.
- Organizations with fragmented documentation practices may experience higher follow-up burden as inconsistencies are identified earlier in the review process.
AI-supported internal review may also shift expectations toward more structured and standardized submissions that support both human and machine-assisted analysis. As AI accelerates regulatory workflows, organizations should anticipate closer scrutiny of data provenance, audit trails, and overall process transparency.
These implications underscore the need for proactive preparation across sponsors, sites, and CROs, beginning with submission strategy and governance.
What sponsors need to do to prepare for Elsa
Sponsors should strengthen submission quality and document structure to support AI-assisted regulatory review. Standardized formats, headings, terminology, and labeling conventions across submission components reduce variability and improve clarity:
- Consistent document structure helps reviewers and AI tools trace concepts across protocols, analyses, and reports without unnecessary interpretation.
- Clear organization reduces the risk that key rationale or context is overlooked during automated summarization.
- Harmonized terminology supports alignment across clinical, statistical, and regulatory narratives.
Eliminating contradictions or duplicative content will help prevent issues that AI tools may quickly surface for reviewer follow-up. Clear summaries, rationale statements, and cross-references allow AI-assisted reviewers to follow submission logic more efficiently:
- Cross-document consistency reviews reconcile overlapping narratives between protocols, statistical analysis plans (SAPs), clinical study reports (CSRs), and labeling before finalization.
- Explicit rationale sections explain design decisions, deviations, and analytical choices and reduce ambiguity in automated review outputs.
Sponsors should enhance data integrity and traceability with tighter governance across datasets, analyses, and reports, ensuring every data point traces back to its source system. Improved alignment across protocols, SAPs, CSR narratives, and labeling reduces the risk of discrepancies, while clear version-control practices help prevent inconsistencies across shared documents.
Preparation for faster, more targeted regulatory questions is also essential. Regulatory teams should be trained to expect accelerated queries informed by AI-generated summaries:
- Establishing defined rapid-response workflows helps regulatory teams coordinate efficiently across functions when questions arise.
- Clear ownership and escalation paths support timely, consistent responses during compressed review cycles.
- Internal readiness exercises boost confidence and reduce delays when addressing AI-highlighted issues.
Internal playbooks for rapid-response workflows can help organizations avoid delays during review cycles, and internal AI tools can pre-screen submissions for inconsistencies or unclear narratives before FDA submission:
- Submission playbooks operationalized across medical writing, regulatory, and data teams support coordinated response processes.
- Internal analytics or AI-enabled review tools flag narrative gaps, conflicting statements, or weak justifications.
- Common regulatory questions and resolution patterns tracked over time support continuous improvement in submission quality and response efficiency.
Building internal literacy around AI-supported review further strengthens readiness. Cross-functional teams (e.g., regulatory, clinical, data science, biometrics, etc.) benefit from education on how Elsa works and which signals it may elevate during review. Still, human validation remains essential, even as AI accelerates early triage of review topics. Experimentation with AI summarization tools helps teams emulate reviewer workflows and identify potential issues earlier.
A similar focus on readiness and documentation discipline extends to research sites, which play a critical role in inspection outcomes.
What sites need to do to prepare for Elsa
Research sites should strengthen documentation quality and consistency.
Complete, accurate, and easily navigable source documentation, case report form entries, and deviation logs support AI-assisted inspection readiness. Consistent terminology across institutional review board (IRB) and institutional biosafety committee (IBC) documents, informed consent forms, and site-level regulatory binders improves clarity, while reducing ambiguity in site reports and correspondence helps prevent issues that AI-assisted inspection targeting may surface more quickly.
Sites should enhance inspection readiness as AI-supported inspections evolve. Preparation for AI-supported FDA inspections includes anticipating a focus on anomalies or patterns identified through Elsa-assisted analysis. Internal audits should target areas where documentation may not align with protocol requirements. Strong corrective and preventive action (CAPA) documentation will help sites withstand increased scrutiny:
- Internal audit programs expanded to identify documentation trends or deviations proactively can reduce AI-assisted inspection focus.
- CAPA records with clear root-cause analysis, corrective actions, and effectiveness checks support defensible inspection readiness.
- Contemporaneous, defensible documentation maintained by trained staff supports inspection transparency.
Improving data entry quality and traceability also remains critical. Timely, accurate, and complete electronic data capture (EDC) entries reduce the likelihood of data discrepancies. Clean, properly documented audit trails support inspection readiness, and close alignment with sponsors and CROs on documentation expectations helps minimize downstream regulatory risk, resulting in clear ownership, faster issue resolution, less rework, and fewer delays across partners.
Sites should also boost staff readiness and training. This training should reinforce the importance of clarity, consistency, and documentation hygiene in an AI-supported review environment.
As regulatory review evolves and sites play an increasingly important role in supporting the overall regulatory narrative, site personnel should anticipate quicker follow-up questions from sponsors responding to AI-highlighted concerns:
- Clear escalation pathways enable sites to respond efficiently when sponsors request rapid clarification or documentation updates.
- Defined roles and responsibilities help ensure consistent, accurate responses across regulatory, clinical, and administrative staff.
- Timely coordination with sponsors and CROs supports alignment and reduces delays during accelerated regulatory interactions.
And like sites, CROs—as coordinators across sponsors and sites—must also adapt their operating models to support AI-enabled regulatory expectations.
What CROs need to do to prepare for Elsa
CROs should elevate cross-functional documentation quality by ensuring alignment across medical writing, data management, clinical operations, and regulatory submission preparation:
- Strong governance across functional teams helps ensure consistent narratives throughout study deliverables.
- Centralized oversight reduces variability introduced across programs and sponsors.
- Clear accountability supports timely resolution of documentation issues before submission.
Standardized templates and structured authoring practices help minimize variability across sponsor projects, but establishing internal quality-control workflows helps check for any inconsistencies that AI tools may flag during review.
Adopting AI-enabled quality checks that mirror FDA workflows further strengthens readiness. By piloting internal AI tools, CROs can summarize protocols, safety narratives, monitoring reports, and clinical study reports to identify issues before submission.
Machine learning-supported review tools help test clarity, detect contradictions, and strengthen regulatory packages, and AI-informed insights allow sponsors to proactively address potential reviewer concerns.
CROs should also strengthen data management and monitoring practices. Improved central monitoring strategies help detect trends or anomalies that AI-assisted regulators may also note by:
- Identifying protocol deviations across sites earlier in the study lifecycle.
- Detecting data patterns that may warrant additional oversight.
- Prioritizing monitoring activities based on risk signals.
Enhanced risk-based monitoring systems ensure that high-risk data remains complete, accurate, and well-documented, while audit-ready documentation and metadata hygiene reinforce regulatory readiness across study systems by supporting traceability across platforms, enabling efficient data reconciliation, and boosting inspection confidence.
Preparation for accelerated regulatory interactions is equally important. Project managers, regulatory specialists, and medical writers should be trained to expect quicker turnaround between reviewer feedback cycles, and their communication strategies should support sponsors when AI-generated inquiries require rapid clarification. This cross-team agility will help reduce bottlenecks and support efficient responses to evolving regulatory expectations.
Navigating the shift to faster, AI-enabled regulatory oversight
As the FDA integrates AI into its regulatory processes, early preparation becomes increasingly important. Elsa represents a shift toward faster, more consistent internal review that rewards clarity, structured documentation, and strong governance.
Early investments in documentation discipline will reduce downstream regulatory friction, strong oversight and interoperability will support consistency across the clinical research lifecycle, and proactive readiness will position organizations to adapt as AI-enabled review continues to mature.
With systems becoming more interconnected, review cycles more compressed, and expectations for clarity more exacting, sponsors, sites, and CROs that invest early in documentation quality and AI literacy will find themselves better positioned for smoother regulatory interactions amid the continued evolution of AI-enabled review.
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