Estimated reading time: 7 minutes
The FDA is now using AI for inspections, and with Elsa, FDA adoption of AI has accelerated. Its introduction signals a meaningful shift in how inspections and compliance oversight may be conducted. As the FDA launches Elsa across more operational areas, organizations face a regulatory environment that emphasizes scale, pattern recognition, and continuous visibility rather than episodic review.
Industry experts with firsthand regulatory and operational experience already are seeing how, for the FDA, Elsa is changing expectations around readiness, visibility, and regulatory engagement. Their perspectives reflect early signals of how AI influences not only inspections, but also everyday compliance decisions and overall resource deployment.
By understanding why Elsa represents a transformational change, how AI expands inspection scale, why “inspection living” is replacing traditional readiness, and why data governance has become compliance-critical, organizations can better prepare for a future in which AI continuously evaluates compliance performance.
But to understand what Elsa changes in practice, organizations must first recognize why AI heralds a fundamentally different regulatory model.
Elsa and AI represent a transformational shift
AI adoption has accelerated faster than many organizations expected. Industry understanding of Elsa moved quickly from “keep it on the radar” to “this is happening now” as the FDA’s intention to expand AI use across operations (including inspections and compliance) became increasingly clear.
This shift creates a new risk when teams treat Elsa like a typical checklist update. Bolting AI onto existing inspection playbooks frames Elsa as a process improvement exercise when the moment calls for a transformation toward an always-on operating model. Elsa challenges organizations to rethink how they define readiness, oversight, and control by:
- Redefining readiness as an operational condition, treating quality, clinical, and regulatory environments as continuously observable, not inspection-triggered, states.
- Rebuilding governance around routine decision cadence and ownership so emerging compliance signals are triaged and acted on in-flight before they surface as inspection questions or pattern-based findings.
- Developing organizational fluency with AI outputs, enabling teams to validate signals, separate true risk from contextual noise, and respond with defensible rationale when questions arise.
Experts have drawn a parallel to the early adoption of 21 CFR Part 11. At that time, uncertainty and inconsistent interpretation led many organizations to overinvest in excessive validation and documentation. That fear-driven response consumed resources without strengthening compliance. The lesson for Elsa remains clear: Measured, risk-based adoption builds lasting readiness, while reactive overcorrection does not.
The transformational nature of Elsa becomes even clearer when considering how AI fundamentally expands the scope and depth of regulatory review.
Elsa’s greatest impact is scale—AI can review everything
Unlike human inspectors, who must prioritize limited samples, AI tools can review entire data repositories. Elsa expands regulatory visibility across all records, fundamentally changing the scope of inspections, which means organizations must now:
- Expect inspections to assess full data populations and require consistent documentation practices rather than selectively polished records.
- Design quality systems assuming every record may be reviewed, not just those most likely sampled during inspections.
- Establish controls that ensure data consistency across systems, sites, and functional areas.
AI also excels at detecting trends. When data across corrective and preventive actions (CAPAs), deviations, training records, and documentation inconsistencies are analyzed collectively, patterns become easier to identify. Relationships that once were overlooked during manual review can now surface quickly through AI-driven analysis. Questions teams should now ask include:
- Are we monitoring cross-system trends proactively to identify emerging risks before they are visible through isolated quality metrics?
- Are we integrating quality and operational data streams to support holistic analysis rather than siloed reporting?
- Are we using trend insights to prioritize remediation based on systemic impact rather than individual event severity alone?
Experts caution that short-term cleanup efforts before inspections may appear as risk signals when AI evaluates behavior over time. Sudden closures of long-lead CAPAs or sharp changes in activity can stand out more clearly than sustained, consistent compliance practices:
- Quality teams will need to standardize CAPA documentation where possible so AI review does not misinterpret local variation as unresolved systemic risk.
- Clinical operations will be expected to demonstrate consistent protocol deviation handling over time, not just during inspection preparation windows.
- As inspection scale expands, organizations must also rethink how readiness is defined and maintained in daily operations.
“Inspection readiness” is being replaced by “inspection living”
“Inspection living” reflects a continuous compliance mindset in which readiness is embedded into everyday operations rather than treated as a temporary state. Instead of preparing only when an inspection is announced, organizations maintain consistent data quality, documentation, and oversight at all times now that AI tools such as Elsa can evaluate performance and decisions across the full historical record at any moment.
Traditionally, many organizations treated inspections as discrete events. Teams scrambled to close gaps, stage documentation, and mobilize resources once inspections became imminent. This reactive approach relied on presenting a curated snapshot of compliance rather than demonstrating consistent, day-to-day control.
Elsa makes this “closet-cleaning” approach risky. AI can identify behaviors that suggest last-minute remediation, such as CAPAs closed immediately before inspection activity. More importantly, AI evaluates patterns across time, not just what appears convenient to show in the moment.
In practice, inspection living emphasizes a continuous compliance posture rather than episodic readiness. Savvy organizations will maintain near real-time visibility into quality system health and ensure documentation reflects consistent practice instead of retroactive narrative repair by:
- Designing routine documentation and monitoring to withstand retrospective review, minimizing reliance on last-minute cleanup, staging, or after-the-fact narrative reconstruction.
- Shifting leadership oversight from lagging reports to live indicators so teams can intervene earlier before issues mature into audit or inspection findings.
- Making training and accountability continuous and role-based so inspection expectations are reinforced through normal execution (e.g., handoffs, deviations, and CAPA decisions) rather than periodic refresher pushes.
Still, successfully sustaining inspection living depends heavily on the quality, structure, and governance of the underlying data the organization is working with.
Elsa makes data governance compliance-critical
A central message from industry experts is simple: AI amplifies the data it’s given. Even when data meets integrity requirements (complete, attributable, and auditable) it can still mislead if definitions remain unclear, structures are inconsistent, or connections to systems and processes are weak. At regulatory scale, Elsa can turn small process weaknesses into systemic compliance findings.
Experts also challenge traditional, delayed management review models, likening them to reading yesterday’s news to manage today’s risks. AI-enabled inspections reveal trends quickly, making lagging indicators more visible and more problematic. Real-time dashboards and live monitoring allow organizations to identify and address emerging issues internally before they become regulatory focal points:
- Data governance teams will need to resolve inconsistent language and definitions across systems before AI analysis exposes them as unexplained discrepancies.
- Delayed management review cycles will subject organizations to findings when AI highlights trends faster than internal escalation processes.
- Cross-functional governance forums will become more critical to interpret AI-identified patterns within proper operational context.
Data dictionaries and shared definitions have also become non-negotiable. In complex organizations, the same data element often carries different meanings across quality, clinical, supply chain, and regulatory systems.
But AI does not respect functional silos. Without clearly defined, shared data dictionaries, mismatches in meaning can be misinterpreted as control gaps or inconsistencies during an AI-driven review.
A strong governance foundation ultimately supports more confident regulatory engagement and more defensible inspection outcomes.
Preparing for AI-enabled inspections at scale
Elsa reflects a broader shift toward AI-enabled inspections that prioritize scale, trend analysis, and continuous visibility. As its adoption expands, inspections increasingly focus on patterns, behaviors, and data relationships rather than isolated samples.
Industry experts consistently emphasize the need for inspection living, strong data governance, real-time monitoring, and internal capability to interpret and challenge AI-driven findings. Organizations that respond thoughtfully, rather than reactively, will position themselves more effectively as FDA use of AI continues to evolve. This calls for:
- Sponsors who stand up cross-functional review huddles so quality, clinical, and IT reconcile AI-flagged trends weekly before they harden into findings.
- Sites that standardize monitoring notes and deviation rationales across studies so comprehensive review shows consistent decision-making over time.
- Compliance teams that deploy live metrics tied to standard operating procedures (SOPs) and training completion, enabling rapid root-cause analysis when patterns shift.
Now is the time to assess readiness through the lens of AI. By evaluating data governance, operational visibility, and inspection living practices today, organizations can prepare before Elsa becomes the norm rather than the exception.
Want to learn more? Check out Advarra’s GxP solutions or contact us to ask an expert a specific question.
