A Few Thoughts on How FDA's AI-Powered Risk Targeting with Elsa Could Change Inspection Readiness
The FDA says its new AI tool, Elsa, can now help pinpoint inspection targets faster and more precisely—here’s what firms might want to start thinking about to stay ahead.
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With the FDA’s new AI tool Elsa now deployed, we’re starting to think about how an agency-wide AI tool could, perhaps quietly, affect how inspections are run and how teams should prepare for them, knowing AI will be helping orchestrate who is inspected and when.
As we covered in our recent update, Elsa is an FDA-built large language model (LLM) tool that the FDA says is already accelerating reviews, aiding inspections, and supporting database development—all part of the agency’s broader modernization strategy.
Over the next few weeks, we’ll start thinking in public here about what the introduction of AI could mean in each of those areas, starting today with inspections—specifically, how best to adapt inspection readiness strategies for this new AI-enabled reality.
The FDA has clearly stated that one of Elsa’s key use cases is helping identify high-priority inspection targets. This means the agency is now—likely—leveraging AI to scan large internal and external data sources for risk signals, helping inspectors triage sites and focus resources where potential problems are most likely to be found.
Let’s think about how this might work and what teams should do, or at least start thinking about, to prepare accordingly.
Before we dive in, please keep in mind that this discussion is speculative. All we know is what the FDA has stated publicly: that Elsa will “identify high-priority inspection targets.” We’ll update our thinking as the FDA shares more over time.
What this might mean for inspection targeting
FDA leaders have stated that Elsa can now process and summarize large, complex datasets—presumably including adverse events, complaints, import alerts, 483 findings, warning letter contents, product quality trends, and more—at machine speed (minutes rather than days of manual work).
Whether or not AI is offering genuinely good direction based on the data it’s being fed, we should expect the agency to conduct much faster, broader, and more nuanced risk-based inspection targeting than was previously possible.
In practical terms, that could mean:
Site risk scores can now update faster—perhaps even in near-real time as new data enters FDA systems—meaning sites with emerging signals can move to the top of the target list far faster than before. For example, if a firm suddenly sees a spike in customer complaints about particulate matter, or if a new adverse event case report mentions product contamination, and the FDA receives that information, it may be fed into Elsa, which would incorporate that new data across systems within hours or days. In the past, such a signal might not elevate a site for inspection until the next quarterly risk review cycle. Now, it could trigger prioritization for inspection, maybe within weeks.
More subtle or emerging risks—such as repeat signals across different data sources, product-specific quality chatter, or weak trending in certain quality attributes—may now surface earlier. It’s probably a smart assumption that Elsa can cross-correlate signals: perhaps a slight but steady increase in out-of-spec results for pH in one product line, coupled with an uptick in consumer complaints and import screening delays. Even if none of these individually trigger an alert, the combined pattern may now raise the site’s inspection priority. Likewise, the AI may identify trends from public forums or online reviews mentioning product defects—data sources not typically used in older inspection models. The point here is to assume that worrying trends will be detected in ways that were previously impossible or unfeasible for humans to monitor.
Inspection planning can happen more quickly as AI-generated triage reports help direct limited investigator resources more strategically, especially as the FDA continues to manage staffing constraints. Instead of waiting for a multi-week manual data review, Elsa may be used to generate inspection triage reports that flag top-risk sites within days—helping compliance officers allocate limited inspector time to the sites where problems are most likely to be found. This would enable the FDA to do more agile scheduling of inspections and faster responses to emerging risks, even amid reduced field staff. Again, a smart assumption here would be to expect that the FDA may dispatch inspectors on shorter notice or combine targeted document requests with focused on-site visits.
Why this changes inspection readiness
Firms that have historically relied on long cycles between inspections—or on the assumption that “we haven’t been inspected in a while, so we’re probably not on the radar”—need to reset those assumptions. Elsa is now part of the targeting process and can bring new signals to FDA attention, even for sites or products that have flown under the radar in the past.
Key shifts RA/QA teams should plan for
Again, we’re thinking out loud here. If we were an RA/QA leader trying to interpret the news that AI was directing inspections to some degree, here’s what we’d be planning for:
Faster, dynamic site triage. Sites may be prioritized for inspection with shorter lead times and fewer traditional warning signs. Be ready for a visit even if your last inspection was “clean.”
Broader signal detection. AI can correlate disparate signals across multiple sources to reveal patterns that would be incredibly taxing for humans to identify; internal inconsistencies may now increase inspection risk, even if no single issue appears critical. In other words, think big picture: is your company broadcasting bad signals that an AI might interpret as a pattern?
Shorter lead time and more unannounced visits. As AI ingests more signals and provides more direction to human officials at the FDA, teams may be wise to expect more surprise inspections and targeted document requests, especially as the FDA seeks to maximize its limited investigator resources.
Higher expectations for data quality. The FDA clearly plans to use Elsa to ingest, parse, and analyze documentation of all kinds. But AI often needs organization and clarity to know what it’s looking at. Poorly organized or unsearchable records will hinder your ability to respond and could raise concerns. AI-ready data practices will give you an advantage. In other words, prepare yourself to cater to Elsa.
Deeper, more targeted inspections. The FDA will likely use Elsa not only to tell them who to inspect, but also to arm those inspectors with insights to aid their inspection before they step on site. Expect that inspectors will arrive with AI-generated insights and hypotheses—walkthroughs and interviews will go straight to potential problem areas.
What firms should do now to stay inspection-ready
Here’s a short action plan we recommend discussing internally or with your RA/QA consultants. Again, this plan reflects what we currently know about how AI will be integrated into the FDA’s inspection program. We’ll update this as we learn more.
Run an internal “Elsa test.” If you have secure access to an LLM tool, use it to review your complaint, quality, CAPA, and supplier data. If patterns or issues surface easily, assume the FDA can see them too.
Tighten your data governance. Make sure that your critical data (batch records, CAPAs, stability data, supplier files) is complete, time-stamped, and stored in machine-searchable formats. If you haven’t already, clearly map data ownership so you can respond quickly to requests.
Close chronic CAPAs and old findings. Elsa will likely prioritize sites with aged CAPAs, repeat 483 findings, or delayed corrective actions. Treat lingering quality issues as visible risk factors—and escalate them accordingly. (If you’re sitting on a backlog of CAPAs, non-conformances, or deviations, now is the time to bring in one or multiple QA contractors to augment your team and eliminate those backlogs. We do this all the time. Read our case study for an example and get in touch to start the conversation.)
Monitor your external signals. Proactively monitor public data (FAERS reports, import refusals, social media, recalls) for your products. The idea is to replicate what the FDA might be using Elsa for to pick up on signals of a problem that warrants an inspection. Prompt corrective action can reduce risk before the FDA identifies an issue.
Modernize your mock inspection playbook. A few things here—contact us if you need to schedule a mock inspection:
Run mock inspections that include AI-assisted inspection scenarios (e.g. rapid document requests, trend analysis inquiries).
Test your team’s ability to respond to unannounced inspection scenarios.
Make sure that staff can quickly locate and explain trends in key quality metrics.
Standardize your data formats and terminology. Now is the right time to harmonize terminology across your documentation and data systems (using standards like ISO, HL7, XEVMPD, where applicable). This can reduce the chance of AI misclassification or misinterpretation of your data.
Train staff on AI-aware documentation. Encourage precise, clear documentation in deviations, investigations, and communications. Ambiguous language propagates through AI summaries and may trigger unnecessary scrutiny.
A quick inspection readiness checklist for the Elsa era
Use this as a working tool to adapt your inspection readiness program for today’s AI-assisted inspection landscape.
Is our critical quality data (batch records, CAPAs, stability data, supplier files) complete, machine-readable, and quickly retrievable?
Are we actively monitoring internal trends and external signals (complaints, FAERS, import alerts, social media) that FDA’s AI might surface?
Do we have any aged or chronic CAPAs (90+ days)—or repeat issues from prior inspections—that could trigger AI-driven inspection prioritization?
Are our mock inspections incorporating modern, AI-relevant scenarios (rapid document requests, trend analysis, short-notice drills)?
Can our team respond confidently to an unannounced inspection or remote document request with little or no lead time?
Are our quality dashboards, management reports, and regulatory submissions telling a clear, consistent story—without data mismatches?
Have we updated supplier quality agreements and oversight practices to reflect modern FDA expectations around data integrity and traceability?
Are our staff documenting deviations, CAPAs, and investigations in precise, clear language that AI tools will not misinterpret?
Are we staying current on FDA’s evolving AI strategy and related guidances—and adjusting our inspection readiness practices accordingly?
Do we treat inspection readiness as an ongoing discipline, not something we scramble to prepare for only when an inspection seems imminent?
The bottom line
Elsa isn’t replacing investigators, but it may make them faster, smarter, and more selective in how they target and conduct inspections. Firms that treat inspection readiness as a real-time discipline, rather than a periodic project, will be best positioned to adapt.
At The FDA Group, we help life science firms build modern, proactive inspection readiness programs. From targeted mock inspections to deep-dive quality data reviews and CAPA remediation support, our team of experienced consultants can help you identify vulnerabilities, close gaps, and ensure you’re prepared for what Elsa—and the next FDA inspection—might bring. If you’d like support assessing your readiness for this shift, running a modern mock inspection, or closing those backlogs, get in touch with our team.
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