Deviation Investigations and AI Drafting: Keeping the Investigator Accountable

Why Deviation Investigations Attract AI Drafting

Every pharma and biotech quality organization knows the pattern. A deviation is opened, an investigator is assigned, and the investigation timeline begins. The investigator gathers the batch record, the equipment logs, the environmental monitoring data, and the notes from interviews with the operators on shift. Then comes the part many investigators dread: turning all of it into a report that follows the site template, reads clearly, and holds up to QA review and, later, to an inspector.

Generative AI appears well suited to that last step. It writes quickly, it follows templates, and it does not get tired at the end of a long week of open investigations. General-purpose tools such as Microsoft Copilot already run inside the Word, Excel, and Teams applications that quality teams use every day.¹ The question for quality leaders is no longer whether investigators will use these tools. It is whether the investigation will still be the investigator’s work when they do.

Where Investigation Time Goes

A November 2025 post on ISPE’s iSpeak blog describes a structured method for using Copilot in deviation workflows.¹ The post reports that in an ISPE webinar on the subject, more than 60 percent of participants named root cause analysis as the most resource-intensive and challenging step of the process, with cross-functional collaboration close behind. The author, a director at an AI company serving life sciences, argues that most investigation time goes to writing, formatting, and reviewing reports rather than to root cause analysis itself. The post describes efficiency gains from an early case study at a contract testing laboratory, but it does not publish the underlying figures, so the size of those gains cannot be judged from the post.

The method in that post is worth noting because it points in the right direction. Each section of the report (problem description, immediate action, impact assessment, investigation, CAPA) is turned into a set of questions. The investigator answers the questions. The AI then assembles the answers into the report format. In other words, the person supplies the content and the tool supplies the structure and the prose. The post is also explicit that AI-generated content must be reviewed, approved, and archived by qualified personnel before it becomes part of the official GMP record.¹ Most of this article is about keeping that division of labor intact once the tool is in daily use, because the pressure of a deviation backlog pushes steadily in the other direction.

What the Regulations Ask of the Investigation Itself

It helps to be precise about what the regulations require, because they require an investigation, not a document. Under 21 CFR 211.192, any unexplained discrepancy or failure of a batch to meet specifications “shall be thoroughly investigated, whether or not the batch has already been distributed.” The same section says that “The investigation shall extend to other batches of the same drug product and other drug products that may have been associated with the specific failure or discrepancy,” and that “A written record of the investigation shall be made and shall include the conclusions and followup.”⁴

The written record is one requirement among several. The thorough investigation and the extension to other batches are separate obligations, and they are the parts a drafting tool cannot perform. A well-written report about a shallow investigation is still a shallow investigation.

EU GMP Chapter 1 sets out the same expectation in quality system terms. Section 1.4(xiv) states that “An appropriate level of root cause analysis should be applied during the investigation of deviations, suspected product defects and other problems.” It adds that where the true root cause cannot be determined, “consideration should be given to identifying the most likely root cause(s) and to addressing those,” and that “Where human error is suspected or identified as the cause, this should be justified having taken care to ensure that process, procedural or system-based errors or problems have not been overlooked, if present.”⁵ Each of those clauses describes a judgment. None of them describes a writing task.

A Drafting Tool Does Not Change Who Is Accountable

The clearest regulatory statement on AI-written GxP documents so far came from the UK’s MHRA in June 2026. Writing on the MHRA Inspectorate blog, the agency described AI-generated responses to inspection findings that cited MHRA guidance that does not exist, cited the wrong regulatory frameworks, and in one case contained material inaccuracies in a response to a serious deficiency that affected patient safety.² Review of that one response grew from around 4 hours to over 20 hours. Another response ran to more than 90 pages without addressing the deficiencies it was meant to answer.

The MHRA’s position is short: “These responsibilities do not change because drafting tools have.” The agency says its concern “isn’t whether you use AI; it’s whether your submissions are accurate, verifiable, and prepared under appropriate oversight.” It then lists five expectations that apply however a submission is drafted. Submissions must be “factually accurate and verifiable,” “technically reviewed by appropriately experienced people,” “signed off by someone with authority and accountability,” “supported by evidence for factual claims,” and “appropriate to the specific regulatory context.”²

That blog post is about responses sent to MHRA compliance teams after an inspection, not about internal deviation reports. But inspection responses are built from investigations, and the MHRA drew the connection itself: “Where responses demonstrate superficial or generic CAPA without evidence of genuine root cause analysis, this will be identified as a quality system weakness regardless of how the response was drafted.”² Our reading is that any inspector reviewing a deviation file will apply the same test. The report must be accurate, verifiable, and owned by a named person who can explain it.

ICH Q9(R1) and the Subjectivity Problem

Deviation investigations are full of risk judgments. How serious is this event? Which other batches could be affected? Is the most likely cause strong enough to act on? Does the product still meet its quality attributes? These are quality risk management decisions, and ICH Q9(R1), the 2023 revision of the ICH guideline on quality risk management, added a section that speaks directly to how they are made.

What the Guideline Says, Word for Word

Because this guideline is often paraphrased loosely, here is the exact language. In its introduction, ICH Q9(R1) says that “subjectivity can directly impact the effectiveness of risk management activities and the decisions made. Therefore, it is important that subjectivity is managed and minimized.”⁶

Section 5.3 also names where subjectivity comes from. It can be introduced “through differences in how risks are assessed and in how hazards, harms and risks are perceived by different stakeholders, (e.g., bias).” It can also be introduced “when risk questions are inadequately defined, and when tools have poorly designed risk scoring scales.”⁶ Note what the guideline does not say. It does not say subjectivity can be removed, and it does not say a tool removes it. It says subjectivity is managed and minimized by addressing bias and assumptions, using tools properly, and making the most of relevant data and knowledge.

The guideline also treats root cause work as expert work. Its annex describes Fault Tree Analysis as a tool that can be used to investigate complaints or deviations to fully understand their root cause, and notes that the method depends on the experts’ own understanding of the process to identify causal factors.⁶ An AI tool can help structure a fault tree. The process understanding that fills it in has to come from people who know the process.

How AI Drafting Can Reduce Subjectivity

Used carefully, AI can help with the part of Section 5.3 about “maximizing the use of relevant data and sources of knowledge.” Most sites have years of closed deviations in their quality management system, and most investigators search them poorly because keyword search is a weak tool for free-text descriptions. A 2024 study by Salami, Smith-Goettler, and Yadav tested three general-purpose large language models on a small but varied set of real pharmaceutical manufacturing deviations.⁷ The authors reported high accuracy from GPT-4 and Claude 2 when extracting specific information, such as the root cause, from unstructured deviation records. They also showed that semantic search on deviation descriptions could find similar historical records, such as those with a similar type of defect, with a high level of accuracy.

That is a real contribution to managing subjectivity. An investigator who sees six prior deviations with the same failure mode is less likely to settle on a convenient cause than one who remembers only the last case they worked. The same study carries a warning, though. The authors describe cases of a complex interplay between the apparent reasoning and the hallucination behavior of the models, and they name it as a risk factor.⁷ A model that retrieves the right historical records can still describe them wrongly.

How AI Drafting Can Add Subjectivity

The risk runs the other way when the AI produces a conclusion before the investigator has formed one. Research on AI-assisted decision making has studied this under the name anchoring bias: people who see a machine’s answer first tend to adjust from it rather than reason independently. Rastogi and colleagues modeled cognitive biases in human and AI decision making, focused on anchoring as a bias commonly encountered in that setting, and found in user experiments that giving people more time to consider the case helped reduce anchoring, especially when the AI was not confident and was wrong.⁸

A 2026 preprint by Chanda and colleagues adds a detail that matters for investigation reports in particular.³ In a study of 46 physicians using a retrieval-augmented LLM that linked its answers to sources, accuracy rose from 70.8 percent unaided to 82.6 percent with the tool. But when an incorrect answer appeared to be supported by citations, physician resistance to it fell from 92 percent to 34.8 percent. An AI-drafted deviation report that cites batch record pages, SOP numbers, and prior deviation IDs carries exactly that kind of apparent support. A reviewer is more likely to accept a wrong conclusion when it looks well referenced.

A third finding comes from outside health care. In a pre-registered experiment with 758 consultants at Boston Consulting Group, Dell’Acqua and colleagues found that on tasks within the model’s capabilities, consultants using GPT-4 completed 12.2 percent more tasks, finished 25.1 percent faster, and produced results rated more than 40 percent higher in quality. On a task deliberately chosen to fall outside those capabilities, consultants using AI were 19 percentage points less likely to produce a correct solution than those working without it.⁹ The practical lesson for investigations is that the tool helps most on tasks it handles well and hurts most on tasks where it looks capable but is not. Writing a clear chronology is the first kind of task. Deciding why a sterile filling line failed is often the second.

What AI Can Draft in a Deviation Investigation

None of this means AI has no place in investigations. It means the tasks need sorting. Some parts of an investigation report are clerical: assembling facts that already exist into a clear, consistent form. Others are analytic: deciding what the facts mean. AI can take on a large share of the first kind. It should only ever offer suggestions on the second kind, and a small set of decisions should stay entirely with the investigator.

Tasks Where AI Drafting Helps

The best candidates share one feature: the correct output can be checked line by line against a source the investigator already has. An event chronology is a good example. The inputs are time-stamped entries from the batch record, the alarm log, and the electronic logbook. The output is the same facts in order. A reviewer can compare every line to its source in a few minutes, and an error is easy to see.

Structuring interview notes works the same way, with one condition. The notes must be the investigator’s own record of what people said, and the tool must organize them without adding to them. Formatting to the site template, fixing inconsistent terminology, and checking that every required section has content are also low-risk tasks. So is drafting the plain description of an immediate action that has already been taken and recorded.

Tasks Where AI Output Is Only a Suggestion

A second group of tasks produces lists rather than text: similar historical events, possible contributing factors, and other batches that shared a piece of equipment, a raw material lot, or an operator. The Salami study suggests that current models can do the retrieval part of this well.⁷ The risk is in treating the list as complete, or treating a factor on the list as confirmed because it appears in a neat table.

The control is simple. Candidate lists go into the investigation file as leads, labeled as tool output. The investigator records which ones were checked, how, and with what result. A lead that was not checked does not appear in the report as a finding. A lead that the tool missed is still the investigator’s to find, which is why 211.192’s requirement to extend the investigation to other associated batches and products cannot be met by accepting the tool’s list as the full scope.⁴

Where Draft Annex 22 Fits

Quality leaders in companies that supply the EU market will also ask how this fits with Annex 22, the proposed new annex to EU GMP on artificial intelligence. The consultation draft, published in July 2025, is still a draft with no final text and no date of application. Its scope says the document “does not apply to Generative AI and Large Language Models (LLM), and such models should not be used in critical GMP applications.” For non-critical applications, the draft says that “personnel with adequate qualification and training should always be responsible for ensuring that the outputs from such models are suitable for the intended use,” which it describes as a human-in-the-loop arrangement.¹⁰

The draft defines critical applications as those with direct impact on patient safety, product quality, or data integrity. A deviation investigation feeds batch disposition, so the conclusions of an investigation plainly have that kind of impact. That is one more reason to draw the line where this article draws it. If the AI only assembles and formats facts that a qualified person has already established, and the decisions that affect patient safety and product quality are made and recorded by that person, the AI’s role is support for a human-authored record. If the AI’s text is where the root cause or impact conclusion first appears, a company would have a hard time arguing that the use is non-critical. Each company needs to make and document its own criticality call for each use, and the final text of Annex 22 may change the details.

The Five Decisions the Investigator Must Own

The simplest way to keep an investigator accountable is to name the decisions that make an investigation an investigation, and to require that a person make each one. Every site’s procedure will phrase these differently, but the same five decisions appear in some form in nearly every deviation procedure we see.

Decision One: The Problem Statement and Its Boundaries

The problem statement defines what went wrong, where, when, and against which requirement. It also sets the boundaries of what is being investigated. ICH Q9(R1) names inadequately defined risk questions as a source of subjectivity.⁶ A problem statement drafted by a tool from a short description tends to be generic, and a generic problem statement produces a generic investigation. The investigator should write it, in their own words, from the evidence.

Decision Two: How Far the Investigation Extends

Section 211.192 requires the investigation to extend to other batches and other products that may have been associated with the failure.⁴ This is a judgment about shared equipment, shared materials, shared procedures, shared people, and time windows. AI can propose candidates. The decision about what is in and out of scope, and why, belongs to the investigator. It is also a decision FDA has recently cited as too narrow, as the Somerset warning letter below shows.¹¹

Decision Three: The Root Cause, or the Most Likely One

EU GMP Chapter 1 asks for an appropriate level of root cause analysis and, where the true cause cannot be found, a justified most likely cause.⁵ This is the step ISPE’s webinar audience found hardest,¹ and it is the step where a drafting tool is most tempting and least reliable. A model that has learned from your site’s historical reports will tend to reproduce the conclusions that were common in those reports. If your history leans on operator error and retraining, the drafts will lean the same way. Chapter 1’s requirement that a human error conclusion be justified, after checking that process, procedural, or system causes were not overlooked, is a direct check on that tendency.⁵

Decision Four: Product Impact

The impact assessment answers whether the affected batches still meet their quality attributes and what that means for disposition. It is a quality risk management decision in the plain sense of ICH Q9(R1), and it is the one where reassuring boilerplate does the most damage. A sentence stating that the process is validated and the risk is low is easy to generate and hard to support. The investigator must state the evidence behind the impact conclusion, and the QA reviewer must check it.

Decision Five: The CAPA

Corrective and preventive actions must follow from the root cause. The MHRA’s June 2026 post singled out “superficial or generic CAPA without evidence of genuine root cause analysis” as a quality system weakness regardless of how the response was drafted.² AI tools are good at producing CAPA text that sounds complete: retrain operators, revise the SOP, add a verification step. Whether those actions address the cause found in this investigation is a judgment only the investigator and the CAPA owner can make.

What Two Warning Letters Show

Recent FDA warning letters show what these decisions look like when they go wrong. Neither of the following cases involved AI, as far as the letters show. They are useful because they show the kind of fluent, reassuring language that an AI draft can produce at scale, and the kind of gap an inspector finds behind it.

In a September 2025 warning letter to Somerset Therapeutics, FDA cited 211.192 and wrote that the firm’s “Investigations lacked data supporting the assigned root cause(s), were not adequately expanded to include all potentially affected drug products, or were not formally documented in your deviation system.”¹¹ One investigation into a media fill failure concluded: “Over the past 2 years, no tubing damage was ever observed in the product pathway which can be readily detected in the form of leakage. Hence, a very low risk is anticipated for previously filled and completed product batches.” FDA’s response was that the firm “did not sufficiently address the detectability of this defect or the possibility of a similar event going undetected on the ampoule filling line.” The sentence in the investigation is grammatical, specific-sounding, and wrong in its reasoning, because the absence of observed damage says little about a defect that may not be detectable.

In a July 2025 warning letter to Janssen Vaccines Corp., FDA again cited 211.192, this time for complaint investigations into recurring stopper problems.¹² The investigations stated that “The manufacturing process is in a validated state of control and is capable to consistently meet specifications.” FDA found that “the investigations did not include a comprehensive assessment of human use, manufacturing, and stability factors” that could increase the risk. The agency asked for a remediation plan with significant improvements in “investigation competencies, scope determination, root cause evaluation, corrective action, and preventive action (CAPA) effectiveness, quality unit (QU) oversight.”

Decide First, Draft Second

The practical answer is a sequence. The investigator makes and records the five decisions from the evidence, and only then does the AI write. The order matters more than any single control, because it determines whose thinking the report reflects.

Why the Order Matters

The anchoring research explains the first reason. When the AI’s conclusion arrives first, the investigator’s own judgment becomes an adjustment to it.⁸ Rastogi and colleagues found that giving people more time to consider a case before relying on the AI helped reduce anchoring. The decision record builds that time into the process, and it does so at the point where it matters most.

The second reason is ownership. A 2025 preprint by Kosmyna and colleagues at the MIT Media Lab studied 54 people writing essays with an LLM, with a search engine, or with no tools.¹³ Self-reported ownership of the essays was lowest in the LLM group, and LLM users struggled to accurately quote their own work. The study is small, it concerns essay writing rather than GMP investigations, and it has not been peer reviewed, so it should be read with care. Still, the finding points at a practical risk. An investigator who did not form the conclusions may not be able to explain them to an inspector two years later. The decision record is the part they wrote themselves, and it is what they will remember.

The third reason is auditability. When the decision record exists as a separate, dated document, anyone reviewing the file can see that the conclusions came from the investigator and that the report was written to match them. Without it, there is no way to show the order in which things happened.

What Goes in the Decision Record

The decision record does not need to be long. One page is usually enough, and many teams will find it replaces working notes they already keep. It should contain:

• The problem statement in the investigator’s words, with the requirement that was not met.
• The scope decision: which batches, products, equipment, and time window are included, which were considered and excluded, and why.
• The root cause or most likely root cause, the method used to reach it, and the evidence IDs that support it. If human error is named, the note on which process, procedural, and system causes were checked and ruled out.
• The product impact conclusion for each affected batch, with the evidence behind it.
• The CAPA, with a one-line statement of how each action addresses the cause.
• Leads from AI retrieval that were checked, and the result of each check.

Some sites will choose to hold the decision record inside the eQMS as a structured form. Others will keep it as an attachment. Either works, as long as it is dated, attributable to the investigator, and retained with the investigation.

How to Review an AI-Drafted Investigation

Reviewing an AI draft is a different task from reviewing a colleague’s draft. A colleague’s errors tend to show up as gaps, awkward phrasing, or missing sections. An AI draft is usually complete and well written. Its errors look like correct text. Reviewers need to know what to look for.

The Failure Modes to Expect

The first is invented references. Chelli and colleagues tested ChatGPT and Bard on generating references for systematic reviews and counted a reference as hallucinated if two of its title, first author, and year were wrong. The hallucination rates were 39.6 percent for GPT-3.5, 28.6 percent for GPT-4, and 91.4 percent for Bard.¹⁴ Those models are older than the tools in use today, and a tool grounded in your own document system should do better. The MHRA’s June 2026 post shows the failure mode has not gone away: it describes responses citing MHRA guidance that does not exist.² In an investigation, the equivalent is a cited SOP number, deviation ID, or batch record page that does not exist or does not say what the report claims.

The MHRA’s list of warning signs of inadequate verification is a good starting checklist for any reviewer. It includes “factually incorrect statements or non-existent references,” “generic language inappropriate to specific circumstances,” “citations of regulatory frameworks without explanation of relevance,” “inconsistent technical terminology across submissions,” and “overly verbose responses failing to deal with the subject matter appropriately.”²

Review Techniques That Work

Read the source, not the summary. The Chanda study shows that citations make wrong answers more persuasive.³ A citation is only a check if the reviewer opens it. For the statements that carry the conclusion (the cause, the scope, the impact), the reviewer should open the cited record every time.

Mark every causal statement. Go through the investigation section and label each sentence that asserts a cause or a contributing factor as either supported by named evidence or assumed. ICH Q9(R1) asks that bias and assumptions be addressed.⁶ Assumptions are acceptable in an investigation when they are stated as assumptions. They are not acceptable when a fluent draft presents them as findings.

Compare against the decision record, not against your expectations. The QA reviewer’s job is easier with a decision record, because the question changes from “does this sound right?” to “does this report say what the investigator decided, and does the evidence support it?” The second question has a checkable answer.

Ask the investigator to explain it. For higher-risk deviations, a short conversation in which the investigator walks the reviewer through the cause and the scope, without the report in front of them, is one of the best tests available. An investigator who owns the conclusions can do this easily. One who accepted a draft often cannot.

The Role of the QA Reviewer

The quality unit’s responsibility to confirm that errors have been fully investigated does not shrink when AI helps write the report, and the MHRA’s expectation that submissions are “technically reviewed by appropriately experienced people” applies to the reviewer as much as to the author.² In practice this means QA reviewers need the same training on AI failure modes as investigators. It also means that review time should not be cut on the assumption that AI drafts are cleaner. They are cleaner to read. That is a different thing from being more accurate.

Documenting AI Use in the Investigation Record

An investigation record should let a reader reconstruct who did what. When AI is involved, that includes what the tool did. There is not yet a binding requirement to disclose AI use in internal GMP records, but there are strong signals about what good practice looks like, and it is much easier to build the record now than to reconstruct it during an inspection.

What to Record

The MHRA’s June 2026 post offers a voluntary disclosure model for inspection responses. Organizations that choose to disclose should “include a brief statement at the start of your submission/response to Compliance Teams,” “identify which sections involved AI assistance,” and “confirm human verification and approval.” The agency says inspectors will consider this positively.² Draft Annex 22 points the same way for human-in-the-loop uses: where the effort to test a model has been reduced because a person reviews its output, “records should be kept from this process,” and the intended use “should include the responsibility of the operator.”¹⁰

For a deviation investigation, a practical record includes:

• The name and version of the AI tool, and the approved configuration or prompt template used.
• Which report sections were drafted or edited with AI assistance.
• The inputs given to the tool: the evidence set identifiers and the decision record.
• Any retrieval results or candidate lists the tool produced, saved as labeled tool output.
• A statement from the investigator that the draft was checked against the evidence and the decision record.
• The QA reviewer’s confirmation that the review covered the known AI failure modes.

What the Signature Means

Under 21 CFR 11.50, signed electronic records must show the printed name of the signer, the date and time, and “The meaning (such as review, approval, responsibility, or authorship) associated with the signature.”¹⁵ When an investigator signs a report as its author, the signature states that they authored it. That remains true when AI helped with the prose, as long as the investigator made the decisions and verified the content. It stops being true if the conclusions came from the tool and the investigator only read them.

This is the plainest way to explain the whole approach to investigators. The signature meaning does not change because the drafting tool did. If you cannot explain and defend every conclusion in the report as your own, you are not in a position to sign it as author. The decision record is how an investigator shows, to themselves and to anyone else, that they can.

Keeping the Drafting Tool Under Control

The tool itself also needs a controlled state. The prompt templates, the instructions that tell the tool not to add conclusions, and the sources it can retrieve from are all part of how it behaves. Changes to them should go through change control, and the intended use should be written down, including the limits described in this article. Periodic review of a sample of AI-assisted investigations (looking for the failure modes above, and for any drift toward the tool adding content it should not) gives the quality unit evidence that the controls are working. The draft Annex 22 text also says that where people provide the human-in-the-loop control, their training and consistent performance “should be monitored like any other manual process.”¹⁰

Keeping Investigators Sharp Over Time

The controls above work on day one. The harder problem is month eighteen, when the tool has become routine, the backlog is growing, and the newest investigators have never written a report without it. Accountability depends on skill, and skill needs to be maintained on purpose.

Train Investigators to Review, Not Only to Write

Most investigator training teaches root cause methods and report writing. AI-assisted investigation adds a third skill: critical review of fluent text. Training should include worked examples of AI drafts that contain each failure mode in the review table, and practice in finding them. It should also cover the research on anchoring and false reliance, in plain terms, so investigators understand why the decide-first sequence exists and do not treat it as paperwork.⁸³

Measure More Than Speed

The ISPE post suggests tracking measures such as on-time closure, CAPA cycle time, and first-pass documentation quality after introducing a drafting tool.¹ Those are reasonable, but speed measures alone will reward exactly the behavior that weakens investigations. Pair them with measures that show whether the investigations are sound:

• Recurrence rate for deviations with the same root cause category after CAPA closure.
• Reviewer findings per report for unsupported statements, invented references, or scope gaps, tracked separately for AI-assisted and unassisted reports.
• Share of investigations concluding human error, watched for any rise after the tool goes live.
• Returned reports from QA, with the reason, to see whether AI drafts are being sent back for different reasons than manual ones.

If AI-assisted reports close faster but recur more often, the tool is producing reports, not investigations.

Protect Investigation Skill

The Dell’Acqua experiment found that AI help raised performance on tasks the model handled well and lowered it on a task outside its capabilities.⁹ Root cause analysis on a novel failure is often the second kind of task. Investigators who have always had the tool may not notice when they have crossed that line. Some practical steps help:

• Require new investigators to complete a set number of investigations without AI drafting before they are approved to use it.
• For major and critical deviations, require the decision record to be reviewed by a second qualified person before the draft is generated.
• Include AI-assisted investigations in the scope of self-inspections and in periodic review of the deviation system, with specific attention to the five decisions.

Conclusion

AI deviation investigation drafting is useful when it takes on the clerical work of an investigation and dangerous when it takes on the thinking. The regulations already draw the line. Section 211.192 and EU GMP Chapter 1 require a thorough investigation, a justified root cause, and an extension to all associated batches, and those are judgments a person makes. ICH Q9(R1) asks that subjectivity in those judgments be managed and minimized, and an AI conclusion that arrives before the investigator’s own is a new source of bias, not a cure for the old ones. The MHRA has now said plainly that the drafting tool does not change who is responsible for accuracy. The practical answer is the decide-first, draft-second sequence: the investigator records the five decisions from the evidence, the AI writes to that record, and review and documentation show which parts came from whom.

Sakara Digital works with pharma and biotech quality organizations that are introducing AI into deviation, CAPA, and other GMP workflows. If you are weighing where AI drafting fits in your investigation process, and how to set it up so your investigators stay accountable and your records hold up at inspection, we are happy to have that conversation.

For Further Reading