What can AI contribute to regulated workflows?
AI can assist teams that search procedures, review records, and prepare documentation. Start with a clearly defined task and identify how an incorrect output could affect product quality or decisions. The proposed workflow should specify its users, source records, permitted actions, and required review. Avoid treating a general-purpose model as an automatically validated system.
A draft summary should link back to the relevant source and version. Protect original records, enforce access restrictions, and distinguish generated text from approved content. If information is missing or conflicting, the system should make that visible. Qualified reviewers need enough context to accept, correct, or reject the proposed output.
Identify the applicable quality-system requirements with the responsible specialists. Define acceptance criteria and test ordinary inputs, exceptions, and failures. Retain evidence showing how the workflow performs within its intended scope. A supplier's security features or a general governance framework do not replace the organization's assessment of its actual process.
Control changes and approvals
Record important changes to models, prompts, data sources, and integrations. Decide which changes require retesting or approval before release. Maintain clear responsibilities for deviations and incidents. Automation should support the approved review sequence, not silently bypass it. Test that the workflow stops or escalates when required evidence or authorization is unavailable.
FDA's January 2025 draft guidance discusses assessing AI model credibility for a particular context of use in drug and biological product regulatory decision-making. The page identifies it as draft, non-binding guidance, not for implementation. Confirm the current applicable requirements with qualified regulatory and quality specialists before deployment. Include the quality owner in pilot reviews and document why each test is relevant to the intended use. Keep acceptance decisions traceable so future reviewers can understand the evidence and its limits.
Define the intended use in operational language
Describe the exact task the system supports, the people reviewing its output, and the decisions that remain outside its remit. For example, preparing a draft comparison of controlled documents is different from approving a quality decision. The assessment should make that distinction explicit before selecting tools or estimating the effort needed to introduce the workflow.
Work with qualified quality and regulatory specialists to determine the applicable requirements. The approach depends on the product, process, records, and jurisdiction involved. General AI guidance cannot establish that a particular implementation is acceptable. Keep the article's planning examples separate from the organization's approved procedures, and treat unresolved requirements as named assessment actions rather than assumptions that engineering can settle alone.
Make the source-to-output trail inspectable
For an illustrative document review assistant, require every material observation to point to the relevant source passage and version. Distinguish an omission in the document from information that the system could not retrieve. A reviewer should be able to reproduce the basis of a finding without repeating the entire search or guessing which document the application actually used.
Record the configuration that produced the output and the review decision applied to it. Avoid replacing original records with generated summaries. WTA's AI strategy and governance services support scoping the use case and its evidence requirements. Our AI-native product engineering work can then connect the approved process to an application designed around traceability and qualified review.
Test the cases that expose weak assumptions
Include superseded documents, inconsistent terminology, incomplete scans, and records with missing context. Have domain reviewers establish expected findings before inspecting the generated results. Measure both unsupported findings and important issues the assistant overlooks. A system that creates many plausible observations can still be unhelpful if reviewers must spend excessive time separating valid points from noise.
Evaluate the actual review process, including how employees correct the output and record their disposition. Check whether the interface encourages a quick approval without examining the evidence. Training should explain what the assistant is intended to support, what it cannot establish, and when the employee must follow an existing escalation route instead of continuing the automated interaction.
Control changes after the initial release
Maintain an agreed approach to changes in source material, model behavior, instructions, and integrations. Determine which changes require additional assessment or testing with the responsible specialists. A stable user interface can conceal a materially different underlying configuration, so release records should describe meaningful changes rather than only visible features.
Review recurring corrections and reported issues as operational evidence. They may reveal a weak source, an unsuitable task boundary, or a training problem rather than a need for a more capable model. Preserve the ability to suspend the assistance and continue through the approved manual process. The objective is to support the quality system without creating an undocumented dependency that teams cannot explain or control.
Frequently asked questions
Does an AI tool make a workflow GxP compliant?
No. Suitability depends on the intended use, applicable requirements, configuration, evidence, and the organization's quality processes. Qualified specialists must assess the specific implementation. A tool's features or hosting environment do not by themselves establish that a regulated workflow has the required controls or is appropriate for its intended purpose.
Can generated summaries replace source records?
Do not assume they can. Preserve the required original records and make generated material traceable to its sources and review status. The organization's approved procedures should determine how outputs are used and retained. A convenient summary is an aid to review, not automatic authority to replace controlled evidence.
What is a reasonable initial use case?
Consider a bounded preparation or comparison task with qualified human review and accessible source evidence. Define exactly what the assistant may conclude and what it must escalate. Assess the use case with quality and regulatory owners before implementation, and use representative failures as well as ordinary examples during evaluation.
Does FDA draft guidance create blanket approval for AI?
No. The cited FDA document is draft guidance with non-binding recommendations, not blanket approval of an application. Check its current status and relevance with qualified specialists. The organization remains responsible for assessing the specific intended use and the evidence needed under its applicable regulatory and quality requirements.
Updated September 18, 2026. FDA draft guidance on AI credibility. Related: Building Your Responsible AI Roadmap: A Practical Guide.
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