Validation evidence often arrives as a mix of attachments, screenshots, logs, PDFs, and assembled evidence packs. Reviewers must interpret each item, compare it with the defined expected results, identify inconsistencies, and document what requires follow-up.

This webinar examines how AI can support that work within a governed validation workflow.

Participants will learn how AI can:

• Extract information from validation evidence, including attachments, screenshots, logs, and PDFs.
• Compare actual execution results with expected conditions.
• Flag potential mismatches and inconsistencies for human review.
• Summarize findings to help reviewers focus on exceptions and follow-up actions.
• Identify execution evidence that requires further investigation or human decision-making.

The session will also define the boundary between assistance and accountability: AI can support verification, but validation experts remain responsible for review, resolution, and approval. Attendees will leave with a practical framework for evaluating the control of source evidence, traceability, defined acceptance criteria, human oversight, and retained approval records when AI is used with validation evidence.

Register for this webinar to explore where AI-assisted evidence review can reduce manual effort while preserving the controls required for defensible validation decisions.