3-day Virtual Seminar

AI in Electronic Quality Management Systems (eQMS)

Overview

This session explores what happens when generative AI and other AI-enabled capabilities begin interacting with electronic quality-management workflows. Rather than focusing on a particular eQMS vendor, the webinar looks at common quality activities and asks where AI may genuinely provide useful support.

Attendees will consider examples such as searching quality information, summarising suitable records, organising content, supporting internal knowledge retrieval and identifying themes that may warrant further human review. The session also examines the controls surrounding these activities, including data integrity, source verification, auditability, access to sensitive information, electronic records, established approval routes and the need to distinguish AI suggestions from approved quality decisions.

The webinar uses presentation-based examples that can be applied across different eQMS platforms and organisational environments.

Why you should Attend

An eQMS can centralise quality information, but adding AI does not automatically make that information more reliable. A generated summary can miss an important detail, an AI search can surface the wrong context, and users may begin trusting recommendations simply because they appear inside an approved business system.

The question for quality teams is not whether AI is impressive, but whether a particular use strengthens or weakens the quality process. This webinar helps attendees evaluate AI-enabled eQMS activities with that distinction in mind, so efficiency gains do not come at the expense of traceability, review or compliance.

Areas Covered in the Session

• Understand how AI-enabled capabilities may interact with common eQMS processes such as document control, deviations, CAPA, change control, training and quality knowledge management
  
• Identify lower-risk areas where AI may support search, summarisation, information organisation and internal knowledge retrieval
  
• Recognise the difference between AI surfacing information for review and AI making or influencing an approved quality decision
  
• Use clear task boundaries and prompting approaches when AI is asked to work with quality-system information
  
• Identify hallucinations, missing context and misleading patterns that can arise when AI interprets quality records
  
• Apply source verification, human review and traceability so AI-assisted outputs remain connected to the underlying quality information
  
• Consider data integrity, access control, confidentiality, audit trails and electronic-record expectations when AI features are introduced
  
• Assess how AI use fits with established procedures, validation or assurance activities, change management and organisational governance
  
• Use a risk-based approach to decide where AI can improve quality-system efficiency and where stronger controls or existing approved processes should take precedence
  

Who Will Benefit

• Quality Assurance Professionals
  
• Quality Systems and eQMS Managers
  
• Quality Compliance Professionals
  
• Document Control and Records Management Teams
  
• CAPA, Deviation and Change Control Professionals
  
• Quality Operations and Continuous Improvement Teams
  
• Computer System and Digital Quality Professionals
  
• IT and Validation Professionals supporting quality systems
  
• Managers responsible for AI adoption within regulated quality functions