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AI Governance Roadmap for QA Leaders: Controls, SOPs, Validation Evidence, and Inspection Readiness

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This course enables QA leaders to establish AI governance programs with defined ownership, documented controls, validation evidence, and inspection-ready oversight that integrate into existing quality management systems without relying on isolated AI initiatives.

COURSE INFO

12 - 13 August 2026
11 AM to 3 PM (Eastern Time - US/Canada)
3 PM to 7 PM (GMT)

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US $390 per learner

30-Days Unlimited Streaming Access

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Subscription include access to entire Learning Library
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  • Faculty
    Charles H. Paul
  • Duration
    2 Days
  • Course ID
    TF1609
  • Live Q&A +
    Post-live Continued Learning
  • Presentation Handout
    & Templates
  • Assessment
    & Certification Included

 

Why take this course?

Artificial Intelligence is becoming embedded across regulated quality operations, yet many organizations still lack formal governance structures capable of controlling how AI supports GMP decisions, quality records, validation activities, supplier oversight, documentation, and risk management. As regulatory attention shifts toward governance, oversight, accountability, validation evidence, and ongoing monitoring, quality organizations face increasing pressure to establish practical controls that fit within existing quality management systems.


This two-day course provides a structured implementation roadmap for Quality Assurance leaders responsible for introducing AI governance into regulated environments. The program addresses governance models, procedural controls, SOP development, validation planning, documentation expectations, lifecycle management, supplier oversight, inspection preparation, and management responsibilities. The material progresses from governance foundations through sustainable operational oversight, providing practical approaches for integrating AI controls into existing quality systems while supporting compliance, data integrity, and responsible organizational adoption.

Build Practical AI Governance Before Inspection Expectations Expand

Quality organizations need governance that keeps pace with increasing AI adoption across regulated activities. This course develops the capability to establish documented oversight, validation expectations, procedural controls, and management responsibilities that reduce operational inconsistency while preparing organizations for growing regulatory scrutiny of AI-supported quality decisions.

A Progressive Roadmap from Governance Planning to Operational Oversight

The program follows a deliberate sequence that begins with governance responsibilities, policies, risk evaluation, and quality system integration before advancing into validation evidence, lifecycle management, supplier oversight, and inspection preparation. Each stage builds upon previous decisions so participants develop an implementation path rather than disconnected technical concepts.

Key Areas Covered

  • Establish governance models, oversight responsibilities, approval structures, and decision accountability for AI-enabled quality activities.
  • Develop AI governance policies, SOPs, documentation standards, and procedural controls supporting consistent organizational practices.
  • Apply AI risk assessment methods, data governance principles, and data integrity expectations across regulated operations.
  • Define validation planning, evidence generation, traceability, output verification, and documentation supporting GMP expectations.
  • Integrate lifecycle management, model monitoring, retraining oversight, performance verification, and change control activities.
  • Establish supplier qualification practices, third-party AI oversight, cloud governance responsibilities, and quality agreements.
  • Prepare governance documentation, audit trails, management reviews, and inspection evidence supporting AI oversight within GMP-regulated environments.
  • Incorporate AI governance into existing quality management systems while maintaining compliance, data integrity, and responsible operational oversight.

Who Must Attend

  • Quality Assurance (QA)
  • Quality Systems Management
  • Regulatory Affairs (RA)
  • Computer System Validation (CSV)
  • Data Integrity Management
  • Information Technology (IT) Quality
  • Manufacturing Operations
  • Supplier Quality Management
  • Compliance Leadership
  • Executive Leadership

Complete Course Agenda

DAY 1

Governance Foundations and Quality System Integration

Module 1 – Understanding AI Risk in Regulated Environments
  • AI technologies and quality applications 
  • Emerging regulatory expectations 
  • GMP implications of AI-enabled systems 
  • Risk categories and governance drivers 
  • AI use case identification and classification
Module 2 – Building an AI Governance Framework
  • Governance models and oversight structures
  • Defining roles and responsibilities
  • Executive sponsorship requirements
  • Governance committees and review boards
  • Decision accountability frameworks
Module 3 – Policies, Procedures, and SOP Development
  • AI governance policies
  • Acceptable use requirements
  • SOP development strategies
  • Documentation standards
  • Procedural controls and approval processes
Module 4 – Risk Management and Data Governance
  • AI risk assessment methodologies
  • Data integrity considerations
  • Training data governance
  • Data ownership and stewardship
  • Risk monitoring strategies

DAY 2

Validation, Inspection Readiness, and Sustainable Oversight

Module 5 – Validation Strategy and Evidence Requirements
  • Validation lifecycle planning
  • Validation traceability requirements
  • Evidence generation methodologies
  • Output verification practices
  • Validation documentation expectations
Module 6 – Lifecycle Management and Change Control
  • Model monitoring requirements 
  • Managing model drift 
  • Retraining governance 
  • Change control integration 
  • Ongoing performance verification
Module 7 – Supplier Oversight and Third-Party AI Systems
  • Vendor qualification requirements 
  • Supplier governance expectations 
  • Quality agreements and responsibilities 
  • Cloud-based AI considerations 
  • Third-party oversight practices
Module 8 – Inspection Readiness and Audit Preparation
  • Preparing for AI-related inspections 
  • Documentation packages and evidence 
  • Audit trail expectations 
  • Management review requirements 
  • Building an AI governance roadmap
COURSE DIRECTOR

Charles H. Paul

Charles H. Paul has more than 30 years of experience helping regulated pharmaceutical, biotechnology, medical device, and advanced therapy organizations implement quality systems, validation programs, governance controls, and inspection readiness practices. His work translating emerging regulatory expectations into practical quality procedures makes him well suited to lead an applied two-day program focused on AI governance implementation.

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Commonly Asked Questions About This Subject

The following questions address practical regulatory, compliance, validation, quality, operational, and inspection-related considerations commonly associated with this subject.

How much evidence is enough to justify a root cause conclusion during a GMP investigation?

A root cause conclusion should be supported by evidence that directly links the identified cause to the observed event. Inspection concerns frequently arise when investigations move from suspicion to conclusion without demonstrating that connection. Statements such as "operator error," "lack of attention," or "procedure not followed" often raise additional questions rather than resolving them.


Reviewers typically look for objective support such as records, interviews, historical trends, process data, equipment performance, environmental conditions, or documented observations that point toward the same conclusion. A root cause becomes difficult to defend when equally plausible explanations were not evaluated or eliminated.


Rework commonly occurs when teams stop investigating after finding the first reasonable explanation. Experienced investigators continue until they can explain both how the failure occurred and why existing controls did not prevent it. The strongest investigations leave little ambiguity about why the selected root cause was chosen over competing possibilities and what evidence supports that decision.

Why do repeat deviations continue to occur even after CAPAs have been completed and closed?

An operational failure point appears when CAPA activities focus on correcting the immediate event while leaving the conditions that enabled it untouched. The deviation may disappear temporarily, yet the underlying system remains unchanged.


A review of recurring events often reveals that training was repeated, procedures were revised, or reminders were issued. Those actions may address symptoms without addressing workload pressures, process complexity, unclear responsibilities, equipment limitations, conflicting procedures, weak oversight, or ineffective management controls.


Inspection friction develops when the same event reappears under slightly different circumstances and prior CAPA records show closure without meaningful system improvement. Investigators frequently discover that effectiveness checks only confirmed short-term compliance rather than sustained performance.


Evidence that carries weight includes measurable process improvement, reduction in recurrence rates, control enhancements, revised workflows, resource adjustments, and objective performance monitoring. Sustainable CAPA outcomes are usually associated with changes to the system rather than changes to individual behavior alone.

When is it appropriate to close an investigation without identifying a definitive root cause?

A definitive root cause is not always obtainable, particularly when evidence has been lost, the event cannot be reproduced, or multiple contributing factors remain equally plausible. What becomes difficult to defend is closing the investigation with uncertainty while treating the issue as fully resolved.


Experienced reviewers generally accept that some investigations end with a probable cause rather than a confirmed root cause. Their focus shifts to whether the investigation was thorough, whether alternative explanations were considered, and whether risk was managed appropriately despite the remaining uncertainty.


Documentation often weakens when teams simply state that the root cause could not be determined. Stronger records explain what evidence was reviewed, what investigative paths were pursued, why additional conclusions could not be supported, and how residual risk will be controlled.


Inspection discussions tend to be far more productive when uncertainty is acknowledged and managed than when unsupported certainty is documented simply to satisfy a procedural expectation.

What distinguishes a strong management review of investigations from a routine approval signature?

A governance concern emerges when management approval becomes a documentation checkpoint rather than an evaluation of investigation quality. Inspectors frequently recognize the difference within minutes of reviewing investigation files.


Strong management oversight focuses on the logic behind conclusions, adequacy of evidence, quality of impact assessments, recurring trends, implementation barriers, and the long-term effectiveness of proposed actions. Questions are raised, assumptions are challenged, and gaps are addressed before closure.


Routine approvals often leave obvious weaknesses untouched. Investigations may contain broad conclusions, incomplete impact evaluations, weak effectiveness measures, or corrective actions that cannot reasonably prevent recurrence. Yet the file carries multiple approval signatures.


Records that demonstrate meaningful management involvement often contain documented comments, requests for additional analysis, escalations, trend reviews, resource decisions, and evidence that leadership evaluated business and quality implications. Those records show active ownership of the process rather than administrative participation.

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Built on 20+ years of global regulatory training experience across FDA, EMA, MHRA, and ICH frameworks.
Trusted by professionals across 80+ countries.

AI Governance Roadmap for QA Leaders: Controls, SOPs, Validation Evidence, and Inspection Readiness

Dates: 12 - 13 August 2026

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