AI Inspection Readiness: A Practical Structure for Preparing Teams, Systems, and Evidence

This blog post explains why readiness matters, identifies the key roles involved, and presents a practical playbook companies can use when preparing for an inspection. It also introduces how the readiness structure aligns with risk-based decision making when determining whether an AI use case is GxP and how validation plays a role in AI-enabled system implementation. 

Why Inspection Readiness Matters in the Age of AI

For decades, inspections have focused on showing that manufacturing processes, quality systems, and computerized tools are reliable and under control. As AI enters regulated operations, inspectors are expanding their questions to understand how these systems support GxP decisions and how companies keep them under control. Inspectors still expect the basics such as fitness for use, reliable data, and a controlled process. At the same time, AI introduces new points of focus that require expanded knowledge, skills, and coordination across quality, business, IT, and technical teams.

Inspection discussions now extend beyond deterministic system behavior to include how AI-enabled systems change over time, how performance is monitored, and how risks are reassessed as data and models evolve. Change management becomes more dynamic, particularly when models are retrained or updated, and traditional validation approaches must be adapted to provide ongoing assurance rather than a one-time approval. Teams must be prepared to explain not only how the AI-enabled system was validated initially, but how it continues to remain fit for use as part of routine operations.

Companies that cannot answer inspector or auditor questions with documented evidence may face inspection delays or observations. Companies that show clear governance, transparency, and readiness position themselves as trusted partners to regulators.

AI-Enabled System Risk Profiles

Even within AI technologies, some fundamental capability and risk profiles arises depending on whether the system is based on deterministic or probabilistic principles (same input always leads to same output vs. same input leading to different outputs respectively). These risk profiles are further complicated by the degree of impact on product quality, patient safety and data integrity as well as degrees of human oversight (decision support vs. decision making).

This requires critical thinking and differentiated risks to be included during definition of the intended use and subsequent validation. An example of system types and risks that can be used to frame your readiness approach is shown in the table below:

Figure 1. Different use cases and risks depending on potential product, data integrity and patient impact along with deterministic or probabilistic AI sub-systems

This blog post focuses on inspection readiness for GxP-impacting systems as they are under regulatory scope. Non-GxP use cases like process optimizations, productivity improvement or analysis without potential impact to product quality, patient safety and data integrity is not discussed here.

Roles and Responsibilities

AI inspection readiness uses the same core audit roles as traditional systems, but with added technical support for AI-related activities. These roles work together to answer questions, present documentation, and give inspectors confidence in the process. The summary below provides representative roles and outlines example questions that each subject matter expert (SME) should be prepared to answer during an inspection based on their area of responsibility.

Organizations should align these roles to their specific organizational structure and governance model. The graphic below reflects an initial view of the key roles that support inspection readiness for AI-enabled systems. Additional detail and representative inspection questions are provided below the graphic.

Quality Unit

Figure 2. An overview of the key roles that support inspection readiness for AI-enabled systems.

The Quality Unit explains the system overview at a high level and how the AI system fits within the quality management system (QMS). This role describes the controls that manage the system, how risk is addressed, and how decisions are supported by documented evidence across the AI lifecycle.

Inspection questions:

• How was AI incorporated into the existing QMS, including policies, procedures, and governance models
• How was validation managed for the AI-enabled system and how ongoing assurance is maintained
• How change management was adapted to address AI-specific changes, including model updates and retraining
• What new or expanded SME roles were required for AI systems and how those SMEs were trained and qualified

Business or Process SME

The business or process SME explains how the system is used and how data supports GxP decisions. This role describes where human review is required, how outputs are interpreted, and how the system supports the business process in practice.

Inspection questions:

• How AI outputs are used to support or replace human GxP decisions
• If human review steps support oversight of the AI
• How false positives and false negatives are identified and managed in daily use
• What actions are taken when AI results do not align with expected or historical outcomes

IT System SME

The IT system SME explains the technical architecture and where AI is integrated within the overall system landscape. This role describes how the system connects with other platforms and how data integrity is maintained throughout the data lifecycle.

Inspection questions:

• How the system maintains data integrity across the data lifecycle
• How data is transferred, stored, secured, and backed up across connected systems
• How access controls, authentication, and system availability are managed
• How system performance, incidents, and technical issues are monitored and addressed

AI Sub-system SME

The AI sub-system SME includes data scientists, AI developers, and technical owners who understand model behavior, data selection, algorithm performance, interfaces, and retraining.

This role, which may be new for many companies, responds when inspectors ask detailed questions about datasets, model design, testing, or drift management.

Inspection questions:

• How datasets were selected, curated, and assessed for representativeness
• What controls prevent bias in the model or dataset
• How false positives and false negatives are measured, tested, and evaluated
• How retraining is performed, how drift is detected, and how model performance is monitored during routine use

Not every SME needs to be in the audit room at the start. They should be prepared to join when needed so the audit stays accurate, focused, and consistent.

AI Inspection Readiness Playbook

An AI inspection readiness playbook provides a practical tool to help teams prepare for inspections, particularly for new AI-enabled systems. It creates a clear, shared path for describing how the system is defined, developed, validated, released, and operated across its full lifecycle, from requirements through routine use. The playbook serves as a central reference where system knowledge is organized and accessible, while pointing to the authoritative source documents, such as validation records or technical reports, so SMEs can quickly retrieve detailed evidence when inspection questions arise.

Chapters of this playbook could include:

Figure 3. Elements of the AI Inspection Readiness Playbook

Intended Use and System Description

A clear intended use provides the foundation for an inspection conversation. It should describe what the AI management system does, how it supports a GxP activity, who uses it, and how it interacts with other systems. The description should focus on purpose, boundaries, data sources, and the decisions that depend on the AI output.

GxP and Data Classification Overview

The AI system needs to be classified so teams know what controls apply. This includes identifying whether the system influences product quality, patient safety, or data integrity. The data classification should describe the type of data used, the level of criticality, and the governance applied to maintain integrity.

Data Map and Flow Diagram

A data flow describes the full lifecycle of data, from entry into the system through processing and output. It should show where the model is applied, where data is stored, how it moves between systems, and what controls apply at each point. This helps inspectors understand how you protect data and maintain accurate model behavior.

Documentation Structure and Validation Approach

Inspection readiness depends on clear and complete documentation. This structure often includes:

• System level documents such as URS, FRS, design documents, and testing records
• AI specific documents such as model training records, model performance summaries, and algorithm verification
• Evidence of dataset controls, versioning, and audit trails

The documentation should show how standard validation activities apply to AI and where AI specific materials extend those expectations.

System Lifecycle and Monitoring

Lifecycle management for AI should follow a structured process. Many teams use a V model approach which includes requirements, design, development, testing, deployment, and maintenance. AI systems also need monitoring for performance drift, quality checks during operation, retraining logic, and periodic reviews. These controls show that the system remains fit for use across its lifecycle.

Deliverables and Release Management

Inspection readiness requires a clear record of all releases, changes, and approvals.

This includes:

• Release notes
• Version control records
• Change history
• Model update or retraining documentation
• Evidence of approvals and assessments

These materials help inspectors verify that the system is controlled and that changes follow an established process.

Access and User Management

Access control is essential for data integrity. Documentation should show how accounts are created, reviewed, and removed, and how privileges support segregation of duties and access to development and test data. Audit trails should capture key actions, and periodic reviews should confirm that users have appropriate access.

Incidents and Post Deployment Review (Optional)

AI systems may show unexpected behavior during use. All anomalies, deviations, and incidents should be captured, investigated, and resolved through corrective and preventive action. Lessons learned should inform system updates and retraining. Inspection outcomes should also feed into continuous improvement efforts.

Additionally, a periodic verification program should be considered to ensure the system is still performing as expected and has maintained the validated state.

Closing Remarks

AI is changing how companies operate in regulated environments. A structured inspection readiness approach helps teams prepare for questions, organize evidence, and maintain control throughout the lifecycle. The ISPE AI Audit Preparedness Working Group will continue to develop guidance and welcomes AI case studies, examples, and feedback for future blog posts and discussions. ISPE members are encouraged to join the AI Community of Practice to participate in future conversations and collaboration opportunities.

Acknowledgement

The authors would like to thank everyone who contributed to the development and review of this blog post, including Kathy Zielinski, David Lerner, John Clapham, Susan Cleary, and Jason Schneider.