Biosimilar Development Covers 2026 ISPE Annual Meeting & Expo Upcoming Session, AI-Enabled Computerized System Validation (CSV)

As pharmaceutical companies increasingly explore artificial intelligence to support CSV, industry experts are emphasizing that success depends not on blind trust in AI, but on understanding where it adds value and where human oversight remains indispensable. These themes will be explored at the 2026 ISPE Annual Meeting & Expo, where Frank Henrichmann, Senior Executive Consultant at Q-Finity will discuss the opportunities and risks associated with incorporating AI into validation workflows.

Henrichmann argues that one of the greatest dangers associated with AI is misplaced confidence. While today’s large language models can produce fluent, convincing outputs, their apparent certainty does not necessarily guarantee accuracy. In regulated pharmaceutical environments, that distinction is critical. Rather than viewing AI as a replacement for validation professionals, Henrichmann advocates using it as a tool to automate repetitive, rule-based tasks while preserving human responsibility for interpretation, judgment, and risk-based decision-making.

According to Henrichmann, the key to determining where AI can be trusted lies in understanding the degree of certainty associated with specific validation activities. Tasks such as completeness checks, reconciliation, identification of missing references, traceability analysis, terminology consistency reviews, and restructuring approved content into required formats can be objectively verified against source documentation. Because these outputs are reproducible and measurable, they represent relatively low-risk applications where AI can provide substantial benefits. In contrast, activities that require interpretation, such as assessing whether test evidence is sufficient, determining the significance of a deviation, or deciding whether a requirement has been adequately implemented, demand human expertise and increased oversight.

A recurring theme throughout the discussion is that human review alone is not enough to ensure quality. Henrichmann cautions against relying solely on the concept of a “human in the loop,” noting that psychological factors can undermine the effectiveness of oversight. He highlights three well-documented risks: automation bias, in which users place excessive trust in machine-generated recommendations; cognitive ease, where polished and well-organized information appears more credible than it actually is; and review fatigue, which can occur when personnel assess large volumes of mostly accurate output. These factors can make it difficult for reviewers to detect subtle errors hidden within otherwise convincing AI-generated content.

To address these challenges, Henrichmann recommends implementing independent technical controls and procedural guardrails alongside human review. Effective AI governance should define what information a tool can access, what outputs it may generate, and under what circumstances it must escalate decisions to qualified personnel. These safeguards help ensure that AI remains a support tool rather than becoming an uncontrolled source of compliance risk.

One area where AI is already demonstrating practical value is automated test case generation. By leveraging approved requirements, functional specifications, risk assessments, system configurations, standard operating procedures, and historical validation data, AI systems can generate draft test cases containing preconditions, test steps, expected results, acceptance criteria, and evidence requirements. However, Henrichmann stresses that success depends heavily on the quality of the underlying inputs. Poorly written requirements will inevitably produce incomplete or inadequate validation outputs regardless of the sophistication of the AI model. In his view, the quality ceiling for AI-generated validation artifacts is determined by the quality of the source data and requirements rather than by the technology itself.

AI is also transforming traceability management, an area that has traditionally required extensive manual effort. Henrichmann describes the emergence of “traceability agents” capable of analyzing lifecycle documentation and automatically identifying orphan requirements, broken document links, inconsistent identifiers, and downstream artifacts affected by requirement changes. These capabilities significantly improve maintenance and structural accuracy. However, he warns that traceability completeness does not automatically guarantee validation adequacy. An AI system may successfully connect a requirement to a related test case while failing to recognize that the test itself lacks sufficient depth to verify the requirement properly. Consequently, experts must continue evaluating the substance of validation evidence rather than relying on formally complete documentation.

Looking ahead, Henrichmann sees validation professionals evolving from document managers into what he calls “quality architects.” As AI automates many administrative activities, validation specialists will increasingly focus on defining intended use, developing precise requirements, structuring risk assessments, and critically reviewing AI-generated outputs. Rather than spending their days manually assembling compliance documentation, professionals will devote more time to designing systems and processes that produce compliance by design. This shift aligns closely with the principles of ISPE GAMP^® 5 Guide (Second Edition), which emphasizes critical thinking and risk-based decision-making over purely procedural approaches.

Training and process design will also play a critical role in safe AI adoption. Henrichmann argues that organizations should redesign workflows to limit review volume, prioritize attention through risk-based review triggers, and introduce mechanisms that require active engagement from reviewers. Examples include requiring written review justifications, rotating reviewers, and incorporating peer reviews rather than allowing simple approval clicks. Organizations should also routinely measure how many errors are detected and how many slip through validation controls, ensuring that oversight mechanisms remain effective over time.

Despite growing enthusiasm surrounding AI, Henrichmann cautions that some highly publicized capabilities remain largely unproven. To the best of his knowledge, he notes that fully autonomous “self-validating” systems do not yet exist in practical pharmaceutical applications, and approaches in which one AI system simply reviews the work of another may create a false sense of security if both models share similar blind spots. While AI can dramatically improve productivity and testing coverage, claims of complete automation or 100 percent validation coverage remain unrealistic. Current success stories are generally less glamorous but highly valuable, including document drafting, completeness reviews, consistency checks, gap identification, and first-pass test script generation

Henrichmann ultimately believes AI is fully compatible with the principles outlined in the ISPE GAMP 5 Guide (Second Edition), provided organizations apply it thoughtfully. AI can support risk-based decision-making by drawing from broader pools of historical information and improving consistency across projects. However, if companies simply use AI to accelerate template-driven documentation practices without improving critical thinking, they risk scaling inefficient validation processes rather than transforming them.

For organizations beginning their AI journey, Henrichmann recommends improving requirements quality before investing in new technologies, conducting parallel pilot projects using previously completed validation packages, establishing AI literacy training, implementing governance policies early, and involving quality assurance teams from the outset. These practical steps, which will be discussed further at the 2026 ISPE Annual Meeting & Expo, can help pharmaceutical manufacturers build trust in AI while maintaining the rigorous compliance standards required for computerized system validation.

Read the full article

Learn More About the 2026 ISPE Annual Meeting & Expo

About Biosimilar Development

Biosimilar Development is part of the Life Science Connect media group. The publication provides in-depth content from pharmaceutical experts who specialize in the biosimilars segment of the industry.