Artificial Intelligence: Quality Friend or Foe

One of the most compelling benefits of AI is its ability to analyze large volumes of data quickly and consistently. In quality-regulated environments, AI can be used to monitor manufacturing processes in real time, identify trends, and detect deviations earlier than traditional statistical methods. For example, machine learning algorithms can analyze process parameters to predict when equipment is likely to drift out of specification, enabling proactive maintenance and reducing nonconformances.

AI can also automate repetitive quality tasks such as document classification, complaint triage, and deviation categorization. This allows quality professionals to focus on higher-value activities such as root cause analysis, risk assessment, and continuous improvement. When implemented correctly, AI can shorten review cycles and reduce human error caused by fatigue or inconsistency.

AI excels at pattern recognition, making it a valuable tool for risk management. In regulated industries, risk-based thinking is a cornerstone of compliance (e.g., ISO 9001, ISO 13485, and FDA quality system regulations). AI can integrate data from multiple sources like CAPAs, audit findings, complaints, and process data to identify emerging risks that may not be obvious when reviewing datasets in isolation.

Predictive models can support decision-making by estimating the likelihood and potential impact of quality failures. This can improve prioritization of corrective actions and resource allocation. Importantly, AI can function as a decision-support tool rather than a decision maker, helping organizations strengthen their quality systems while maintaining human oversight.

Human judgment, while essential, can be influenced by bias, experience level, or organizational pressure. AI systems apply predefined logic consistently, which can help standardize quality assessments such as visual inspections, anomaly detection, or trend analysis. For example, computer vision systems used in inspection can provide more consistent defect detection than manual inspection alone, reducing variability and improving overall product quality.  It should be noted however that at the end of the day, the results should be reviewed and approved or rejected by a human, never rely solely on AI for decision making, as some of the cons below can better explain.

One of the greatest challenges of AI in regulated industries is demonstrating compliance. Regulations typically require validated systems with well-defined, repeatable behavior. Many AI models, for example machine learning models, are dynamic and may change over time as they learn from new data. This learning and adaptation without true change control will not sit well with traditional validation approaches that assume static systems.

Regulators and auditors expect organizations to understand how their systems work, including limitations and failure modes. AI models can be difficult to explain, making it challenging to justify decisions during inspections or audits. Without clear guidance and robust documentation, organizations risk noncompliance. Additionally, there’s a human factor to consider (more on that below) that should never be ignored or eliminated.

AI systems are only as good as the data they are trained on. In quality-regulated industries, poor data quality, incomplete records, or biased datasets can lead to inaccurate or misleading outputs. This is especially important and a larger issue overall when AI is used to support quality decisions that affect patient safety or consumer protection.

Additionally, historical data may reflect outdated processes or past noncompliant practices. Training AI on historical (and potentially noncompliant) data can unintentionally reinforce undesirable behaviors. Ensuring data integrity, traceability, and appropriateness for use adds significant complexity and requires strong governance.

This is the most important item to consider when looking at AI and its uses. While AI can enhance decision-making, the decision should still be a human one and never be replaced or eliminated from the process. Overreliance on automated outputs poses a risk. Quality and regulatory frameworks emphasize accountability, and organizations remain responsible for decisions regardless of whether AI was involved (anyone who has ever read a warning letter will know the paraphrased verbiage of “While you contract out these processes, you cannot ultimately contract out your responsibility for the quality of the products you make”). If personnel defer too readily to AI recommendations without critical review, not only will errors potentially go unnoticed, but it will also eliminate the human decision and accountability for the decision being made, which in and of itself is noncompliant against most regulations. Additionally, as explained above, over-reliance on AI that takes away the critical thinking of an organization or the Quality Unit and takes away the overall understanding of a system and how it functions, and therefore how to show compliance.

This last point also leads to the tribal knowledge factor: excessive automation may limit or even delete tribal knowledge over time, making organizations less agile and able to react when systems fail or when new situations arise that fall outside the AI model’s training.