How to use Artificial Intelligence tools to enhance your QMS based on the 5 Maturity Levels

Whether you are certified to ISO 9001, AS9100, IATF 16949, or any other quality management system (QMS), you may not realize how Artificial Intelligence (AI) could be used to implement and maintain a more efficient and effective QMS. AI tools can significantly enhance implementation and compliance by improving documentation management, performance analysis, internal audits, and corrective action tracking. This article is intended to shed some light on how AI may be used, the challenges involved, and the benefits of potential AI integration.

Companies that integrate and utilize AI in their QMS implementation and compliance, in a variety of industries, have shown improvements in operational efficiency and compliance. It is important to understand that all the QMS standards are written as a minimum requirement, a baseline expectation of acceptability, with many ways to demonstrate compliance. It is therefore necessary to understand the varying levels of maturity a company’s QMS may have, to see the types of AI tools that might be used.

Five Maturity Levels of a QMS

1. Minimal Compliance (Lowest Maturity) – In this level, leadership seeks certification only for appearances. The quality manager is solely responsible for ISO 9001 compliance but lacks authority, leading to weak implementation across the company. Internal audits do not provide value. Management doesn’t understand the importance of root cause analysis and corrective actions, so the quality manager ends up trying to address someone else’s problems that they don’t have responsibility for. 
2. Procedural Adoption – Processes are documented and followed to pass audits, but buy-in from leadership and process owners is limited. Compliance is seen as the quality manager’s job, with minimal integration into daily operations. Processes get changed by the process owners without updating QMS documentation.
3. Systematic Implementation – Management acknowledges the value of a QMS and enforces compliance within their teams. However, improvements are mostly reactive, and audits are seen as necessary formalities rather than opportunities for growth.
4. Continuous Improvement Culture – Leadership actively participates in quality initiatives, fostering a culture of improvement. Teams engage with the QMS standard as part of daily operations, and compliance is viewed as a means to enhance efficiency.
5. Full Integration & Proactive Engagement (Highest Maturity) – Top management owns their processes and QMS compliance, driving improvements independently of the quality manager. Audits are embraced as opportunities for learning, with leadership developing and challenging auditors to uncover weaknesses for continuous enhancement. Fear of reprisal is absent, and the business system is the QMS, fully embedded in the company’s culture.

AI Tools for Minimal Compliance (Lowest QMS Maturity)

Understanding of QMS Requirements: Before integrating AI, it’s important to have a comprehensive understanding of the QMS standard and its requirements as it relates you your functional area. Tools like ChatGPT or PerplexityAI can be used to generate summaries and explanations of the QMS standard for easy comprehension by your team and can be tailored to specific functional areas. Other possible AI tools for search and information retrieval would be Google Bard (Gemini) or Microsoft Copilot (Bing AI). These are all free AI tools.

Caution: Asking the right questions of AI tools, regardless of which tool you use, is critical throughout this entire article.  The basic use of AI’s free tools is for Q&A, process analysis, and document review for compliance. AI can do much more, but not at this QMS maturity level. When you don’t think the answers from AI are adequate, drill deeper with further clarifying questions and information to get better responses that can apply to your company, industry, department and QMS maturity level. I rarely accept the first answer to a question from an AI tool. Based on the answers, I realize there may need to be more context and information to get helpful information. AI tools can be wordy, and you may need to tell it to be more concise in your request.

AI Tools for Procedural Adoption (AI-Powered Document Analysis, Document Compliance & Management)

Department workflows, procedures, manuals, work instructions, and policies can be uploaded to an AI-powered compliance tool to help perform a document review to identify gaps and missing requirements. Natural Language Processing (NLP) can highlight missing elements such as nonconformity handling, risk assessment, or documented procedures.  AI can identify redundant, outdated, or inconsistent policies that may need updating.  If you are implementing a QMS from scratch, AI could be used for automating document creation, control, and compliance tracking.

Examples of AI Tools for purchase:

• ETQ Reliance – AI-driven compliance verification and document analysis. Uses AI to manage controlled documents and track changes to ensure compliance.
• Qualio – AI-based QMS with automated document audits.
• DocuPhase – AI-powered workflow automation and document compliance tracking. Automates workflows for document version control, approvals, and compliance auditing.
• Gavdi AI Document Assistant – Uses AI to analyze compliance documents and ensure alignment with QMS requirements.
• MasterControl – AI-driven document management for maintaining QMS compliance.

Caution:  You have to purchase most of these AI tools, and some can be quite expensive.  You can implement corrective actions based on AI-driven recommendations.  Some AI tools provide suggestions and templates to fill gaps automatically. It is common for AI to recommend more than what is required to meet minimal QMS requirements. For example, I have seen where AI recommended a risk management procedure, which for ISO 9001 and many other QMS standards, is not a requirement.  Without a process owner having adequate knowledge of the QMS standard and its requirements, they might blindly develop an unnecessary procedure.

Caution:  One AI tool when specifically asked “What are the minimum required procedures and their topics for ISO 9001?”, responded with this answer:

“The ISO 9001:2015 standard does not explicitly mandate specific procedures but emphasizes maintaining documented information to ensure the effectiveness of a Quality Management System (QMS). However, organizations often implement the following six key procedures to meet compliance requirements:

1. **Control of Documents** – Ensures proper management, accessibility, and updates of documents.
2. **Control of Records** – Maintains records for traceability and evidence of conformity.
3. **Internal Audits** – Conducts regular audits to verify QMS effectiveness.
4. **Control of Nonconforming Products** – Manages nonconformities in products or services.
5. **Corrective Actions** – Addresses and eliminates causes of nonconformities to prevent recurrence.
6. **Preventive Actions** – Identifies potential issues to prevent their occurrence.”

Note:  #6 Preventive Actions has not been a specific ISO 9001 requirement for a long time, and is now unified under risk management. When asked the same question for AS9100 and IATF 16949, it yielded imperfect results. AI is still learning, albeit at a very fast pace.

Caution:  Having a QMS procedure in place doesn’t guarantee it is being followed consistently. Effective internal audits are essential to confirm that documented procedures are actively practiced, rather than merely serving as formalities to satisfy auditors. When uploading documents, exercise caution, as it is common for written procedures to meet QMS requirements on paper but not in practice. This discrepancy can create a false sense of security, especially when using AI tools to assess compliance. Similarly, pre-made QMS document packages might appear comprehensive when analyzed by AI, revealing few gaps. In reality, significant customization is often necessary to align these generic documents with actual organizational practices, sometimes requiring extensive modifications.  Or the documentation requires changes in practice to match what is written, which may be cumbersome and difficult to follow.

AI Tools for Higher Levels of QMS Maturity

1.  AI for Performance Data Analysis and Business Intelligence (BI)

Use Case: Monitoring key performance indicators (KPIs) and identifying trends
Examples of AI Tools for purchase:

• Tableau with AI (Einstein Analytics, Power BI Copilot) – AI-driven data visualization and analytics to track quality metrics and identify deviations.
• Sisense – AI-powered predictive analytics to detect quality performance issues before they escalate.
• Power BI Copilot – AI-driven business intelligence tool.
• Looker (part of Google Cloud) – comprehensive BI solution with strong data exploration capabilities.
• Domo – monitor and analyze data in real time through highly visual, customizable dashboards.

2.  AI for Internal Audits & Risk Assessment

Use Case: Conducting internal audits, identifying risks, and ensuring compliance
Examples of AI Tools for purchase:

• AuditBoard – AI-powered internal audit and gap analysis, audit management, risk assessment, and compliance tracking.
• Intellect QMS – Uses AI for risk-based auditing and compliance assessment.
• ComplianceQuest – To plan and execute audits with automated, customizable workflows.
• Qualityze EQMS – End-to-end control over the entire audit lifecycle.

3.  AI for Corrective & Preventive Actions (CAPA)

Use Case: Identifying root causes and tracking corrective actions
Examples of AI Tools for purchase:

• ETQ Reliance CAPA Module – Automates corrective action workflows and root cause analysis.
• QCBD (Quality Collaboration By Design) – Uses AI to detect recurring issues and recommend corrective actions.
• Intellect QMS – Automate from nonconformances to CAPAs.
• Compliance Quest – Manage corrective and preventive action (CAPA) processes.
• Qualityze EQMS – AI-powered CAPA management system

4.  AI for Training & Knowledge Management

Use Case: Automating training, documentation, and knowledge sharing for an ISO QMS
Examples of AI Tools for purchase:

• Docebo AI LMS – AI-driven learning management system for QMS training.
• SAP Litmos – Uses AI to personalize compliance training and track employee progress.
• Cornerstone Learning AI – Automated training recommendation system for QMS.

5.  AI for Supplier & Vendor Compliance

Use Case: Ensuring suppliers meet QMS quality requirements, manage supply chain risks, automate
Examples of AI Tools for purchase:

• Synertrade AI Supplier Management – Uses AI to assess supplier risk and compliance.
• SAP Ariba – AI-driven supplier lifecycle, supplier evaluation, performance and compliance tracking.
• C3 AI – Leverage AI for efficient logistics and inventory management.
• GEP Software – AI powered platform that integrates all source-to-pay (S2P) functions for AI-powered supplier quality and performance monitoring.

6.  AI for Predictive Maintenance & Quality Control

Use Case: Preventing defects and ensuring product quality
Examples of AI Tools for purchase:

• IBM Maximo AI – Predictive maintenance to avoid quality issues.
• Siemens MindSphere – AI-powered quality monitoring in manufacturing.
• Uptake AI – Uses machine learning for predictive maintenance and defect prevention.
• ThingWorx (PTC) – AI-driven IoT platform for real-time quality monitoring.

7.  AI for Advanced Product Quality Planning (APQP)

Use Case: Integration, maintenance and update of FMEA’s, Control Plans, PPAP packages, and change control. 
Examples of AI Tools for purchase:

• Intellect QMS- Intuitive tools for project management, risk analysis, and compliance tracking for APQP, FMEAS, Control Plans, Deviations.
• Ideagen – APQP process software
• ETQ Reliance – New product introduction with Project Control (APQP)
• AQuA Pro Software – Integrates FMEA into APQP (Advanced Product Quality Planning), PPAP (Production Part Approval Process), and connects to Process Flow and Control Plan.
• QI Macros – Provides APQP software as an Excel add-in for various core tools. 

Final Thoughts About AI Integration into the QMS

Integrating AI into a QMS can drive efficiency, improve compliance, and enhance operational effectiveness. However, selecting the right AI tools requires an understanding of the organization’s QMS maturity level. While AI can support compliance efforts, it should not replace fundamental QMS knowledge and human oversight. Organizations should carefully evaluate AI recommendations, ensuring they align with actual requirements and operational needs.

This article was written by Jim Lee, CEO of simpleQuE. With 36 years of experience in quality, engineering and manufacturing. Jim was also a partial owner of an accreditation body, a former president of a certification body, and former RABQSA/IRCA certified lead auditor, IATF® certified ISO/TS 16949 lead auditor, and certified Aerospace Auditor. He holds a bachelor’s degree in electrical engineering from Ohio University, has been on ANAB’s Accreditation Council, and various ANAB subcommittees. Jim has also served on various ASTM subcommittees, on two previous AS standard writing committees, and is currently on the standard rewrite committees for AS9110 and AS9120. As a participant on various aerospace standard development committees, Jim gains firsthand insight into the rationale and intent behind evolving requirements. This involvement allows him to better interpret the meaning and purpose of the language used in the standards, ensuring our clients and team members have a clear understanding of not just what the requirements are, but why they exist. That deeper clarity supports more effective implementation strategies, promotes compliance, and helps organizations navigate changes with confidence and accuracy.

SimpleQuE is a leader in AS, IATF® and ISO consulting, internal auditing, and training. Contact us for a consult and see the difference that our experts can bring to your quality, environmental and safety management processes.