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AI in Quality Management Systems (QMSs)

Price: Starting at USD 1,950
Publish Date: 04 Nov 2026
Code: AN-6709
Research Type: Report
Pages: 12
Actionable Benefits

Actionable Benefits

  • Understand how Artificial Intelligence (AI) is shifting from a premium Quality Management System (QMS) feature to a baseline requirement for new implementations and long-term vendor competitiveness.
  • Evaluate how Agentic AI, Human-in-the-Loop (HITL) requirements, and regulatory constraints will shape automation strategies for QMSs.
  • Prepare for predictive quality adoption by understanding how connected factory data, time series analysis, and AI-powered data cleaning can reduce non-conformance and post-production quality events.
Research Highlights

Research Highlights

  • Forecast of AI-attributable revenue in QMSs from 2025 to 2035.
  • Evaluation of HITL as the central barrier to agentic QMS autonomy, with analysis of ISO 9001:2026, European Union (EU) AI Act Annex 22, Food & Drug Administration (FDA) warning activity, and manufacturer trust.
  • Market segmentation of QMS providers across three taxonomies: point solution providers, digital thread powerhouses, and life sciences specialists.
Critical Questions Answered

Critical Questions Answered

  • How much of future QMS revenue will be attributable to AI and AI-related services, and why is AI becoming a base requirement for QMS implementations?
  • How are ISO 9001:2026, regional regulations, and manufacturer trust concerns shaping AI governance and verification requirements in QMSs?
  • How will AI accelerate consolidation across QMS-adjacent software categories while also increasing pressure for open, interoperable industrial software ecosystems?
Who Should Read This?

Who Should Read This?

  •  QMS product leaders and strategy teams evaluating how AI, agentic workflows, HITL design, and predictive quality will reshape product roadmaps and competitive differentiation.
  • Industrial software vendors, Product Lifecycle Management (PLM) providers, Manufacturing Execution System (MES) suppliers, and digital thread platform teams assessing how QMS integration, knowledge graphs, and interoperability affect broader manufacturing software strategies.
  • Cloud, infrastructure, and platform providers positioning sovereign data, AI trust layers, and secure hosting capabilities for QMS vendors and regulated manufacturers.

Companies Mentioned

Table of Contents

1. KEY FINDINGS

2. KEY FORECASTS

3. KEY COMPANIES AND ECOSYSTEMS

3.1. POINT SOLUTION PROVIDERS
3.2. LIFE SCIENCES SPECIALISTS
3.3. DIGITAL THREAD POWERHOUSES

4. KEY AI USE CASES AND THE IMPORTANCE OF THE KNOWLEDGE GRAPH

5. HOLISTIC QMS: A NEW CATEGORY

6. KEY BARRIERS TO ADDRESS FOR SUCCESSFUL AI DEPLOYMENTS

6.1. HOW HITL WILL IMPACT USE CASES
6.2. REGULATORY PRESSURES

Companies Mentioned

  • Autodesk
  • ComplianceQuest
  • Dassault Systèmes
  • Greenlight Guru
  • Honeywell
  • IQVIA
  • MasterControl
  • Octave
  • PTC
  • QT9 Software
  • Rockwell Automation
  • Siemens
  • Veeva Systems