Almost every quality team I speak to has tried an AI tool. Very few have changed how their quality system works because of it. The data says the same.
The numbers
McKinsey’s State of AI survey (fielded 4 May to 8 June 2026, 1,719 participants in 97 countries) reports that nearly nine in ten respondents use AI regularly in at least one business function. 44% report scaling AI across the enterprise, up from 38% a year earlier. 37% attribute at least some EBIT impact to AI. Only 6% qualify as high performers with 5% or more EBIT impact.
In quality engineering — software testing in this case — the World Quality Report 2025 by OpenText, Capgemini and Sogeti (more than 2,000 senior executives, 22 countries) shows the same pattern: 89% are piloting or deploying generative-AI workflows, yet only 15% have reached enterprise-wide deployment. Top barriers: data privacy risks (67%), integration complexity (64%) and hallucination and reliability concerns (60%).
What that means for the quality manager’s job
From my own perspective as a consultant, and this is opinion, not data: AI is moving the quality manager’s work from producing documents to judging them. Drafting a procedure, summarising an audit report or clustering nonconformities takes minutes now. Deciding whether the output is correct, traceable and fit for an authority audit still takes a professional.
Three shifts I see:
From author to reviewer. The skill is to spot what is missing, not to write faster.
From data collection to data quality. AI on poor nonconformity data produces confident nonsense. The data model behind your NCR system matters more than ever.
From tool rules to governance. ISO/IEC 42001:2023 provides a management-system standard for AI. Under the EU AI Act, transparency obligations under Article 50 apply from 2 August 2026, while the Digital Omnibus (Regulation (EU) 2026/1744, in force since 27 July 2026) moved high-risk obligations to 2 December 2027 (Annex III) and 2 August 2028 (Annex I).
A note on cost
The ASQE 2025 Insights on Excellence report found that only 31% of respondents fully understand the impact of quality costs on their organisation’s financial performance. AI that speeds up reporting without that understanding will not move EBIT either.
Three actions
1. Pick one use case with measurable output — for example NCR clustering — and measure time saved and error rate.
2. Define who reviews AI output and how that review is recorded.
3. Clean your NCR categories before you feed them to any model.
Sources
- McKinsey & Company — The state of AI (survey fielded May–June 2026), mckinsey.com
- OpenText, Capgemini, Sogeti — World Quality Report 2025 (13 November 2025), capgemini.com
- ASQ — Cost of Quality, incl. ASQE Insights 2025, asq.org
- ISO/IEC — ISO/IEC 42001:2023 Artificial intelligence management system, iso.org
- White & Case — EU AI Omnibus enters into force, amending the AI Act (July 2026), whitecase.com
- Lewis Silkin — The Digital Omnibus on AI enters into force today (27 July 2026), lewissilkin.com
- Jones Walker — Yes, August 2 Still Matters (16 July 2026), joneswalker.com