In short:
Interest in AI for quality management is growing – but there is a significant difference between having access to the technology and actually getting value from it. Where should you start? Which processes are actually suited for AI and automation? And what separates organisations that succeed from those that get stuck at the pilot stage?
This article walks through which quality processes are the best fit, how to move step by step towards a working implementation, and the most common mistakes you will want to avoid.
New to the topic? Read our introductory article on what AI in quality management actually means and why it is relevant right now.
Not all quality work is suitable for AI. The key is to identify processes that are repetitive, text-intensive, and rule-based—tasks that take time but do not require human judgment at every step. Within the management system, there are five main types of work that are particularly well-suited:
Deviations, incidents, complaints, and internal audits quickly accumulate a long history of free-text entries. AI can create a factual overview in just a few sentences—what happened, the scope, and the current status—or a decision-oriented summary for the management team that highlights the essentials and filters out the noise. Technically, this is based on abstract summarization, a field within deep learning that has matured rapidly in recent years.
Typical benefits: a faster overview, shorter processing times, and simpler escalation and reporting.
Much of quality assurance work involves writing: customer responses, supplier communications, final memos, and periodic reports. AI can generate an initial draft directly from the case’s own data —for example, a problem description following the 8D method for a supplier investigation, or a technical handoff to engineering and R&D—which the case handler can then adjust and approve instead of starting with a blank page.
Typical benefits: faster feedback, more consistent quality, and a unified language in communication.
Proper categorization, description, and titling are essential for finding the right information later. AI can suggest document type, category, and a searchable description during registration or import—particularly valuable for bulk registration, where fields would otherwise be left blank.
Typical benefits: higher data quality, improved searchability, and less manual registration work.
In contracts and policy documents, the most important information is often buried in the text. AI can highlight key terms, commitments, references, and critical dates, or flag clauses that warrant special scrutiny. Extracting concepts and terms from legal text is an active field of research where language models are performing increasingly well, even with limited training data. The result serves as a basis for human assessment, not a definitive answer.
Typical benefits: fewer missed deadlines, more reliable contract management, and a stronger basis for review.
When deviations, actions, and observations are collected and structured, it becomes possible to see what individual cases overlook: recurring root causes, processes that frequently go wrong, and lessons that can be reused. This is where quality work eventually shifts from addressing individual cases to improving the system behind them.
Typical outcomes: more preventive work, a growing knowledge base, and decisions grounded in the bigger picture.
The common thread is that the AI works with the information already available in the management system, makes suggestions—never decisions—and that a human always reviews the results. This is not a compromise but best practice: research on “human-in-the-loop” systems shows that a human reviewer catches the errors the model makes, especially in high-stakes decisions. It is this model that makes AI useful even in highly regulated and traceability-sensitive operations.
The most common mistake when getting started is trying to do everything at once. A step-by-step approach delivers value faster and reduces risk.
Which quality processes take the most time and are the most repetitive? Where do errors occur that “should have been detected earlier”? Where do you have data that you don’t systematically analyze? There are almost always one or two clear candidate areas—usually deviation handling, document management, or contract monitoring.
AI and analytics require structured, consistent data. Excel spreadsheets and email chains aren’t enough. Centralize your quality management work digitally—deviations, documents, contracts, and records—preferably in a shared management system. This step is often underestimated, but it’s the foundation for everything that follows. Unstructured data yields worthless analysis.
Automate repetitive workflows: notifications when action is required, automatic assignment of tasks, and reminders for overdue actions and deadlines. This delivers concrete, measurable benefits quickly, without requiring advanced models.
Once the data is reliable and the workflows are automated, it’s time to integrate AI support into a specific workflow—as a ready-made, defined function that users simply need to click, not a general-purpose AI they have to learn how to control. Start simple—not with your most critical workflow. Measure the impact, make adjustments, and document your findings.
Expand to more processes, modules, and units once you’ve validated that the support works and that the team is actually using it. Build in feedback loops—features and rules that aren’t reviewed regularly lose their relevance over time.
A tool cannot solve an unclear problem. First, define what you want to achieve, then determine which technology can help you get there.
Poor data leads to poor insights. If anomalies are classified inconsistently or documentation is scattered across email chains – address these issues before you begin your analysis.
Don’t expect everyone to learn how to formulate good AI instructions. Package the application as ready-made functions so that the technology adapts to the user, not the other way around.
The value comes from redesigning the workflow around AI, not from simply slapping a model on top. Ask, “What would the work look like if AI were part of it?” rather than “How can we speed up today’s process?”
AI support requires ownership, oversight, and traceability over time. Processes change, requirements are updated—and AI that isn’t managed loses both accuracy and trust.
Measuring the impact is crucial, both to justify the investment and to drive continuous improvement. Here are some KPIs that provide useful insights:
|
KPI |
What it measures |
Why it’s relevant |
|
Time to resolution |
Days from report to closed action |
Measures efficiency throughout the entire CAPA process |
|
Lead time for documentation |
Time to complete and publish a document |
Measures administrative efficiency |
|
Percentage of fully completed records |
What percentage of records contain all necessary information |
Indicator of data quality and traceability |
|
Missed deadlines and key dates |
Number of overdue actions and contract dates |
Shows how well preventive efforts are working |
|
Adoption rate |
Percentage of the team that actually uses the features |
The single most important indicator—without usage, there is no value |
|
Recurring types of deviations |
Percentage of deviations that recur |
Measures whether the organization learns from its cases |
AI in quality management isn’t a project for the future—it’s something many quality teams are already actively working on. You don’t need a multi-year IT project to get started; you need:
The best quality assurance work going forward will combine the consistency of automation with human judgment. Start with the former, build on the latter.
Once you know where to start, the next question is which system will actually support it. We’ll cover that in our final article: why the management system determines whether AI in quality assurance actually works.