The arrival of artificial intelligence (AI) in quality management has been met with a mixture of hype and skepticism. Is it just a faster anomaly detector, or is it truly transformative?

The answer depends on how we frame the problem. If we see AI merely as a way to speed up quality processes we already have, we miss 90% of its potential. This approach only gets us a slightly faster horse when we could be building a spaceship.

To unlock the revolutionary value of AI, we must adopt the strategic framework laid out by Richard Susskind, one of the leading experts on the effects of AI on society: We must think beyond automating what we currently do to innovating new capabilities, and most powerfully, eliminating the need for entire tasks altogether. This is the blueprint for the next generation of quality that changes the quality department from the company’s necessary hand brake into its strategic accelerator.

Let’s break it down.

1. Automate: Do the current job better

The first wave of AI adoption is the necessary foundation. It’s about granting quality teams immediate relief by making current, repetitive human tasks faster, cheaper, and more reliable.

Inspection and anomaly detection

This is the most established application. AI-powered machine vision systems inspect products on the line with unprecedented precision, spotting microscopic flaws invisible to the human eye—all without needing a coffee break or worrying about shift fatigue.

Administrative streamlining

The traditional quality management system (QMS) is notorious for paperwork. AI is already tackling this administrative chaos. AI can automatically read, tag, and route incoming supplier certifications or assign the correct root cause investigation template to a nonconformance report (NCR).

Basic predictive monitoring

AI analyzes sensor data (e.g., vibration, temperature) to predict equipment failure (predictive maintenance). It’s essentially automating what a savvy data analyst would do with a vast spreadsheet—just executing faster and with perfect vigilance.

While automation delivers essential efficiency, it only earns us a marginal improvement. The real disruption occurs when we move to the next stage.

2. Innovate: Redefining quality outcomes

Innovation happens when we stop trying to improve the existing process and start asking, “What is the ultimate outcome we need, and what is the best way—human or machine—to achieve it?” AI’s value is its ability to achieve desired outcomes through entirely new, nonhuman methods.

For quality management, this means redefining the expected outcomes of core processes.

Innovation No. 1: The traditional root cause process involves a lengthy investigation. The desired outcome is a minimal time between a defect and a verified corrective action. AI achieves this outcome instantly by correlating massive, multisource data such as sensor logs, ERP material receipts, corrective and preventive action (CAPA) history, and training records to generate a high-probability root cause hypothesis.

Outcome: Verified root cause in minutes, not days.  

Innovation No. 2: The desired outcome is an always-on, real-time process control system. Traditional risk management relies on static, failure mode and effects analysis (FMEA) documents that are notoriously hard to keep current (often collecting dust faster than they’re updated). AI innovates by moving risk from a static document to a live, predictive system.

Outcome: Perfectly aligned process control. 

Innovation No. 3: The ultimate quality outcome is seamless, end-to-end assurance from supplier to customer. AI achieves this by acting as the single intelligence layer across siloed ERP, MES, and supplier quality systems. This innovation provides complex, previously invisible correlations. For example: “Supplier X’s recent packaging change correlates with a rise in field failures, but only for batches processed on Machine 3 during the night shift.”

Outcome: Global, vertically integrated quality insight. 

3. Eliminate: Removing the need for the job

The highest-leverage application of AI is the elimination of unnecessary complexity, waste, and even certain human tasks by redesigning the system entirely. This is where quality becomes a self-optimizing engine.

Eliminating reactive stop-and-fix cycles

The need for human intervention to adjust process parameters is largely eliminated. AI detects a subtle process drift and, within prevalidated limits, autonomously initiates the necessary corrective action (e.g., slightly adjusting the pressure or temperature) to bring the process back into spec before a nonconformance is even created. The human’s job of fixing minor deviations is gone.

Eliminating manual validation and compliance documentation

For regulated industries, compliance means mountains of documentation. AI, leveraging modern risk-based methodologies like the U.S. Food and Drug Administration’s Computer Software Assurance (CSA) road map, will monitor, record, and verify compliance through constant, auditable data streams. This eliminates countless hours of manual validation and documentation maintenance, making compliance less about paper and more about verified data.

Eliminating the search for knowledge

Every successful CAPA, every effective audit finding, and every validated control becomes part of a continuously learning global knowledge base. AI then automates the creation of new procedures and control plans based on this institutionalized history. This eliminates the need for quality engineers to constantly reinvent the wheel or struggle with knowledge transfer, allowing them to finally focus on high-value, strategic risk mitigation.

How much should manufacturers trust the machine? 

This powerful transformation requires a clear strategy around trust. AI should be an assistant, not a replacement.

For all critical quality events (NCR/CAPA sign-offs, change management approvals), the final authority must remain with the qualified quality professional. AI provides a probabilistic recommendation (e.g., 97% likelihood this is the optimal CAPA), but the human provides the deterministic sign-off, confirming the context, verification, and ethical implications. Trust is built on transparency: AI must provide not just an answer, but also a transparent rationale based on traceable data.

It’s also important to remember that data quality is king. AI models built on inconsistent, siloed data will only amplify organizational chaos. “Garbage in, gospel out” is the biggest risk facing users when they believe the output of their AI is accurate, despite being based on poor-quality data.

AI isn’t just a tool for optimization; it’s a catalyst for transformation.  Manufacturers that move beyond mere automation to embrace the principles of innovate and eliminate will be the ones that truly redefine quality as a strategic driver of margin, brand equity, and business excellence.