What is the #1 core skill for quality professionals?
Whenever Temple have the opportunity to discuss the future of quality management, the conversation inevitably turns to Artificial Intelligence.
The level of engagement is always remarkable, highlighting a widespread recognition that our profession is on the cusp of a major transformation.
It raises an important point: why is this topic resonating so strongly with quality professionals right now? The answer is clear. According to a LinkedIn survey from March 2025, AI literacy is the fastest growing professional skill in most parts of the world. The world of work is undergoing a seismic shift. LinkedIn's data suggests that by 2030, a staggering 70% of the skills required for most jobs will have changed, with artificial intelligence driving this transformation.
As Salesforce CEO Marc Benioff stated, "AI is the most important technology of our lifetimes, and we need to understand it." For those of us in the quality profession, this is not a distant forecast; it is a present-day reality.
What Do We Mean by AI Literacy?
Before we proceed, let's establish a clear definition. AI literacy is the ability to understand, evaluate, and responsibly use artificial intelligence systems. It is not just about technical know-how. It encompasses a grasp of how AI works, its practical applications, and its wider impact on society. In essence, it is the toolkit needed to navigate an AI-driven world with confidence and critical awareness.
Crucially, AI literacy is often domain-specific. The way AI will disrupt the legal profession is fundamentally different from how it will reshape the work of a quality professional.
The Global Drivers of AI Literacy
This push towards AI literacy is not just a trend; it is rapidly becoming a requirement. Two major drivers highlight its importance:
The EU AI Act: This landmark regulation will affect most products that incorporate IoT or embedded AI. For any product linked to safety or critical infrastructure, compliance with Article 4 of the Act will be mandatory. This article requires that providers and users of high-risk AI systems ensure their staff possess a sufficient level of AI literacy, tailored to their role and the context of the system's use.
Governmental Education Initiatives: Across the globe, governments are recognising the need to prepare the next generation. In the United States, for example, initiatives are in place to integrate AI education into school curricula, even at the primary and secondary levels.
For the quality professional, these developments represent both a significant opportunity and a risk. If your organisation designs or deploys products for the EU market, compliance is non-negotiable. Globally, the next generation entering the workforce will be AI-native. We must adapt to lead.
The Current State of the Quality Profession
Each year, the World Economic Forum (WEF) publishes its influential "Future of Jobs Report" The 2025 report offers a sobering perspective on our profession. In a graph mapping skills, "Quality Control" is situated in the lower-left quadrant, labelled "Out of Focus Skills."
This quadrant represents skills that employers neither consider core to their business in 2025 nor expect to be in high demand by 2030. Our goal must be to reposition our profession into the upper-right quadrant, "Core Skills in 2030." This is where we find competencies like "AI and Big Data" and "Technology Literacy," which are the very pillars of AI literacy.
What AI Literacy Means in Practice
Many assume AI literacy is simply about writing effective prompts for chatbots. While important, that is only a fraction of the picture. For quality professionals, it involves leveraging AI in our core functions:
AI and Big Data: Our work is built on data. AI provides the tools to manage, analyse, and derive decisive insights from the vast datasets generated by modern processes, ensuring products meet standards and that processes are continuously improved.
Technological Literacy: Technology is at the heart of quality. AI can enhance our capabilities by detecting microscopic defects beyond human ability, identifying subtle patterns in statistical analyses, and predicting potential product failures before they occur.
Creative Thinking: AI is not infallible. It can produce incorrect or illogical outputs, often called "hallucinations." A quality professional's critical thinking is essential to validate AI-generated results, understand the technology's limitations, and correct its course when it goes wrong.
Your Action Plan: Moving to the Core Skills Quadrant
The key is to start now, start small, learn quickly, and remain curious. Here are ten practical steps you can take today to build your AI literacy and secure your relevance for the future:
Deepen Your Risk Management Skills: You are familiar with FMEA. Expand this knowledge to enterprise risk management (ERM), as nearly every AI regulation and standard is built on principles of risk governance.
Apply ISO 31000 to a Problem: Use this risk management framework to tackle a current quality challenge, clearly defining its boundaries, constraints, and assumptions.
Use an AI Data Analysis Tool: Many modern eQMS and inspection platforms have built-in AI features. Use these tools to explore your process data and detect anomalies, expanding your skills as an AI-enabled quality engineer.
Conduct a Risk-Based Supplier Evaluation: Go beyond standard compliance checks. Evaluate a supplier based on total risk, including cost, delivery, quality, and schedule. You can use tools like ChatGPT to help structure a weighted average model, but remember never to input confidential company information.
Identify Potential Bias in an AI Application: Examine a quality product that uses AI and try to identify any potential social or technical bias risks in its design or output.
Understand ISO 42001: This is the new international standard for AI management systems. Its adoption will grow exponentially over the next five years. Obtain a copy, learn its principles, and consider how to apply them.
Run an AI Pilot Project: Select a critical quality process in your workflow. Identify the risks and controls, and then use AI tools to automate and improve the process.
Learn Basic Prompt Engineering: Move beyond simple questions. Learn to structure sequential prompts to state a hypothesis, test its logic, and refine your analysis.
Integrate AI into Root Cause Analysis: Use advanced prompting techniques with an AI tool to explore potential root causes for a persistent quality problem, augmenting your traditional methods.
Review a Supplier's AI Compliance: As suppliers develop their AI governance systems, evaluate them against emerging regulations like the EU AI Act or relevant national laws.
Is Your Company Ripe for AI Disruption?
The quality profession is drowning in data. From SPC charts and inspection reports to supplier audits and non-conformance records, our work is data-intensive. And AI thrives on data.
The WEF puts it best when comparing industries: some have vast amounts of useful data for AI to learn from, while others have very little. Industries with rich data could see AI adoption rates as high as 60-70%. Given the nature of our work, the quality profession is a prime candidate for this transformation.
Your Future with AI
AI will not replace you, but a quality professional who knows how to leverage AI will.
Think of developing AI literacy in the same way you approached learning Lean Six Sigma or a new ISO standard. It is a critical new competency.
The most valuable quality leaders of the next decade will be those who can seamlessly merge deep domain expertise with the power of artificial intelligence. This is not just a skill upgrade; it is your career assurance.