QUALITY ASSURANCE
HARNESSING THE AI IN TQM
Sanjeewaka Kulathunga presents a range of activities that AI can undertake
TQM has served as a vital foundation for promoting excellence by emphasising customer centricity, staff engagement and continual process enhancement. Historically, Total Quality Management depended on structured human led practices such as quality circles, the Plan-Do-Check-Act (PDCA) model and defect prevention strategies.
These methods, which were inspired by thought leaders, provided strong systems to ensure product and service quality.
But with markets growing more complex and digital technologies accelerating change, organisations must now look beyond traditional tools. And AI offers the transformative potential to evolve TQM into what is now termed ‘Quality 4.0.’
Artificial intelligence reshapes quality management by shifting it from a reactive discipline that’s focussed on post-production checks to a proactive and predictive approach.
Previously, fault identification relied on human observation and inspection after production. Today, AI is introducing real-time monitoring and optimisation.
Technologies such as computer vision can identify minuscule defects that humans may miss while predictive analytics can interpret sensor data to anticipate machine malfunctions before they happen. This predictive maintenance reduces downtime, minimises waste and ensures smoother operations.
Many organisations are already reaping the rewards of integrating artificial intelligence into their quality systems. This has led to fewer production defects, shorter manufacturing cycles and less operational delays.
A defining feature of this transformation is the ability to gather and analyse data continuously. The closed feedback loop enables teams to make informed and timely decisions, and adjust operations swiftly to meet changing conditions.
AI enhanced quality practices extend far beyond the manufacturing floor; their applications span the entire organisational value chain from procurement and logistics, to customer service and product design.
For instance, NLP tools can use Natural Language Processing to process vast amounts of customer reviews or complaints to pinpoint recurring issues or new demands.
These insights empower businesses to implement quick corrective action while shaping strategy. AI driven sentiment analysis also helps track customer satisfaction in real time and shows that quality isn’t simply a technical domain but is experiential too.
HUMAN COMPONENT Nevertheless, technology alone can’t drive this transformation. An organisation’s culture plays an equally crucial role.
To leverage artificial intelligence fully, businesses need strong leadership, continuous learning opportunities, interdepartmental cooperation and a readiness to embrace change.
Without these human elements, even the most advanced AI solutions won’t deliver their intended outcomes. The successful integration of artificial intelligence depends on people who understand both the capabilities of technology and need for innovation.
Artificial intelligence’s contributions also extend to strategic planning. Generative AI tools can simulate various scenarios in product design, predict trends in demand and improve logistical efficiency.
These applications drastically shorten the time needed to bring products to the market and increase business responsiveness to fluctuating consumer preferences. They also improve risk evaluation and resource planning, and help enterprises to become more agile and resilient.
Nevertheless, AI is not without its challenges. One major issue is the lack of transparency in how artificial intelligence models make decisions – a phenomenon known as the ‘black box problem.’
If the logic behind AI generated conclusions is unclear, it can undermine accountability and trust – especially in matters related to safety, compliance or customer satisfaction. So organisations are increasingly adopting Explainable AI (XAI) frameworks, which make artificial intelligence decisions more understandable and traceable.
DATA SECURITY Other crucial considerations are data integrity and cybersecurity. Since AI systems thrive on data, ensuring their accuracy, privacy and protection is essential. Any flaw or breach can compromise not only AI systems but the entire quality assurance process.
Therefore, modern TQM must include strong data governance, secure digital infrastructure and ethical AI development practices to safeguard operational quality.
The accessibility of artificial intelligence by SMEs is also changing, thanks to advancements such as cloud computing, pre-built AI services and intuitive platforms.
Only large companies could afford these tools in the past but today, these democratised technologies allow even resource limited businesses to enhance their TQM with sophisticated analytics and automation.
AI is not without its challenges
