The 5 Areas Where AI Is Set to Impact Quality Management in 2025

As the regulatory environment becomes stricter, and the marketplace grows increasingly complex, traditional quality management approaches relying heavily on human intervention will prove inadequate.

To be agile and responsive to changing trends without compromising on quality, performance, and safety, businesses will need to rely on technologies to augment human efforts. In this context, artificial intelligence and machine learning (AI/ML) will play a key role in 2025.

In one of our recent blogs titled ‘Task Automation & Better Decision Support with ComplianceQuest’s 7 AI Agents’, we spoke about the role of intelligent automation in driving efficiencies and saving time for quality leaders.

In another blog titled ‘AI and Humans: Better Together’, we wrote about how mundane, repetitive tasks can be taken over by automation and AI, while higher-level tasks that require the time and effort of quality leaders will be done by people.

Some of the key emerging trends of AI infusion in quality management include:

• Data-driven Decision Making: As businesses embrace Industry 4.0 technologies such as the cloud, IoT, and Big Data to improve process efficiency, access to information and augmented analytics will improve. This will enable the efficient use of AI for improving decision-making capabilities and quality management by predicting future risks and facilitating proactive management.
• Automation: Business processes have been moving towards automation for the last few decades. AI/ML will augment the processes by learning newer ways to continuously improve processes and enhance business capabilities.
• Simulations: The use of AI/ML will also enhance the effectiveness of training on potential quality control issues by providing simulations to enable realistic training. This will improve the skills of professionals and employees, helping them better understand and align their tasks with quality standards, thereby fostering a culture of quality.
• Visibility and Transparency: AI/ML will also increase visibility into processes, enabling transparency and accountability. This will help the quality management teams identify areas that need improvement, the impact of those improvements across the supply chain, and choose wisely to minimize disruptions.

Key Impact of AI On Quality Management: Quality Leaders Will Have More Time for Major, Critical Tasks

Access to data, improved visibility, and enhanced capabilities of the teams will enable businesses to create a culture of quality. This improves compliance as all employees contribute to quality management through proactive participation and alignment with standard operating procedures and policies.

The direct impact of AI in Quality Management can be seen in the following areas:

• #1 - Quality Control: AI in Quality control will improve the efficiency of quality control processes and free up resources to focus on problem-solving. AI in Quality control can be leveraged to improve the accuracy of detecting flaws and deviations, reducing human errors, and improving the quality and performance of the products.
• #2 - Risk Management: AI can help the quality team with continuous improvement by identifying risks using historical data. It can help identify patterns and enable risk assessment by scoring the risk factors based on frequency and intensity. This can help with resource optimization to focus on critical risks and enhance the risk tolerance of businesses.
• #3 - Change Management: Any change may have an impact not only on the associated processes but also upstream and downstream, which cannot be always detected by the human mind. Using AI-based processes enables identifying the organization-wide impact and assessing the risks of such an impact proactively. This can help the business leaders decide on implementing the change or the control measures needed to mitigate the risks.
• #4 - Process Standardization: Automation using AI/ML will ensure standardizing processes for consistent quality and performance. This can also improve compliance, leading to customer satisfaction and gaining a competitive advantage. This will also help align with evolving business environments and regulatory requirements.
• #5 - Predictive Maintenance: The performance of equipment and machinery is critical to maintaining quality and consistency. AI/ML infusion into quality management processes can enable predictive maintenance with planned downtimes to improve the life of the equipment and minimize disruptions to operations due to unplanned downtime.

Effective Implementation of AI in Quality Management

According to a Forbes report, the global artificial intelligence market size is expected to grow at a compound annual growth rate (CAGR) of 37.3% between 2023 and 2030, to $1,811.8 billion. Since 2017, the adoption of AI by global enterprises has more than doubled and continues to grow. What this means is that AI itself will not be the differentiating factor but how well it is implemented and leveraged.

• Assess your current quality management: Businesses wishing to improve their quality management processes with AI will need to assess the current state of quality management, identify gaps, and assess the effectiveness of filling those gaps. This will help with prioritizing the areas of investment for better ROI.
• Evaluate AI/ML Tools Based on QMS Maturity: There is an abundance of AI/ML tools and solutions in the market today. However, which one will be the best fit for each organization will depend on the maturity level of the QMS and the processes being automated. Therefore, evaluating the tools for quality assurance and selecting the best fit will enhance effectiveness.
• Foster Employee Acceptance and Provide Leadership: The employees must be willing to change the way they work once AI/ML is adopted. Often, there is apprehension about losing the job. Also, resistance to change is a common factor in AI projects being abandoned mid-way and employees reverting to early ways of getting the job done. Leaders must lead from the front, create awareness, and provide training to improve adoption.
• Monitor the Effectiveness of AI Implementation: Tracking and monitoring the effectiveness of AI implementation is a must to assess the effectiveness of AI infusion. Establishing metrics and measuring performance are key here. If the desired outcomes are not obtained, mid-course correction may be required.
• Future-Proof Your AI Implementation with Scalable Solutions: The AI implementation must also be future-ready. Therefore, a scalable, flexible solution is critical to ensure the solution can grow with the organization to minimize large investments later in upgrading the systems. Therefore, the AI partner must also possess the capability and vision to grow alongside the organization, providing continued service as required.

ComplianceQuest is a cloud-based AI-powered QMS that enables businesses to be ready and on the go quickly. The solution can be customized to meet the unique needs of organizations and is also aligned with ISO standards, thereby enhancing compliance. It is a scalable solution that enables end-to-end quality management with integrated workflows. Some of the areas where businesses can benefit from AI power in quality management include:

• Audit
• Inspection
• Document
• Training
• Risk
• Change
• Equipment
• Supplier
• CAPA
• Complaints
• Management Review
• Nonconformance