Quality issues in manufacturing, such as production or assembly defects, spoiled packaging or dimensional inaccuracies, are problems that come from deviations from the quality norms. Those issues can occur at various stages of production, warehousing, and logistics. Traditionally, manufacturers relied on manual inspections to spot quality issues. However, with the rise of advanced technology, it’s possible to fully automate quality inspections and improve quality control processes.

Step #1: Understand What AI Can and Cannot Do

Before you come up with a plan of how to use AI for quality control, you need to understand AI’s strengths and weaknesses. Based on Deloitte’s research, most of the manufacturing industry uses AI to drive optimizations in areas such as quality control, but also to make predictions. Some of them use it for visual surveillance, localizing robots, and identifying images. Are those the most effective uses of AI?

Our answer is: yes! AI is especially useful when it comes to processing data from images, recordings or sensors. It can identify patterns, recognize deviations from the norm, make predictions, and automate routine tasks that lead to optimization of quality control processes in manufacturing (spotting good quality products, detecting defects, and anticipating failures before they happen). Here you have it!

Read our previous article: Benefits of AI Quality Control.

However, artificial intelligence has its shortcomings. It often struggles with understanding context, and its accuracy depends heavily on proper training and dataset quality. While those limitations can be fixed with engineer’s help, there are some things that your factory needs to accept before proceeding with AI, and that is:

• AI cannot adapt on its own; it needs support and advocacy from the team.
• AI cannot build or optimize itself; it needs a professional to fine-tune it.
• AI struggles to solve problems beyond what it has been trained for.

Those limitations are reiterated in the same Deloitte report which found that common challenges in AI implementation include:

• Organizational resistance and lack of experience;
• Infrastructure and integration constraints;
• Data availability, consistency, and quality issues;
• Shortage of engineering expertise;
• Overly ambitious scope and complexity.

So while AI is good at analyzing the input from your facility, it won’t be the perfect solution for optimizing quality control until you define its scope, consult with the right experts, make the data available, consistent, and of good quality, define technical feasibility, and advocate for AI in your organization. Don’t worry. The next steps tell you how to do that.

Step #2: Pick a Quality Issue You Want to Optimize

Once you understand what AI is capable of, take a piece of paper and write down what kind of use case you want to focus on. Do you want to tackle defect detection? Then, you need to invest in computer vision (read more about defect detection with computer vision). Do you want to eliminate prone-to-defect variants as the product gets designed? Then, maybe a digital twin is the answer. Or, maybe you want to track the environmental conditions to make sure that humidity or low temperature won’t spoil your product? Then, IoT sensors can be of help.

Take a piece of paper and write down what needs to be fixed with AI. Consider what will bring you the highest return on investment, and interview the rest of your team to find out what needs to be done first. If you need inspiration of what can be done, read our blog.

• AI-powered Drones for Better Operations
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Discover how AI Quality Control can help you. Schedule an Industry AI Workshop.

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Step #3: Define What You Already Have

Before implementing AI, start by evaluating your current setup. Here’s what you need to do:

• talk to your team
• examine your plant floor 
• learn about your IT infrastructure

Talk to Your Team and Stakeholders

Engage with quality inspectors, production staff, and IT teams. Understand their pain points, existing workflows, and any manual process

Examine Your Plant Floor and IT Infrastructure 

AI relies on strong digital systems. Evaluate your factory’s hardware, network capabilities, and data storage. Do you have the right hardware and software to collect and store quality-related data? If not, plan for necessary upgrades or investments.

Check out what AI can be integrated with.

Step #4: Create a Scope and Plan

You completed the research, so it’s time for planning. The next step is identifying high-impact use cases for AI. It involves finding a realistic business case and defining expectations that an AI project should meet. To make this happen, a lot of manufacturers host AI workshops – a structured meeting with key stakeholders and a facilitator that results in determining where and how AI can help.

We did the same when we created our proprietary product for examining precast concrete: OkKast.

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Form a Business Case

Once you have a clear picture of your AI readiness, the next step is to form a strong business case. AI is an investment, and like any investment, it needs to provide measurable value. Identify key performance indicators (KPIs) that will help measure success, such as reduced defect rates, faster inspection times, or cost savings. If you need help in defining the right KPIs, check West Monroe’s Manufacturing Industry 2025 report that has some examples ready.

Don’t know how to form a business case? Take part in a consulting session with our expert Marek S. Tatara, PhD.

Gather Requirements

After defining the business case, it’s time to gather requirements. Determine what your manufacturing site wants from AI for quality control, what is the technical feasibility of AI implementation, and how it fits into the production line. Assess how AI will integrate with your existing systems, such as manufacturing execution systems (MES) or IoT networks.

Identify Success Criteria

Set clear, quantifiable goals that define what success looks like. By establishing these criteria upfront, you ensure that AI adoption is not just experimental but results-driven. Regular monitoring and iteration will be key to optimizing performance over time. Take the same KPIs you used to form a business case as success criteria.

Develop a Structured AI Implementation Plan

Build a roadmap of the AI project that you’re about to begin. Similarly to software development, AI also has a roadmap that includes MVP, PoC, iterations, and deployment and scale.

MVP and PoC are small-scale AI implementations that test feasibility, performance, and real-world impact. They help refine the model, gather feedback, and address potential issues before full-scale deployment. Once you’re happy with our model, you can go into full deployment, but remember – plan deployment with an expert at MLOps who can help you pick the right infrastructure and tool stack to avoid overspending and deployment issues.

Step #5: Deploy the Project

Let’s assume that the goal is to integrate AI-powered computer vision and automated defect detection into the manufacturing process. Here’s how to do it.

Start with a PoC to test the core AI functionality using a small sample dataset. The AI model should be trained on real images of products, identifying defects such as scratches, misalignments, or irregularities. At this stage, the goal is to prove that AI can accurately classify defects and provide actionable insights before investing in full-scale deployment.

Once the MVP demonstrates potential, move to a MVP by testing AI in a real factory environment. Set up cameras and sensors along the production line to capture real-time images. The AI model should analyze these images to detect defects and flag faulty products. This phase helps identify integration challenges, edge cases, and system limitations before moving to full deployment.

AI for quality control relies on high-quality visual data. During the PoC, collect a diverse dataset of defective and non-defective products across different lighting conditions, angles, and production speeds. Properly label this data to improve model accuracy and reduce false positives or missed defects.

Before rolling out AI at scale, conduct rigorous testing, including:

• Defect detection accuracy – Measure the precision of AI in identifying poor quality products.
• False positive/negative rates – Ensure the AI is not rejecting good products or missing defects.
• Speed and latency – Test AI’s ability to analyze products for surface or internal defects without slowing production.
• Adaptability – Evaluate how AI handles variations in products, materials, or manufacturing conditions.

If you’re happy with the solution, proceed to a full deployment.

Step #6: Find AI Advocates in Your Facility

AI adoption requires leadership. Identify key factory employees and stakeholders who will drive AI implementation, advocate for its benefits, and ensure alignment between engineering, IT, and management. This could be a quality engineer, IT manager, or a cross-functional team. If you struggle with this step, take our short AI project management masterclass.

Step #7: Monitor and Improve

You should account for long-term strategy and continuous improvement. AI models need regular updates, retraining, and adaptation to new data and industry trends. Businesses must establish governance frameworks, compliance measures, and feedback loops to maintain AI’s reliability and effectiveness. Need help? Ask us for MLOps services.

Need Help with AI for Quality Control Projects? Contact us!

At DAC.digital, we don’t just provide AI solutions; we become your strategic partner who guides you through the entire journey from concept to deployment. Our team of AI specialists, software engineers, and MLOps experts works closely with you to develop a solution tailored to your factory’s specific needs. What sets us apart? Our team includes PhD-level scientists who have contributed to numerous AI R&D projects, ensuring that our approach is backed by the latest advancements in the field. With experience deploying AI in industrial environments, we understand the challenges of data variability, system integration, and real-time processing—and we know how to overcome them.

By partnering with DAC.digital, you gain a dedicated team committed to working for your long-term success. We help you plan, drive implementation, optimize performance, and future-proof your AI project.

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