As the shift towards smart manufacturing accelerates, more companies are implementing AI solutions to streamline core manufacturing processes, with AI-powered defect detection standing out as one of the most popular use cases in the industry. Nonetheless, achieving true efficiency of defect detection in smart manufacturing requires more than just installing AI-enabled systems across factory sites. Proper user adoption and integration of the technology into the daily workflows of a company’s employees is not less important.
In this article, experts from Itransition, a company offering end-to-end AI services, emphasize the importance of smoothly adopting AI-based defect detection solutions and provide multiple practices that can be helpful in this regard.
Why successful user adoption is vital when implementing AI-based defect detection
AI-based defect detection systems can operate at various levels of autonomy. For instance, they can function semi-autonomously as decision-support tools for human operators. Within this approach, the AI model detects potential product defects, flags them for human review, and presents the information about the identified defects on a dashboard or workstation screen. After reviewing the provided information, the human worker makes the decision whether to scrap, repair, or pass the product.
AI tools can also function in an autonomous setup, managing the defect detection process end-to-end without constant human intervention. This means that after a defect is found, the system can make the decision independently and trigger an automated sequence of events, signaling to machinery to move a faulty product into a scrap bin, routing a product to a repair, or halting the production line.
In practice, human interaction with AI-enabled systems is necessary regardless of the scenario; however, its depth, intensity, and timing can vary. For example, in semi-autonomous environments, employees need to actively confirm or reject the AI’s findings, while in fully autonomous environments, specialists can be involved to resolve various edge cases. Additionally, humans can also participate in AI model training, audits, and tuning.
To ensure that workers can successfully execute these and other tasks, a company needs to ensure that new AI-enabled suggestions software properly integrates it into their daily routines. Otherwise, the ROI of implementing these solutions can decrease drastically, causing stakeholder dissatisfaction with the AI initiative.
1. Incorporating explainable AI practices and feedback loops
Skepticism and mistrust towards AI-generated outputs and decisions represent a major obstacle to AI adoption among workers in the manufacturing sector, as highlighted by OECD in its 2026 report. This issue can stem from several distinct factors. One of them is the so-called “Black Box” problem, or the lack of user transparency into how an AI system justifies its decisions, making them consider these decisions as unreliable. Rare erroneous AI suggestions – such as false positives or false negatives – are another factor, which can erode trust in AI.
To overcome this obstacle and unlock the business benefits of AI-based defect detection, manufacturers can employ various tactics. One useful tactic involves implementing explainable AI dashboards on factory floors. These dashboards can visualize how and why the defect detection system generated its suggestion and therefore improve transparency into AI decision-making. For instance, explainable AI can indicate the exact product surface areas and dimensions that influenced the system’s decision to flag the defect and highlight specific dimensional deviations.
Additionally, manufacturers can implement human-in-the-loop feedback mechanisms, allowing operators to correct AI’s assessments when it mistakenly flags a harmless anomaly or misses a genuine product flaw. AI-based systems can also continuously learn from operator input to improve their reasoning and avoid similar miscalculations in the future, which can enhance the overall accuracy of its suggestions and contribute to user trust.
2. Offering personalized upskilling plans
Many manufacturing companies lack personnel capable of efficiently utilizing AI-enabled systems, which hinders AI adoption across their organizations, and companies implementing AI-based defect detection technology are not the exception. For instance, 38% of manufacturing decision-makers who participated in Cisco’s 2026 survey cited lack of skilled talent as the most significant barrier to successful AI adoption.
In this context, it is critical for manufacturers to provide human operators with proper training prior to system launch and, if needed, after the software goes live. Since generic, one-size-fits-all upskilling programs often fail to match individualized needs and actual work roles of employees, which can cause a lack of engagement in the training process, manufacturers should develop personalized upskilling plans instead. The following algorithm can come in handy in this regard:
- To start with, a company should evaluate each operator’s existing knowledge level, including their domain expertise – the understanding of manufacturing processes, common defect types, and product quality standards – as well as overall tech literacy and their ability to navigate digital interfaces and dashboards.
- Since different workers can interact with AI systems in distinct ways, companies need to tailor training plans to their specific responsibilities. For example, line operators can focus on how to interpret AI dashboards and verify flagged defects, while quality inspectors can review false positives and negatives and provide feedback to AI.
- Companies also should establish performance metrics, such as false acceptance rate or defect escape rate, to continuously track operator performance and understand whether they might need additional training.
3. Employing proactive preventive maintenance strategies
Even if employees have confidence in AI technology and are properly trained, user satisfaction still can decrease over time, especially if a defect detection system no longer matches their expectations and needs regarding functionality and usability. To prevent this deterioration, a company should implement an actionable perfective maintenance framework for continuously updating, refining, and improving AI defect detection software. Key focus areas of this strategy should include:
- Human-AI interaction
It is important to iteratively review and refine user interfaces based on accumulated user feedback to ensure that they remain properly contextualized and easy to navigate, as it enables operators to understand more quickly why the AI flagged a certain defect.
- Model drift monitoring
As AI models’ performance can degrade over time, which can increase the number of false positives and false negatives, companies should continuously monitor and fine-tune the accuracy of AI algorithms.
- Latency and inference speed
If the pace at which AI can identify defects falls behind the speed of the assembly line, the production process can slow down. Since data pipelines and AI model architecture directly impact how quickly data is ingested and processed by AI, companies should focus on regularly optimizing them.
If factory floor workers mistrust the AI technology or cannot utilize it efficiently, the defect detection system implementation project is likely to fail, which is why maintaining high user adoption rates is critical, and practices listed in this article can be useful in this regard. To maximize user adoption, it is also recommended that manufacturers involve AI experts in their projects right from the start. These specialists can analyze the company’s technical and business requirements to deliver a functional and intuitive solution tailored to user needs. If needed, they can also help implement feedback loops, teach employees how to use an AI-driven system efficiently, and assist with perfective maintenance tasks.
Roman Davydov is a Technology Observer at Itransition with over five years of experience in the IT industry. Roman monitors and analyzes the latest technology trends, helping businesses make informed software decisions that align with their strategic goals.
