Human-AI Collaboration Quality Control: How AI Can Augment and Support QC Teams

The real role of artificial intelligence (AI) in quality control – at least within the fresh produce industry – is workforce augmentation. Far from replacing human expertise and human judgment, AI-powered quality control software offers quality control managers unprecedented decision-making support. 

Through this human-AI collaboration, the quality process in a wide range of industries is being transformed, from automotive to manufacturing to fresh produce. Quality professionals in these industries are using AI-assisted quality control and predictive analytics to scale up inspections without needing to commit extra manpower to their daily operations.

The Role of AI in Quality Control

When most people think about the role of AI in quality control, they probably think about a ChatGPT interface for customer service. And that’s not wrong; this capability is an important one, and many platforms include it in order to streamline access to data for users across the length and breadth of an organization.

But it’s even deeper than that. AI is now involved in every step of the quality control process, from inspections to data analysis and predictive analytics. 

Computer Vision 

As the name suggests, this AI technology equips computers to “see” objects and make judgments about them (in this case, fresh fruits and vegetables). These evaluations are precise and automated – a huge advantage for quality inspections. These systems can detect minute defects and inconsistencies that might be missed by human inspectors. By automating visual inspections, computer vision enhances the consistency and reliability of quality control.

Machine Learning

When inspectors use devices equipped with computer vision, they generate vast amounts of data. Machine learning algorithms analyze these large datasets to identify patterns and predict potential defects. This enhances accuracy and efficiency in quality control processes. By learning from historical data, machine learning continuously improves its predictions and recommendations.

Predictive Analytics

AI also gives businesses something they’ve never really had before: a way to accurately predict problems before they occur, and take appropriate action while there’s still time. Predictive analytics allows for proactive quality management by forecasting issues, rather than reacting to them after the fact. At Clarifresh, we have begun to integrate our platform with analytics platforms to give our customers these capabilities.

Human-AI Collaboration in the Fresh Produce Quality Control Processes

The work of a fresh produce quality control manager is never really done. These professionals work around the clock, evaluating pallets of fruits and vegetables with tools like sizers, color charts, defect charts, scales and refractometers. The vast amounts of data that result from this all need to be documented – manually – before managers can input it into the company system. Usually, that’s an Excel spreadsheet in the office.

This is as labor-intensive (and error-prone) as it sounds, which is why these methods are steadily giving way to human-AI collaboration. Technology integration provides a way to automate away the more routine, mechanical parts of their workflow. Using Clarifresh, an inspector can instantly evaluate an entire pallet of goods through the powerful computer vision and LiDAR capabilities of the app. This in turn allows inspectors and quality control managers to focus their irreplaceable human expertise on more complex business processes and decisions. Each automated inspection saves them hours of work.

AI in Quality Control: Much Needed Decision-Making Support for Quality Assurance Teams

In the hands of a knowledgeable inspector, AI powered quality control software is a powerful tool that enhances decision-making and accuracy. Human expertise remains crucial for interpreting AI-generated data to make nuanced judgments, but the synergy between them leads to continuous learning on both sides. AI algorithms continuously adapt to real-world conditions, and human inspectors get better at making strategic use of the data. The combination of human intuition and advanced technology creates a robust quality control system that maximizes efficiency and maintains high standards.

Challenges in Technology Integration: Key Considerations for Fresh Produce Businesses

If generative AI really does hold the keys to accurate and fully automated inspections – the ideal state for busy quality managers – why has the fresh produce industry been relatively slow to adopt AI technology?

One of the main reasons is the nature of the industry itself. Any fresh produce operation – from growers to retailers – is a complex set of processes that relies on a range of technologies spanning logistics, refrigeration and warehousing, on top of normal day-to-day management. 

It’s not easy to integrate a single technological solution across the entire breadth of these businesses. The quality inspector in a remote field with poor network coverage has a very different experience to a warehouse manager receiving palettes of goods – but they need access to the same data, and they have the same requirements when it comes to data quality. That’s why we have prioritized ease of use in the design of the Clarifresh platform. Our customers benefit from straightforward training and swift onboarding, so they can start to reap the benefits of AI-powered workforce augmentation right out the gate. 

The Impact of AI in Quality Control: Building a Business Case for Digital Workforce Augmentation

Leading fresh produce businesses are gaining a competitive edge by adopting modern AI systems. This advantage plays out in a number of crucial areas:

• Efficiency improvement due to streamlined operations and time savings, in some cases doubling overall productivity.
• Significant error reduction, because all parties in the supply chain have real time access to the same quality standards. 
• Slashing food waste – a critical challenge for the fresh produce industry – by establishing clear and error-free product quality standards. This enables suppliers to effectively segment produce in order to meet retailers’ specific standards, avoiding quality mismatch and reducing complaints.

Far from a luxury or nice-to-have, AI algorithms are now the backbone of a modern, scalable quality control system. At Clarifresh, we are committed to smoothing the AI transition for organizations seeking to scale and expand in a sustainable way.

Contact our team to talk about your brand’s key challenges and goals, and define a clear pathway to AI-augmented success.