Tools & Platforms
AI on the line: How AI is transforming vision inspection technologies
In an era of tightening global regulations and rising consumer expectations, the F&B industry is increasingly turning to advanced vision inspection technologies. From spotting defects to ensuring compliance, these automated inspection tools are reshaping quality control, enhancing efficiency, reducing waste and boosting safety. FoodBev’s Siân Yates explores how cutting-edge technology is reshaping the industry, one perfectly inspected product at a time.
In the food and beverage industry, traditional quality inspection methods have always relied on human observation – an inherently inconsistent and flawed process. Automated vision inspection systems offer a transformative alternative. By detecting foreign objects, assessing product uniformity and ensuring that only items meeting strict quality criteria reach consumers, these systems significantly enhance operational efficiency and minimise errors.
“As the food industry moves towards more automation, applications are becoming increasingly complex, largely due to the variability in food products,” said Anthony Romeo, product manager at US-based vision solutions company Oxipital AI. This complexity stems from the need for automated systems to adapt to the wide range of textures, sizes and ingredients in food, making precise automation a key challenge.
Stephan Pottel, director of strategy at Zebra Technologies, highlighted the rising demand for intelligent automation: “There’s a growing need for machine vision and 3D solutions, powered by deep learning, to address more complex food and packaging use cases, along with vision-guided robotics for tasks like inspection, conveyor belt picking and sortation workflows”.
Key features of vision inspection
1. Defect detection
Vision inspection systems excel in identifying defects that may go unnoticed by human inspectors. These systems utilise high-resolution cameras and advanced algorithms to detect foreign objects, surface defects, and inconsistencies in size and shape. For example, in the fruit packing industry, vision systems can identify bruised or rotten fruit, ensuring only high-quality products are packaged and shipped.
2. Label verification
These technologies are increasingly used for label verification, ensuring compliance with regulatory standards. Systems can check for correct placement, legibility and adherence to labelling requirements, such as allergen information and expiration dates. Vision is usually deployed for label verification, rather than food surface defects, enhancing compliance and reducing the risk of costly recalls.
3. Product uniformity assessment
Maintaining product uniformity is crucial in the food and beverage sector. Vision inspection systems can assess visual aspects such as size, shape and colour. For instance, a snack manufacturer might use vision inspection to ensure that chips are uniformly shaped and coloured, meeting consumer expectations for quality and appearance.
4. Adaptive manufacturing
Advanced vision systems, particularly those incorporating AI and 3D technology, enable adaptive manufacturing processes. These systems can adjust production parameters in real time based on the visual data they collect. For example, in a bakery, vision systems can monitor the size and shape of pastries as they are produced, allowing adjustments to baking times or temperatures to ensure consistent quality.
Advancements in AI
Recent advancements in AI, automation and 3D technology have greatly enhanced machine vision systems, increasing accuracy and providing realistic visual sensing capabilities. 3D imaging technologies are being used to assess the shape and size of products, ensuring they meet packaging specifications. For instance, in the seafood industry, 3D scanners can evaluate the dimensions of fish fillets, ensuring they are cut to the correct size before packaging. This not only reduces waste but also ensures consistency in product offerings.
What is more, 3D profile sensors improve depth perception and refine quality control, making them indispensable tools in industrial automation. Oxipital AI’s Romeo highlighted the potential of these technologies: “Removing defects before they reach customers is a key first step where vision inspection technology plays a role, but there’s even more data to be leveraged”. By preventing defects from the outset, manufacturers can boost yield and reduce waste.
AI-powered vision inspection systems can also facilitate real-time monitoring of production lines, identifying potential issues before they escalate. This capability allows manufacturers to implement predictive maintenance, reducing downtime and improving overall efficiency.
AI and food safety
Consumer safety remains a top priority in the food and beverage industry. AI plays a crucial role in monitoring and analysing processes in real time, helping manufacturers navigate the complexities of compliance with legal requirements and certification pressures from major retailers.
As Zebra Technologies’ Pottel explained: “AI is ideal for food and beverage products where classification, segmentation, and object and anomaly detection are essential. It is also enhancing asset and inventory visibility, which is crucial for predicting contamination risks and maintaining high safety standards throughout the supply chain.”
“Vision technologies can help check the presentation of food products…offering a quick, repeatable and reliable way to assess the visual aspects of food products like size, shape and colour,” added Neil Gruettner, market manager at Mettler-Toledo Product Inspection.
He continued: “Deployment of this type of AI provides context to support rule-based machine learning and improve human decision-making. It also gives inspection equipment the tools to extract and interpret as much data as possible out of a product, facilitating the evolution and refinement of production processes through the continuous exposure to vast datasets.”
AI-enhanced vision systems also guide robots in handling food products, particularly those that are delicate or irregularly shaped. “AI has proved to be a great method for tackling applications with a high frequency of naturally occurring organic variability, such as food,”Oxipital AI’s Romeo explained, adding that this adaptability ensures gentle and precise handling, particularly important when sorting fresh produce or packaging baked goods.
Fortress Technology uses AI to reduce contamination risks and identify defects. The company’s commercial manager, Jodie Curry, told FoodBev: “Streamlining processes reduces the risk of contamination and ensures consistent quality. Implementing automated technology and digital tools helps identify inefficiencies and boosts responsiveness.”
The role of combination inspection systems
The integration of multiple inspection technologies into single systems is another key trend in this space. These systems integrate various inspection technologies, such as X-ray, checkweighing and vision inspection, to provide a comprehensive assessment of food products. By combining these technologies, manufacturers can ensure higher quality control, better detection of defects and more efficient production lines. This trend allows for more accurate and reliable monitoring, helping to reduce waste, improve safety standards and enhance overall product quality.
For its part, Fortress offers combination systems that enable comprehensive and multi-layered inspection. The company is already leveraging its proprietary data software package, Contact 4.0, across its metal detection, X-ray and checkweighing technologies. Contact 4.0 allows processors to review and collect data, securely monitor and oversee the performance of multiple Fortress metal detectors, checkweighers or combination inspection machines connected to the same network.
Deep learning and quality control
Deep learning is revolutionising visual inspection by enabling machines to learn from data and recognise previously unseen variations of defect As Zebra Technologies’ Pottel explained: “Deep learning machine vision excels at complex visual inspections, especially where the range of anomalies, defects and spoilage can vary, as is often the case with food.
This technology is vital for automating inspections and ensuring quality. Deep learning optical character recognition (OCR) also improves packaging inspection by ensuring label quality, regulatory compliance and brand protection. It can verify label presence, confirm allergen accuracy and prevent mislabeling.
“The goal is to strengthen quality control by capturing an image and processing it against set quality control parameters,” Mettler-Toledo’s Gruettner pointed out.
Vision systems are increasingly deployed for label verification, ensuring compliance with legislative food labelling requirements. The Mettler-Toledo label inspection portfolio features Smart Camera systems (V11, V13, V15) for basic label inspections, including barcodes, alphanumeric text and label quality. For more advanced applications, the PC based V31 and V33 systems offer a larger field of view, faster throughput and enhanced inspection capabilities.
Oxipital AI uses 3D product scans and synthetic data generation to eliminate the need for hand-labelling images. “All training is done at Oxipital AI, enabling food and beverage customers to deploy AI without needing a team of experts,” said Romeo. “Our solutions are designed for immediate impact, requiring no coding, DIY or machine-learning expertise to implement and maintain.”
Real-world applications and future prospects
According to Zebra’s Global Manufacturing Vision Study, which surveyed leaders across various manufacturing sectors, including F&B, 66% of respondents plan to implement machine vision within the next five years, while 54% expect AI to drive growth by 2029.
These figures, coupled with the expanding market for vision inspection systems, suggest
that the majority of manufacturing leaders are prioritising the integration of these advanced technologies, seeing them as crucial tools for both immediate improvements and long-term growth.
This shift is partly driven by increasingly stringent government regulations, which demand more accurate labelling and packaging. Many companies are already successfully leveraging AI to enhance their operations, particularly in labelling processes.
Despite its clear advantages, the uptake of AI has been slow. The main barrier appears to be cost. While the initial integration can be expensive, AI has demonstrated significant long-term cost savings, making it a worthwhile investment over time.
Zebra’s studies have shown that the pressure to maintain quality while managing fewer resources is intensifying for manufacturers. As a result, cost remains a significant consideration when implementing AI solutions.
Fortress recommends consolidating AI systems into a single interface, which helps reduce costs in the long term. Curry told FoodBev: “The future of our food supply chain depends on advanced inspection systems that enhance food safety, reduce product waste and require minimal factory floor space”.
She continued: “Combination systems offer the benefit of space efficiency, as all sales, services, parts and technical support are handled by one provider. A single interface simplifies training, improves operational safety and drives cost savings through faster installation and reduced training time.”
As AI continues to evolve, its role in vision and inspection is set to expand. Advancements in machine learning, sensor technology and robotics will lead to even more sophisticated and efficient inspection systems, raising quality and safety standards for consumers worldwide.
Tools & Platforms
The AI Era Soft Skills to Prioritize for Career Growth
The Solutions Review and Insight Jam team has identified several soft skills that professionals throughout the enterprise technology market must prioritize in the AI era, according to proprietary research.
As artificial intelligence (AI) reshapes industries and transforms the nature of work itself, professionals face an unprecedented challenge: how to remain relevant and thrive in an increasingly automated world. While technical skills and AI literacy are undoubtedly important, the most successful professionals of the AI era will be those who master distinctly human capabilities that complement rather than compete with artificial intelligence.
The traditional career playbook gives way to a new paradigm where human insights fueled by adaptability and emotional intelligence become the primary drivers of professional success. Organizations are discovering that their most valuable employees aren’t necessarily those who can outperform AI at computational tasks, but those who can work alongside AI systems while bringing irreplaceable human judgment, creativity, and connection to their roles.
However, a proprietary study of over 200 senior tech professionals across markets and roles (you can check out the Solutions Review team’s research here) reveals a disconnect. While 94 percent of tech leaders agree that soft skills are more critical than ever, most admit their organizations lack the structure, time, or training mechanisms to develop them.
Findings like this should be a wake-up call for professionals, now more than ever. That’s why our team conducted the research in the first place and compiled some of the soft skills respondents identified as particularly valuable for the current market trends we’re seeing. Professionals prioritizing the five skills below will differentiate themselves from AI systems, enabling them to leverage AI tools more effectively, lead diverse teams through constant change, and create value that transcends what technology alone can provide.
5 AI Era Soft Skills Professionals Must Prioritize for Career Growth
Curiosity
In an era when information becomes obsolete faster than ever, curiosity has evolved from a “nice-to-have” trait to a career-critical capability. The half-life of skills continues to shrink as AI automates routine tasks and creates entirely new categories of work, which means professionals who maintain an active, systematic approach to learning and questioning will consistently outperform those who rely on static knowledge.
Our respondents agree, as 93.3 percent rate curiosity as “very” or “extremely important” to their careers. The problem is that nearly half of them also say they lack the time to commit to that learning. Curiosity in the AI era must go beyond passive learning, as professionals must actively seek an understanding of how AI systems work, where they excel, and crucially, where they fall short. The most successful professionals will be those who ask probing questions about AI outputs, challenge assumptions, explore the boundaries of what these systems can (and cannot) do, and identify opportunities for human-AI collaboration that others might miss.
Relationship-Building
The ability to build and maintain meaningful professional relationships has become more valuable, not less, especially with work becoming increasingly digital and AI-mediated. While AI can analyze communication patterns and even generate personalized messages, it cannot replicate the trust, empathy, and genuine connection that form the foundation of effective collaboration.
The complexity of modern organizations requires professionals who can navigate intricate networks of stakeholders, each with different priorities, communication styles, and levels of comfort with new technologies. While many tech professionals prefer working alone, despite recognizing the need for strong networks, 84.5 percent still acknowledge the importance of relationship-building. Once again, though, industry leaders say that prioritizing professional relationships is a struggle.
As AI democratizes access to information and tools, competitive advantages come from having access to diverse perspectives and early insights into emerging trends. The ability to cultivate relationships with thought leaders, potential collaborators, and industry pioneers becomes a significant differentiator.
Humility
Perhaps counterintuitively, humility has become one of the most powerful professional attributes in the AI era. As the pace of change accelerates and the complexity of challenges increases, the professionals who acknowledge the limits of their knowledge and actively seek input from others, including AI systems, often outperform those who rely solely on their existing expertise.
81 percent of tech leaders say humility (seeking and using feedback) is essential to their career success.
Intellectual humility manifests in several ways that can directly impact career growth. First, it enables professionals to embrace AI as a collaborator rather than a threat. Instead of viewing AI capabilities as diminishing their value, humble professionals recognize that these tools can amplify their effectiveness when used thoughtfully. They’re comfortable saying “I don’t know” and turning to AI systems for analysis, while also recognizing when human judgment is necessary to interpret and apply AI-generated insights.
This humility also extends to learning from failures and mistakes. In an environment where experimentation with new technologies and approaches is essential, the professionals who can quickly acknowledge when something isn’t working and pivot their approach are more likely to succeed than those who persist with failing strategies to protect their ego or reputation.
Resilience
Thanks to AI, technologies that once seemed permanent have become obsolete within years, entire job categories are disappearing as new ones emerge, and the skills required for success continue to evolve. In this context, resilience has become essential for long-term career success. Professionals and executives are well aware, too, with over 90 percent claiming resilience is a crucial skill, while also acknowledging that it can be challenging to recover from setbacks.
That’s why continuous learning remains essential. The more attention given to projects that promote resilience, the easier it will be for teams to acquire new skills, adapt to changing workflows, and maintain productivity during organizational transformation. Whether it’s an AI implementation that doesn’t deliver expected results or a skill that becomes automated, resilient professionals can bounce back quickly and extract valuable lessons from positive and negative experiences.
Perspective-Taking
According to survey findings, 84 percent of tech professionals value perspective-taking, yet 31 percent struggle to reconcile conflicting viewpoints. Further, an alarming 29 percent believe their perspective is the “best” one, even in teams with diverse views, which can become a major blocker to innovation.
Understanding and considering multiple viewpoints has become critical as AI systems reshape how work gets done and decisions are made. This ties into the importance of “humility” as a soft skill, as effective perspective-taking requires professionals to serve as interpreters between different stakeholders who may have vastly different comfort levels with AI and other assumptions about its capabilities. This skill proves particularly valuable when implementing AI solutions that affect diverse groups of users, customers, or colleagues.
The professionals who excel at perspective-taking often become the most trusted advisors and change leaders in their organizations, helping others see opportunities and navigate challenges that might otherwise seem overwhelming.
Conclusion
The AI era represents both an unprecedented opportunity and a significant challenge for professional development. While technical skills and AI literacy are important, the professionals who will thrive are those who develop distinctly human capabilities that complement rather than compete with artificial intelligence. The report shows that 94 percent of professionals agree that curiosity, resilience, and other critical soft skills are required for the future. And yet, most also state they lack the time, coaching, and feedback to improve these skills.
That lack of time is a problem, considering those soft skills are integral to successful business practices and professional development. For example:
- Curiosity drives continuous learning and innovation.
- Relationship-building creates the trust and networks necessary for effective collaboration.
- Humility enables productive partnerships with both AI systems and human colleagues.
- Resilience provides the foundation for adapting to constant change.
- Perspective-taking facilitates understanding across diverse viewpoints and technologies.
These soft skills work synergistically, reinforcing each other and creating a foundation for sustained career growth regardless of how AI continues to evolve. Promoting these soft skills requires support from the top down, and vice versa. Individuals should voice their desire for upskilling or ongoing learning programs, and executives must respond by providing them with the time and resources they need to focus on those skills. Doing so will create value that transcends what technology alone can provide and build careers that remain relevant and rewarding in an increasingly automated world.
The future belongs to those who can combine human wisdom with artificial intelligence, and these five soft skills provide the roadmap for making that combination both powerful and sustainable.
Take the Next Step: Help Shape the Future of AI-Ready Workforces
The best technologists of the future will not simply know how to build, prompt, or deploy AI. They’ll learn how to work with others, weather change, and see the bigger picture. Human-centered skills are the foundation of that future, and the time to start building them—systematically, strategically, and sincerely—is now.
To that end, the Solutions Review and Insight Jam teams are conducting a follow-up study to deepen our understanding of the human-AI skills gap, and we need your input.
Note: These insights were informed through web research and generative AI tools. Solutions Review editors use a multi-prompt approach and model overlay to optimize content for relevance and utility.
Tools & Platforms
Shape Tomorrow’s Technology Today with Lewis University’s AI Degree – Shaw Local
Artificial Intelligence, or AI, is no longer just a concept for the future. It is actively transforming how we work, live, and communicate. From voice assistants and predictive healthcare to self-driving cars and fraud detection, AI drives innovation across every industry. As the demand for skilled professionals continues to grow, Lewis University is launching a new master’s degree in Artificial Intelligence.
Lewis University’s Master of Science in Artificial Intelligence is designed for professionals looking to expand their expertise in this rapidly growing field. This program emphasizes real-world applications, ethics, data analysis, and machine learning, giving students both technical skills and a strong ethical foundation. Courses are taught by experienced faculty who understand the evolving AI landscape and the need to stay ahead.
Students in Lewis University’s Master of Science in Artificial Intelligence program study deep learning, natural language processing, and the ethics of AI. The program features project-based learning and flexible course options, preparing graduates for roles in AI development, strategy, and implementation.
According to the World Economic Forum, AI and automation are expected to create 97 million new jobs worldwide by 2025, providing more opportunities for those skilled in designing, implementing, and managing AI systems responsibly. This graduate program prepares you for a career in data science, robotics, cybersecurity, and more, regardless of your background in computer science, engineering, business, or healthcare.
What sets Lewis University apart is its focus on personalized learning. The program offers flexible formats, including online and hybrid options, allowing students to balance career growth with current responsibilities. Students benefit from small class sizes, hands-on projects, and strong professional networking opportunities.
By earning a master’s degree in Artificial Intelligence from Lewis University, students gain knowledge, but also become leaders in one of the most dynamic and in-demand fields today.
To learn more or to apply today, visit https://www.lewisu.edu/academics/msai/.
Lewis University
One University Parkway
Romeoville, IL 60446
(815) 838-0500
Tools & Platforms
New York can lead the AI revolution – Bronx Times
With artificial intelligence driving growth, New York is home to the world’s second most valuable startup ecosystem, according to the latest report from Startup Genome. In previous years, the Big Apple was tied with London, which has slipped to third place. This is great news as AI is transforming industries—from financial services to Main Street shops and restaurants—and revolutionizing the way we do business. As this technology reshapes how we learn, communicate and conduct transactions, it is crucial that we ensure the next generation is equipped to use it responsibly, creatively and ethically. We must ensure, too, that New York and the United States continue to lead the AI revolution.
Across sectors, AI is enhancing accuracy, streamlining processes and driving better decision-making. It’s automating repetitive tasks and improving efficiencies. For small businesses, AI levels the playing field by enabling faster customer service and more targeted and effective marketing strategies. These advances are opening opportunities once out of reach for many entrepreneurs, enabling them to scale, compete and succeed. The technology is revolutionizing healthcare, government services and disaster preparedness, too.
As we move into an era where AI is embedded in everything, tech literacy is essential. We need to make sure our students are well-equipped to strategically use and build with these tools before they enter the workforce. This underscores the need for more developers and students in STEM fields generally. Local startups are already building the next generation of AI-powered tools that will fuel the economy of the future, and many of these entrepreneurs will come from communities like the Bronx, where Lehman College is integrating AI to motivate students to be creative and harness these tools to nurture intellectual growth.
Education in AI must go beyond simple use cases––it must also emphasize the values that should guide its application. We must ensure that the ethical guidelines we set for AI are not just theoretical, but practically integrated into the development and use of this technology. Contrast this with what we’re seeing in countries like China, where AI is being used for authoritarian purposes—tracking citizens, suppressing dissent and controlling access to information. This is why we must ensure the US leads in both open- and closed-source AI. While China is rapidly advancing in AI and utilizing the technology to spread authoritarian values globally, every state has a role to play to ensure we are not undermining our own ability to lead on all AI development fronts.
Digital inclusion is paramount. Let’s acknowledge the digital divide by proposing targeted investments in under-resourced neighborhoods, ensuring that Bronx-based students and entrepreneurs have not only the tools but also mentorship and initial seed funding to launch the next breakthrough AI startup or at least give them a chance.
Now, to truly maximize New York’s potential as a global leader in AI, we must do more than just prepare our students. We must ensure the government properly invests in education, supports local startups and encourages businesses and institutions of higher learning to embrace AI-powered solutions. At the same time, lawmakers must avoid enacting laws that could suppress innovation or create barriers to the adoption of AI technologies. Regulations should promote both open and closed source models, ensuring New York’s AI ecosystem remains robust and accessible to the state’s innovators. If we fail to do so, we risk undermining the entrepreneurial spirit that makes New York’s tech ecosystem so valuable and sending our brightest minds to other states or countries with more supportive regulatory environments.
The next breakthrough in AI should come from a startup founded by a CUNY or University of Mount Saint Vincent graduate right here in New York City. By equipping our students with the right skills, ethical guidance and entrepreneurial support, we can ensure that New York remains a leader in this transformative field and that our workforce is prepared to meet the challenges and opportunities of the AI-driven future.
Lawrence Fauntleroy is Vice President of Strategy at the University of Mount Saint Vincent. Sean Stein Smith, DBA, CPA, is an Associate Professor of Accounting at City University of New York’s Lehman College.
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