Data management and analytics has moved from a future-facing concept to a present-day necessity for pallet companies that want to operate efficiently and remain competitive. In today’s market, a strong data strategy is no longer optional—it is foundational. As companies look ahead to 2026, now is the time to evaluate what data you are collecting, how you are using it, and where gaps may exist. Smart companies are also evaluating how Artificial Intelligence (AI) can improve data gathering and analysis functions.
To help pallet companies sharpen their approach, Pallet Enterprise spoke with technology providers and industry leaders to identify practical lessons and best practices. From benchmarking key performance indicators and improving data analysis to breaking down operational silos and connecting systems across the business, these insights offer a roadmap for building a smarter, more integrated operation.
Participants in this story include the following: Brian Glass vice president of operations of Automated Machine Systems; Mitchell Kamps, CEO of Kamps Inc.; Chris Manz, vice president of strategy for Alliance Automation, Alan Miceli, director of automation systems for 48forty Solutions and Pallet Track ERP, and Nunzio Presta, head of strategic growth, global for Pallet Connect; and Clay Wells an account executive for Universal Logic. Each of these companies is experimenting with various aspects of the data gathering and analysis puzzle.
How are you or your customers using improved data collection and AI analysis to provide more robust reporting to pallet users? What is the biggest challenge that your customers face in this process?
Brian Glass: Our customers are using AI and tighter data collection to take the guesswork out of pallet inspection. Instead of someone saying, “Yeah, that looks fine,” they can now give their customers hard numbers on their pallet criteria, and how consistent each repair actually is. Big pallet users seem to be starting to expect this level of transparency. The hardest part isn’t just collecting the data—it’s making it useful. Recyclers get flooded with information but turning that into something that ties into their ERP, customer reporting, or day-to-day decisions takes time. And honestly, getting operators to trust the AI is just as much of a challenge as the technology itself.
Mitchell Kamps: Our focus at Kamps has always been the customer. On the reporting end, Kamps is leveraging improved data collection and AI analysis largely to augment our current reporting models. That means more accurate data, faster data, and unbiased data. Better data collection is helping with order patterns, forecasting, and communication with our customers. The biggest challenge we had faced was standardizing our input data as we integrated each acquisition.
Chris Manz: Alliance Automation has developed an Industry 4.0 platform called STATS, designed to enhance the business performance of our customers. STATS replaces manual, email-based reporting with continuous, automated data capture that eliminates missing or incomplete reports. The platform provides pallet companies with transparent production and quality data, including production counts, repair rates, scrap rates, and historical performance trends. By leveraging more than two years of stored historical production, STATS offers data to show customers long-term reliability, yield trends, and service-level performance. Instead of relying on outdated manual exports, STATS provides automated reporting, giving customers live dashboards that support faster decision-making and accurate reporting.
Alan Miceli: Pallet users are now able to log onto a cloud-based system from their office or anywhere in the world. They can confirm counts, sorts, and production for any of their products and have the ability (if allowed) to view live production data and inventory levels. Permissions can be set for customized access and reporting for each user. Prior to our Online Dashboards, it was very difficult for customers to audit their products. Ex. How many broken pallets do you have in stock? How many ready to go? How many products were you able to sort in a single day, shift, or hour? Providing this accountability to the pallet user is priceless and puts the pallet company way ahead of the competition.
Nunzio Presta: Our customers are using improved data collection and AI analysis to move from basic reporting to meaningful insights they can share with their own customers. With cleaner data across production, inventory, orders, and pricing, and with our open API or upcoming AI reporting tools, they can provide clearer trends, stronger performance metrics, and better explanations behind pricing and service levels.
The biggest challenge they face is time. Many pallet businesses have been focused on meeting daily demand in a fast-changing market, which makes it hard to set up advanced reporting or explore new analytics on their own. Tools like our National Trends Data help fill this gap by giving immediate visibility into market activity, while our AI reporting add-on coming in 2026 will make deeper insights more accessible and easier to use.
Clay Wells: Our customers are using AI-driven inspection to give pallet users something they have never had before. They can now capture a full record of every pallet they ship, along with a consistent quality determination that does not drift from shift to shift. It shows their customers exactly what was inspected, what passed, and why. That level of reporting builds trusts quickly because it removes subjectivity from pallet grading and replaces it with clear, repeatable standards.
The impact goes well beyond documentation. With real-time quality data, suppliers can plan repairs with greater accuracy, manage A, B, and C-grade inventory with confidence, and reduce the number of credits or rejected loads. They also use the information to create customer-specific quality programs that strengthen long-term relationships and help them stand out in a highly competitive market.
The biggest challenge is rethinking long-standing grading habits. Many facilities have relied on operator judgment for years, so shifting to objective, AI-based standards require alignment across the team. Once they make that transition, the data becomes a real business tool that improves quality, lowers rework, and gives pallet users a better product with fewer surprises.
What is the biggest misconception that your team discovered that your customers have about AI analysis?
Brian Glass: The biggest misconception is that AI will magically make all the decisions. AI is great at detecting patterns and being consistent, but it doesn’t think like an experienced operator. Another common misunderstanding is the idea that AI is plug-and-play. You still need good lighting, solid calibration, and a clear definition of what “good” looks like. The AI still learns from the people running the operation…it doesn’t replace their knowledge.
Mitchell Kamps: That it was ‘all knowing.’ You still need an experienced and dedicated team at the end of the day. AI is an incredible tool, and the technology is evolving quickly. It’s a learning curve that you need to lean into and figure out where it’s best in your business. Start small and get some gains then move into larger parts of your business once you’re more comfortable with its gains.
Chris Manz: Many clients believe that utilizing AI will complicate their processes. Our systems simplify this by transforming AI-driven insights into easy-to-use dashboards, making identifying issues and implementing simple improvements easier. Clients assume AI can fix bad or incomplete data. However, missing reports, inconsistent transcription, and manual errors are major challenges. Many pallet companies expect AI to compensate for weak processes, when in reality, AI actually magnifies underlying data problems. AI cannot generate meaningful insights if the data isn’t consistently collected. AI needs accurate timestamps, counts, downtime logs, and repair classifications. Our STATS platform collects all the data in real time and helps minimize these issues.
Alan Miceli: Customers see A.I. and think it’s smart enough to work without intervention. We wind up replacing A.I. Systems with Pallet-Track® A.I. so the cameras can use the data from Mill Manager to properly adjust inventory, quality control the correct products and operate autonomously. Those systems that do not integrate with ERP require manual input.
Nunzio Presta: The biggest misconception is often about how to apply it. Customers want to understand exactly how it adds value and increases efficiency immediately. The “low-hanging fruit” is the automation of sales and customer follow-up.
I would say customers have shifted from “all-in” excitement six months ago to a more pragmatic approach, looking for ways they can slowly and effectively integrate AI into their specific business workflows.
Clay Wells: A lot of the hesitation around AI right now comes from what people see in the rest of the world. Most of the AI they’ve interacted with feels superficial. It struggles to understand the question, it doesn’t know their business, and often gives an answer that sounds confident but isn’t actually useful. When that is your reference point, it is fair to wonder how AI could make a quality determination on something as physical and nuanced as a pallet.
That is the misconception we encounter most often. Customers assume AI is trying to overreach or act like a general problem solver. What we use it for is very different. Our system has one core job. It inspects pallets, measures real defects with real sensor data, and gives a consistent quality decision every time. There is no guesswork and no interpreting human language. It is grounded in measurement, not opinion.
Once customers see that, the fear drops fast. They realize the AI is not pretending to understand their business broadly. It understands the specific task it was built for, and it does that one task with accuracy that people cannot match at high speed. The insights come later when they start mining that data. They can spot trends, improve repair planning, tighten their quality programs, and give their customers better reporting. None of that requires the AI to overstep. It only requires the AI to do its job well.
How have your customers found that AI vision technology has helped them save money and provide a relatively fast ROI?
Brian Glass: Most of the ROI comes from eliminating inconsistencies:
- No more over repair
- No more under repairing that leads to returns
- Less wasted material
- Less labor tied up making judgment calls
- Less handling of customer ready pallets
Because the AI handles the decision-making, the job requires less specialized skill than before. Customers don’t have to rely as heavily on high-skill operators, and training new employees becomes faster and easier. Put all of that together—more consistency, a lower skill requirement, fewer touch points, and less wasted material.
Chris Manz: We work closely with our customers on AI vision technology to help them understand how it can improve data tracking. AI vision systems typically provide ROI through labor saving by reducing manual checking and rework to identify broken deck boards, missing stringers, improper repairs, and poor-quality lumber. By integrating data from our products with the STATS program, we can deliver customers more accurate insights while eliminating the need for a separate system installation.
Alan Miceli: They gain the ability to track people and products that were once very difficult to track and almost impossible to provide accountability. Now they can offer piece rates without degrading quality. Whenever you can provide incentive with accountability, production, and quality increases.
Nunzio Presta: Most pallet companies haven’t seen a real ROI from AI vision yet because the technology is still new in the industry and the existing options weren’t built for pallet operations. Most current systems come from outside the industry, built to serve multiple sectors, which makes it difficult for them to perform well in the fast-moving, chaotic environment of a pallet yard. Customers who have tried these tools often report unreliable data, extremely high pricing that doesn’t reflect pallet-industry margins, and systems that operate in isolation. Since the data isn’t connected to their ERP or daily workflows, it rarely leads to meaningful operational or financial improvements.
What’s changing with our upcoming offering is that it was developed inside the pallet industry from the start. It uses existing camera infrastructure and, more importantly, connects directly to the ERP so companies can track production counts, piece rate, productivity, and labor performance in real time. By integrating vision data with the systems that run their business, the insights become immediately actionable. This makes AI vision finally capable of delivering the accuracy, visibility, and cost structure required for a realistic and relatively fast ROI.
Clay Wells: Our customers tell us the real financial lift from AI vision shows up at the per-pallet level. If you can tighten grading accuracy enough to save just a few cents on every pallet that moves through the line, the system pays for itself very quickly. Most facilities handle hundreds of thousands of pallets a year, and small improvements in quality control or repair planning can add up to meaningful dollars. That is where AI vision has been a game-changer.
The system catches defects that are missed during busy periods and prevents teams from repairing pallets that were already good to go. It also protects revenue by preventing bad pallets from reaching customers, reducing credits, and keeping accounts running smoothly. The more reliable grading also helps customers recover higher value inventory, since the line is no longer drifting between A, B, and C standards. These corrections alone can shift a plant’s economics.
Customers also appreciate that the inspection step becomes automatic and predictable. Their teams can stay focused on production rather than making judgment calls on every pallet that rolls by. The clarity of the data leads to better forecasting, better inventory decisions, and far fewer surprises. When you stack all of this together, the ROI comes fast because every pallet is treated with the same level of accuracy every single shift. That consistency is what protects margins in a business where pennies matter.
