Manufacturers have heard plenty about what AI could do. The question now is what AI can improve in the business. Can it increase throughput, cut downtime, shorten changeovers, improve procurement, or help someone on the frontline catch a problem while there is still time to change the outcome?
For manufacturers, an end-to-end strategy means business context can move across ERP, procurement, production, quality, and frontline operations without being rebuilt at every handoff. That is why AI value does not arrive alongside ERP. It arrives through it. The transactions, workflows, and operating history are already there. Connect that foundation to AI and automation, and ERP can move from recording what happened to helping determine what happens next. That is the move from a system of record to a system of action.
That is the theme around QAD at Champions of Manufacturing 2026 in Chicago. Adaptive ERP, Redzone Connected Workforce, supplier capabilities, ES, and ChampionAI can each address a different part of the manufacturing process. QAD’s focus is to help customers work backward from a measurable problem and use the technology that fits. The test is the proof. What changed in operations or the financials? For QAD, the proof comes from customers on stage at the Champions of Manufacturing event, providing examples of how AI can be put to work quickly to shift systems of record to systems of action.
Start With The Manufacturing Problem
Midmarket manufacturers can start from different places. One may need ERP modernization while another needs better production visibility, stronger frontline engagement, or less manual work in procurement. There is no reason to force the same AI roadmap on all of them.
Work backward from the outcome. Find where time, money, inventory,y or capacity is being lost. Then decide what could improve it. ERP can provide the business record. Redzone can connect the frontline. ChampionAI can add intelligence and selected automation. AI is easier to justify when the problem and the baseline are clear.
Build The Foundation
Tenneco, an automotive components manufacturer, shows why the ERP foundation still matters. Years of growth had created a mix of systems, customizations, and manual work. Working with QAD, the company moved to QAD Adaptive in about 120 days, while standardizing core processes, removing hundreds of customizations, and bringing MRP, warehouse management, traceability, and financial structures into a more consistent model.
The move was completed without missing a production day or shipment. Expected gains include lower scrap, better labor productivity, and improved inventory synchronization. Not every manufacturer needs a full ERP transformation before using AI. QAD can help identify where customization, inconsistent data, or process debt could block the outcome and leave working processes alone. Trusted ERP context becomes more valuable as AI takes on more work. I discussed that direction in my Forbes article Why ERP Became the Execution Layer, Not Just the System of Record.
Bring Better Context To The Frontline
ERP can tell a manufacturer a lot, but many outcomes are decided on the plant floor. A line stops, a changeover runs lo,ng or a quality issue appears. Redzone can bring performance, communication, and problem-solving closer to those situations. The question is what people can now see, understand, decide, and do that they could not before.
The coaching model matters just as much as the software. Redzone coaches work directly with employees and supervisors to build daily routines, huddles, and problem-solving habits. That can make change management part of the work instead of something handed back to the customer after go-live. A key takeaway I like to highlight is that technology enables transformation. People determine the outcome. Hershey and Ferrara provide additional examples of that frontline model, where better visibility and daily routines help teams respond while production is still underway.
Follow The Economics
The customer examples are useful when the outcomes are real. Raining Rose, a personal care manufacturer using Redzone, reported a 50% reduction in changeover time and a 57% drop in product giveaway. Amtico, a flooring manufacturer, reported a 14% improvement in sitewide OEE after connecting QAD ERP and Redzone. Sauder, a furniture manufacturer, cut a recent full changeover from about 19 minutes to one minute and 24 seconds, a 93% reduction, while productivity improved about 40% during the first year.
Different industries, same point. A small loss repeated across every batch, package,e or production period can become a margin issue. QAD can help customers identify those leaks first, then decide whether better data, a process change, Redzone, or AI could improve the result.
Reyes Automotive started from a different place. With OEE already around 75%, Redzone helped make excess capacity visible, support IATF certification, and open OEM growth conversations. It is a good example of how better context can support growth in an operation that is already performing well.
Use The Same Discipline In Procurement
The same discipline can apply away from the production line. KION, a materials handling manufacturer, used QAD Supplier Relationship Management to cut RFQ creation time by 90% and increase sourcing savings by 32%. Dyer Engineering, using Procurement Champion, reported up to a 70% reduction in manual procurement administration along with better ERP accuracy and visibility.
ITW Automotive took a different route. A two-hour Agentic Process Re-engineering Workshop mapped procure-to-pay, found where time and money were being lost, and produced a business case for leadership. QAD could use that same discipline more broadly by establishing the baseline first, identifying where judgment is still needed, and then deciding which work an agent can take on.
Make the Pieces Work Together
QAD can be even more useful when the pieces work together around the customer. Adaptive ERP can provide the business and process record. Redzone can bring in what is happening on the plant floor. Supplier Relationship Management can add sourcing context. ChampionAI can help people understand what is happening and automate selected work. QAD calls this a system of action. I agree and see it as part of ERP becoming an enterprise execution layer.
Redzone is what can make this more than an ERP story. Manufacturing AI needs process context, plant-floor reality, and input from the people closest to the work. Trusted context is the currency for enterprise AI. In manufacturing, that context can include both the transaction and what is happening around it. When ERP is integrated with shop floor systems, we can see how technology drives transformation while empowered people determine the outcomes.
QAD can leverage the Redzone model to promote continuous engagement with the technology. A recommendation only helps when someone trusts it enough to act. Automation only helps when it removes work, not when it adds another process to manage. The better outcome can be more productive work, stronger skills, and better performance. I explored that connection between people, process, technology, and governance in Forbes in Why AI Requires A New Enterprise Operating Model.
Keep The Difference Clear
QAD is not the only vendor moving AI closer to ERP and operational work. Epicor Prism, Infor Industry AI, IFS Industrial AI, and SAP are all bringing agents or AI into manufacturing workflows. Buyers hear similar language across the market, making it harder to explain the difference if the story stays at the feature level.
QAD could keep the difference simple. Adaptive ERP can provide the business context. Redzone can bring plant-floor activity and a coaching model that supports adoption. ChampionAI can add role-based agents and selected actions. The story could be how those pieces improve one manufacturing outcome together through manufacturing depth, frontline adoption, and faster time to value.
What QAD Could Prove Next
QAD can keep building proof in three areas. The first is repeatability. Buyers could want to see the same results across more plants, industries, and geographies. The second is interoperability. Manufacturers rarely run one stack, so ChampionAI and Redzone could need to work cleanly with MES, PLM, supply chain, data platforms and ERP systems outside QAD. The third is control. As agents move from recommendation to action, permissions, auditability, exception handling, and recovery can matter as much as the model.
QAD is already addressing part of that with its Authorized AI Agent Library and AI Action Auditing. The next step could be to make those controls visible in customer stories, especially when an agent crosses systems or when a person needs to step in. More action can require more control. That is not a reason to slow adoption. It is part of making AI usable in production.
Measure The Business, Not The AI
QAD and Redzone can point to meaningful ERP modernization, frontline productivity, and process outcomes. Agent use is earlier, especially in production, which makes the next step less about adding more agents and more about proving where they can repeat a result.
Manufacturers already know the scorecard. Throughput, downtime, quality, inventory, working capital, productivity, service, operating cost, and margin can tell the story better than agent counts. The advisory question should stay the same. What changed in the business because of the technology?
QAD can help with the technology, manufacturing context, security, resiliency, and adoption support. Customers can own the process, baseline, data quality, and business outcome. That shared accountability can make it easier to decide what to scale and what to leave alone.
From System Of Record To System Of Action
QAD describes its direction as moving manufacturers from systems of record toward systems of action. ChampionAI can take on selected work within defined boundaries. As that role expands, manufacturers could tighten permissions, human oversight, audit trails, recovery paths, and controls around the actions that matter most.
The goal does not need to be AI in every process. A manufacturer could start with a process workshop, a value engineering assessment, a focused Redzone deployment, or a targeted agent. Prove one outcome, then decide what to modernize or scale next. The next stage of manufacturing AI can be measured less by agent count and more by what people, systems, and agents accomplish together. If a manufacturer can clearly explain what changed because of the technology, AI has moved from experimentation into execution.
