ERP is becoming a system of action. Infor and QAD show how trusted context, AI and execution drive measurable manufacturing outcomes.
By Robert Kramer
ERP has spent the past few years getting smarter. Generative AI moved into the user experience, copilots followed and agents are now being embedded into finance, procurement, supply chain, manufacturing and other enterprise workflows.
The technology is moving quickly. The more important question is whether the business is moving with it. Manufacturers need to know whether the technology can increase throughput, reduce downtime, improve inventory, shorten changeovers, remove manual work from procurement or help someone catch a problem while there is still time to change the outcome.
I have written for years about ERP moving from a system of record toward a system of action. In my recent Forbes analysis, Why ERP Became The Execution Layer, Not Just The System Of Record, I described ERP entering a third era where systems move beyond transactions and visibility toward recognizing business conditions and initiating controlled action. I see ERP becoming part of a broader execution layer where trusted operational context can move from transaction to decision and, within defined boundaries, to action.
Recent work with Infor and QAD | Redzone shows two approaches to getting there. Infor is focused on shortening the distance between an AI use case and production through forward-deployed engineering (FDE) and its AI Adoption Hub. QAD is connecting ERP, frontline operations, procurement and ChampionAI around what it calls a system of action. The models are different, but the question is the same: What changed in the business because of the technology?
Plenty of AI is available inside enterprise software. Adoption is harder because putting an agent into a product is very different from putting AI into a production process people depend on every day.
That was my main takeaway in my recent ERP Today analysis, Infor’s Forward-Deployed Engineering Pushes AI From Availability Into ERP Execution. The issue is whether Infor can connect AI capabilities to real processes quickly enough to produce measurable outcomes and make that learning reusable across customers.
Infor describes FDE as placing engineers closer to customer operations to scope, build, validate and deploy AI solutions. Rick Rider, Infor’s senior vice president of AI innovation, has positioned FDE as a way to move organizations from pilots to business value faster.
Putting engineers closer to customers is not new. The test is whether each engagement produces reusable product learning. If every customer requires a customized project, FDE becomes difficult to scale. If one deployment improves the platform and reduces the work required for the next, the economics look very different.
Infor’s AI Adoption Hub is designed to move FDE beyond individual engineering engagements. It uses AI across four stages: prototype, refine, deploy and evolve. AI can help create initial solution blueprints, refine them against workflows and data, move validated solutions into production and manage them after go-live.
Infor says most of its AI solutions are reaching production in roughly four weeks. The timeframe gets attention, but four weeks is not the outcome. The better question is what the organization can do differently four weeks later.
Team Air Distributing provides one example. Infor says three agents were deployed in under two weeks, with faster customer credit inquiry resolution, less employee time spent on manual work and substantially faster inventory sourcing searches. The measure is not that Team Air has three agents. It is the time saved in processes employees and customers depend on.
I also sat down with Rider on KramerTALK to go deeper into FDE, the AI Adoption Hub and the harder question of moving customers from AI availability to adoption. If more of the build and deployment work becomes automated as Infor expects, FDE could become less about adding engineering capacity and more about creating a repeatable path from customer problem to working solution, then feeding that experience back into product development.
QAD | Redzone approaches execution from a different direction. ERP can hold the order, inventory, financial and planning record, but many manufacturing outcomes are decided elsewhere. A line stops, a changeover runs long, a quality problem develops or a supplier misses a commitment. Someone on the plant floor may understand the issue before it reaches an ERP dashboard.
QAD Adaptive ERP provides the core business and process record. Redzone Connected Workforce adds frontline activity. QAD Supplier Relationship Management adds sourcing and supplier information. ChampionAI can help people interpret that context and automate selected work.
QAD calls this movement from systems of record toward systems of action. I see it as part of a broader ERP evolution: System of record → trusted operational context → decision → governed action → business outcome. AI value does not arrive alongside ERP. It arrives through ERP because the transactions, workflows and operating history needed to understand the business are already there.
Tenneco shows why the ERP foundation still matters. Years of growth had created a mix of systems, customizations and manual processes. Working with QAD, the automotive components manufacturer moved to QAD Adaptive in about 120 days, standardizing core processes, removing hundreds of customizations and bringing MRP, warehouse management, traceability and financial structures into a more consistent model. The deployment was completed without missing a production day or shipment.
Not every company needs a full ERP transformation before using AI, but process debt, inconsistent data, unnecessary customization and fragmented workflows can limit what AI can understand and what an agent can safely do. Trusted ERP context becomes more valuable as AI takes on more work.
Redzone broadens the story beyond ERP by bringing performance data, communication and problem solving closer to production. Its coaching model also puts people directly into adoption through daily routines, huddles and continuous improvement practices. I like that part of the model because it doesn’t treat change management as something handed back to the customer after go-live.
That connects directly to a point I made in Forbes in Why AI Requires A New Enterprise Operating Model. Technology, process, data, governance and people cannot be separate programs once AI starts participating in decisions and execution. Technology creates the capability. People determine the outcome.
The results help make the case. Raining Rose reported a 50% reduction in changeover time and a 57% reduction in product giveaway. Amtico reported a 14% improvement in sitewide OEE after connecting QAD ERP and Redzone. Sauder reduced a full changeover from about 19 minutes to one minute and 24 seconds, a 93% reduction, while productivity improved about 40% during the first year. Reyes Automotive, already operating around 75% OEE, used Redzone to make excess capacity visible, support IATF certification and open OEM growth conversations.
The system-of-action idea does not stop at production. KION used QAD Supplier Relationship Management to reduce RFQ creation time by more than 90% and reported 32% savings in one sourcing initiative. Dyer Engineering used Procurement Champion to automate supplier coordination, document handling and selected ERP updates in a process where manual supplier follow-up had consumed as much as 70% of an average workday.
ITW Automotive took another route. A two-hour Agentic Process Re-engineering Workshop mapped procure-to-pay, identified where time and money were being lost and produced a business case for leadership. That is the right order: establish the baseline, understand where judgment is still required, then decide what AI should recommend, what it can automate and where a person needs to remain in control.
The common thread across Infor and QAD is context. An ERP transaction can tell an AI system that there are 8,000 units of inventory. Knowing how many are available, committed, on quality hold, at the wrong location or needed for a customer order is context.
I explored this more directly in InfoWorld in Why Trusted Context Is Becoming the Currency for Enterprise AI. Broader data access helps, but access is not the same as understanding. As AI moves into production, governed and business-relevant context becomes essential to whether agents create value or operational risk.
Infor is building around industry-specific data, workflows and orchestration across agents and systems. QAD combines ERP transactions with plant-floor activity, sourcing information and input from the people closest to the process. Neither approach makes the system of record less important. As agents take on more work, the quality of the context behind their decisions matters more.
Trusted context is the currency for enterprise AI.
A generative AI answer can be reviewed before someone acts. An agent changing an order, updating ERP, contacting a supplier or triggering another process creates a different level of operational risk.
I explored that issue in Forbes in How AI Agents Are Changing ERP And What CIOs Need To Know. The question is becoming less about what an agent can technically do and more about what the business should allow it to do, when someone needs to step in and who remains accountable for the result.
Permissions, auditability, exception handling, human intervention and recovery paths need to be part of the execution model. QAD is addressing this through its Authorized AI Agent Library and AI Action Auditing. Infor is making a similar governance argument around auditable agent actions and moving validated solutions from prototype into production. More action can require more control. That is part of making AI usable in production.
Infor and QAD are not alone. Epicor, IFS, SAP and other enterprise application vendors are bringing AI and agents closer to manufacturing and operational workflows. Buyers will hear similar language around agents, automation, industry AI and time to value, making feature lists less useful for understanding the difference.
I would watch three areas. Repeatability: can the vendor reproduce an operating result without rebuilding the solution each time? Interoperability: can ERP, MES, PLM, supply chain systems, data platforms and plant technology share enough trusted context to support the process? Outcomes: did the technology measurably improve the business?
Infor needs to keep showing that faster FDE deployments create reusable product learning rather than faster projects. QAD needs to continue connecting ChampionAI and Redzone to repeatable plant, procurement and financial outcomes across a broader customer base. The market needs fewer demonstrations of what an agent can do and more evidence of what changed afterward.
Manufacturers already know the key metrics: throughput, downtime, quality, inventory, working capital, productivity, service, operating cost, and margin. These indicators can provide a clearer picture than the number of agents used or AI features launched.
Infor adds another measurement of how quickly an organization can go from identifying a problem to putting a solution into production, then reuse what it learned. QAD adds the connection between enterprise systems, frontline work and measurable operational improvement.
Put those together and the next phase of ERP becomes stronger. ERP can provide trusted business context. AI can help interpret it. People can make better decisions. Agents can perform selected work. Governance can define the boundaries. The outcome determines whether any of it was worth doing.
The goal is not AI in every process. Start with a business problem, establish the baseline, understand the context and decide where people should remain in control. Use automation where it removes work or improves the process. Prove one outcome, then decide what is worth scaling.
That is what a system of action should look like, and for manufacturers, it is a much more useful measure of what is new in ERP than another AI feature list.
About the Author:
Robert Kramer covers AI, ERP, SCM, data, and security, with a focus on real-world performance. He is the founder of KramerERP, a boutique advisory firm advising on ERP modernization, data strategy, and AI readiness. With over 30 years of experience in enterprise systems, manufacturing, and operations, he was previously VP & principal analyst at Moor Insights & Strategy. He has an MBA in International Marketing and teaches graduate courses with an emphasis on marketing, strategies, pricing, and consumer behavior. His work focuses on execution, data quality, and outcomes, helping organizations understand what works in production. Follow Kramer for insights on ERP evolution, data platforms, and the shift from systems of record to systems of action.
Forging the Next 250 Years: Powering the Next Era of American Manufacturing
As manufacturers offer more customization than ever before, managing product complexity has become a critical challenge. Tune in with Dan Joe Barry, Vice President of Product Marketing at Configit, who explores how companies are tackling the growing number of product configurations across engineering, sales, manufacturing, and service. He explains how Configuration Lifecycle Management (CLM) helps organizations maintain a single source of truth for configuration data. The result: fewer errors, faster quoting, and the ability to deliver customized products at scale.
Get In Touch
Google news and SEO compliant, Industry Today’s state-of-the-art digital media platform offers bespoke media campaigns that target key decision makers and buyers to achieve your marketing and promotional goals.
Contribute
Showcase your brand and promote your business to our highly targeted audience. We offer detailed Google Analytics with measurable ROI to assure success. Submit your content for review by our Editorial team who will contact you to discuss the project further.
About Us
Reach Your Targeted Audience and Grow Your Business. Learn more About Industry Today.
Contact Us
© 2026 Industry today. All Rights reserved.
