QQAD Champion AI

“I want to be the best in manufacturing,” QAD | Redzone CEO Sanjay Brahmawar said at the November 2025 launch of Champion AI, the agent layer now built into the company’s QAD Adaptive ERP. The target is specific: manufacturers with $200 million to $5 billion in annual revenue, and the agents run on Amazon Web Services, with AWS CEO Matt Garman backing the launch.

Champion AI is best for mid-market manufacturers already running QAD, or choosing a new ERP and willing to consider QAD Adaptive. It comes as part of the ERP rather than as a standalone product, and QAD shares pricing through sales. We rate it 7.3/10 on our scale, mainly reflecting that entry cost and the ERP dependency rather than the agent design. We have not used Champion AI ourselves; this review is based on QAD’s announcements and coverage by Constellation Research, SDCExec and App Developer Magazine.

Architecture and Target Market Over Customer Results

QAD has published more about Champion AI’s architecture and target market than about customer results so far.

Two facts matter for buyers. The AWS foundation means Champion AI uses Amazon’s managed agent runtime rather than a homegrown one, which matters to IT teams that already standardize on AWS. The Kavida.ai acquisition shows QAD chose to buy procurement agent expertise rather than build it, so the supply chain agents have a specialist origin.

The revenue band also tells you who QAD is focused on. QAD is aiming below the largest global manufacturers and above small job shops. For companies in that middle band, the length and cost of an ERP rollout is a common worry, which helps explain why implementation agents sit next to planning and procurement agents in QAD’s pitch. Brahmawar has said the company does not want to be everything to everybody, and Champion AI reflects that narrow focus on manufacturing.

Launch coverage centered on partners, analysts and architecture. Before buying, ask QAD for reference customers using the agents in production, and for the specific actions each agent can take.

The Agent Layer in QAD’s Next-Generation ERP

Champion AI is the agent layer inside QAD Adaptive, which QAD describes as its next-generation ERP. QAD frames the change as a move from systems of record to systems of action: the ERP should not only store what happened but prompt and carry out what should happen next.

Three Layers

Constellation Research describes QAD | Redzone’s offering as three pillars: the adaptive ERP backbone, Champion AI as the agentic engine, and Redzone as the system for frontline workers. Redzone Connected Workforce brings process intelligence to people on the plant floor, while Champion AI works on the planning, procurement and operational data in the ERP.

Spotting Supply Problems and Starting Next Steps

• Watch for problems: agents flag emerging issues in supply chains, production schedules and demand signals.
• Recommend and start next steps: they suggest adjustments to production or procurement and can initiate them within defined parameters.
• Automate routine decisions: everyday choices are handled automatically, while strategic decisions stay with managers.
• Explain trade-offs: managers see the trade-offs of suggested changes in real time.
• Speed up implementation: Champion Pace Rapid Implementation automates implementation tasks, part of a group of implementation, productivity and business optimization agents.
• Handle procurement work: procurement and supply chain agents, from the Kavida.ai acquisition, focus on supplier and purchasing tasks.

Users stay inside their existing ERP screens rather than learning a separate AI tool, according to App Developer Magazine’s January 2026 coverage.

The AWS Foundation

Champion AI uses Amazon Bedrock AgentCore and Amazon SageMaker. AWS CEO Matt Garman framed the partnership as moving agentic AI from proof of concept to production by connecting it securely to a company’s own data. QAD’s stated goals include faster time to value, higher productivity, lower total cost of ownership and better cyber resilience.

The Rest of the Release

Champion AI arrived alongside other changes to QAD Adaptive. The November 2025 release added a fully embedded warehouse management system, enhanced production scheduling and tools for Unique Device Identification labeling, which matters to medical device makers. These features give the agents more of the operation to see: inventory, schedules and labeling all sit in the same system the agents watch.

Who Is Behind It

Sanjay Brahmawar joined QAD | Redzone as CEO in March 2025 after running Software AG. Amit Sharma leads the manufacturing ERP business and Ken Fisher is president of Redzone, which QAD positions as the frontline arm of the company. At launch, Brahmawar described the offering as a new model rather than a better version of ERP, and argued that the biggest risk for manufacturers is inaction. Constellation Research analysts at the event, Mike Ni and Holger Mueller, framed the shift around faster decisions and AI reaching the shop floor.

Managers Set Agent Boundaries and Approve Strategy Changes

Managers set the boundaries agents operate within, approve strategic changes and decide how much routine work to automate. ERP data still needs care: demand, capacity and planning data must be accurate for the agents’ alerts to be useful. Amit Sharma, president of manufacturing ERP at QAD | Redzone, told Constellation that this data will become more valuable than the software itself.

Part of the ERP, Not Priced on Its Own

QAD does not publish prices for Champion AI or QAD Adaptive. Based on QAD’s announcements, the agents are part of the ERP offering.

Planner and Buyer Time the Agents Could Return

Without list prices, the useful question is how much planner and buyer time the agents could return. The scenarios below use our own illustrative assumptions.

• A planning team of five: if each planner spends 8 hours a week reacting to supply and schedule exceptions, that is 40 hours a week. If agents catch issues earlier and handle routine adjustments, cutting that by a quarter returns 10 hours a week, about 520 hours a year, worth $31,200 at an assumed $60 an hour.
• A company switching ERP: here the agents are one part of a larger project. Champion Pace Rapid Implementation targets rollout time, so ask QAD for a written implementation timeline and how much the agents shorten it.
• An existing QAD customer: the main cost is upgrading to QAD Adaptive and any added modules; ask whether Champion AI agents are included or priced separately.

Other costs to plan for: ERP licensing and upgrades, implementation services, data cleanup for items, suppliers and routings, and training planners and buyers to trust and supervise the agents.

Questions worth asking in a demo: which agents are live today versus planned, which actions each agent can take without approval, how agent activity is logged for audit, whether AWS model usage is billed separately, and what data cleanup QAD expects before agents go live.

QAD Champion AI Compared With Didero, Tulip AI Agents and Cognite Atlas AI

Each of these rivals approaches the factory from a different system: supplier email, frontline apps and plant data.

If you already run QAD, Champion AI is the obvious first look because it needs no new system. If you run SAP or Oracle and want help with supplier follow-up, Didero is the more direct fit. Tulip and Cognite work closer to the shop floor and plant data. For more choices, see our AI agents for manufacturing and enterprise AI agents guides.

Current QAD Customers and Mid-Market ERP Shoppers

• Current QAD customers: the agents arrive in the ERP they already use, with no separate tool to learn.
• Mid-market manufacturers choosing a new ERP: QAD built Champion AI for companies with $200 million to $5 billion in revenue.
• Planners dealing with frequent supply and schedule changes: agents flag issues early and suggest adjustments.
• Companies standardized on AWS: Champion AI runs on Amazon Bedrock AgentCore and SageMaker.
• Regulated manufacturers: the same release added Unique Device Identification labeling tools alongside the agents.

Manufacturers on SAP or Oracle

• Manufacturers on SAP, Oracle or another ERP: Champion AI is part of QAD Adaptive and does not run on other systems.
• Small manufacturers: the target market starts at about $200 million in revenue.
• Buyers who want to test before talking to sales: there is no published price, free plan or trial.
• Teams that want to pick individual agents a la carte: the agents come as part of the ERP rather than as separate products.
• Plants focused on machine data and maintenance: a plant data platform such as Cognite or a maintenance tool fits that job better.

Questions on Agent Scope, Data and ERP Commitment

• ERP commitment: the agents are part of QAD Adaptive, so switching ERPs means leaving them behind.
• Agent scope: QAD describes agents in groups; confirm the exact agents, actions and limits included in your contract.
• Customer references: ask for manufacturers of a similar size using the agents in production, and what they automate.
• Boundaries and approvals: confirm which decisions agents can make on their own and how those limits are set and audited.
• Data quality: alerts and recommendations depend on accurate demand, capacity and planning data in the ERP.
• Pricing clarity: ask whether Champion AI is included in QAD Adaptive or billed as an add-on, and how usage is counted.

Constellation Research on the Launch and Kavida.ai Deal

• On November 13, 2025, Constellation Research reported on the launch of QAD Adaptive with Champion AI, the Kavida.ai acquisition and new warehouse and scheduling features at QAD | Redzone’s Dallas event.
• In November 2025, QAD and AWS presented Champion AI as an agentic AI platform for mid-market manufacturers, built on Amazon Bedrock AgentCore and Amazon SageMaker.
• On January 6, 2026, App Developer Magazine described how Champion AI flags supply chain, production and demand issues and acts within defined parameters.

Worth Evaluating During a Move to QAD Adaptive

For mid-market manufacturers already on QAD, Champion AI is worth evaluating as part of a move to QAD Adaptive. It scores 7.3/10 on our scale: its design, with agents embedded in the ERP, running on AWS and backed by a procurement acquisition, is sound, and the score reflects an ERP-sized entry cost and pricing shared through sales. QAD could help buyers with published pricing guidance and customer results from production use.

For manufacturers on another ERP, Champion AI would come with a switch of systems, and that decision should rest on the ERP as a whole rather than on the agents. If you are evaluating QAD, ask for a live demo of the procurement and planning agents on your own data, the boundaries they work within, and two reference customers using them in production.

Sold Separately or Not, and What It Runs On

Is QAD Champion AI Sold Separately From QAD Adaptive?

Public material presents Champion AI as the agent layer inside QAD Adaptive ERP. Ask QAD’s sales team whether specific agents are included in your contract or priced as add-ons.

What Does QAD Champion AI Run On?

Champion AI is built with Amazon Web Services and uses Amazon Bedrock AgentCore and Amazon SageMaker. AWS CEO Matt Garman spoke in support of the launch.

What Did QAD Get From Kavida.ai?

QAD | Redzone acquired Kavida.ai to add procurement and supply chain AI agents to Champion AI.

Can QAD Champion AI Make Decisions Without Approval?

It can automate routine decisions and initiate next steps within parameters the manufacturer defines. Strategic choices remain with people, who see suggested changes and their trade-offs.

Which Manufacturers Is Champion AI Built For?

QAD | Redzone targets manufacturers with roughly $200 million to $5 billion in annual revenue. Its CEO has said the company wants to focus on manufacturing rather than serve every industry.