Agentic AI that Empowers the Entire Workforce

Smarter, Faster, Sharper Manufacturing Intelligence

Champion AI is QAD’s intelligence layer, a network of purpose-built agents that automate, optimize and accelerate how you work.

Artificial Intelligence. Real Benefits.

Visualize bottlenecks and performance anomalies.

Benchmark process performance across sites.

Surface actionable insights that feed directly into agents.

Frequently Asked Questions

What exactly is agentic AI?

Agentic AI is artificial intelligence designed to act autonomously toward a goal. Instead of just responding to prompts or offering recommendations, it can plan, make decisions, and take actions across systems to complete tasks and drive real business outcomes with minimal human input. In manufacturing, this means AI that can proactively balance supply and demand, adjust production schedules, resolve exceptions, automate routine ERP processes, and respond to disruptions in real time, helping manufacturers operate more efficiently, resiliently, and at scale.

What is the difference between generative AI and agentic AI?

Generative AI focuses on creating content such as text, images, or code in response to prompts. Agentic AI goes further by taking action. It can set plans, make decisions, and execute tasks across systems to achieve a goal, not just generate an output. In manufacturing, generative AI might help summarize reports or answer questions, while agentic AI can automatically adjust production plans, manage supply chain exceptions, and coordinate actions across ERP, planning, and execution systems to keep operations running smoothly.

How would one define agentic AI for manufacturing/ERP context and differentiate from traditional automation/RPA?

Agentic AI in a manufacturing and ERP context refers to AI systems that can understand business goals, reason across data, and autonomously take actions across enterprise systems to achieve outcomes. Unlike traditional automation or RPA, which follows predefined rules and scripts, agentic AI can adapt to changing conditions and make decisions in real time. For example, RPA might automatically enter a purchase order when inventory hits a fixed threshold, while agentic AI can recognize a supply disruption, evaluate alternate suppliers, adjust production schedules, update ERP transactions, and notify planners, all without manual intervention.

What types of quantitative business outcomes does agentic AI deliver?

Agentic AI drives measurable business outcomes by autonomously optimizing decisions and actions across manufacturing and ERP processes. In practice, this can translate into reduced planning and execution cycle times, lower inventory and logistics costs through smarter demand-supply balancing, and increased order intake by improving service levels and on-time delivery. By continuously monitoring performance and adjusting in real time, agentic AI helps manufacturers achieve sustained KPI improvements, not just incremental efficiency gains.

What are some role-specific use cases for agentic AI?

Agentic AI supports role-specific use cases across manufacturing and ERP by acting as a digital teammate for planners, schedulers, and operators. For planners, agents can continuously balance demand and supply, auto-reschedule plans, and resolve material or capacity exceptions. For schedulers, agents can sequence production runs, adjust schedules in response to disruptions, and coordinate changes across plants and suppliers. For operators and back-office teams, agents can triage exceptions, match and validate purchase orders and invoices, update ERP transactions, and escalate only what requires human judgment, reducing manual work while keeping operations on track.

What governance, safety, and responsible AI assurances are there with regard to agentic AI?

Agentic AI is designed with strong governance, safety, and responsible AI controls to ensure trust and compliance in manufacturing and ERP environments. This includes human-on-the-loop oversight for critical decisions, clearly defined decision boundaries that limit autonomous actions, and full audit trails for transparency and traceability. These safeguards help ensure AI actions are explainable, controllable, and aligned with regulatory requirements, including readiness for frameworks such as the EU AI Act.

STYLES

SCRIPTS