How Agentic AI is Redefining Automotive ERP

QAD’s Tom Roberts explains how agentic AI is transforming automotive ERP, enabling planning, resilient supply chains, and faster decisions.

Automotive manufacturers are navigating a period of fundamental change. Electrification, shifting regulations, ongoing supply chain disruption, and the growing complexity of software-defined vehicles are challenging OEMs and suppliers to rethink how they plan, build and deliver at scale.

These forces are also prompting a closer examination of the digital systems that underpin operations – particularly ERP, which plays a central role in coordinating production, materials and global supply networks.

Against this backdrop, QAD has introduced the latest evolution of its Adaptive ERP platform, now enhanced with Champion AI, its new agent-based artificial intelligence framework. Designed to support more responsive, action-oriented operations, the update reflects a broader industry move toward systems that can automate routine decisions and adapt quickly to changing conditions. To explore the implications for production planning, inventory management, workforce enablement and wider manufacturing performance, Automotive World spoke with Tom Roberts, Vice President, Strategic Industry Development at QAD.

How will Champion AI reshape automotive manufacturing as the industry moves toward electrification, software-defined vehicles, and more complex supply chains?

One of the most visible challenges we’re seeing is demand volatility, particularly around electrified vehicles. Fluctuations in demand can significantly impact inventory levels and working capital if not managed proactively. Champion AI includes inventory optimization capabilities that help organizations adjust more dynamically as demand patterns change. On the sourcing side, our optimization tools provide greater flexibility in post-purchase order management, enabling buyers to evaluate and respond to date or quantity changes and improve visibility into external risks.

For example, we’ve begun incorporating geopolitical event mapping – initially focused on regions such as the Strait of Hormuz – so customers can visualize affected suppliers and logistics routes. This capability is being expanded more broadly as part of our roadmap.

You describe QAD Adaptive with Champion AI as shifting ERP from a “system of record” to a “system of action.” What does that look like in practice for OEMs and suppliers?

ERP systems have historically been very good at recording transactions, but much of the follow-up work has remained manual. I’ve been in the ERP space since the late 1990s, and many of those core workflows haven’t fundamentally changed.

With Champion AI, the system can detect when critical information is missing – such as during an RFQ process or post-purchase order management – and initiate actions to resolve it, such as contacting a supplier to request the missing data. This reduces the need for buyers to spend time chasing information, allowing them to focus on higher-value activities. That shift from passive record-keeping to active problem resolution is what we mean by a “system of action.”

You’ve said customers can see ROI within weeks. What early gains should manufacturers realistically expect? One challenge with ERP projects is that success is rarely measured after go-live. Organizations often move on without formally assessing whether the system is helping them meet their original objectives. We address this through post-hypercare coaching, which helps customers measure performance against KPIs and continuously refine processes using Adaptive ERP and Champion AI capabilities. Whether the focus is on inventory and working capital, on-time-in-full delivery, or procurement efficiency, the goal is to translate system adoption into measurable operational outcomes and sustained value.

Inventory volatility remains a major challenge in the automotive industry. How does Champion AI differ from traditional MRP or forecasting tools?

Rather than replacing existing planning tools, Champion AI enhances them. In many organizations, inventory parameters were set years ago and rarely revisited because reviewing them manually is time-consuming and complex.

AI allows those parameters and performance indicators to be analyzed much more quickly and consistently. What once required extensive spreadsheet work and dedicated resources can now be reviewed and optimized far more frequently, helping manufacturers respond to changing conditions instead of relying on static assumptions.

Automotive production scheduling is highly complex. What enhancements in the new release help plants manage variability and last-minute changes? The key is visibility combined with dynamic re-sequencing. Our solution provides a single source of truth for orders, inventory positions, and resource capacity, so planners and scheduling engines are working from the same, up-to-date information.

When disruptions occur – whether that’s a supplier delay, equipment issue, or material variance – the system can evaluate alternatives such as overtime or split runs. It then proposes optimized re-sequences that minimize changeovers and protect critical customer deliveries, reducing the need for costly manual firefighting on the shop floor.

As connectivity across automotive plants increases, how does an AWS-based architecture support cybersecurity and compliance?

AWS provides a strong foundation for Champion AI, including data-at-rest encryption, data isolation, and established security guardrails. This allows customers to integrate AI capabilities with their operational data while maintaining security and compliance requirements. As AWS CEO Matt Garman has noted in the context of our partnership, the goal is to enable agent-based AI solutions that customers can deploy securely within their own environments.

How do QAD Redzone and Champion AI together support frontline operators on the shop floor?

One example is the use of AI-driven “Game Ready” shift summaries. Incoming teams receive a clear view of key events across safety, production, quality, and reliability, along with prioritized action recommendations before a shift even begins. When downtime occurs, Champion AI can augment troubleshooting by analyzing the operational context, drawing on historical resolutions, real-time data, and industry standards to suggest likely root causes and next actions. It can also automate follow-up tasks, such as creating maintenance work orders.

In addition, operators receive run predictions at the start of each production run, including estimated run times and likely issues, enabling teams to prepare proactively rather than reacting after problems arise.

Looking ahead, what additional agentic AI capabilities will automotive manufacturers need as the industry continues to evolve?

One major shift will be in how users interact with enterprise systems. AI-driven interfaces are likely to reduce reliance on traditional transaction screens. For example, a user could create a purchase requisition through a conversational interface, with the system guiding them to provide the required information and learning from prior behavior over time.

Beyond usability, manufacturers are focused on reducing waste and cost. Advanced causality detection can help identify the drivers of inefficiency and guide corrective actions before suboptimal decisions are executed.

Finally, supply chain resilience will remain critical. Agentic AI embedded across demand management, scheduling, forecasting, and alternative sourcing can help organizations respond more effectively to disruption and uncertainty as platforms, regulations, and ecosystems continue to change.