Key Points
- KPMG analysis identifies hyperautomation as the critical foundation layer needed before enterprises can deploy agentic AI systems effectively
- The research positions autonomous enterprises as the next evolution beyond traditional automation, where AI agents make independent decisions across business processes
- The framework suggests that companies must first achieve comprehensive process automation before they can successfully implement AI agents that operate with minimal human oversight
Why KPMG Says You Need Hyperautomation Before Agentic AI
A new analysis from KPMG argues that enterprises rushing toward agentic AI are missing a crucial step. The consulting firm's research suggests that hyperautomation, the comprehensive automation of business processes using multiple technologies, must come first.
The report positions this as a foundational requirement rather than just another technology trend. According to KPMG's framework, companies trying to deploy agentic AI without a robust hyperautomation infrastructure will struggle to achieve the autonomous operations they're aiming for.
The analysis focuses on what KPMG calls "autonomous enterprises", organizations where AI agents can make independent decisions across interconnected business processes. But the key insight is that this level of AI autonomy requires a foundation of established automated workflows and integrated systems that most companies haven't built yet.
What This Means for Supply Chain Operations Ready for AI Agents
The reality that most supply chain teams are discovering is that you can't jump straight to AI agents making procurement decisions or routing shipments if your basic processes aren't automated and connected.
Think about what agentic AI actually means in supply chain context. It's AI that can independently negotiate supplier terms, reroute inventory based on demand signals, or adjust transportation modes without human approval. That sounds compelling, but those agents need reliable data flows and standardized processes to work with.
If your invoice processing still requires manual review, your inventory data lives in disconnected systems, or your transportation planning involves spreadsheets, agentic AI becomes a solution looking for infrastructure that doesn't exist yet.
The Infrastructure Gap Most Teams Are Missing
Hyperautomation isn't just about having automated tools. It's about having those tools work together seamlessly across your entire operation. Your procurement automation needs to talk to your logistics systems, which need to connect to your warehouse management, which feeds your planning tools.
Most supply chain organizations have automated pieces of their operation, but haven't connected those pieces into the kind of integrated foundation that agentic AI requires. That's the gap KPMG is highlighting.
Why This Foundation Matters for AI Decision-Making
AI agents make decisions based on data patterns and established workflows. If your automated processes are inconsistent or your data flows are manual, those agents can't learn reliable patterns or make trustworthy decisions.
The companies that will succeed with agentic AI in supply chain are the ones building a comprehensive automation infrastructure first. They're connecting their procurement, logistics, and operations systems into cohesive workflows that generate clean, consistent data.
Building Your Hyperautomation Foundation for Future AI Agents
If you're planning for agentic AI in your supply chain operations, start with the infrastructure that will support it. Here's where to focus your automation efforts now to be ready for autonomous AI later.
- Connect your document processing to downstream systems: Invoice automation that feeds directly into procurement analytics and supplier performance tracking creates the data foundation AI agents will need to make purchasing decisions.
- Automate the handoffs between functions: The gap between procurement, logistics, and warehouse operations is where most automation efforts stall. Focus on connecting these workflows before adding AI decision-making on top.
- Standardize your data flows across all processes: Agentic AI needs consistent data formats and reliable update cycles. Audit where your automation creates different data standards and fix those inconsistencies first.
The goal isn't perfect automation before you touch AI. It's comprehensive enough automation that AI agents have reliable workflows to build on and consistent data to learn from.
Preparing Supply Chain Systems for Autonomous AI Operations
The path from hyperautomation to agentic AI isn't just a technology upgrade. It's about building supply chain systems that can support truly autonomous decision-making across procurement, logistics, and operations.
Trax Technologies helps supply chain teams build the kind of connected automation infrastructure that creates a foundation for advanced AI applications. Our approach to invoice processing and spend management connects procurement data across systems in ways that support both current automation needs and future AI capabilities.
Explore how automated invoice processing and integrated spend management create the data foundation your supply chain needs for both current efficiency gains and future AI agent deployment.