The Bubble Question   If you follow technology trends, the AI agent conversation sounds familiar. New technology arrives. Vendors make expansive claims. Investors deploy capital aggressively. Implementation reality lags marketing promises. Then correction occurs.    Mid-market finance and operations leaders have seen this pattern before with blockchain for supply chain, IoT platforms, and earlier automation waves. The skepticism about AI agents following this pattern is reaso

The Black Box Problem   Most explanations of AI agents focus on underlying technology - language models, APIs, workflow orchestration. These explanations satisfy curiosity but do not help operational leaders understand what the agent actually does in their ERP environment.    The operational question is more practical:   What does the agent do with your data?   How does it decide what action to take?   When does it escalate to humans?   How does information flow between agent

The Automation Confusion   Most ERP automation discussions focus on transaction processing - purchase orders generating automatically, invoices posting to the GL, inventory reservations triggering on sales orders. These automations work well and deliver clear value.    Exception automation operates differently. Exceptions do not follow predictable paths. Each requires individual attention, but the decision logic follows patterns. Understanding this difference prevents misappl

The Confusion   Mid-market companies evaluating automation options encounter overlapping terminology. Vendors describe chatbots, RPA, and AI agents as if they solve similar problems. Marketing materials blur distinctions. The confusion about which technology addresses which operational need is understandable.    These technologies serve different purposes. Understanding the actual differences prevents misapplication and implementation failure.    This article clarifies what e

The Expectation Problem   Marketing materials describe impressive capabilities. Vendor demos show seamless operation. The gap between claims and operational reality creates implementation failures when expectations exceed actual capability.    Mid-market companies need accurate understanding of what AI agents can and cannot do today to prevent misapplication and ensure appropriate oversight.      What AI Agents Can Do Today   Handle Standard Exception Processes   AI agents ex

The Question   Controllers and operations managers ask why AI agents are necessary when their ERP system already has automation, workflows, and business process management capabilities. The question is reasonable. Modern ERP systems provide extensive automation features.    The distinction matters because applying the wrong automation approach to operational problems creates implementation failure and wasted effort.      What ERP Automation Actually Does   ERP automation exec