The Accuracy Question  Marketing materials show AI accuracy approaching 95-100%.   Controllers implementing AI agents experience 60-80% complete handling with 20-40% requiring human escalation.   The gap between marketing and reality creates disappointment unless expectations align with operational outcomes.  Understanding what accuracy means, why 100% is the wrong goal, and how to measure success prevents misaligned expectations.      What 80% Accuracy Actually Means  In one

The Set-and-Forget Expectation   Vendors market AI automation as "set it and forget it."   Controllers implementing AI agents discover ongoing oversight, monthly refinement, and pattern monitoring are necessary for continued effectiveness.    Understanding realistic ongoing effort requirements prevents disappointment and ensures sustained value.      What "Set It and Forget It" Actually Requires   AI agents require 3-5 hours monthly staff oversight including call review, rule

The Misunderstanding Around Automation   Automation discussions often drift toward absolutes.  Either processes are automated or they are not.    In ERP exception handling, this framing leads to poor outcomes.    Exceptions exist precisely because judgment, context, or discretion is required. Attempting to remove humans entirely misunderstands the nature of the work.    The goal is not elimination of human involvement.  The goal is elimination of unnecessary human coordinatio

The Platform Question   "Which AI platform should we use?" requires understanding capabilities, costs, and practical differences for ERP exception handling. Both OpenAI (GPT-5) and Anthropic (Claude) work well. Selection depends on specific priorities.    Reality: Platform choice matters less than implementation quality for mid-market ERP use cases.      Platform Overview   OpenAI (GPT Family)   Models:   GPT-5 (Recent)  GPT-4 (original, most capable)  GPT-4 Turbo (faster, lo

The Pattern Blindness Problem   Staff handling exceptions individually miss patterns across accounts and time. AI analyzing all exceptions systematically identifies recurring issues enabling proactive resolution.    Reality: 15-25% of exceptions are symptoms of systemic patterns, not isolated incidents.      Patterns AI Identifies   Pattern Type 1: Seasonal Payment Delays   What AI detects: Construction customers consistently delay payment November-February  Human perception

The Escalation Importance   AI effectiveness depends on knowing when to escalate to humans. Too few escalations create quality problems. Too many negate efficiency benefits. Understanding escalation rule design balances automation with judgment.    Reality: Well-designed escalation (20-30% rate) maintains quality while achieving 60-70% automation.      Escalation Rule Categories   Category 1: Complexity Thresholds   Principle: AI handles routine, humans handle complex  Examp