Avoiding the Hidden Risk in AI Rollouts: Prepare Your Finance Function Before You Deploy AI Agents

AI agents are arriving faster than most finance teams can absorb. The promise is powerful. Automated reconciliations, self-directed close cycles, real-time commentary, and operational speed that outpaces traditional workflows.

Yet in practice, AI does something far more provocative. It exposes everything finance has quietly tolerated for years.  

After years overseeing ERP rebuilds, reconciliation automation, and AI readiness efforts across tightly regulated and high-complexity environments, I have seen the same pattern repeat. Leaders want the benefits of autonomous agents without addressing the structural weaknesses those agents depend on.  

The result is predictable.  

AI produces errors that look like technology failures, but they are really signs of foundational instability. 

The illusion of clean structures 

The first cracks surface in the chart of accounts. Over time segment drift becomes normal. New business lines, regional variances, acquisitions, and ad hoc reporting needs generate slow chaos.  

Finance teams compensate with manual fixes, custom mappings, and unwritten rules that make the structure look more stable than it is. Humans can work around inconsistency. AI cannot. When an agent begins processing the structure at scale, it highlights every misalignment instantly. What feels like an AI malfunction is simply the system reflecting hidden fragmentation. 

Narrative generation presents a similar problem. Many organizations want AI to create management-ready commentary directly from ERP and planning data. On the surface this looks achievable.  

Yet when definitions of revenue, margin, or forecast drivers vary across systems or regions, the agent produces commentary that appears crisp but contains subtle inaccuracies. The polish hides the conflict.  

Process entropy meets automation 

Month-end close is often viewed as a stable ritual, but most closings rely heavily on tribal knowledge. Teams remember exceptions. They know which account always ties out late. They keep track of who approves which adjustment.  

An AI agent does not inherit this tribal wisdom. It demands consistent triggers, predictable sequences, and definitive endpoints. When close processes shift based on who is available or which subsystem is behaving that month, the agent stalls. Leaders think AI slowed the close. In reality the close was never truly stable. 

The same pattern plays out in reconciliations and reporting. Many operational workflows depend on analysts correcting upstream issues manually. The human reviewer absorbs the inconsistency and moves on. An autonomous agent has no such intuition. It runs the logic exactly as written and magnifies the structural issues humans quietly fix each month. 

Data lineage as a fault line 

Data lineage reveals the final set of vulnerabilities. Many finance teams feel confident in their lineage because analysts can trace the logic by memory. But lineage is not an oral history. It is documentation, definition ownership, and control.  

When AI agents begin linking source data to transformations to outputs, small inconsistencies cascade. I have watched a single undocumented calculation in a planning model produce weeks of downstream disruption once an agent attempted to scale that logic across business units.  

Again, the agent did not fail. The structure did. 

These weaknesses appear more frequently as AI adoption accelerates. Gartner reports that 58 percent of finance functions used AI in 2024, a sharp increase from the prior year, signaling rapid experimentation without full operational grounding.  

IBM’s research on data quality reinforces this risk by noting that poor or inconsistent data can erode insights, slow decision making, and increase compliance exposure. These findings align with what many executives experience. The appetite for AI rises faster than the foundation required to support it. 

A practical readiness framework for leaders 

Executives who want autonomous agents to deliver meaningful impact need a stable environment first. A four-part readiness framework helps teams understand where to focus before AI deployment takes hold. 

Start with structure: Reevaluate the chart of accounts, financial hierarchies, allocation logic, and planning models at the depth an AI agent will experience them. Look for conflicting definitions, outdated segments, and rules that evolved informally. If reconciling these elements requires tribal knowledge, the structure is not ready. 

Confront process truth: Document the real process, not the intended one. Follow the actual sequence of steps for close, forecasting, capital planning, vendor payments, and reconciliations. Ask a simple question. If all manual workarounds disappeared tomorrow, would the process still function. If the answer is no, AI will magnify the instability. 

Strengthen controls and lineage: Validate how source data moves through systems, where transformations occur, and who owns each definition. Ensure naming conventions, reporting rules, and metric logic have clear, centralized governance. If lineage depends on a handful of experts rather than documentation, autonomous agents will expose the gap. 

Assess environmental readiness: Many companies operate across a mix of cloud platforms, legacy ERPs, analytics tools, and point solutions. Identify which systems produce consistent outputs and which need remediation before becoming inputs to AI workflows. Agents thrive in predictable conditions. They struggle in fragmented ecosystems. 

The real value of preparation 

Preparing for AI forces operational maturity. Teams build stronger definitions, cleaner structures, and more resilient processes. Finance becomes easier to scale. Visibility improves. Reporting sharpens. And when the environment is stable, AI agents accelerate work in ways that are reliable instead of risky. 

Finance is not failing because AI is too advanced. Finance struggles because modern automation removes the buffer of human improvisation.  

The sooner leaders stabilize their environment, the sooner AI agents can amplify the value they were designed to deliver. The promise is still real. The work simply needs to start earlier.