Everyone's being told they can "just build it.” Wire up an LLM, automate a few recs over a weekend, and call it an AI strategy. And the early results are often encouraging. Until month-end hits.

By the second close cycle, cracks appear. By the fourth, the hidden costs of DIY AI frequently exceed the price of a purpose-built platform. Hallucinated numbers, missing audit trails, SOX exposure, and an unplanned $200K on tokens that wasn't in the business case.

This session examines where DIY approaches fall short when applied to a real close. And, what "purpose-built" must mean for accounting teams operating at a standard that general-purpose AI was never designed to meet.

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What You'll Learn

• Why DIY AI looks like the smart move — until you see where that logic breaks down in production
• The hidden costs that only appear when you're live: auditability gaps, maintenance treadmills, key-person dependency, and SOX exposure
• The bar for accounting AI is non-negotiable — explainability, traceability, human-in-the-loop controls, and always tying back to source‍
• Before you commit to a build, every CFO and Controller should pressure-test true TCO, including maintenance, who audits the AI, and what happens during close crunch