Dirty Data,
Shrinking Margins

KPIT Technologies posted a 33% YoY profit decline this quarter despite 12% revenue growth — operating expenses outpaced everything. That pattern is showing up inside accounting firms too: teams investing in AI tooling are seeing costs climb before efficiency gains materialize, usually because the underlying data — inconsistent GL codes, non-standard vendor names, manual journal entry habits — isn't clean enough for automation to work reliably. You don't have a technology problem. You have a data hygiene problem wearing a technology costume.

Think of it like this: a surgeon doesn't need to build the robot, but they absolutely need to know what it can and can't cut. The accountants gaining ground right now aren't the ones who learned Python — they're the ones who can sit down with an AP automation tool and define exactly which exception types require human review, and why. That skill — translating accounting judgment into decision logic — is not something AI replaces; it's what makes AI deployable. Pick one recurring manual process on your team this month and write out its decision tree on paper before you touch any software.

The business problem: your month-end close still runs on a shared Excel checklist, a string of Slack messages, and institutional memory. A new class of agentic workflow tools — think Numeric, Trunk, and similar close management platforms — now assign tasks, track preparer-reviewer status, flag late items, and surface anomalies without a controller manually chasing status. The shift matters because these tools are beginning to ingest GL data directly and flag reconciling items before a human opens the account. Early adopters are reporting two to three day reductions in close cycle time within the first quarter of deployment.