Most finance leaders at large organizations have made the right investments. A modern ERP, cloud data platforms, planning tools, and more. And now, increasingly, AI — for forecasting support, anomaly detection, close acceleration, and reporting at scale.
The technology stack looks right. But when the board starts asking about results, the returns are harder to point to than the investments were.
What your ERP was built to do — and what it wasn’t
Your ERP is excellent at what it was designed for: capturing transactions, enforcing accounting standards, managing the chart of accounts. It is the system of record, and it performs that job well.
But it doesn’t encode how your organization has decided to handle intercompany eliminations across a complex entity structure. It doesn’t carry your FP&A team’s cost allocation methodology, refined over three budget cycles. It doesn’t know what variance threshold triggers a controller review versus a VP escalation, or how your tax team has mapped jurisdictions for Pillar Two. That logic — specific, documented, organization-defined — isn’t in your ERP. It’s not in your data warehouse either.
For most finance organizations, it lives in spreadsheets. Sometimes in the heads of the people who built them.
Where AI runs into trouble in finance
There’s a finding that gets cited a lot in finance AI conversations: research from MIT found that 95% of organizations are seeing no measurable return on their generative AI investments. Bain & Company looked at the same picture and reached a different conclusion for finance specifically. The fastest payback from AI in finance comes from embedding it in workflows — not from running pilots. The distinction matters because it explains why so many finance AI efforts stall after the proof of concept.
AI can process data at speed and surface patterns across large datasets. What it cannot do is infer your business logic from raw inputs. Without that context, AI outputs in finance look confident but aren’t defensible — and in a function where auditability is a baseline requirement, that gap is not a minor limitation. It validates that trustworthy AI is critical for scaling workflows and AI pilots.
Our own survey of 1,400 IT and business leaders asked what their biggest barriers to success with AI workflows were. One in two (49%) said inaccurate or biased outputs. 38% said it was a reluctance to allow AI to make decisions without human oversight. While you don’t need perfect data to start using LLMs, you absolutely need trustworthy data.
The layer that’s actually missing
The gap between your ERP and your AI ambitions isn’t a data gap. It’s a business logic gap — the layer where your organization’s specific rules, methodologies, and decision criteria live, and where AI needs to operate to produce outputs you can stand behind.
When that layer is built correctly — logic documented, workflows repeatable, outputs traceable — AI has validated, structured inputs rather than raw data it has to interpret. Outputs can be explained to auditors and to the board. And the sequencing question resolves itself: getting the process right is how you adopt AI.
What it takes to build that layer
Closing the gap takes more than a mandate to “use AI responsibly.” It takes three specific things, built and owned inside finance rather than handed off to IT.
- A purpose-built data asset for each process. Not another warehouse but a narrow, well-defined data set scoped to one process that reflects how your team measures it, not just what your ERP happens to store.
- Encoded logic, not tribal knowledge. The allocation methodology or the variance threshold that triggers escalation — built into a repeatable workflow instead of a senior analyst’s spreadsheet. The shift is building it once; in a form AI can use.
- A way to update it when the business changes. Comp plans get revised, tax jurisdictions shift, and the chart of accounts gets restructured after an acquisition. Logic that can only be changed by submitting a ticket to IT will be stale before it’s deployed — the people who own the process need to be the ones who can adjust the rule.
None of this requires waiting for a perfect architecture. The highest-value starting point is whatever process has your analysts fielding the same question, the same way, every single cycle. Encode that one workflow first, connect it to the AI tools your team is already using, and the logic compounds from there: the same governed calculation that answers one controller’s question can feed the scenario model that runs your next planning cycle.