The problem
A company's finance team of five spent the first ten business days of every month on month-end close. The process involved pulling data from multiple systems, reconciling accounts, preparing management reports and producing commentary. The work was repetitive, time-pressured and left no time for the analysis that would make the reports more valuable.
The close process had grown organically as the business added systems, products and markets. It worked — the books closed every month — but it consumed disproportionate time and produced reports that were more descriptive than analytical.
What we built
We built an AI finance workflow assistant that automated the mechanical aspects of the close process:
- Data gathering. The assistant pulled data from the ERP, CRM, billing system and other sources automatically on the close schedule.
- Reconciliation. Accounts were reconciled automatically, with discrepancies flagged for review.
- Report generation. Management reports were populated with data and draft commentary on significant variances.
- Exception handling. Items requiring human judgement were surfaced with context, rather than the team having to search for them.
The results
Six months after deployment:
- Month-end close time reduced from ten business days to four
- Finance team time redirected from data gathering and reconciliation to analysis and business partnering
- Report quality improved — the team had time to add strategic commentary rather than just describing the numbers
- Error rate in financial reporting decreased as manual data manipulation was eliminated
How it worked
The assistant did not replace the finance team's expertise. It replaced the mechanical work that consumed their time — the data gathering, the reconciliation, the report population. The team reviewed the assistant's work, investigated the exceptions and added the analysis and interpretation that only they could provide.
The critical design decision was to make the assistant's work transparent. The team could see what the assistant had done, verify it and adjust where needed. This transparency built trust and meant the team was comfortable relying on the assistant for the mechanical work.
What we learned
The most important lesson was that the close process improved not just in speed but in quality. When the team had ten days, the first eight were spent on mechanics and the last two on analysis — which meant analysis was rushed. When the assistant handled the mechanics in days, the team had more time for analysis than before, despite the overall process being shorter.
We also learned that the transition requires the team to shift their self-perception from processors to analysts. This is a positive shift — it is more interesting and more valuable work — but it requires active management to help the team see themselves in the new role.
For a broader look at AI in finance operations, see AI automation for finance. For an overview of the assistant category, see AI finance assistant.
This case study describes a composite of real implementations. Results vary based on the specific process, team and context.