From Spreadsheets to Staff Agents: How We Automated Our Monthly Close

When I joined Turing’s finance team, I saw a familiar pattern in how we handled accounting data: everything was accurate, but not necessarily actionable.

Each month, we recorded a large volume of contractor payout costs into a number of high-level categories. The entries were technically correct, but they lacked the level of detail our Finance and FP&A teams needed to analyze performance by customer or project.

We couldn’t easily answer questions like:

• What’s our gross profit by customer?
• How much cost is tied to a specific engagement?
• Where are margins improving or tightening?

In short, the data was right, but not useful. We needed a way to deliver more detailed financial insights without adding hours to our monthly close process.

Rather than expanding headcount or layering on another system, we asked:
‘Can we train AI to behave like a staff accountant?’

That’s how our Staff Accounting Agent was born, an intelligent workflow designed to replicate the data preparation and reconciliation steps that a human accountant would perform manually.

Traditionally, this process would require combining multiple reports, one with payout information, another with work allocation or time data, and another to format the data correctly for upload into our accounting system, and then calculating how to distribute costs across customers or projects.

We trained the AI agent to handle that entire process:

• Combine datasets from separate systems
• Determine allocation percentages based on time worked
• Apply those percentages to costs
• Generate a file formatted for direct journal entry upload into our accounting platform

What once took a few hours now takes a few minutes.

1. Speed and efficiency
The automation reduced a multi-hour data manipulation task into a near-instant process. That freed the accounting team to focus on validation, not formatting.

2. Accuracy and consistency
The agent calculates allocations directly from source data, no sampling, no manual mapping, ensuring full consistency across accounting and FP&A.

3. Strategic visibility
For the first time, our finance partners can analyze costs and margins at a granular level, supported by verified accounting data.

Here’s how it works today:

1. Collect monthly payout and work allocation data from our core systems.
2. Prompt the Staff Accounting Agent to merge the datasets, calculate allocations, and format the result for upload.
3. Review a sample of outputs to confirm accuracy.
4. Post the journal entry into our general ledger with detailed customer- or project-level expense breakdowns.

The workflow is simple, repeatable, and reliable, the kind of automation that compounds its value every month.

The measurable gains are clear:

• Time saved: Hours of manual preparation reduced to minutes.
• Data accuracy: Full alignment between accounting and FP&A, using a single version of truth.
• Team impact: Finance can focus on insights instead of reconciliation.

Even more valuable than the efficiency gains is the quality of the data we now produce. Our accounting entries are not only correct, they’re decision-ready. That means finance leaders can trust what they see, run faster analyses, and make better calls without waiting for data clean-up.

To me, this project represents what proprietary intelligence truly looks like:
AI that knows your data, follows your workflows, and is governed by your rules.

This wasn’t about chasing the latest tool; it was about solving a real business problem using AI responsibly, within our own systems and controls.

The Staff Accounting Agent demonstrates that AI in finance doesn’t need to be flashy to be transformative. Sometimes the most valuable innovations are the ones that make the process invisible, fast, accurate, and quietly reliable.

The current version still includes a light human review step. The next iteration will go further by adding automated validation, where the agent checks its own output against source data and explains any discrepancies.

We’re also exploring how similar logic could be applied to revenue accounting, using an agent to review and summarize contracts for accounting treatment before they are even entered into the general ledger.

Each of these use cases points to the same goal: Build systems that make humans faster, data cleaner, and decisions clearer.

In finance, progress often comes from improving the unseen, the workflows behind the numbers. By teaching an AI agent to perform the repetitive parts of accounting, we’ve not only saved time but also raised the standard of insight across our team.

This isn’t about replacing people. It’s about freeing them to think bigger, and letting the systems handle the rest.

If you are ready to transform one of your workflows with proprietary intelligence, talk to a Turing strategist today.