Building an AI Finance Agent to Rethink Monthly Reporting

Every month, my team works through the same cycle. The month closes. Accounting finalizes the numbers. We pull data from multiple systems, reconcile the differences, validate everything, and then begin the real work of building the story that explains how the company performed.

Those reports are critical. They guide leadership decisions, inform investors, and help budget owners understand the cost dynamics of their organization. But they also take hours, sometimes days, of manual effort. The work is not strategic. It is necessary, but repetitive.

We spend too much time assembling and formatting, and not enough time analyzing and advising.

That imbalance led us to a question that kept coming up inside my team:
Could we teach AI to take care of the reporting so we could focus on the thinking?

To explore that idea, we built what we call the Finance Agent. 

The agent is able to query financial data, generate initial drafts of reports, and even write narrative summaries in the tone and structure that our team already uses. It connects multiple data sources, including our actuals, budgets, forecasts, sales pipeline, headcount, and more.

It can respond to practical, detailed questions that every finance team gets:

“Show me the top 10 accounts by revenue, and how much of that came from X company?” “How much did we spend on digital advertising last year?”

The answers appear in seconds, fully formatted and written in our internal reporting style.

It is not perfect, but it is a meaningful step toward something better: using AI to handle the repetitive work so the humans can focus on interpretation and judgment.

• Time savings: We have reduced the time spent on monthly reporting by several hours per cycle. The agent produces a first draft, and my team focuses on analysis and recommendations.
• Accessibility: Leadership can now self-serve quick insights rather than wait for a full report.
• Consistency: Each section of the monthly financial summary follows a clear structure, with comparables across time periods and variance explanations written in a finance voice.
• Engagement: With less time in spreadsheets, our analysts spend more time partnering with business owners to identify risks, opportunities, and patterns in the data.

Step 1 — Curate the data
We began by consolidating datasets across finance, accounting, HR, and sales operations. The goal was to ensure accuracy, consistency, and proper access controls.

Step 2 — Teach the agent where to look
We trained it to understand the shape of our data, including how to locate and filter specific values.

Step 3 — Define the writing style
Using past financial updates, we taught the agent how to describe performance the way a finance professional would: with context, clarity, and comparables such as month-over-month or forecast versus actual.

Step 4 — Validate and refine
We tested every output for accuracy and tone, adjusting prompts and rules until the summaries met our expectations.

Step 5 — Deploy and review
Now, each month, the agent produces an initial draft that serves as our foundation. My team reviews, corrects, and finalizes it for distribution.

We built the Finance Agent because we wanted to change how we work, not who does the work.

Finance professionals are hired for their judgment, not their ability to copy data between spreadsheets. When AI can handle the mechanical aspects of reporting, our team can spend more time where it matters: analyzing results, identifying trends, and collaborating with leaders to inform decisions.

This was never about efficiency for its own sake. It was about improving how we think, communicate, and add value to the business.

• Speed: Hours of manual reporting replaced with an automated first draft.
• Focus: More time for analysis, planning, and business partnership.
• Scalability: A framework that can eventually extend to forecasting, investor reporting, or scenario modeling.
• Proof of value: Demonstrates how proprietary intelligence, when applied to internal operations, can create measurable impact and improve the quality of work.

We are already planning the next evolution of the Finance Agent.

The vision is an autonomous workflow that connects directly to our data warehouse, refreshes data after each monthly close, drafts the full report, and highlights the key insights I need to review. It would act like an additional member of the team: reliable, fast, and always up to date.

We are not there yet, but we have proven the concept. We have demonstrated that AI can accelerate finance reporting, enhance accuracy, and increase focus on informed decision-making.

That is what it means to be AI-powered and human-led.

This project matters because it reflects how Turing operates.

We do not just help enterprises build proprietary intelligence systems. We apply the same principles to enhance our own workflows. The Finance Agent is one example of that mindset: using AI safely, responsibly, and creatively to make the work itself more meaningful.

It is a small step toward the future of finance, where teams spend less time assembling data and more time shaping strategy.

Explore how Turing Intelligence helps enterprises transform financial workflows into real-time, decision-ready systems.
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