Cash Flow Forecast

The finance team spends time every week rebuilding the cash flow forecast: exporting transactions, consolidating in Excel, recalculating for what-ifs. Automation removes this routine and shifts the forecast from a once-a-month snapshot to a current picture updated every day.

The AI agent pulls data from the accounting system and data warehouse / BI, builds a 30/60/90-day forecast, and accompanies it with a written explanation. The finance manager does not receive a bare table — they receive a short report explaining what changed and why.

What automation does

1. Pulls actual data from accounting (receipts, payments, liabilities) and from the data warehouse (contract schedules, recurring subscriptions, sales forecast).
2. Categorizes transactions: operating receipts, taxes, payroll, rent, one-off payments.
3. Builds a forecast across three horizons: 30, 60, and 90 days.
4. Generates three scenarios: base (actual data and confirmed liabilities), optimistic (including pipeline), pessimistic (with delays to key receipts).
5. Compares against the previous forecast version: what changed, which new risks emerged, what the trend is for cash position.
6. Prepares a written narrative — a short explanation of the numbers in a format accessible to a business owner without a finance background.
7. Delivers the report on a schedule (for example, on Monday mornings) or on demand — via email, Slack, or Notion.

What automation does not do

• Does not replace the treasurer or CFO. Decisions on payments, bank negotiations, and securing financing remain with the human.
• Does not forecast revenue that is not in the system. If the pipeline exists only in the manager's head and not in the CRM — the forecast will be incomplete.
• Does not fix data at the source. If the books are not closed or transactions are delayed — the forecast will reflect this, but will not fix it.

Under the hood — a combination of source connectors, an ETL layer for data normalization, a forecasting model, and an LLM for generating a text narrative. Implementation — custom-code in Python: off-the-shelf SaaS solutions rarely cover the specifics of working with invoices, counterparties, and the tax schedule of a particular company.

Architecture flow

Raw data comes from two categories of sources. Accounting (1С, QuickBooks, Xero, NetSuite — whichever is in use) provides actual balances, transactions, counterparties, open invoices. Data warehouse / BI (BigQuery, Snowflake, Metabase) provides product metrics, contract schedules, extended pipeline. If there is no warehouse, the AI agent works directly with the CRM and spreadsheets.

The ETL layer then brings the data to a unified model: invoices are mapped to cash flow categories, recurring payments (rent, subscriptions, payroll) are identified as known obligations, one-off payments — as scenario-based. The forecasting model builds three trajectories and calculates the balance for each day of the horizon.

The LLM (AI model or equivalent) takes numbers and context as input and generates a text narrative: what is in focus this week, which risk is most significant, what has changed since the last forecast.

Implementation steps

1. Source audit. Identify where the actual data lives (accounting), where the planned data is (CRM, contracts), which expense categories need to be tracked.
2. Account mapping. Align the chart of accounts and CRM categories to a unified taxonomy for the forecast (operating expenses, payroll, taxes, investments, one-off).
3. Connector setup. Accounting and data warehouse / BI APIs; if the API is limited — export via regular dump.
4. Building the forecasting model. Core logic — recurring + scheduled + scenario adjustments. No ML on the first iteration, so the result is explainable.
5. Scenario configuration. Parameters for optimistic and pessimistic scenarios are agreed with the CFO: % delay in receipts, % of lost pipeline, accelerated payments.
6. Connecting the LLM layer for narrative. A prompt with company context, the latest forecast, changes. The tone and format of the commentary are aligned over several iterations.
7. Delivery channel. Email with PDF, Slack message, Notion page, BI dashboard — chosen based on how the team already works with financial reporting.
8. Launch in shadow mode for 2-4 weeks. The AI agent builds the forecast in parallel with the manual version; discrepancies are reviewed and calibrated.

Components

Orchestration — a low-code platform or code in Airflow / cron, depending on what the team already uses. For SMB, a workflow engine is more common: a visual workflow is easier to maintain without a dedicated DevOps.

Implementation requires access to data sources and some preparatory work from the finance team.

Access and data

• Access to the accounting system (1С, QuickBooks, Xero, NetSuite, or another) — API is preferred, an export as a last resort.
• Access to a data warehouse / BI or to the sources that the pipeline is built on (CRM, billing, contract tables).
• Chart of accounts and movement taxonomy in current form.
• Information on recurring obligations: rent, subscriptions, payroll, taxes.

Team readiness

• A CFO or finance manager who will align the taxonomy and scenario parameters. This is not a one-time task — the first iteration requires 4-6 working sessions.
• Willingness to operate in shadow mode for 2-4 weeks: manual forecast and AI forecast running in parallel for calibration.
• Access to a person who understands the data structure in the source systems (accountant, BI owner).

Timeline

Basic implementation takes 6-10 weeks:

1. Source audit and mapping — 2 weeks.
2. Model build and integrations — 3-4 weeks.
3. Shadow period and calibration — 2-4 weeks.

Timelines increase if source data is fragmented or if non-standard scenarios are required (multi-currency forecast, factoring, handling advances from large clients).