Stop Manual Forecast Consolidation: Use Gemini to Automate Finance Planning

Standardise Forecast Templates in Google Sheets

Start by enforcing a single, standardised forecast template across all regions and business units in Google Sheets. Lock structural elements like time periods, account codes, and cost center fields, and make variable inputs (volumes, prices, drivers) clearly identifiable. This consistency is what allows Gemini to process and consolidate submissions reliably.

Use data validation and dropdowns for entities, cost centers, and account codes to reduce free-text input and mapping errors. Store the master template in a controlled folder and use Apps Script or Workspace add-ons to distribute copies to contributors with controlled sharing permissions.

Load Submissions into BigQuery as a Staging Layer

Don’t let consolidation live only in spreadsheets. Configure an automated pipeline (using Apps Script, Dataform, or similar) that writes each submitted Google Sheet into a BigQuery staging table. Include metadata like region, business unit, version, and submission timestamp so you can filter and audit later.

From there, define transformation queries that unify currencies, time granularity, and account mappings into a standard planning schema. This is the dataset Gemini will read, clean, and summarise – giving you a single, reliable layer for analytics and reporting beyond the spreadsheets.

Use Gemini to Clean, Map, and Flag Anomalies

Once your forecast submissions land in BigQuery, use Gemini to automate data cleaning and anomaly detection. For example, you can have Gemini review new forecast lines against historical actuals and prior forecasts, then flag unusual variances or missing entries for finance review.

In a Gemini-connected environment, prompts might look like this:

Role: You are a financial planning assistant helping with forecast consolidation.

Task: Analyze the latest forecast data (current_forecast) against:
- Last forecast (previous_forecast)
- Last 4 quarters of actuals (actuals_4q)

For each cost center and account combination:
- Flag any variance > 20% vs previous_forecast
- Flag any variance > 30% vs average of actuals_4q
- Suggest likely reasons based on seasonality and driver changes
- Propose a cleaned version where obvious data entry mistakes exist

Output a JSON payload with:
- cost_center
- account
- original_forecast
- suggested_correction
- variance_flags
- explanation

Embed this into a scheduled process so controllers receive a pre-analysed list of issues each cycle rather than hunting manually through thousands of lines.

Automate Consolidated Views and Management Summaries

After cleaning and validation, use Gemini to generate consolidated forecast views and narrative summaries that finance can directly review with management. Have Gemini pull from the curated BigQuery tables and produce P&L, cost center, and region-level aggregates, then generate commentary on main drivers and changes.

Example prompt for a management summary:

Role: You are an FP&A analyst summarizing the new consolidated forecast.

Input data:
- consolidated_forecast table (current cycle)
- previous_forecast table (last cycle)
- variance_analysis table (by region and account)

Task:
- Summarize total revenue, gross margin, and EBIT vs last forecast
- Highlight top 5 positive and top 5 negative variances with concrete drivers
- Identify 2-3 risks and 2-3 opportunities based on the forecast
- Keep language concise and suitable for an executive audience

Output: 5-8 bullet points followed by a short narrative (max 400 words).

This turns raw consolidated numbers into “finance-ready” output in minutes, while leaving review and approval firmly with your FP&A team.

Enable What-If Scenarios Directly from the Consolidated Data

One of the biggest advantages of having consolidation automated and centralised is that you can finally explore dynamic, driver-based scenarios without rebuilding everything manually. Use Gemini to apply parameter changes (e.g. volume growth, FX rates, price changes) to your BigQuery-based forecast and generate alternative views.

Here’s an example prompt pattern:

Role: You are a scenario planning assistant for the finance team.

Input:
- consolidated_forecast_base
- scenario_parameters: { volume_delta_pct_by_region, fx_rates, price_change_pct_by_product_line }

Task:
- Apply the scenario_parameters to the base forecast
- Recalculate revenue, gross margin, and EBIT by region and business unit
- Compare scenario results vs base forecast
- Output tables plus a short explanation of the key changes.

Connect these scenarios back to Google Sheets dashboards or Looker Studio to let finance and business leaders interact with the results without touching the underlying logic.

Track KPIs for Process Quality and Speed Improvements

To prove the value of Gemini-based forecast consolidation, define and monitor a small set of KPIs. Examples include: time from submission deadline to consolidated view, number of manual adjustments after AI cleaning, number of detected anomalies per cycle, and the share of templates submitted correctly on first attempt.

Visualise these in a simple dashboard so you can show stakeholders how automation has reduced cycle times and error rates over successive planning rounds. This also helps you prioritise where to refine templates, rules, or prompts next.

When implemented in this way, organisations typically see consolidation time cut from days to hours, a measurable reduction in manual errors, and a higher share of time spent on analysis and scenarios instead of mechanical data work. The goal is not to remove finance judgment, but to free it from low-value tasks so planning can become faster, more dynamic, and more strategically relevant.