Fix Scenario-Based Cash Planning with Gemini-Powered Forecasts
Finance teams struggle to build robust cash scenarios when everything is stuck in manual spreadsheets. This article shows how to use Gemini with Google Sheets and BigQuery to automate scenario-based cash planning, model real shocks, and give CFOs a clear view of how quickly liquidity can change.
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The Challenge: Poor Scenario-Based Cash Planning
Most finance teams know they should be running best, base and worst-case cash scenarios — but in reality, building these models is slow, manual and shallow. Analysts spend days copying data into spreadsheets, tweaking assumptions cell by cell, and reconciling versions, which means only a few simple variants ever get modeled. The result is a planning process that cannot keep up with the speed and volatility of the business.
Traditional approaches to scenario-based cash planning were built for a world of quarterly updates and stable conditions. Static Excel models, manual data pulls from ERP systems, and one-off what-if analyses do not scale when you need to test shocks like demand drops, FX moves or rate hikes weekly or even daily. By the time a scenario is ready for a steering meeting, the underlying data is already outdated, and the team is reluctant to redo the work for new questions.
The business impact of not solving this is significant. CFOs are effectively blind to how quickly liquidity could erode under realistic stress events. Early-warning signals for cash shortfalls are missed, working capital remains sub‑optimised, and the organisation either holds too much buffer cash (dragging down returns) or runs dangerously close to covenant breaches. In turbulent markets, this lag between reality and planning becomes a real competitive disadvantage and increases financing costs and risk.
The good news: this challenge is real but solvable. Modern AI for finance can automate much of the data handling, generate rich scenario sets on demand, and surface the few scenarios that truly matter. At Reruption, we’ve helped organisations replace fragile spreadsheet workflows with AI-first tools and operating models. Below, you’ll find practical guidance on how to use Gemini together with Google Sheets and BigQuery to turn your scenario-based cash planning into a fast, repeatable and decision-ready capability.
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Our Assessment
A strategic assessment of the challenge and high-level tips how to tackle it.
From Reruption’s experience building AI-powered forecasting and planning tools, Gemini is most valuable when it sits directly on top of your existing finance data stack. By connecting Gemini with Google Sheets and BigQuery, finance teams can keep their familiar tools while adding AI-driven scenario generation, anomaly detection and narrative insight on top. Instead of trying to replace your models, we focus on using Gemini to orchestrate data, create consistent scenarios, and explain what the numbers actually mean for liquidity and risk.
Anchor Gemini in a Clear Cash Forecasting Framework
Before introducing AI, finance leaders need a shared framework for cash forecasting and scenario planning: time horizons (e.g. 13-week vs 12-month), granularity (weekly vs monthly), and which levers matter (collections, payables, capex, credit lines). Without this, Gemini will generate scenarios, but the organisation will not know how to interpret or act on them. Start by documenting your current cash model structure and key drivers so Gemini can augment a known process, not invent a new one.
Strategically, this also clarifies where AI should and should not be used. For example, you might use Gemini to create and stress-test revenue and working capital assumptions, but keep treasury policy and covenant logic in deterministic models. That balance keeps regulatory and audit stakeholders comfortable while still benefiting from AI’s pattern recognition and speed.
Design Governance Around Assumptions, Not Just Numbers
In scenario-based cash planning, assumptions are often more important than the raw numbers. Gemini can rapidly propose demand drops, FX moves, or rate hikes based on historical volatility or external data, but without governance, assumptions can drift and undermine trust. Define who can approve new scenario templates, what ranges are acceptable for key drivers, and how often base assumptions must be reviewed.
Use Gemini as a structured co-pilot: it can propose assumption sets, but the finance team validates and documents them. Strategically, this builds a robust audit trail and helps risk, controlling and treasury teams stay aligned on which scenarios are considered “official” for planning and communication.
Build Cross-Functional Ownership for Scenario Outcomes
AI-enhanced cash scenario planning affects more than finance. Sales, procurement, operations and HR all contribute drivers that shape cash outcomes. If Gemini is only used by a central FP&A team, it will generate scenarios that are technically impressive but disconnected from operational reality. Instead, treat Gemini as a shared decision tool and involve business owners in designing and interpreting scenarios.
Strategically, this means defining clear ownership for each driver: sales pipeline conversion, payment terms, inventory policies, hiring plans. Gemini can help simulate the impact of decisions in these areas, but leaders in each function must commit to the assumptions and be ready to adjust plans when scenarios point to upcoming liquidity stress.
Mitigate Model Risk with Human-in-the-Loop Reviews
As you increase reliance on AI for forecasting and scenario generation, model risk becomes a strategic concern. Gemini can surface counterintuitive but plausible paths where liquidity deteriorates quickly — yet those insights must be challenged. Set up a cadence where senior finance staff systematically review AI-generated scenarios, challenge outliers, and compare them against historical crises or stress periods.
This human-in-the-loop review builds trust and creates a feedback loop: finance experts correct or refine AI outputs, and you gradually converge on a robust, institution-specific scenario library. Over time, Gemini becomes a tool for disciplined risk exploration rather than an opaque black box.
Prepare Your Data Architecture for Scale
Strategically, you will not get far with Gemini if your finance data is scattered across manual exports and local spreadsheets. To use AI effectively in cash forecasting, you need a minimum viable data architecture: transactional data consolidated in BigQuery, key drivers mapped to clear tables, and Google Sheets acting as the presentation and control layer. This does not require a multi-year data lake project, but it does require deliberate design.
By aligning data structures and naming conventions upfront, you make it much easier for Gemini to query, join and explain data across sources. Reruption typically prioritises a small but clean subset of data for the first use case, proving value quickly and then expanding coverage once the approach is validated.
When used deliberately, Gemini for scenario-based cash forecasting turns slow, manual planning into a fast, repeatable and explainable process that surfaces liquidity risks early. It works best when you combine a clear forecasting framework, robust data architecture and human review with Gemini’s ability to generate, stress-test and narrate scenarios at scale. Reruption has built similar AI-first planning capabilities in complex environments, and we bring that hands-on engineering and co-founder mentality to your finance stack as well. If you want to explore how Gemini could safely power your cash scenario planning, we’re ready to help you test it in a focused, real-world setup.
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Best Practices
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Connect BigQuery and Sheets as Gemini’s Single Source of Truth
Start by centralising the core data required for cash forecasting in BigQuery: bank transactions, AR and AP ledgers, sales orders, payroll and major capex. Create views that aggregate cash inflows and outflows by week or month and tag them by business unit or product line. This gives Gemini a consistent, queryable base.
Then, use Google Sheets as the control panel where finance defines high-level assumptions (e.g. DSO changes, FX rates, volume scenarios). With Gemini integrated into Sheets, you can ask it to pull scenario-ready aggregates from BigQuery, apply your assumptions, and write back forecasted cash balances into structured tabs.
Use Gemini to Generate and Document Scenario Sets
Instead of manually building best, base and worst cases, use Gemini to generate structured scenario sets based on your drivers. Create a dedicated “Scenario Config” sheet where you store driver ranges (e.g. revenue growth, DSO, FX, interest rates). Then prompt Gemini to propose realistic combinations and label them clearly.
Example prompt in Google Sheets (via Gemini sidebar):
You are assisting with scenario-based cash planning.
1. Read the driver ranges from the 'Scenario_Config' sheet.
2. Propose 10 scenarios, including: base, best, worst, and 7 stress scenarios.
3. For each scenario, specify:
- Revenue growth rate
- DSO change (in days)
- FX EUR/USD change (in %)
- Average interest rate on debt
4. Ensure at least 3 scenarios reflect severe but plausible stress.
5. Write the scenarios into the 'Scenarios' sheet with clear names and descriptions.
This approach allows finance teams to generate richer scenario libraries in minutes and ensures assumptions are logged and reproducible for audits and steering committees.
Automate Cash Flow Projections per Scenario
Once scenarios are defined, use Gemini to apply them across your transactional history and pipeline. For example, for each scenario, Gemini can shift expected collection dates based on DSO assumptions, adjust FX exposures, and recalculate interest expenses based on rate paths. Implement this as a repeatable workflow: Gemini reads a selected scenario, queries aggregated cash data from BigQuery, and writes projected cash balances into a dedicated forecast tab.
Configuration sequence:
1. In 'Scenarios' sheet, select a scenario ID.
2. In 'Forecast_Control', define horizon (e.g. 13 weeks) and granularity (weekly).
3. Trigger Gemini with a prompt:
- Read scenario parameters for the selected ID.
- Query BigQuery view 'cash_hist_agg' for the last 24 months.
- Apply scenario assumptions to:
* Collections (shift by DSO change)
* Payables (optional extension or compression)
* FX (revalue foreign-currency flows)
* Interest cost (apply scenario rate path)
- Write projected weekly cash inflows, outflows, and balances to 'Forecast_[ScenarioID]'.
4. Use standard Excel formulas or Apps Script to visualise cash curves and buffer levels.By standardising this workflow, your team can re-run all scenarios whenever new transactional data arrives, without rebuilding models.
Let Gemini Explain Variances and Liquidity Risks in Plain Language
Beyond raw numbers, Gemini is effective at summarising what changed and why. After forecasts are generated, have Gemini compare the new base case against the previous iteration and produce a narrative explanation of key variances and risk points. Store these narratives next to charts in Sheets or paste them into your monthly cash committee decks.
Example prompt for variance explanation:
You are a finance analyst explaining cash forecast changes.
1. Compare the latest 'Forecast_Base' sheet with the previous version stored in 'Forecast_Base_Prev'.
2. Identify the top 5 drivers of change in:
- Peak cash drawdown
- Minimum cash buffer
- Timing of cash low point
3. Write a concise explanation (max 300 words) in business language, focusing on:
- What changed
- Why it changed (link to drivers)
- Recommended actions (collections, payables, credit lines, investment deferral)
4. Output the explanation to the 'Narratives' sheet, cell A2.This builds decision-ready insights directly into your planning process and shortens the time from forecast refresh to management action.
Set Up Alerts for Upcoming Cash Shortfalls
To move from reactive to proactive liquidity management, combine Gemini with simple threshold logic. In your forecast sheets, calculate minimum projected cash balance and weeks until breach of your target buffer. Then ask Gemini to scan these values across scenarios and generate alerts or recommended actions when thresholds are crossed.
Example alert workflow:
1. In each 'Forecast_[ScenarioID]' sheet, compute:
- MIN_CASH_BALANCE
- WEEK_OF_MIN_BALANCE
2. Create a 'Risk_Overview' sheet that aggregates these metrics per scenario.
3. Trigger Gemini with:
Review the 'Risk_Overview' sheet.
- Flag any scenario where MIN_CASH_BALANCE < 0 or < target buffer.
- For each flagged scenario, propose 3-5 concrete actions to mitigate risk
(e.g. accelerate collections in specific regions, delay capex, renegotiate terms).
- Write a prioritised list of scenarios and actions into 'Action_Plan'.
4. Optionally, connect this to email or chat notifications via Apps Script.This lightweight setup gives CFOs early warning and structured action plans without expensive treasury systems.
Continuously Refine Prompts and Data Based on User Feedback
Gemini’s effectiveness depends heavily on prompt quality and data structure. Treat your initial configuration as a version 1.0 and iterate. Encourage analysts to flag where Gemini’s suggestions are off (e.g. unrealistic DSO shifts or FX assumptions) and capture these as prompt improvements or data filters.
For example, you might update prompts to limit scenario shocks based on historical volatility, or to treat certain customer segments separately due to known payment behaviour. Regularly review the prompts and Sheets/BigQuery schema to align with how the business and its risk profile are evolving.
When implemented with this level of discipline, finance teams typically see scenario generation time drop from days to hours, refresh frequency increase from quarterly to monthly or weekly, and a measurable reduction in unplanned liquidity stress events. The exact metrics will vary, but a realistic expectation is a 50–70% reduction in manual scenario-building effort and significantly earlier visibility into upcoming cash shortfalls.
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Frequently Asked Questions
Gemini improves scenario-based cash planning by automating the repetitive work around data aggregation, scenario creation and narrative analysis. Connected to BigQuery and Google Sheets, it can pull updated cash data, apply defined assumptions (e.g. demand drops, FX moves, interest rate changes) and generate multiple forecast scenarios in a consistent structure.
Instead of manually editing spreadsheets for each new question from the CFO, your team can re-run and extend scenarios on demand. Gemini can also explain variances between forecast versions in plain language and surface which drivers cause the fastest erosion of liquidity, making steering meetings more focused and actionable.
You need three main capabilities: access to your finance data, basic cloud/BI know-how, and finance experts who understand your cash drivers. Technically, someone should be able to expose AR/AP, bank and sales data in BigQuery or another structured source and connect it to Google Sheets. This is usually achievable with existing analytics or IT resources.
On the business side, FP&A or treasury teams define the forecasting logic, scenario definitions and constraints. Reruption typically works with a small cross-functional team (finance + data/IT) to set up the first workflows, so you don’t need a large internal AI team to get started. Gemini itself is accessed through familiar tools like Sheets, which lowers the learning curve for finance users.
With a focused scope, you can see tangible results within a few weeks. A first version usually includes a 13-week base forecast, 3–5 scenarios, and automated narrative explanations — all powered by Gemini integrated into Sheets and BigQuery. This is often enough to significantly reduce manual effort and improve the quality of cash discussions.
From there, you can expand to more scenarios, additional data sources (e.g. detailed pipeline data, capex plans), and automated alerts for upcoming shortfalls. Many organisations move from a proof of concept to an operationally useful setup in 6–10 weeks, depending on data readiness and stakeholder availability.
The ROI comes from both efficiency gains and better risk management. On the efficiency side, teams typically cut manual scenario-building time by 50–70%, freeing analysts to focus on interpretation and decision support instead of spreadsheet maintenance. This alone can justify the investment, given the relatively low incremental cost of using Gemini with existing Google tools.
On the risk side, better and more frequent scenarios help avoid expensive surprises: emergency funding at unfavourable rates, covenant breaches, or missed investment opportunities due to overly conservative cash buffers. While the exact figures depend on your size and capital structure, even a single avoided liquidity event can create a strong positive ROI case for AI-enhanced planning.
Reruption supports you from idea to working solution. Our AI PoC offering (9.900€) is designed to prove that Gemini can handle your specific scenario-based cash planning needs using your real data. We define the use case with your finance team, set up the data flows (e.g. from ERP to BigQuery and Sheets), and build a functioning prototype that generates and explains cash scenarios.
Beyond the PoC, we work with a Co-Preneur approach: we embed with your team, operate in your P&L, and take entrepreneurial ownership for getting the solution into day-to-day use — not just into a slide deck. That includes hardening the workflows, defining governance, training finance users, and creating a production roadmap so that AI-powered cash forecasting with Gemini becomes a reliable part of your planning process.
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