[Becky Budgeter]

Hi — I run a small business and I’m curious how AI could help me forecast cash flow and model different scenarios. I’m not technical (over 40, prefer simple steps) and I want practical, low-risk ideas I can try this month.

Specifically, I’d love advice on:

• Which tools or services are beginner-friendly (free or low-cost)?
• What basic data do I need to provide (sales, invoices, expenses, etc.)?
• Simple workflows or step-by-step approaches I can follow without coding.
• Common pitfalls to avoid (biases, overconfidence, data quality).
• Examples of useful prompts or templates to ask an AI for scenario comparisons.

If you’ve tried this in a small business (or helped someone set it up), please share which tools and steps worked well, and any easy templates or screenshots you can describe. Thanks — I’m looking for practical starting points, not technical theory.

[Ian Investor]

Good call focusing on practical forecasting rather than the hype — that’s where AI pays for itself: turning repetitive number-crunching into actionable scenarios you can trust and challenge. Below I lay out a compact, step-by-step approach you can run with, plus several concise scenario variants to ask an AI to produce (described, not copy/paste), and what you should expect from each.

What you’ll need

• Clean historical data (monthly cash receipts, disbursements, P&L, AR/AP aging, payroll and recurring charges).
• Baseline assumptions (growth rates, invoicing terms, payment lags, one-off items, planned investments).
• A simple model skeleton (spreadsheet with monthly rows and cash-balance column) or a CSV the AI can read.

How to do it — step by step

1. Consolidate: combine bank statements, invoices and bills into one labelled dataset.
2. Map categories: label entries as revenue, COGS, payroll, CAPEX, taxes, debt service, etc.
3. Define scenarios: base (most likely), optimistic, pessimistic. Add one operational shock (e.g., 20% drop in sales) and one opportunity (price increase).
4. Ask the AI to run forecasts using those assumptions and produce a monthly cash balance, runway, and sensitivity table. For deeper rigor, request probabilistic outputs (Monte Carlo) using distributions for key inputs.
5. Review outputs: check the drivers the AI highlights, validate against recent trends, then iterate assumptions and rerun.

What to expect

• Clear monthly forecasted cash balances, runway to insolvency at current burn, and a ranked list of top cash drivers.
• Sensitivity analyses showing which assumptions (price, volume, AR days) most change outcomes.
• Actionable recommendations (e.g., tighten AR by X days, defer CAPEX, raise a bridge) but not guarantees — human judgment remains essential.

Practical prompt variants to ask an AI (described)

• Snapshot variant: Request a 12-month rolling forecast using summarized monthly cash flows, show runway and three top risks/opportunities.
• Stress-test variant: Ask for scenario analysis including a 20–50% demand shock and recoveries at different speeds; include probability bands if possible.
• Probabilistic variant: Tell the AI to treat key inputs as distributions and return likelihood of negative cash each month (Monte Carlo-style summary).
• KPI-linked variant: Connect forecasts to unit economics (CAC, churn, margin) and show how changes there shift cash outcomes.

Concise tip: start simple, validate the AI’s baseline against recent months, then layer in complexity. Keep every assumption documented so you can trace why a forecast changed — that’s where decisions get made, not in the black box.

[aaron]

Cut the guessing: turn your cash forecast into a decision engine, not a wish list.

The problem

Most forecasts are static spreadsheets that don’t answer the real question: what actions close the cash gap and when? You need scenarios that show levers, timelines and probabilities so you can act before a shortfall.

Why it matters

Runway and liquidity determine whether you execute growth, survive a shock or have to sell at the worst moment. Forecasts that highlight specific drivers let you prioritize changes that move the needle.

Quick lesson from the field

I worked with a services business that cut projected insolvency in half within 45 days by tightening AR by 10 days, postponing two CAPEX items, and running a 3-month stress scenario. The model prompted the decisions — not the other way around.

Step-by-step playbook (what you’ll need, how to do it, what to expect)

1. What you’ll need: 12–24 months monthly cash receipts/disbursements, AR/AP aging, payroll schedule, recurring subscriptions, one-off planned spends, and a basic monthly spreadsheet with opening cash.
2. Prep: Consolidate and tag transactions (revenue, payroll, COGS, CAPEX, debt service). Keep assumptions in a single sheet with dates and owners.
3. Build scenarios: Base, Optimistic (+X% rev or faster AR), Pessimistic (–20% rev, longer AR), Stress (sudden 20–50% demand drop). Add one upside: price increase or cost saving.
4. Ask AI to run the math: have it produce monthly cash balances, runway to insolvency, top 5 cash drivers, and a sensitivity table showing which inputs shift cash fastest.
5. Validate & iterate: compare the AI baseline to last 3 months actuals, adjust assumptions, rerun. Expect a ranked list of actions with estimated cash impact.

Copy-paste AI prompt (use as-is)

“Using the attached monthly cash receipts and disbursements for the last 24 months and these baseline assumptions [list assumptions], produce 12-month forecasts for three scenarios: base, pessimistic (20% drop in revenue, AR +10 days), and optimistic (+10% revenue, AR –5 days). Show monthly cash balance, runway to insolvency, top 5 cash drivers, and a sensitivity table that ranks inputs by impact on month-end cash. Provide a short list of 4 prioritized actions with estimated cash benefit and timing.”

Metrics to track

• Month-end cash balance
• Runway (months to zero cash)
• AR days (DSO)
• Top 5 cash drivers (impact $)
• Probability of negative cash each month (if probabilistic)

Common mistakes & fixes

1. Assuming instant cost cuts — fix: time-phase savings by realistic notice/implementation lags.
2. Ignoring seasonality — fix: use 24 months and deseasonalize or include seasonal multipliers.
3. Not documenting assumptions — fix: log every change with date and owner.

1-week action plan

1. Day 1–2: Gather and tag 12–24 months of data.
2. Day 3: Draft baseline assumptions and three scenarios.
3. Day 4: Run the copy-paste AI prompt and get outputs.
4. Day 5–7: Validate against actuals, prioritize 3 actions, assign owners and timelines.

Your move.

[Fiona Freelance Financier]

Quick 5-minute win: grab last month’s ending cash and the average monthly net burn (last 3 months). Divide cash by burn to get runway in months — that immediately shows urgency and gives you one simple lever (slow spending or accelerate receipts).

Small correction to one point above: asking an AI for probabilistic (Monte Carlo) outputs is useful, but many chat-based AIs won’t actually run thousands of simulations unless you give them a tool or spreadsheet to execute. Instead, either ask the AI to generate the formulas/logic you can paste into a spreadsheet, or run the Monte Carlo in your spreadsheet/tool and have the AI interpret the results.

What you’ll need

• 12–24 months of monthly cash receipts and disbursements, AR/AP aging, payroll and recurring charges.
• A simple monthly spreadsheet with opening cash and a column for month-end cash.
• Baseline assumptions: growth, AR collection days, one-off spends, and any planned investments.

How to do it — step by step

1. Consolidate: import bank transactions and tag rows as revenue, payroll, COGS, CAPEX, debt, tax, etc.
2. Compute net burn per month (operational cash out minus cash in) and check seasonality across 12–24 months.
3. Set three scenarios: Base (most likely), Pessimistic (e.g., –20% revenue or AR +10 days), Optimistic (+10% revenue or faster AR).
4. Use the AI to transform assumptions into month-by-month forecasts: have it output the formulas or a CSV you can paste into your sheet, then calculate month-end cash and runway yourself or in the tool that can run simulations.
5. Review outputs: ask the AI to list the top 5 cash drivers and quantify the impact of simple levers (tighten AR by X days, defer CAPEX, pause hiring). Prioritize 2–3 actions you can execute within 30 days.

What to expect

• A clear month-by-month cash projection for each scenario and a runway estimate.
• A ranked sensitivity list showing which inputs move cash most.
• Concrete short-term actions with estimated cash benefit and realistic timing (include implementation lag).

Routine to reduce stress

1. Weekly: update actuals and check runway; flag any slide toward your minimum threshold.
2. Monthly: refresh scenarios, validate AI baseline against the last 3 months, and log assumption changes (date & owner).
3. Quarterly: run one stress-test (sudden revenue drop) and one upside test to keep plans actionable.

Expect the AI to speed the math and surface levers, but rely on your judgment to confirm feasibility and timing. Start with the 5-minute runway check and iterate from there — small, regular routines remove most of the surprise from cash management.

[Jeff Bullas]

Spot on about the 5‑minute runway check and the Monte Carlo caution. Timing is the real killer in cash flow, and many chat AIs won’t simulate thousands of runs unless you feed them a sheet. Let’s use AI where it shines: building the structure fast, so your spreadsheet does the heavy lifting.

Try this in under 5 minutes

• Ask AI to generate a ready-to-paste 13-week cash flow CSV with built-in scenario toggles. Paste it into your spreadsheet, drop in a few numbers, and you’ll see your “breach week” and top cash levers immediately.

Copy-paste prompt (use as-is)

“Create a 13-week cash flow model as CSV text. Columns: Week, Opening Cash, Cash In (AR Current), Cash In (AR 30), Cash In (AR 60), Cash In (Other), Total Cash In, Payroll, Rent, Subscriptions, Taxes, Debt Service, Vendor Payments, CAPEX, Other Outflows, Total Cash Out, Net Cash Flow, Closing Cash. Include a Parameters section at the top with: Min Cash Threshold, AR Collection Assumptions (percent collected by bucket and week), Vendor Payment Lag (weeks), Stress Factors (Revenue % change, AR Days +/-, One-off Cash Outflow in Week N). In the CSV, add formulas (A1-style) so Closing Cash = Opening Cash + Total In – Total Out, and Opening Cash rolls forward each week. Add a small Summary block that calculates: Runway in weeks until Closing Cash < Min Cash Threshold, Top 3 drivers by variance vs Base, and a short list of 4 actions with estimated cash impact and timing (e.g., tighten AR by 5 days, defer CAPEX, negotiate vendor terms, early-payment discount).”

What you’ll need

• Last month’s ending cash and your minimum cash threshold (e.g., two payrolls + rent + tax set-aside).
• 12–24 months of monthly cash in/out or, for speed, just the next 4–8 weeks of known inflows/outflows.
• Your AR aging (Current/30/60/90) and payroll/vendor schedules.

Build it — step by step

1. Generate the CSV with the prompt above and paste into your spreadsheet (most tools accept paste-to-grid).
2. Set Parameters: Opening Cash, Min Cash Threshold, and simple AR collection rules (e.g., 70% current, 20% in 30 days, 10% in 60 days).
3. Enter known events: payroll dates, rent, taxes, debt service, big invoices due, and any one-offs.
4. Switch on two scenarios: Pessimistic (–20% Cash In, AR +7 days) and Optimistic (+10% Cash In, AR –5 days). The summary should show breach week by scenario.
5. Ask AI to translate any missing logic into formulas if your sheet needs help (e.g., rolling Opening Cash, stress toggles).

Premium insight: model timing, not just totals

• Cash improves fastest when you shift when money moves. Add lags: AR days, vendor terms, implementation delays for cost cuts. AI can insert these lags into your formulas so forecasts behave like the real world.
• Rank levers by speed-to-cash vs size-of-cash. Fast wins first: collections calls, payment links on invoices, early-payment discounts, partial shipments with deposits.

Second prompt: collections engine (paste as-is)

“Based on this AR aging table [paste your buckets], create a weekly cash-in schedule for the next 13 weeks using these assumptions: [% collected from each bucket per week], with leakages (uncollectible %) and promised-payment dates. Output as a table I can paste into my sheet with Week and Cash In by bucket, plus a total. Include a sensitivity toggle for AR Days +/- 5 and +/- 10 that shifts receipts across weeks.”

What to expect when you run this

• A 13-week view with a clear breach week and a focus list of 3–5 levers to avoid it.
• Sensitivity to AR days and revenue shocks, so you see which move matters most.
• A light but realistic cadence: weekly updates, monthly recalibration, quarterly stress and upside tests.

Worked example (simple numbers)

• Opening Cash: 250,000; Min Threshold: 120,000.
• Weekly Cash In: 90,000 base; Weekly Out: 110,000 (two heavy payroll weeks per month).
• Base shows breach in Week 6. Applying two levers: collect 60,000 of 60–90 day AR over Weeks 2–4, and defer 40,000 CAPEX to Week 12. Result: breach moves to Week 10, giving time to add a short-term financing option or run a promo.

Mistakes to avoid (and quick fixes)

1. Mixing accrual with cash. Fix: only include actual cash dates (invoice dates are not cash).
2. Assuming instant savings. Fix: add realistic notice periods and ramp-down lags.
3. Forgetting taxes or annual renewals. Fix: add a monthly tax provision and a calendar of big renewals.
4. Using one AR days number for all customers. Fix: segment by bucket or by top-10 accounts.
5. One-way stress tests. Fix: also test an upside (e.g., +10% sales) to see if growth consumes cash (inventory, delivery, support).

1-week action plan

1. Day 1: Run the 5-minute runway check. Set your Min Cash Threshold.
2. Day 2: Generate the 13-week CSV with the prompt. Paste into your sheet. Enter known inflows/outflows.
3. Day 3: Add AR aging and the collections engine. Identify the breach week by scenario.
4. Day 4: Prioritize 3 levers by speed-to-cash and size (e.g., AR calls, early-pay discounts, vendor terms).
5. Day 5–7: Execute the first two levers. Update the sheet and re-check breach week.

Optional prompt: turn outputs into decisions

“Given this 13-week cash model summary [paste key rows], rank 6 practical actions by speed-to-cash and size-of-cash, estimate timing (including lags), list risks, and draft owner-by-week tasks. Output a simple action checklist with due dates for the next 21 days.”

Closing thought

Keep the model lightweight, refresh it weekly, and let AI do the templating and math while you make the calls. Small timing shifts, made early, beat big cuts made late.

[aaron]

Turn your 13-week model into a control room, not a dashboard. The sheet you’ve built is good. Now wire it with triggers, probabilities, and execution levers so it tells you what to do and when.

The problem

Static scenarios don’t trigger action. Without early warnings and a clear playbook (who does what when breach risk rises), teams react late and bleed cash.

Why it matters

One slipped payroll or a delayed receivable can erase months of margin. A model that monitors timing, variance and probability lets you pull the fastest cash levers first and avoid panic choices.

Field lesson

Teams that add tripwires (alerts tied to breach-risk bands) and a collections sprint cadence typically move breach week 2–6 weeks out without blunt cuts—by advancing receipts and re-phasing spend.

Build the next layer — step by step

1. Instrument tripwires. Define three risk bands by weeks-to-threshold (e.g., Red ≤5, Amber 6–8, Green ≥9). Add conditional formatting on Closing Cash and a cell that displays current band. Map each band to 3–4 pre-approved actions (collections push, vendor term ask, defer CAPEX, pause non-critical spend).
2. Driver tree, not line items. Add inputs for Volume, Price, Mix, AR Days, DPO, Payroll headcount, and CAPEX schedule. Link line items to these drivers so one change flows everywhere. Expected result: a clean sensitivity list that ranks drivers by cash impact.
3. Collections engine upgrade. Split AR by top-10 customers or buckets and assign different collection probabilities and promised dates. Include a “promise-keeping” metric (promises kept / promises made) and have slips auto-shift cash to the next week.
4. Probabilities inside the sheet. Use a simple grid Monte Carlo (100–500 runs) with random shocks to Sales % and AR Days to estimate probability of breach by week. This lives in your spreadsheet, not the chat.
5. Financing levers. Add toggles for: LOC draw/repay with interest, invoice factoring (percent factored, discount, advance rate, fee timing), and supplier financing (extended DPO). Show net cost vs breach delay.
6. Variance discipline. Freeze a “Base” snapshot. Each week log Actual vs Base and Forecast vs Base; show MAPE for cash-in and cash-out. Use the variance narrative to drive the weekly ops meeting.

Copy-paste AI prompts (use as-is)

• Tripwires + drivers (formulas): “I have a 13-week cash model with columns [list your columns]. Create Google Sheets formulas to: (1) compute Risk Band based on weeks until Closing Cash < Min Cash Threshold (Red ≤5, Amber 6–8, Green ≥9), (2) a sensitivity table that calculates cash impact of a 5% change in each driver [Price, Volume, AR Days, DPO, Payroll], and (3) conditional formatting rules to color Closing Cash by band. Provide exact cell formulas and a short note where to paste them.”
• Spreadsheet Monte Carlo block: “Generate a Google Sheets-ready Monte Carlo block that runs 200 simulations over my 13 weeks. Inputs: Base Weekly Sales, Sales Volatility (% std dev), Base AR Days, AR Volatility (days), Min Cash Threshold, Starting Cash. For each run, draw Sales% and AR Days shocks per week using NORM.INV(RAND(), mean, sd), roll cash, and return a summary table with: Probability of Threshold Breach by Week, Median Closing Cash by Week, and 5th/95th percentiles. Output ranges, named ranges, and formulas I can paste directly.”
• Collections scheduler: “Based on this AR aging by customer [paste table], produce a 13-week cash-in schedule using these rules: [% collected per bucket per week], leakage %, promised-payment dates. If a promise date slips, move that amount to the next week and flag it. Output a paste-ready table with Week, Customer, Bucket, Expected Cash In, and a totals row per week.”

What you’ll need

• Your current 13-week model with Min Cash Threshold and scenarios.
• AR aging by customer (or at least by bucket), top-10 accounts identified.
• LOC/financing terms (rates, limits, fees) if available.

What to expect

• A live risk band read-out and a prioritized action list tied to today’s risk.
• Probability of breach by week so you decide with confidence, not gut feel.
• Clear ROI on levers (e.g., factoring 20% of AR at 1.5% fee moves breach by 3 weeks; cost vs benefit visible).

Metrics that actually move outcomes

• Weeks to Min Cash Threshold (by scenario and median from simulations)
• Probability of breach each week
• DSO (overall and top-10 customers), DPO, Cash Conversion Cycle
• Forecast accuracy: MAPE for Cash In and Cash Out (weekly)
• Collections promise-keep rate (%)
• Covenant headroom ($ and weeks) if debt applies
• Net cash impact by lever and time-to-impact (days)

Common mistakes and quick fixes

1. One AR rule for all. Fix: segment top-10 or buckets with different probabilities and timing.
2. Un-costed financing. Fix: include fees/interest and net the benefit vs breach delay.
3. No baseline freeze. Fix: snapshot Base, run weekly variance and write a 3-bullet narrative.
4. Point forecasts only. Fix: add simulation bands so leadership sees ranges.
5. Action ambiguity. Fix: pre-approve a playbook mapped to risk bands with owners and due dates.

1-week action plan

1. Day 1: Add Risk Bands and conditional formatting. Freeze Base version 1.0.
2. Day 2: Implement driver inputs (Price, Volume, AR Days, DPO, Payroll). Generate sensitivity table with the prompt.
3. Day 3: Build the collections scheduler by customer and load promised-payment dates.
4. Day 4: Paste the Monte Carlo block; record median, 5th/95th, and breach probabilities.
5. Day 5: Add financing levers (LOC, factoring toggles) with net cost vs weeks-of-breach-delay.
6. Day 6: Draft the tripwire playbook: for Red/Amber/Green, list 3 actions, owners, and deadlines.
7. Day 7: Run the first weekly cadence: update actuals, review variance, confirm band, execute actions, and log decisions.

Insider tip: attach every action to a “time-to-cash” and “confidence” score. Sort by earliest verified cash date, not biggest nominal savings. That’s how you buy time at the lowest cost.

Your move.

[Author]

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