AI-Powered Runway Modeling

Predictive Cash Flow Intelligence Cash Flow Driver Mapping AI identifies every cash flow driver: customer payment patterns (actual days-to-pay vs. contractual terms), recurring expenses (payroll, rent, SaaS subscriptions), variable expenses (commissions, raw materials), debt service (principal and interest), and one-time items (capex, tax payments). Creates a granular, bottom-up cash flow model. Customer Payment Behavior Analysis For each customer (or customer segment), AI calculates: average days-to-pay (weighted by invoice size), payment variability

(standard deviation), seasonal payment patterns (quarter-end rushes), and 'payment at risk' probability (based on customer credit signals and aging patterns). Monte Carlo Simulation AI runs 50,000+ simulations varying: customer payment timing (based on individual customer distributions), new bookings (based on pipeline probability), and expense timing (based on vendor payment terms). Generates a probability distribution of cash positions at each future date — not a single-point forecast, but a range. Scenario Analysis AI models named scenarios: Base Case

(current trends continue), Upside (key deals close, collections improve), Downside (largest customer churns, collections slow by 15 days), and Stress Test (combination of adverse events). Each scenario shows: month-by-month cash balance, runway months remaining, and the date when action must be taken. Treasury Decision Support Based on the forecast probability distribution, AI recommends: optimal cash reserve (enough to cover the 95th percentile adverse scenario), investment of excess cash (sweep to money market, T-bills allocation), credit facility draw

decisions, and early payment discount opportunities where cash allows. Update the cash flow forecast weekly, not monthly — cash positions can change dramatically based on a single large receipt or disbursement. Track 'Cash Conversion Cycle' (CCC = DSO + DIO - DPO) as the primary efficiency metric — AI should benchmark your CCC against industry peers and show the working capital freed by a 1-day improvement. Build 'Cash Trigger Alerts': AI notifies the CFO when: projected cash drops below 3 months runway, a customer > $100K is 60+ days overdue, or monthly

burn rate exceeds the forecast by >15%. Don't use a single-point cash forecast — 'We'll have $2M next month' is useless without the confidence interval. AI should say: '$2M ± $400K (90% confidence).' Don't ignore intra-month cash timing — a company can have positive monthly cash flow but go negative on the 15th when payroll hits and AR doesn't arrive until the 25th. AI should model daily cash positions. Don't treat all receivables as equally collectible — AI should apply aging-based probability adjustments: 0-30 days = 98%, 31-60 = 90%, 61-90 = 70%, 90+

= 40%. The '13-Week Cash Flow' Discipline Implement the '13-Week Cash Flow' model that private equity firms use: a rolling 13-week (one quarter) forecast updated every Monday. It's short enough to be highly accurate and long enough to see upcoming cash crunches. AI generates this automatically by pulling: AR aging (when will we collect?), AP aging (when must we pay?), payroll calendar (fixed), and known one-time items. This single report prevents more cash crises than any other financial tool.