Water Treatment Company Implements AI for Sales Forecasting — Landing Accuracy from ±20% to ±0.4% vs Target

What this case study covers

A water treatment service company could not land its sales forecast — reps predicted revenue, then dropped expectations through the month, leaving a ±20% miss the business couldn't plan against. This case study documents the full intervention: from sales-operations standardization to inventory-to-pipeline reconciliation to an AI forecasting layer in the CRM — and what the accuracy gain is worth in dollars.

Inside this case study

• Why fixed-percentage forecasting caps out around ±8% — and why regression alone wasn't enough
• How the AI layer scored true pipeline quality down to the manager–rep level
• Connecting supply chain, customer inventory, and predicted chemical run-out
• Backtest vs realized results: ±0.4% / <1% quarterly backtested, ~0.8% / ~4% realized
• The dollar translation: forecast swing, working capital, SG&A, and recovered pipeline