• Client: $50M home goods manufacturer, cash-constrained
• Engagement: Execution Sprint, Jane Gentry & Company
• Results: gross margin 34% to 38%, sales +16%, inventory turns 2.5x to 3.4x, $2.4M cash freed, net income doubled
• Timeline: cash crisis resolved in 12 months, with no new systems, hires, or debt

The CFO put the cash forecast on the table. Ninety days. Then he turned to the order book, which was full, and the room went quiet, because both things were true and nobody could explain how.

We were growing and going broke at the same time, and I couldn’t tell you why.
CEO

This is where a lot of CEOs reach for financing, or a year-long systems project. Money would have bought another ninety days. A software build would have taken a year the company didn’t have. Neither one fixed the real problem, which wasn’t a shortage of cash or systems. It was that no one could see where the cash already was.

Sales, operations, and finance each kept their own spreadsheets, and none of them agreed. Inventory got ordered on instinct. Marketing spend went untracked. Working capital sat frozen inside product that hadn’t moved in a year.

So Jane Gentry ran an Execution Sprint. Instead of the systems build the company couldn’t afford, AI pulled the disconnected spreadsheets into one live view of the business in weeks, then surfaced what the old reports buried: the dead SKUs, the customers quietly shrinking, the products losing money on every unit shipped.

Jane Gentry did the diagnosis and the operating model redesign: the decision architecture, the workflow mapping, the sequencing. Peter O’Sullivan of HighBeam Analytics built the tools that architecture called for on top of the company’s existing data: the alerts that flagged aging inventory, the demand signals that replaced gut-feel purchasing, and the attribution that tied marketing spend to margin. No new platform, no migration, no year-long build.

Jane Gentry does not build AI tools. She designs the decision architecture that determines which tools are worth building and where to point them, then brings in the technical partner to build exactly what the operating model requires, not what a vendor wants to sell.

The AI didn’t replace the team’s judgment. It gave them something to judge. The first moves were obvious once they could see them. Clear the dead inventory. Reprice the bottom fifth of the catalog, where the company had been losing money on every sale.

Inside twelve months, gross margin moved from 34% to 38%. Sales grew 16%. Inventory turns went from 2.5x to 3.4x, pulling $2.4M in frozen cash back into the business. Net income doubled. No new systems, no new hires, no new debt.

Most companies don’t need more data or a bigger system. They need to see the data they already have. If that sounds like yours, that’s where an Execution Sprint starts.

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