Lean Six Sigma 30 Years Later: The Discipline AI Needs
Lean Six Sigma may look unfashionable, but it builds exactly the disciplines AI needs: process mapping, simplification, data quality, and statistical thinking.

Lean Six Sigma may look unfashionable, but it builds exactly the disciplines AI needs: process mapping, simplification, data quality, and statistical thinking.

Most people think the deal is done when it is signed. It is not. Signing is the easy part. The real work starts the day after.

When margins compress and cash gets tight, most companies manage the numbers. Durable gains come from fixing what drives them.

AI pilots fail on processes that were never stabilized and data that was never governed. Technology amplifies the operating engine it is given.

A joint venture is built with two bosses by design. It only works when governance is as practical and enforceable as the shareholder agreement.

Why does execution succeed or fail? Ten years inside General Electric’s culture of execution reveal what actually separates real delivery from busy activity.

Most acquisitions fail in the months after signing. A GE story shows how fast it can happen, and how the damage gets fixed.

AI pilots rarely scale on their own. Companies need senior operators who can redesign workflows and turn AI ambition into measurable business performance.

Contracts uninvoiced for six months, taxes unpaid, drivers stopped by the police. Inside a GE integration recovery, and the method that saved the business.

Operating cadence turns strategy into a weekly rhythm of KPIs, ownership, and decisions, the discipline that separates execution from a strategy deck.