Skip to content

Your AI Pilot Worked. That’s Exactly Why It’ll Fail in Production.

The most dangerous sentence in AI right now is “the pilot was a success.” A failed pilot tells you the truth while the check is small. A successful one tells you a flattering lie—that the hard part is behind you—right before you wire seven figures into the part that was never tested. Here are the four things a pilot quietly hides, why production is where projects actually die, and how to read pilot success without being fooled by it.
Short Excerpt (151 chars): A successful AI pilot isn’t a green light—it’s a trap. The four things it hides are exactly what kills the project in production. Here’s how to scale.

[Click on the image to view post]

Uber Burned Its Entire 2026 AI Budget in Four Months. The Real Lesson Isn’t About Money.

In April, Uber admitted it spent its full 2026 AI budget in four months — and its own COO couldn’t connect the spend to customer value. The easy read is “AI is expensive.” That read is wrong, and it will cost you. This wasn’t a tool problem or a budgeting mistake. It was a foundation mistake, and foundation mistakes are the only kind that scale. Here’s the boring, unglamorous discipline that separates the companies getting ROI from the ones getting a surprise invoice.

[Click on the image to view post]

What AI-Ready Infrastructure Actually Costs to Build

A client wanted one number for getting “AI-ready” before our call ended. Three vendors had already given him one — and that’s exactly the problem. The number you’re quoted is almost always the model, the cheapest part. The real budget lives in data readiness, integration, security, and adoption — the work that has to be true before any model does anything useful. Here’s what each category actually costs, why two companies the same size can land 3x apart, and how to get a real number before you commit a dollar.

[Click on the image to view post]

How to Write an AI Business Case Your CFO Will Actually Approve

Most AI business cases die in the CFO’s inbox — not because the idea is bad, but because the document reads like a vendor brochure with a price tag stapled to it. After 20 years sitting in budget meetings at IBM and Kyndryl, I’ve seen the patterns. The approved cases share a structure. The killed cases share a different one. Here’s the framework — cost of doing nothing, infrastructure vs. AI separation, risk framing, and the one slide that closes the deal.

[Click on the image to view post]

From Manual Chaos to AI-Powered Operations: A Manufacturing Transformation Story

Six months into the project, a senior stakeholder asked the question that kills more AI initiatives than anything else: “Can’t we just skip ahead to the good stuff?” Here’s what happened when this manufacturing client said no, stayed the course on the hard infrastructure work nobody puts on a press release, and walked away with 38% faster incident detection, $2M+ in savings, and a team finally free to do the work they were hired for.

[Click on the image to view post]