Skip to content

Your Enterprise Playbook Is Why Your AI Project Failed

I spent 20 years running technology at IBM and Kyndryl leading 200+ engineers, governance boards, phased rollouts, the whole apparatus. That same enterprise playbook is quietly strangling AI projects right now. Not because the discipline is wrong, but because it’s aimed at the wrong problem, at the wrong speed. Here’s why enterprise process front-loads approval and back-loads the infrastructure truth that actually decides whether your AI project lives or dies and what to do instead.

[Click on the image to view post]

Your AI Vendor Is Hoping You Don’t Ask About Security

I’ve sat in a lot of vendor demos. The slides are beautiful, the demo runs flawlessly, and nobody says a word about where your data goes or who can read it. That silence isn’t an accident, it’s the strategy. Here are the five AI vendor security questions the industry hopes you’ll skip, why they matter more for small businesses than enterprises, and how to tell a serious vendor from one who bolted AI onto a weak foundation.
Short Excerpt (150 chars): Most AI vendors bet you won’t ask about security. The 5 questions to ask before you sign and how to tell a serious vendor from a risky one.

[Click on the image to view post]

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]

9 Questions to Ask an AI Vendor Before You Sign Anything

Last month I sat across from a CEO who’d just signed a $340,000 AI contract that was already failing. He didn’t miss the warning signs — he was never equipped to see them. After 20+ years in enterprise transformation and 25 active AI consulting clients, I’ve watched the same vendor patterns play out again and again. Here are the nine questions I’d ask if I were sitting in your seat — the ones that, when a vendor can’t answer them clearly, tell you to walk away.

[Click on the image to view post]

The Silent Killer of AI Projects: Why Infrastructure Fails Before AI Does

After 20 years leading enterprise technology at IBM and Kyndryl, I’ve seen the same pattern destroy AI initiatives at Fortune 100 companies and small businesses alike. The AI model works. The infrastructure underneath it doesn’t. Here are the 4 silent killers most organizations discover too late and how to find them before they cost you millions.

[Click on the image to view post]

The $2.4 Million AI Mistake: What Most Companies Get Wrong Before They Even Start

A manufacturing CEO spent $2.4 million on an AI initiative that completely failed. Six months of work, nothing worked, back to manual processes. The problem? They skipped the infrastructure assessment everyone said was “boring.” This pattern repeats across industries: companies make expensive mistakes in the same order for the same reasons. Here’s what actually separates AI success from disaster, and why foundation-first isn’t slower, skipping it is.

[Click on the image to view post]