74% of Companies Fail at AI. Most Never Even Started Right.

74% of companies struggle to achieve and scale value from AI.

That's not my number. That's BCG surveying 1,000 executives across 59 countries.

And here's the uncomfortable part: Most of these companies aren't failing because they're doing AI wrong. They're failing because they never actually started. They're still "getting ready." Still planning. Still in committee meetings about their AI strategy.

The world moved on.

The Mental Model That's Killing You

This is a pattern I keep seeing repeating. Executives approach AI with the same mental model they used for IT projects in 2010.

Big upfront planning. Lengthy requirements phases. 12-month timelines before anything ships. Massive teams. Careful, slow, expensive. Or at least that's the mindset they approach it.

That model made sense when technology was expensive to change. When getting it wrong meant costly rework. And seldomly a solution went live without rework in the clip. When deployment meant physical servers and permanent decisions.

But that's not how it works anymore.

The technology has changed. Dramatically. The question is whether your mindset has caught up.

A Conversation in Spain

I'm in Spain this week, catching up with a friend who runs a startup.

In June last year, he was nowhere near AI. Not even on his radar. Then 2025 happened. A brutal year for him. Market pressure. A sophisticated cyber attack that nearly took his company down. The kind of year that forces you to rethink everything.

Yesterday, he told me he's deploying two AI solutions tomorrow. Not "exploring." Not "piloting." Deploying. Into production. And he's already planning the next experiments.

Then he asked me something that surprised me.

"You know, I struggle with having an overview over everything in AI. Can you help me?"

My answer: "I could, but you would lose focus. The way you're doing it is exactly how it works. Just keep going like that. The only thing to be aware of is to keep evaluating the results and iterate where needed."

He doesn't need a comprehensive AI strategy document. He doesn't need to understand the full landscape. He needs to keep doing what he's doing: picking problems, building solutions, testing them, and iterating.

That's the speed lane. And he's on it.

Why IT Projects Used to Be Slow

Traditional IT projects were slow for legitimate reasons:

Waterfall methodology - You planned everything upfront because changes were expensive. Once you started building, deviations meant rework, delays, and budget overruns.

Fear of getting it wrong - Deploying meant committing. Rolling back was painful, sometimes impossible. Better to plan for 6 months than ship something broken.

Infrastructure constraints - Physical servers. Long procurement cycles. Dependencies that took weeks to provision.

Limited iteration capability - Testing required dedicated environments. Feedback loops measured in sprints, not hours.

These weren't irrational fears. They were responses to real constraints.

But those constraints are mostly gone.

What Changed Everything

Three shifts collapsed the timeline:

1. AI-assisted development

GitHub's 2024 data shows 92% of developers now use AI coding tools. Stack Overflow reports 76% save at least 30% of their time on routine programming tasks.

AI doesn't just write code faster. It eliminates the bottleneck of translating ideas into implementation. You describe what you want. It builds the first version. You iterate.

2. Modular systems

Modern architecture means components snap together. APIs connect services. You're not building from scratch every time. You're assembling.

What took months of custom development now takes days of configuration and connection.

3. Iteration as default

Cloud deployment means you ship, test with real users, and improve. The feedback loop collapsed from months to hours. You don't need to be right the first time. You need to be fast enough to learn.

McKinsey reports a 55% reduction in development time through AI-assisted coding. Y Combinator startups are delivering MVPs in 6 weeks that used to take 6 months.

What My Friend Actually Did

He didn't hire an AI consultant to map out his strategy. He didn't spend three months building an AI roadmap. He didn't wait until he "understood AI" before starting.

He picked a problem. Built something. Tested it. Learned. Moved to the next one.

No lengthy requirements document. No architecture review committee. No 3-month planning phase.

Start. Build. Test. Ship. Learn.

If something breaks, fix it. Today. Not next quarter.

That's not reckless. That's the new standard.

The Competitive Math

Here's the math that should worry you.

If your competitor ships and tests 4 ideas per month while you're still in planning committees, they're learning 12 times faster than you are per quarter.

Every week you spend "getting ready," they're shipping, testing, and iterating. Every month you spend in planning committees, they've built and discarded 5 approaches that didn't work and found 3 that did.

Speed isn't about being sloppy. Speed is about compressing the learning cycle. The companies that win aren't the ones with the best strategy document. They're the ones who figure out what works before everyone else does.

And the only way to figure out what works is to try things. Fast.

The Mindset Shift

Speed is possible now. But only if you update your mental operating system.

Stop treating AI like a 2-year IT project. Treat it like a series of small experiments. Ship something in a week. Learn what works. Build on it.

Stop waiting for perfect requirements. You can't know the perfect requirements until you've seen what's possible. Build something rough. Show it to users. Let reality refine your understanding.

Stop protecting your timeline. The timeline is the enemy. Every day you spend planning is a day your competitors spend learning.

My friend in Spain figured this out the hard way. After one of the toughest years of his business life, he stopped waiting and started building. Tomorrow, two AI solutions go live.

Speed is possible. But only if you know what to speed toward.

If you've been waiting to "get ready" for AI, consider this your wake-up call.

Speed isn't sloppy. It's the new standard. And the longer you wait to adopt it, the harder the gap becomes to close.

Ready to move faster?

Reach out about a Sprint week where we build working prototypes for your business.

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