MIT put numbers on what everyone in the trenches already knew: 95% of AI pilots never make it into production.
A trillion dollars torched worldwide. Countless "AI transformation roadmaps" written. And for what? Models that choke on logic puzzles your kid could solve. GPT-4 fails Tower of Hanoi even when you spoon-feed it the algorithm. But sure—let's run another six-month discovery phase to "explore use cases across the enterprise."
While You're Writing Plans, Your Team Is Already Using It
Nobody in your company is waiting for the strategy committee.
The finance analyst is copying transaction data into ChatGPT to find anomalies.
The customer support lead is running angry client emails through Claude to draft "calmer" replies.
The developers are dumping production error logs into GPT to debug at 2 AM.
No logging. No guardrails. No oversight. Just staff doing what works, faster than procurement can approve a license.
So when the auditor shows up, your "15-month rollout plan" won't matter. What will matter is that someone pasted raw customer PII into ChatGPT last quarter from a company laptop.
Executive Strategy Theater
A bunch of scale-ups I talk to are staging the same play:
Phase 1: Discovery & assessment (6 months) - mostly politics
Phase 2: Pilot programs (6 months) - build proof-of-concept nobody uses
Phase 3: Enterprise rollout (12 months) - integration hell
Phase 4: "AI Team" (ongoing) - maintain the thing that doesn't work
Ok, that’s how you do things in C-land. That's also two years to officially sanction what your team has already been doing unofficially since 2023.
Meanwhile, your competitor hired two engineers who spent a weekend and came back with AI-assisted ops that actually work. No deck. No steering committee. Just problems solved.
What Actually Works
The companies pulling ahead aren't running "transformation programs." They're doing boring operational hygiene:
Audit reality: What tools are staff already using? With what data?
Put in guardrails: Mask PII, log prompts, human review for production changes. Not a project. A checklist.
Aim AI at grunt work: Reconciliation scripts, log analysis, documentation. The time-sink stuff nobody enjoys and sits at the bottom of the backlog.
Measure actual impact: Deploy speed, error rates, support tickets. Not "AI readiness scores."
It's janitorial, not visionary. It also fucking works.
The Real Numbers
The 95% failure rate isn't because AI doesn't work. It's because leadership treats working tools like magic objects.
Failed approach: €500k "AI program," custom model RFP, dedicated task force.
Working approach: Create a task force, €200/month in GPT/Claude/Copilot accounts used by engineers to ship faster and screw up less.
One path produces PowerPoints, the other fixes production.
You don’t need an AI “strategy.” You need AI hygiene.
Instead of roadmaps → Audit what’s already happening.
Instead of “centers of excellence” → Train staff to use tools safely.
Instead of “custom models” → Get really good at using the ones that exist.
You don’t have a calculator strategy. You don’t need an AI strategy. You just need to make sure nobody is dividing by zero in production.
The Actual Risk
Not machine consciousness. Not job extinction. The real risk of not controlling AI usage is operational stupidity:
Support pasting full email threads with client data into free tools.
Engineers deploying AI-written code they don't fully understand.
Finance uploading transaction exports into public APIs for "analysis."
Tomorrow's governance headache shouldn’t take priority over today’s compliance breach...
Then we get to pure market forces. What do you think happens when the competition starts pushing features faster, tightening their operational resilience and getting hacked less?
The Bottom Line
The companies that survive won't be the ones with the most ambitious AI vision decks. They'll be the ones who figured out how to use flawed but useful tools safely, while everyone else was still "exploring use cases."
Good developers have already transformed their processes. They're shipping faster, debugging smarter, and solving problems while you're still writing vision statements.
The only question left is whether you're going to manage the risks they've already taken or spend another quarter planning to plan.