"Why is my company's AI strategy not working?"

"Most corporate AI strategies fail because they focus on theater over action. Common patterns include: adding 'AI-powered' labels to existing features without real changes, creating AI labs with no budget or authority, and spending months identifying use cases without shipping anything. The companies winning at AI are actually building and learning through iteration, not stuck in endless stakeholder meetings."

"What is AI theater and how do I avoid it?"

"AI theater is when companies pretend to adopt AI without meaningful implementation. Signs include: labeling existing if-else statements as 'AI-powered,' having a CEO say 'AI' 60 times in meetings while only having a ChatGPT Plus subscription, creating impressive slide decks but shipping zero products. To avoid it: let engineers experiment, ship something (even if internal), and eliminate committee-based decision making that prevents action."

"Why does my AI lab produce no results?"

"AI labs often fail when they have no budget, report through multiple management layers, and lack authority to make decisions. They spend months 'exploring use cases' with expensive consultants producing unread PDFs. The solution is to give them actual resources, direct authority, and clear permission to ship something—even if it's just an internal tool."

"How long should identifying AI use cases take?"

"If you've been identifying use cases for more than 3 months without building anything, you're stuck in analysis paralysis. While it's reasonable to want low-risk, high-reward use cases, waiting for the perfect scenario means you're learning nothing. Successful companies identify a use case quickly and start building to learn through iteration, not endless planning."

"What should I do instead of having more AI strategy meetings?"

"Stop the committee-driven approach and take three actions: (1) Let engineers experiment—they probably already know what to build, (2) Ship something, even if it's just an internal tool for internal users to learn from, (3) Consider eliminating the AI committee if it's blocking progress. Your engineers likely have better ideas than what emerges from stakeholder meetings where everyone has opinions but no one has authority."

"How do companies that are winning at AI do it differently?"

"Winning companies aren't necessarily smarter—they're just taking action. They're in a learning phase with rapid iterations rather than stuck in meetings. They ship products, gather feedback, and improve. They empower engineers to experiment and build rather than waiting for committee approval. The key difference is action over planning, shipping over slide decks, and learning through doing rather than through consultant reports."

"What are the warning signs that my company is doing AI theater?"

"Key warning signs include: using 'AI-powered' labels on unchanged features, having an AI initiative with no budget or decision-making authority, producing impressive presentations but zero shipped products, spending 6+ months in 'use case identification,' holding endless stakeholder meetings where no one makes decisions, and hiring consultants who produce lengthy reports that no one reads. If your engineers are laughing at your AI strategy, that's the clearest warning sign."

Your Company's AI Strategy Is EMBARRASSING And Everyone Knows It

Your company just announced a new AI initiative. You hired a Head of AI. You’re “exploring use cases.” You have 56 meetings on your calendar about it.

And your engineers? They’re laughing at you. Not with you. AT you.

The worst part? You have no idea.

After two decades in technology and software engineering, I’ve watched this pattern repeat itself across countless organizations. Let me show you exactly why your AI strategy is failing—and what you can do about it.

The Three Patterns

Pattern 1: The “AI-Powered” Label Strategy

You’ve added the “AI-powered” label to all your features. But underneath? Just if-else statements. Nothing changed except the name.

Your CEO mentions “AI” 60 times in every meeting. When you claim “we are using AI across the organization,” the reality is simpler: someone in marketing has a ChatGPT Plus subscription.

That’s the entire strategy.

Pattern 2: The AI Lab With No Budget

You created an “AI Lab.” Sounds impressive. The company has an AI Lab!

But it’s just two people with no budget, reporting to someone who reports to someone who reports to someone who reports to the person in charge.

They’ve been “exploring use cases” for 8 months. You hired expensive consultants who delivered a 200-page PDF with beautiful charts.

Nobody reads it. It sits in a folder accessible to three people.

You’ve shipped zero products. But you have an impressive slide deck.

Pattern 3: The “Use Case” Death Spiral

You’ve been identifying use cases for over a year. Still identifying. Still building nothing.

Stakeholder meetings pile up. Nobody decides anything. Everyone has opinions. Nobody has authority. Nothing happens.

So you schedule another meeting.

You’re waiting for the perfect use case—low risk, high reward. The search for perfection becomes the excuse for inaction.

The mantra: “We need more data before we can start.”

What You Can Actually Do

Here’s what should really concern you: everybody knows. Your engineers know. Your customers know. Everyone knows that nothing is happening.

The uncomfortable truth? Your team isn’t actually behind—because everyone is behind. AI is moving fast, and every organization is trying to figure it out.

The difference? The companies winning at AI aren’t necessarily smarter. They’re just taking action. They’re in a learning phase with rapid iterations. They ship products, gather feedback, and improve. They experiment, fail fast, and adapt.

Meanwhile, you’re stuck in meetings.

The solution isn’t complex:

Let engineers experiment. Give your engineers permission to explore. They likely already have ideas about what to build. Stop requiring approval for every experiment.

Ship something—anything. Build and deploy something. It doesn’t need to be customer-facing. Start with an internal tool for internal users. Learn from real usage, not hypothetical scenarios.

Eliminate the committee. If you have an AI committee that’s blocking progress rather than enabling it, consider eliminating it. Committee-driven decision making often produces consensus without action.

Your engineers know what to build. They’re waiting for permission to build it.

The Bottom Line

The patterns are clear. The solutions are straightforward. What’s missing is action.

The Pattern	The Reality
“AI-powered” labels everywhere	Just if-else statements with new names
AI Lab sounds impressive	2 people, no budget, 8 months of “exploring”
200-page consultant PDF	Nobody reads it, sits in a restricted folder
Identifying use cases for a year	Still no shipped products
Stakeholder meetings	Everyone has opinions, nobody has authority

The key insight: The companies winning at AI aren’t smarter—they’re just shipping and learning while others are stuck in meetings.

The question isn’t whether you can adopt AI successfully. The question is whether you’re willing to stop the theater and start building.

If this article makes you uncomfortable, good. That’s the point.

The choice is yours: keep pretending, or start shipping.

This article challenges corporate AI theater. The goal isn’t criticism for its own sake—it’s to push organizations from endless planning toward meaningful action. From slide decks toward shipping. From meetings toward learning.

Watch the Video

I also shared this perspective in video format. You can watch it here:

The Three Patterns#

What You Can Actually Do#

The Bottom Line#

Watch the Video#

The Three Patterns

What You Can Actually Do

The Bottom Line

Watch the Video