AI Transformation Fail
Read on my website / Read time: 5 minutes
For the last several years, company boards have been asking: What's our AI strategy?
CEOs want to show progress. Everyone is launching pilots. (And co-pilots.) There's a race to build "something with AI" before the next earnings call.
Maybe you've been tasked with leading an AI initiative that's meant to modernize the business. You've got the funding, the executive support, and the slide deck with all the right buzzwords.
Now, you're 3 months in, and already feel stuck.
AI initiatives are launched with energy and intention. The org celebrates pilot launches, renames teams, installs tooling, maybe even spins up a shiny innovation hub.
But inside 6–12 months, the momentum stalls. Teams are confused. Leaders quietly lose faith. And the returns? Hard to find.
Under the hood it's the same old incentives, decision patterns, and risk-aversion.
In the end, the AI initiative starts to look more like expensive theater. The promise of transformation is real. But so is the letdown.
In a recent call I had with a CTO, he admitted to me: "AI transformations are already headed down the same dead-end path as digital transformations."
We've Seen This Movie Before
Remember when digital transformation was going to reinvent everything?
It was supposed to streamline operations, reinvent customer experiences, and future-proof the business.
Instead, we got a lot of agile theater, new job titles, and flashy dashboards.
And surprisingly few lasting results.
Now, we're watching the same playbook run again, just with different tech and a faster hype cycle.
Most AI Initiatives Are Expensive Theater
So many companies are engaging in "AI theater" without having any real results to show for it.
- Shiny demos, but no real impact
- No real understanding what AI actually requires
- AI treated as a "fix-it" project, not a strategic capability
- No connection to customer or business outcomes
- No investment in the messy foundations (data, talent, ops, culture)
- Worst of all: no plan to scale what works
Let's break it down.
1. Everyone's Building, Few Are Solving
Co-pilots, chatbots, auto-summarizers — everywhere you look, someone's got a pilot going.
But most of these are outputs in search of outcomes. No problem to solve. No adoption path. No business case.
It's innovation theater, not transformation.
What we need to do instead:
- Anchor every AI initiative in a real business problem. What pain are we relieving? For whom? How will we measure impact?
2. AI Lipstick on a Legacy Pig
Most initiatives chase the flashy stuff — tech demos and prototypes — without tackling the deep systems underneath. Or they slap an "AI layer" on top of legacy tech and celebrate the cheap "win."
It's easy to rebrand teams, launch AI training, or spin up an AI app. It's much harder to change how decisions get made, how teams are funded, and how performance is measured.
Superficial change allows leaders say, "Look, we're innovating!" But if no one is truly empowered to solve real customer and business problems, nothing transformative will actually happen.
AI transformation isn't about launching a new interface or experimenting with a co-pilot. It's about changing how the business works — the way it learns, adapts, and delivers value.
What we need to do instead:
- Start with the business model, not the tech. We need to ask: How will this change how we serve customers? How we compete? How we create value differently than before?
- Fund strategies, not projects. Define real outcomes. Give teams the space to discover the solution, not just build requirements handed down from above.
3. Skipping the Foundations
Everyone wants GenAI results. No one wants to invest in the unsexy work:
- Data quality
- Model governance
- Experimentation infrastructure
- AI-literate business teams
Without these, our AI investments will stay stuck in pilot purgatory.
What we need to do instead:
- Invest now in not just the technical and organizational foundations, but the business and operational ones too. Otherwise, our AI promise will collapse under its own hype.
4. No Clarity on What Success Looks Like
Too often, AI initiatives celebrate outputs, like new features shipped, tools installed, org charts updated, without ever defining how those things drive real business results.
AI teams are set up, but they're not tied to results that matter: cost to serve, customer retention, revenue per user, better margins, etc.
What we need to do instead:
- Tie every initiative to business outcomes. Make success measurable, visible, and owned by empowered teams.
5. No Path to Scale
Even when AI pilots show promise, most stall before scaling. Why?
Because the org isn't set up to absorb the change. Incentives don't support it. Workflows don't adjust. And no matter how much executives hype it, teams don't trust it.
We don't just need a working model. We need change management, process redesign, and executive alignment.
What we need to do instead:
- Once the pilot is proven, start planning for scale. Who needs to change how they work? What systems need to evolve? Who owns success?
5. Déjà Vu: Digital All Over Again
If this is all sounding familiar, it should.
We ran this same playbook a few years ago with digital transformation:
- Big talk, small follow-through
- Surface-level change
- Disconnected "innovation hubs"
- No alignment with real business priorities
- Burned out teams, underwhelmed customers
The pattern is repeating. Just faster this time.
What we need to do instead:
- Learn from the past. Don't confuse experimentation with transformation. This isn't about launching tools or shiny press releases. It's about rewiring how our business creates value.
If We Want Real AI Transformation, Here's the Playbook
To avoid another round of expensive innovation theater, we need to commit to these five moves:
- Start with business problems, not technology showcases
- Integrate AI into the core, not bolt it on the side
- Invest in foundational capabilities, like data, teams, processes, and culture
- Define what success looks like, and measure it relentlessly
- Tie transformation to strategy, not optics.
Bottom Line:
The AI transformation we're trying to lead is not really that new. It just has new tools.
We can follow the old, familiar pattern: hype → pilot → stall → fade.
Or we can lead differently: with focus, discipline, and a relentless commitment to solving real problems.
The opportunity is real. So is the risk of wasting it.
We must choose wisely.
