AI's First Success is Exposing Our Failure!

The "perfect storm" happened when three forces collided. Are we seeing the same in AI?

Artificial intelligence promised transformation. Yet for all the hype, the reality is sobering: most AI (particularly in-house) projects are apparently failing.

AI is a messy marriage of technology, data, and human behavior. Too often, companies run headlong into this complexity, only to find that their projects stall, sputter, and eventually get shelved.

So why is this happening? Three overlapping issues as MIT, The Economist and Pavilion point to in recent articles and posts —human denial, structured resistance, and dirty data. These three forces create a perfect storm of failure. And together they raise a bigger question: is AI actually showing us that we’re not ready for it, by quickly revealing  all these deficiencies we have.

The Human Response: Denial and Distraction

The first roadblock is simply human nature. The MIT article highlights the very human reaction of denial. Leaders greenlight AI projects, but when the going gets tough, they retreat into old habits. Teams convince themselves that maybe the project was “too ambitious” or “ahead of its time.” The novelty wears off, enthusiasm wanes, and resources drift to shinier initiatives.

This isn’t a purely technical failure, it’s psychological. Humans resist confronting the uncomfortable truth that transformation requires hard, messy, sustained effort. We’d rather believe the problem lies elsewhere than face the reality that our own systems, processes, and even leadership resolve are inadequate.

The Economist’s Take: Structured Failure

If denial is the emotional response, inertia is the structural one. The Economist recently argued that organizations often engage in what amounts to “structured sabotage” of AI initiatives. Why? Because many employees—and sometimes managers—are invested in the status quo, and AI can be seen as an existential crisis.

AI, by its very nature, challenges the way things have been done. It shines a spotlight on inefficiencies, redundant roles, and outdated practices. For many, that feels threatening. So resistance builds quietly, sometimes consciously, often unconsciously. Meetings stall, committees debate endlessly, and pilots never scale. Leaders, eager to maintain consensus, allow inertia to set in.

It’s easier to preserve a quiet life than to confront existential fears. The possibility that AI might one day make us irrelevant only deepens the instinct to slow-walk adoption.

Dirty Data: The Silent Killer

Even if organizations overcome denial and inertia, they still face a technical trap: dirty data. As Sam Jacobs of Pavilion pointed out in a post on LinkedIn recently, AI is only as good as the data that feeds it. Yet many corporate data sets—especially CRM systems—are riddled with inaccuracies, duplications, missing fields, and outdated information.

The result? AI systems trained on polluted data deliver polluted outputs. Predictions become unreliable. Recommendations fall flat. Users lose trust in the system, and the project collapses under its own weight.

In other words, the biggest barrier to AI success may not be futuristic fears of “superintelligence.” It may be the mundane reality that we’ve spent 50 years building sloppy, fragmented data environments that AI ruthlessly exposes.

Is AI Showing Us the Problem Behind the Problem?

When you put these three forces together—denial, structured resistance, and dirty data—it can feel like success in AI is out of reach. Maybe AI isn’t failing us at all. Maybe it’s holding up a mirror to decades of systemic deficiencies: weak leadership resolve, consensus-driven indecision, and decades of (expensive, but) neglected data hygiene.

Seen this way, AI’s “failure” is actually its first success. It reveals the rot in our organizational foundations. It forces us to confront questions we’ve long avoided: Are we as clever as we think we are? Is our data trustworthy? Are our leaders willing to push through resistance? Do we really want transformation, or just the appearance of progress?

So Where Do We Go From Here?

The path forward is uncomfortable but clear.

1. Outsource and Leverage Domain Expertise

   
   

   For most companies, building AI in-house is a recipe for disappointment. The skill sets, infrastructure, and specialized expertise required are immense. Outsourcing to third-party experts—especially those with deep domain knowledge—offers a faster, safer, and more effective path. You wouldn’t build your own ERP system from scratch. Why should AI be different?

2. Leaders Must Lead, Not Manage by Consensus

   
   

   Transformation requires conviction. Leaders must be willing to push past resistance, make what might appear as unpopular calls, and carry the organization forward. Even AI projects can’t survive death by committee. They need champions.  People only fall into one of three camps here – they’re either part of the solution, part of the scenery or part of the problem. It's time for leaders to figure out who’s where and act accordingly.

3. Admit That Much of Our Data Is Junk

   
   

   Instead of endlessly cleaning up decades of bad CRM data, maybe it’s time to start fresh. Too many organizations have apparently wasted millions trying to polish the unpolishable. Accept that most legacy data is unreliable, and focus instead in the current on cleaner, leaner, more purposeful data sets going forward. Think addition by subtraction.

   
   

Final Thought

AI is not failing—it’s success lies in exposing our failure. It shows us the denial in our culture, the weakness in our leaders, the inertia in our systems, and the dirt in our data. Whether we treat that as a reason to retreat or advance is up to us.

The first step on the road to redemption is admitting the problem (actually there's three!!)

The companies that succeed will be those that accept the hard truths AI reveals, take action, and rebuild stronger foundations. The real question isn’t whether AI is beyond our capabilities, it’s whether we've got the stones to admit and confront the flaws it has exposed.