Why AI Projects Fail — and How Technology Leaders Can Turn Them into Wins

A Leadership Briefing with Richard Doran, CEO of Sierra ITS

Executive Summary

AI is at the top of every board agenda — and Technology Leaders feel the pressure. But as Richard Doran, CEO of Sierra ITS, explains in this leadership briefing, AI initiatives almost never fail because of the technology. They fail because of misaligned goals, weak data readiness, lack of cross-functional collaboration, and gaps in talent.

In this conversation with Alex Jarett, Richard outlines why so many AI projects stall or collapse — and what successful leaders do differently. If you’re responsible for AI strategy, delivery, or business transformation in your organization, these insights will help you navigate expectations, avoid hidden pitfalls, and set your teams up for real-world transformation.

AI Projects Rarely Fail Because of AI

Richard begins with a blunt truth that many leaders quietly acknowledge:

“AI projects rarely fail because of the technology. They fail because the wrong people are in the wrong seats.” — Richard Doran

While boards and CEOs increasingly demand “AI initiatives,” the focus on tools and models often overshadows the fundamentals of execution. Technology leaders are being pushed hard to “deliver AI” — whether or not the business problem is defined.

Richard notes that the best leaders always start with the same question:

What is the business issue or challenge we are solving?

Leaders who reverse-engineer from the business problem create clarity, alignment, and value. Those who start with the tool create friction, disappointment, and rework.

Failure Point #1: Misaligned Goals Between Business & Technology

One of the biggest reasons AI projects fail is also the most human:

Business leadership and technology leadership aren’t aligned.

According to Richard:

“If the business leadership and technology leadership aren’t in sync on the goal, expectations won’t be met — and there’s disappointment.”

And disappointment in AI is expensive.

It costs:

• Time
  
  
• Money
  
  
• Credibility
  
  
• Momentum
  
  

Executives feel the pressure. Many say:
“AI is a game changer — we need to start using it now.”

But the technology leader is responsible for balancing that urgency with reality. If they choose the wrong project, the transformation never materializes — and they’re the ones who ultimately pay the price.

Failure Point #2: Poor Data Readiness

If there is one universal theme in AI execution today, it’s this:

Data readiness is the make-or-break factor for AI success.

Richard explains that even when the business case is clear and the leadership team is aligned, AI initiatives can fall apart because:

• The data is incomplete
  
  
• The data is biased
  
  
• The data structure is inconsistent
  
  
• There is no governance framework
  
  
• Teams don’t fully understand their own data landscape
  
  

He puts it plainly:

“If your data is incomplete or biased, your AI will be too.”

Skilled data engineers, analysts, and governance leads become essential — not optional. Without them, even the smartest AI models will misfire.

A Real-World Example: Fixing Data Governance Before AI

Richard shares a story from a national healthcare insurance firm grappling with massive governance issues.

They initially approached major consulting firms — only to discover the price tag for their data governance initiative was astronomical.

They turned to Richard’s team for a different approach.

What Sierra ITS Delivered

• A senior data governance consultant
  
  
• A clear framework and structure
  
  
• Alignment across groups and departments
  
  
• A roadmap for a successful AI rollout
  
  

The result?

“They saved a significant amount of expenses… and had an amazing outcome.”

The project not only succeeded — the new framework is now part of their strategic initiatives for 2026.

This is the essence of what great talent, aligned correctly, can accomplish.

Failure Point #3: Lack of Cross-Functional Collaboration

AI isn’t a technology initiative.
It’s a business transformation initiative.

Richard emphasizes that the organizations who win at AI have people who can translate between both domains:

“AI isn’t just a tech project. The winners and losers have translators who can speak business and technology.”

These cross-functional teams:

• Learn together
  
  
• Share context
  
  
• Build trust
  
  
• Increase speed
  
  
• Reduce risk
  
  
• Improve adoption
  
  

This mirrors what we’ve seen in ERP projects, digital transformation, and other major organizational initiatives:

The broader the collaboration, the higher the success rate.

The Thread That Connects Everything: Talent

At the end of the briefing, Richard brings it back to the root cause behind nearly every failure or success:

“Do you have the right talent in the right seat?”

Whether it’s:

• Strategic leadership
  
  
• Data engineering
  
  
• Architecture
  
  
• Governance
  
  
• Project selection
  
  
• Business analysis
  
  
• Cross-functional facilitation
  
  

AI demands people who understand both the business challenge and the technical opportunity.

And when the organization doesn’t have that talent internally, they must acquire it — through:

• Consulting
  
  
• Staff augmentation
  
  
• Direct strategic hires
  
  

That’s where Sierra ITS specializes, and where they’ve helped many clients turn “red or yellow light” projects into green-light successes.

Key Takeaways for Technology Leaders

1. Start with the business problem — not the tool.

Reverse-engineer the solution from the value the organization needs.

2. Align leadership early.

If expectations diverge, the project will stumble.

3. Prioritize data readiness.

Clean, consistent, governed data is the foundation of reliable AI.

4. Build cross-functional teams.

AI is a business transformation effort — not an IT-only initiative.

5. Ensure the right people are in the right seats.

From governance to engineering to leadership, talent determines outcomes.

Closing Thoughts

AI initiatives are accelerating across every industry — and with them, the stakes. As Richard notes, the technology isn’t the barrier. The challenge is alignment, data, collaboration, and talent.

Get those elements right, and AI becomes a powerful engine for transformation.

Get them wrong, and even the best tools won’t save the project.

Learn more about Richard and watch all of his Leadership Briefings here.