Over the last decade, I’ve helped business leaders across industries navigate digital optimization to rise on the digital maturity scale, and more recently, the unique challenges of AI integration. My interest in this space grew from seeing promising AI investments fall short not because of the technology, but because of misaligned priorities, weak foundations and cultural resistance.

As companies rush to adopt AI, many are drawn to its promise but face real challenges. While some AI projects have delivered success, others have fallen apart, costing time, money and trust. In this article, I highlight 10 common AI strategies that fail to deliver long-term value and share lessons to help business leaders avoid making the same costly mistakes. These challenges are widely recognized in research by RAND and Melbourne Business School (MBS).

Top Reasons Behind AI-Strategy Failures

1. Chasing The Wrong Problem: Many leaders start AI projects without clearly understanding the real problem. This causes confusion and wasted effort. Over 80% of experts say leadership mistakes are a top reason AI projects fail.

2. Bad Or Incomplete Data: AI depends on good data. If the data is messy, missing or not useful, the AI can’t work well. Some reports say up to 85% of AI models fail because of poor data.

3. Focusing On Fancy Tech, Not Real Needs: Some companies get excited about the latest AI tools but forget to ask, “What problem are we solving?” This leads to cool tech that no one actually needs.

4. No System To Support The AI: Even if the AI model works, it needs a system to run on—like pipelines, storage and monitoring. Without these, the project stalls and doesn’t get used.

5. Picking Projects That Are Too Big: Trying to solve complex problems with AI before you’re ready can backfire. If the task is too hard or vague, the project often fails.

6. No Process To Improve Or Test AI: Projects fall apart when teams don’t have clear steps for testing, improving and using AI. Without a feedback loop, the AI can’t get better or stay useful.

7. People Aren’t Trained Or On Board: If your team doesn’t have the right skills or doesn’t believe in the project, the AI won’t succeed. Many projects fail because people aren’t involved early or don’t understand the changes.

8. Tracking The Wrong Results: Some AI models aim for high accuracy but miss what actually matters to the business. If your success goals aren’t clear or don’t match your work, the project may deliver little value. 

9. No Rules Or Safety Checks: Without good rules and oversight, AI can go off track. Systems need clear steps for testing, catching errors and staying fair. Skipping this step makes the AI risky or useless over time.

10. Can’t Grow Or Connect The AI: Many companies build small AI demos but never grow them into full business tools. Studies show up to 85% of AI projects fail to make a real impact because they don’t scale or connect with other systems.

Additionally, a 2025 study by MIT suggests:

• The scale of failure (95%) with recent data
• The core barrier is not compute, but systems that learn and integrate
• The divide between experimentation and true transformation
• The superior outcomes achieved via strategic partnerships over internal builds

From Lessons To Action

In nearly every conversation I’ve had with business leaders about AI, one challenge consistently stands out: While interest is high, most organizations are stuck in the early stages of digital maturity, looping between digital awareness (knowing AI matters) and digital experimentation (trying disconnected pilots or tools). These companies rarely move into full-scale integration or impact.

Leaders jump in with hope but no clear map. They chase trends instead of solving real problems. This article isn’t here to point fingers—it’s here to help leaders pause, step back and re-strategize.

For a deeper dive into the five stages of digital maturity, and how to assess and advance your organization’s readiness for AI and beyond, you can explore my Digital Maturity Model here.

Better Way Forward: Doing AI Right

While this article focused on what can go wrong, here’s a quick, proven framework to help you do it right:

• Start with a business problem, not a tech solution.
• Invest in full-stack enablement: from data governance to secure, scalable infrastructure.
• Ensure data readiness: clean, connected and compliant.
• Build cross-functional teams: business, IT, ops and data must collaborate.
• Plan beyond the pilot: design for scale, integration and long-term use.
• Embed human oversight and ethics from day one.
• Prioritize change management and training.
• Use key performance indicators (KPIs) that tie directly to business value.

Now you know enough about what can go wrong when businesses rush into AI without the right foundation. The success begins with a digital maturity strategy built on a broader vision—one that connects green sustainability, operational efficiency, financial economics and customer experience. When these pillars are aligned, trust me, the challenges like poor data, unclear goals and lack of buy-in start to fade. The work still takes effort, but with clear direction and shared purpose, progress becomes far more achievable.

This article was originally posted on Forbes.com.