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AI Project Failures: What Technology Alone Can’t Solve

Myth #2 – “If an AI Project Fails, It’s the Technology’s Fault”

We’ve all heard about the low success rates of technology projects: according to McKinsey, only 30% of technology projects meet their objectives, and some studies, like HBR’s in 2023, estimate this rate even lower, at 20%.

Some argue that the low success rate is linked to the maturity level of the technology.

Yet, AI is nothing new.

What if I told you that the Montreal Computer Research Center has been working on AI projects for nearly 30 years?

Or that Google has been using machine learning algorithms for almost 25 years?

Artificial intelligence is not a new technology. What is new is its democratization, made possible by increased computing power and public access to solutions like ChatGPT.

The real problem isn’t the technology—it’s… us. Yes, us humans! Because the success of an AI project relies primarily on our ability to integrate it intelligently.

The good news is that it is possible to defy the statistics by becoming aware of the pitfalls to avoid right now.

5 Factors That Cause AI Project Failures and How to Avoid Them

1. The project isn’t aligned with your business objectives

It may seem obvious, yet the lack of strategic alignment remains one of the main causes of failure for technology projects.

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Photo by Shopify Partners from Burst

Why?

Technical experts responsible for identifying use cases don’t always have a strategic understanding of the business. Think about your IT team, for example.

  • Are they involved in your strategic decisions?
  • Do they have a deep understanding of your challenges, priorities, and those of your customers?
  • If they propose an AI project, do you have the skills to assess whether it is truly relevant to your business?

✅ Best practice: Bring your management team together for joint AI training before starting a project. This will help you better understand AI’s potential, align it with your business objectives, and ease upcoming change management.

2. The project scope is too large for available resources

Driven by excitement around AI’s possibilities, many companies embark on projects that are too ambitious and exceed their means (falling victim to the infamous “scope creep”).

Why is this risky?

  • A large-scale project requires more human and financial resources.
  • The more stakeholders involved, the more complex change management becomes.
  • Failure can hinder future AI adoption and slow innovation within the company.

✅ Best practice: Start with a pilot project and progress gradually. Test your approach on a specific case before expanding the initiative.

3. Too much focus on technology, not enough on people and processes

Have you ever attended a governance meeting for an important technology project? How much time was spent discussing technology, backlog, technical issues, costs, or timelines?

Now, do you remember if you discussed:

  • Change management?
  • New roles and behaviors to adopt?
  • New processes to implement?

Like a chicken keeping its head down to peck for seeds, we often focus solely on technology.

Yet, in 70% of cases, human factors are responsible for project failures.

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Source: Beyond Performance 2.0, McKinsey

✅ Best practice: Seek support for change management. Having a neutral person with an external perspective on the organization is essential.

📢 I assist manufacturing companies with change management and technology adoption. Contact me to discuss your challenges and we’ll find tailored solutions together.

4. Lack of a comprehensive digital strategy

AI shouldn’t be an isolated project but integrated into a broader digital strategy. Many of the software solutions you already use in your company include AI, and it’s important to understand how to use them effectively before investing in custom solutions.

Why?

  • A fragmented approach results in disconnected solutions that are difficult to manage efficiently in the long term.
  • Without an overall vision, it’s hard to assess the real impact of AI initiatives on the organization.

✅ Best practice: Take the time to build a digital strategy that includes AI. This ensures smoother adoption and better alignment with your business ambitions.

5. Lack of executive alignment

When different departments have conflicting objectives and compete for the same resources, it can undermine the success of an AI project.

Why?

  • Lack of cohesion can slow decision-making and generate internal tension.
  • Project team engagement may suffer if they feel a lack of support from leadership.
  • Technology adoption becomes more difficult without clear alignment among stakeholders.

✅ Best practice: Establish a clear company strategy and ensure buy-in from all stakeholders in positions of power before starting a project. Strong alignment from the start prevents friction and facilitates AI implementation.

Summary of Best Practices

To maximize your chances of AI project success:

1️⃣ Train your management team on AI before launching a project.
2️⃣ Build a detailed business case and link your project to your company’s strategic priorities.
3️⃣ Seek support for change management and ensure an external perspective.
4️⃣ Start with a pilot project and progress gradually.
5️⃣ Develop a digital strategy that includes AI to avoid a fragmented approach.

Conclusion

Artificial intelligence can be a powerful driver of transformation, but it doesn’t work on its own. Its success depends on a clear strategic vision, a gradual approach, and well-orchestrated change management.

By avoiding common mistakes and applying these best practices, you set yourself up to fully leverage AI.

Don’t let your AI project join the list of predicted failures. Make the right choices now and ensure a successful transition to an organization truly augmented by AI.

📢 Looking to implement an effective AI strategy for your manufacturing company? Contact me to discover how my training and support can help you succeed in your AI transformation!

Note: This article was conceived and written by a human, then reviewed by AI.

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