Why most AI projects fail: The myths holding businesses back

September 12, 2025

If you’ve been following AI news, you’ll know the hype is relentless. Promises of automation, efficiency and transformation are everywhere. Yet the reality is sobering: according to a new MIT report, 95% of AI pilot projects fail.

For leaders eager to bring AI into their organisations, this failure rate should be a wake-up call. Not because AI lacks potential, but because too many projects are still being built on myths.

At CIM, after more than a decade working hands-on with AI in property operations, we’ve seen three myths crop up again and again. And if you don’t challenge them, they will quietly sabotage adoption and value.

Myth 1: The data myth

“AI doesn’t need clean, structured data.”

In reality, if you want people to trust the process and the outputs, data quality is everything. Without it, users disengage, projects stall, and results become unreliable.

A Deloitte survey found that nearly 40% of AI adopters cite “data issues” as the top barrier to scaling. Investing in structured, accessible, and transparent data is the foundation for any successful AI deployment.

Myth 2: The easy myth

“AI is simple. Just tell it what to do.”

The truth? AI doesn’t deliver value unless people change how they work. Without change management, new processes and accountability, adoption fails. The result is shelfware: tools that technically function, but go unused.

McKinsey cites that organisations with structured change management programs are six times more likely to meet AI project goals. In other words, technology is the easy part. The hard part is getting humans to embrace it.

Myth 3: The DIY myth

“Build your own AI. It’s quicker and more cost-effective.”

This myth is especially tempting for large enterprises with internal tech teams. But as the long-running build vs. buy debate shows, homegrown solutions often become slow, costly, and high-risk.

Off-the-shelf SaaS platforms have matured rapidly, offering speed, usability, and security that are hard to match in-house. Even some of the world’s most recognised tech companies - including Google, Slack and WhatsApp - outsourced critical pieces of their early software development before scaling.

For most businesses, the smarter play is not to build everything, but to integrate the best tools and manage them effectively.

So, how do you win?

If the myths derail projects, the winners are those who move past them. Success comes from acting as the conductor of best-of-breed tools, not betting everything on a single platform or a DIY experiment.

That means:

Prioritising data quality and accessibility
Embedding change management into AI rollouts
Selecting and integrating proven SaaS tools rather than reinventing the wheel

AI is not magic. It’s a system of people, processes and technologies that needs orchestrating. For leaders willing to play the role of conductor, AI can shift from 95% failure to measurable, lasting value.