85 Percent of Data & AI Projects Fail!

This always gets me thinking as a headline!

Gartner loves to headline it. Analysts repeat it. Consultants wring their hands over it.

85% of data and AI projects fail.”

The implication? That this is a disaster.

That your organization should be ashamed if a pilot crashes and burns.

But, maybe, if we think about it for a second, maybe it’s meant to be!

Stick with me here!

POCs and pilots aren’t supposed to be flawless.

They are meant to kill bad ideas early so only the strong survive. Just read that one again. I know you might find it difficult.

A high early-stage failure rate means your team is doing the most underrated thing in corporate innovation: learning fast, cheaply, and without betting the farm.

In fact, the real danger isn’t 85 percent failing, it’s actually when 85 percent succeed in pilots, and then stall, deliver no value, or quietly die in production because they were never strategically aligned.

So, how do you make sure that “fail fast” doesn’t turn into “fail forever?”

Here’s the 3-part executive playbook to turn AI experimentation into measurable business wins:

 Fail Intelligently: Not all Failures are Equal

• Set explicit kill criteria before the project starts.
• Demand a post-mortem for every failed POC. One page max, focused on insights gained, not excuses.
• Track learnings in a central “value log” so the next team doesn’t repeat the same dead ends.

Align Before You Build: The Biggest Cause of Value Leakage isn’t Bad Tech, it’s Misalignment

• Start every AI initiative with a clear business problem, not a shiny model.
• Tie success metrics to business KPIs that the CFO actually cares about (cost savings, revenue growth, risk reduction).
• If the link to business value is fuzzy, don’t greenlight the project.

 Scale the Survivors: Your Winners Need a Runway

• Treat successful pilots like investments. Assign them owners, budgets, and timelines for scaling.
• Build a lightweight AI operating model that covers governance, talent, and change management. You MUST do this before you scale.
• Publicize the business impact internally to fuel more high-quality ideas.

The goal isn’t to make every AI project work. It’s to make the right ones work, at scale, for the business.

Failure is the tuition you pay for future value. The smart executive knows when to stop paying and start cashing in.

Don’t let the FOMO effect from this overused statistic, fear tactic, make you go headless first into an “let’s just do AI for AI’s sake.”

Step back, remember the purpose of the organization, don’t waste money and do it with integrity, not because Gartner keeps piling the pressure on and making you feel sick!

It’s time to change and that change is to get rid of this 85 percent noise and make it real.

Can you do that?