AI Success Series [Part 1]: Why 85% of AI projects fail

Businesses of all shapes and sizes are rushing to implement AI right now.

And most of them are failing at it spectacularly.

According to Gartner research, 85% of AI projects fail.

In real-world terms, that represents:

• Enough wasted budgets to fund a small space program

• Industry credibility deflating faster than a soufflé in a slamming door

• Countless product managers who now twitch uncontrollably when someone says “Can't we just use AI for that?”

Fortunately, this stat is actually a bit misleading.

The high failure rate isn’t because AI tech itself is flawed or overhyped…

It’s just that most companies don't know how to implement AI correctly.

Over the last few years, we’ve helped startups, middle-market companies, and Fortune 500s across dozens of industries implement AI into their businesses.

And along the way, we've identified three fundamental mistakes most companies make that doom their AI projects to failure.

Now we’re going to pass that knowledge on to you 😉

The next few issues will be a short mini-series.

We’ll start off by breaking down each mistake and showing you how to avoid it.

Then to round things out, we'll share the battle-tested strategies we've developed to consistently land our clients' projects in that successful 15%.

Today’s focus: the first and most fundamental mistake…

Mistake #1: Using AI with no clear “why”

Stop us if you’ve heard this before:

“Our competitors are deploying AI, so we need it too!”

This is literally how most corporate AI initiatives begin. Someone in leadership hears about what competitors are doing, or reads about the latest ChatGPT breakthrough, and suddenly everyone's scrambling to “do AI.”

But, no one stops to ask what problem they're actually solving.

So really, they’re just setting fire to piles of cash while chanting “AI” like it's a magic spell that summons business value.

Not a sound strategy!

Companies often take a backwards approach

Sometimes, executives hope that shiny new AI tech will reveal whatever problem is plaguing the company.

But that's like buying an expensive hammer, then wandering around your house looking for nails. Sure, you might eventually find some, but was that really the most efficient approach?

Without a clear pain point or workflow to improve, AI just adds complexity, cost, and confusion, with no meaningful upside.

So, what problem are you trying to solve?

If you're about to greenlight an AI project (or wrestling with one that's already lost its way), let’s chat.

👉Book a 30-minute consultation call with us here

We'll explore your AI project together and see if we're a good fit to build what you need.

For more info about AE Studio and what we do, click here.

And stay tuned for the next issue, where we'll tackle the second major AI project killer!

Until then,

— The AE Studio Team