you can't spell fail without AI.

I’m an AI Engineer, been doing this for a long time. I’ve seen plenty of projects that stagnate, wither and get abandoned. I agree with the top 5 in this article, but I might change the priority sequence.

Five leading root causes of the failure of AI projects were identified

• First, industry stakeholders often misunderstand — or miscommunicate — what problem needs to be solved using AI.
• Second, many AI projects fail because the organization lacks the necessary data to adequately train an effective AI model.
• Third, in some cases, AI projects fail because the organization focuses more on using the latest and greatest technology than on solving real problems for their intended users.
• Fourth, organizations might not have adequate infrastructure to manage their data and deploy completed AI models, which increases the likelihood of project failure.
• Finally, in some cases, AI projects fail because the technology is applied to problems that are too difficult for AI to solve.

4 & 2 —>1. IF they even have enough data to train an effective model, most organizations have no clue how to handle the sheer variety, volume, velocity, and veracity of the big data that AI needs. It’s a specialized engineering discipline to handle that (data engineer). Let alone how to deploy and manage the infra that models need—also a specialized discipline has emerged to handle that aspect (ML engineer). Often they sit at the same desk.

1 & 5 —> 2: stakeholders seem to want AI to be a boil-the-ocean solution. They want it to do everything and be awesome at it. What they often don’t realize is that AI can be a really awesome specialist tool, that really sucks on testing scenarios that it hasn’t been trained on. Transfer learning is a thing but that requires fine tuning and additional training. Huge models like LLMs are starting to bridge this somewhat, but at the expense of the really sharp specialization. So without a really clear understanding of what can be done with AI really well, and perhaps more importantly, what problems are a poor fit for AI solutions, of course they’ll be destined to fail.

3 —> 3: This isn’t a problem with just AI. It’s all shiny new tech. Standard Gardner hype cycle stuff. Remember how they were saying we’d have crypto-refrigerators back in 2016?

I think the whole system of venture capital might be garbage. We have bros spending millions of dollars like gif sharing while the oceans boil, our schools rot, and our infrastructure rusts or is sold off. Or, I guess I'm just indicting capitalism more generally. But having a few bros decide what to fund based on gutfeel and powerpoints seems like a particularly malignant form.

Capitalism wastes money chasing new shiny tech thing

Yeah, we know. AI's not special.

This isn't unique to AI.

80% of new businesses fail, period.

80% of AI projects so far...

Isn't it good that the money is being put back into circulation instead of being hoarded? I'm all in for the wealthy wasting their money.

When did brute force switch from being an antipattern to the preferred pattern?

Most people don't want to pay for AI. So they are building stuff that costs a lot for a market that is not willing to pay for it. It is mostly a gimmick for most people.

Wasting?

A bunch of rich guy’s money going to other people, enriching some of the recipients, in hopes of making the rich guy even richer? And the point of AI is to eliminate jobs that cost rich people money?

I’m all for more foolish AI failed investments.

Here's a fitting AI generated Porky

I've been reading a book about Elizabeth Holmes and the Theranos scam, and the parallels with Gen AI seem pretty astounding. Gen AI is known to be so buggy the industry even created a euphemistic term so they wouldn't have to call it buggy: Hallucinations.

AI is a ponzi scheme to relieve stupid venture capitalists of their money.

Ooh ooh now do restaurants!!!

To be fair, a large fraction of software projects fail. AI is probably worse because there's probably little notion of how AI actually applied to the problem so that execution is hampered from the start.

https://www.nbcnews.com/id/wbna27190518

https://www.zdnet.com/article/study-68-percent-of-it-projects-fail/

100%, the remaining 20% grift their way to success

That's great. Like 5% more fails than regular software projects. Why do people see this as validation for AI failing? Lol

The interviews revealed that data scientists sometimes get distracted by the latest developments in AI and implement them in their projects without looking at the value that it will deliver.

At least part of this is due to resume-oriented development.

Is that better or worse than IT and software projects in general? It sounds like it might be better.

As I said in a project call where someone was pumping up AI, this is just the latest bubble ready to pop. Everyone is dumping $$ into AI, a couple decent ones will survive but the bulk is either barely functional or just vaporware.