The AI Tool Graveyard

The AI Tool Graveyard: Why Companies Buy 12 AI Subscriptions and Use None of Them

Sam McRobertsArtificial Intelligence

Let me describe a scene I’ve watched play out at company after company over the last couple of years, and tell me if any of it sounds familiar.

Somebody on the leadership team reads a breathless post about how AI is going to eat the world, gets a hot flash of FOMO, and decides the company needs to “do AI.” A budget appears out of nowhere. Over the next few months people sign up for ChatGPT Team, and then somebody else grabs Jasper, and then marketing buys a writing tool or three, and sales buys a different one, and somebody in ops is paying for an automation platform nobody else even knows about. Six months later you’ve got a dozen AI subscriptions humming away on the company card, a couple of half-built workflows everyone’s afraid to actually rely on, and a team that more or less went right back to doing everything the old way because the new way was never really thought through.

That, my friends, is the AI Tool Graveyard. And it is EXPENSIVE.

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Why does this keep happening?

Because buying a tool feels like progress, and actual progress is harder and less fun than swiping a credit card.

Here’s the thing nobody wants to admit… signing up for an AI tool gives you a little hit of “we’re doing something about this,” and that feeling is addictive. It’s so much easier than the genuinely hard work, which is sitting down and figuring out which specific parts of your business AI should actually touch, in what order, and what it’s worth to you. So companies skip the hard part and go straight to the dopamine, and they end up tool-rich and outcome-poor.

And to be fair, the vendors are great at making this worse. Every demo looks incredible. Every tool promises to 10x your team. The slick thirty-minute walkthrough always works perfectly, and nobody mentions the part where your actual messy data and your actual busy team and your actual undocumented processes turn that magic into a frustrating slog. So you buy it, the honeymoon lasts about three weeks, and then it joins the graveyard.

What does the data say about AI projects failing?

It says most of them flop, and the numbers are honestly kind of brutal.

A 2025 report out of MIT’s Project NANDA, called “The GenAI Divide,” made a big splash when it found that roughly 95% of enterprise generative-AI pilots were delivering essentially zero measurable return. Ninety-five percent. Let that sink in for a second. Around the same time, S&P Global Market Intelligence reported that the share of companies abandoning the majority of their AI initiatives jumped to about 42%, up sharply from the year before. So this isn’t me being a grump, the failure pattern is well-documented and widespread.

Now, the AI itself isn’t the problem here (we use it constantly, and when it’s pointed at the right work it’s genuinely fantastic). The problem is that the overwhelming majority of these efforts were tool-first instead of plan-first. Somebody bought capability and hoped strategy would magically follow. It doesn’t.

And while we’re talking numbers, this isn’t even a new disease, it’s the old SaaS-shelfware problem wearing a shiny new outfit. Companies have wasted money for years on software licenses nobody uses, with various studies pegging something like a third of SaaS spend as effectively wasted on unused or underused tools. AI just gave everybody a brand new category of stuff to overbuy.

How do you know you’re heading for a graveyard?

There are a few tells, and once you know them you’ll start spotting them everywhere, including possibly in your own company.

The first one is when nobody can tell you, in plain English, what problem a given tool was bought to solve. If the answer is some version of “well, it’s AI, and we figured we should have it,” congratulations, that’s a future headstone. The second tell is overlap… you’ve got three tools that all basically do the same thing because three different people each bought their favorite, and now nobody wants to admit they wasted the money so they all just quietly coexist. The third is the orphaned subscription, the tool that one enthusiastic person championed and then that person changed roles or left, and now it’s just auto-renewing into the void with nobody at the wheel.

And the big one, the tell that matters more than all the others… is there a single person who actually owns the company’s AI efforts and is accountable for results? In most graveyards the answer is no. Everybody’s dabbling, nobody’s steering, and “AI” is technically everyone’s job, which in practice means it’s no one’s. When responsibility is that diffuse, the tools pile up and the outcomes never show, because there’s nobody whose actual job it is to make sure they do.

If two or three of those tells are ringing a bell right now, you’re not alone, and it’s fixable. But it won’t fix itself by adding tool number thirteen.

The real problem isn’t the tools

The tools are fine. The problem is you went shopping before you knew what you were cooking.

And there’s a hidden cost here that the subscription fees don’t even capture. Every tool you roll out demands training, integration with the stuff you already use, and a change in how people actually work, and that’s the expensive part nobody budgets for. A $40-a-month tool that takes ten hours of somebody’s time to set up and then gets abandoned didn’t cost you $40, it cost you those ten hours plus the opportunity cost of everything else that person could’ve been doing. Multiply that across a dozen tools and the graveyard gets a whole lot pricier than the line items on your card suggest.

Think about it like this… nobody walks into a grocery store, fills three carts with random ingredients, and THEN tries to figure out what’s for dinner. That’s insane. But that’s exactly what “we need to do AI, everybody go find some tools” amounts to. You end up with a fridge full of stuff that doesn’t combine into a single actual meal, half of it goes bad, and you order takeout anyway (which, in this metaphor, is your team quietly reverting to the old manual way of doing things).

What actually works is the boring, unsexy thing… you start with a plan. You figure out where AI genuinely fits in YOUR specific business, which of your workflows are worth automating and which absolutely should be left alone, what each opportunity is actually worth in real dollars, and what to tackle first so you bank some early wins instead of trying to boil the ocean. THEN, and only then, you go find the two or three tools that serve that plan. Not twelve. Two or three.

That research everybody loves to quote, the big BCG and Harvard study, actually backs this up in a way most people miss. The productivity gains from AI were real and large, but only when the AI was pointed at the right tasks. Point it at the wrong ones and performance actually got worse. Which is the entire ballgame… it was never about having the tools, it was about knowing which work to point them at. (I dug into that whole study and its sneaky little catch in a separate post if you want the full breakdown.)

What to do instead of buying more software

Stop buying. Start planning. Then buy a little.

If you’ve already got a graveyard, the first move is honestly to do an audit of what you’re paying for and whether anybody’s using it (you’ll probably find a few subscriptions you forgot existed). After that, the highest-value thing you can do is get an actual AI strategy in place, a real roadmap built around your specific business that tells you where AI belongs, what it’s worth, and what to do first. That’s the difference between “we bought some AI tools” and “we know exactly how AI is going to move the needle for us.” It’s cheap insurance against torching another year and another pile of cash learning what a plan could’ve told you in a few weeks.

And if you don’t have anybody senior in-house who can own this stuff, that’s a real and common gap, which is exactly why we offer a Fractional Chief AI Officer and hands-on AI consulting. Somebody who’s done this before can save you from refilling the graveyard.

And if you want the single most practical piece of advice here, it’s this… don’t try to “do AI” everywhere at once. Pick ONE workflow. One painful, repetitive, high-volume process that’s eating your team’s time, and get AI genuinely working on just that, end to end, until it’s reliable and people actually trust it. Bank that win, learn from it, and then move to the next one. That sequencing is the whole difference between an AI effort that builds momentum and one that sprawls into a graveyard. Companies that try to transform ten things at once usually finish zero of them. Companies that nail one thing, then another, then another, quietly end up transformed a year later without ever having a dramatic flameout.

The tools will still be there when you’re ready for them. They’re not going anywhere, and frankly they get better every few months, so there’s zero downside to getting your plan straight first.

FAQ

Why do so many AI tools end up unused?

Because they get bought tool-first, based on slick demos and FOMO, without a plan for which specific workflows they serve. With no plan and no owner, adoption fizzles and people revert to old habits.

Is buying AI tools a waste of money?

The tools aren’t the problem, the lack of strategy is. Used against the right, well-chosen tasks, AI delivers real gains. Bought at random, it becomes expensive shelfware.

What should I do before buying any AI software?

Build a plan first. Identify where AI genuinely fits in your business, what it’s worth, and what to tackle first, then buy only the few tools that serve that plan.

How many AI tools does a company actually need?

Usually far fewer than they end up with. Most businesses are better served by two or three well-integrated tools tied to a clear plan than by a dozen overlapping subscriptions nobody fully uses.

How do I clean up an AI tool graveyard I already have?

Start with an honest inventory of every AI subscription you’re paying for and whether anyone actually uses it, then cancel the dead weight. After that, build a real plan for the workflows that matter and keep only the few tools that serve it. The goal isn’t zero tools, it’s the right two or three with someone accountable for the results.

Got a graveyard of your own, or trying to avoid digging one? An AI Strategy is the fix, and it costs a fraction of what you’ll waste flailing without one. Let’s talk.

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