Three Questions to Ask Before Investing in AI in 2026

Here’s what nobody wants to say out loud: most AI projects are failing.

Not because the technology doesn’t work. When it’s applied well, the results are real. Studies show 10-40% productivity gains. The problem isn’t the AI. The problem is that companies are buying solutions before they’ve defined problems.

The numbers tell the story: 42% of companies abandoned most of their AI initiatives this year, up from 17% last year. 95% of enterprise pilots never deliver measurable returns. Billions of dollars chasing the wrong questions.

This isn’t a technology failure. It’s a thinking failure, and you can’t buy your way out of that.

The opportunity

When AI works, you barely notice it. It lifts the tedious, repetitive work off your team’s plate and gives your best people room to do what only they can do. The goal was never automation for its own sake. The goal is leverage.

But you don’t get there by chasing the latest trend or buying the most impressive demo. You get there by asking better questions before you write the first check.

Here are three that matter.

1. Is this a model problem or a data problem?

Everyone wants to talk about models. Almost nobody wants to talk about data, which is strange, because data is where most projects go to die.

Your model is only as good as what you feed it. If your data is messy, siloed, or incomplete, no algorithm will save you. The most sophisticated AI in the world will just give you confident wrong answers faster.

Before you evaluate any AI solution, ask: Do we actually have the data this requires? Can we describe exactly what we’d feed this system and where it lives?

If that question takes more than a few minutes to answer, stop. You don’t have an AI problem yet. You have a data problem, and that’s the one to solve first.

2. Does this need the most powerful tool, or the right one?

There’s a bias in this industry toward complexity. Bigger models, more parameters, cutting-edge everything. It sounds impressive in a pitch deck. Most of the time, it’s exactly wrong.

Gartner predicts enterprises will use small, task-specific models three times more than general-purpose large language models by 2027. The reason is simple: they’re faster, cheaper, and often more accurate for focused problems.

The question to ask isn’t “what’s the most advanced option?” It’s “what’s the simplest thing that actually solves this?” Simplicity requires understanding the problem deeply enough to know what you can leave out. That’s hard. It’s also where the value is.

Ask your vendor, or your team: Why this approach instead of something simpler? If the answer is “because it’s state-of-the-art,” walk away. That’s not a reason, that’s marketing.

I’ve sat through enough vendor demos to know the pattern: impressive capabilities, vague answers about what happens after the pilot.

3. What decision does this actually support?

This is the question that separates useful AI from expensive theater.

It’s easy to get excited about capabilities. Dashboards, chatbots, predictions. But capabilities aren’t outcomes. The only question that matters is: what will someone do differently once this exists?

If you can’t answer that clearly, you’re not ready to build.

Try this: Finish the sentence “Once this is implemented, we’ll be able to ______ that we couldn’t before.” If you can’t complete it in concrete terms, the technology can wait. The clarity can’t.

What comes next

2026 is going to be a shakeout.

The companies that treated AI as a checkbox will quietly shelve their projects. The ones that started with the problem, not the tool, will pull ahead. This isn’t about who spent the most or moved the fastest. It’s about who thought clearly, who resisted the pressure to just do something and instead asked whether that something was worth doing.

The best technology disappears into the work. You stop noticing it. It just makes everything a little bit better.

That’s what we’re building toward at Gravitas Grove. Not AI for AI’s sake, but tools that earn their place by making people more capable.

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If you’re working through these questions and want a second opinion, we’re happy to help.