I just came across this interesting MIT research that shows most AI projects are not delivering the financial results companies expected. According to their findings, only 5% of businesses actually see increased profits after implementing artificial intelligence solutions.
This seems pretty shocking to me since there’s been so much hype around AI lately. Every company seems to be rushing to add AI features or automate processes with machine learning. But if 95% are not seeing profit increases, that means a lot of money is being wasted.
The report apparently has Wall Street worried too. I guess investors are starting to question whether all these AI investments will actually pay off. Has anyone else seen similar statistics or experienced this firsthand at their workplace? I’m curious if this matches what people are seeing in the real world with AI implementations.
Companies keep building AI from scratch instead of just connecting their existing tools better.
I’ve seen teams waste months training models and hiring data scientists when their real problem was moving data between systems. Customer info here, billing there, support tickets somewhere else. No wonder AI can’t find patterns.
Most failed AI projects would work if companies automated data flows first. Get everything talking to each other, then worry about the fancy stuff.
The 5% that succeed probably have solid automation already. They feed clean, connected data into AI systems. The other 95% are building rockets without basic plumbing.
Skip the expensive AI consultants. Automate workflows first. Once data flows smoothly between apps and databases, AI projects actually make sense.
Seen this pattern countless times. Fix connections, automate boring tasks, add intelligence on top. Latenode makes this simple - connect everything without code.
Not surprised at all. Been through three AI rollouts at different companies - same story every time.
Most places don’t know what problem they’re solving. They hear “AI makes things efficient” and throw money at it. Six months later they’ve automated something that already worked fine.
The one successful AI project I worked on took two years to show real ROI. We rebuilt our entire data pipeline, retrained half the team, and fixed tons of edge cases we never saw coming. Most companies quit way before that.
Companies also measure success wrong. They want immediate revenue bumps, but AI usually saves costs or prevents problems - doesn’t directly make money. Try putting a dollar amount on “we avoided this issue.”
The 5% that work pick one specific problem and stick with it. They don’t try to AI everything at once.