Fishing rods and AI: understanding the tool

A friend of mine shared a video the other day about SaaS prices going up across the board. The guy in the video made a point that stuck with me: these companies aren't raising prices because their products got better. They're raising prices because they bolted AI onto their platforms and called it an upgrade.

“Enhanced by AI.” That's the pitch. And I keep asking the same question every time I see it: enhanced how?

What does it actually do for me? Does it save time on a specific task? Does it catch errors I'd miss? Does it change the way I work in some concrete way I can point to? Most of the time, the answer is vague at best. There's a chatbot in the corner now, or a “smart suggestions” feature nobody asked for, and the subscription went up 20 percent.

The data says the same thing, just at a much larger scale.

McKinsey's 2025 State of AI report found that only about 5 to 6 percent of enterprises attribute more than 5 percent of their earnings to AI. Not vague sentiment. Actual bottom-line, show-it-on-the-books impact. MIT's NANDA initiative studied 300 AI deployments and surveyed 350 employees and found that 95 percent of generative AI pilot programs never produced measurable financial returns. That stat has some caveats. MIT used a narrow definition of success, measured over a short window, and excluded productivity gains that hadn't hit the P&L yet. But the direction is corroborated everywhere. RAND Corporation found that over 80 percent of AI projects fail, roughly double the failure rate of non-AI IT projects.

So companies are spending billions on AI, packaging it into products, passing the cost to customers, and the vast majority can't even prove it's working inside their own organizations.

That's a real problem.

The pattern I keep seeing is the same whether it's a SaaS company bundling AI into a product or a company mandating AI use internally. The technology shows up, but the understanding doesn't.

ManpowerGroup reported in January 2026 that AI usage among workers jumped 13 percent in 2025. In the same period, worker confidence in using AI dropped 18 percent. And 75 percent of workers don't feel confident using AI in their daily work. Usage going up and confidence going down at the same time tells you something important about how these tools are being rolled out.

KPMG and the University of Melbourne ran a global study across 48,000 respondents in 47 countries. They found that 57 percent of employees hide their AI use and present the output as their own. Another 32 percent use unauthorized AI tools their companies don't even know about. And 66 percent rely on AI output without checking whether it's accurate.

People are using the tools. They're just using them badly, secretly, and without support. None of that is adoption. It's chaos dressed up as progress.

And it mirrors what's happening on the product side. SaaS companies are integrating AI the same way employers are rolling it out internally: here's the tool, figure it out, pay more for the privilege.

The guy in the video said something I agree with completely. People don't want AI-enhanced products. They want better products — software that's more intuitive, that takes their actual needs into account, that solves problems they care about. Whether AI is involved in making that happen is beside the point. The outcome is what matters, not the label.

This is the same thing I see in the organizations I work with. When someone tells me their team tried AI and it didn't stick, the first thing I ask is whether anyone sat down and figured out which specific tasks AI could actually help with. The answer is almost always no. Someone bought a license, sent an email saying “we have AI now,” and expected results.

The Canadian nonprofit data makes this painfully clear. About 92 percent of Canadian nonprofits report using AI in some capacity, but only 7 percent report mission-level impact. Which makes it an implementation problem, not a technology one. And the Imagine Canada research is explicit about what's holding organizations back: “uncertainty and limited hands-on experience, not financial resources.”

It's the same story at the enterprise level. S&P Global found that 42 percent of companies abandoned most of their AI initiatives in 2025. Not because the technology failed. Because they couldn't figure out how to make it work within their actual operations. KPMG Canada found that 93 percent of Canadian businesses use AI, but only 2 percent see returns on generative AI investment. 93 versus 2. That gap should alarm anyone who's being told AI is the answer to everything.

AI works. It can save significant time, reduce repetitive work, and open up capacity that didn't exist before. But only when someone takes the time to understand the tool and match it to specific tasks in a specific context. There's no shortcut past that step.

The old saying about teaching someone to fish applies here more than anywhere. If you hand someone a fishing rod and walk away, they're not going to eat. If you show them where the fish are, how to cast, and what bait to use for the conditions they're dealing with, they'll feed themselves every day after that.

AI is the same. When someone learns how to use it within their actual workflows, for the problems they actually face, the speed and quality of their work can change significantly. But that requires investment in people, not just in software licenses with “AI-powered” stamped on the label.

The SaaS price hikes and the enterprise failure rates are symptoms of the same problem. Organizations and product companies are treating AI as a feature to sell rather than a capability to build. And until that changes, most of the money being spent on AI is going to keep producing the same result: expensive tools that nobody really knows how to use.

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