"We're adding AI" has become the default move for companies that feel they need to do something about AI. It sounds strategic. It isn't. It's a technology in search of a problem — starting from the tool and hoping a use for it appears. The companies actually winning with AI didn't begin with "let's use AI." They began with a problem worth solving and discovered that AI was the right tool to solve it. The order matters more than almost anything else.
Here's why technology-first thinking fails, and how to get the sequence right.
AI is a tool, not a strategy — "we're adding AI" is technology in search of a problem, and that approach fails.
The right order:
Don't ask "how do we use AI?" Ask "what problem do we have, and is AI the right tool for it?"
"We're adding AI" is backwards because it starts from the solution and works toward a problem, which is the reverse of how good decisions get made. A technology is only valuable insofar as it solves a real problem better than the alternatives. Starting with "we need to use AI" and then hunting for somewhere to apply it inverts that logic — you've committed to a tool before identifying the job, which means you're now motivated to find problems that justify the tool rather than tools that solve your problems.
This produces the familiar pattern of AI features that exist to say "we have AI," not because they make anything meaningfully better. The technology-first approach optimizes for using AI rather than for solving problems, and those aren't the same goal — they're often in tension. When the objective is "add AI," you get AI bolted onto things that didn't need it, complexity added for the sake of a buzzword, and resources spent demonstrating modernity instead of delivering value. The tool became the goal, which is exactly the error: a tool is never the goal. The goal is the problem solved.
The companies winning with AI are the ones that kept the order right — problem first, tool second:
| Technology-first (fails) | Problem-first (wins) |
|---|---|
| "How do we use AI?" | "What problem are we solving?" |
| Solution hunting for a problem | Problem reaching for the right tool |
| AI features that justify themselves | AI features that solve real needs |
| Optimizes for using AI | Optimizes for solving problems |
Problem-first thinking works because it keeps AI in its proper place: a means to an end, evaluated by whether it solves a real problem better than the alternatives. You identify something genuinely worth solving, consider the available tools, and reach for AI when it's the best fit — and reach for something else when it isn't. That discipline produces AI applications that actually matter, because they exist to solve real problems rather than to wave a flag. The winners didn't set out to "use AI"; they set out to solve problems and found AI was the right tool for some of them. This is the same hype-versus-reality discipline applied to strategy: judge the tool by the problem it solves, not by how impressive it sounds. The technology is an input; the solved problem is the output that matters.
The corrective is to treat AI as what it is — a tool — and to subordinate it to the problem, every time. Before any AI initiative, ask the problem-first questions: What are we actually trying to solve? Is it worth solving? Is AI genuinely the best tool for it, or are we reaching for it because it's trendy? If AI is the right fit, use it with conviction. If it isn't, the discipline is to not use it, even when there's pressure to "do something with AI."
That willingness to say "AI isn't the right tool here" is the mark of a genuine strategy rather than a trend-chase. A strategy is about solving problems and achieving outcomes; AI is one possible means among many. Confusing the means for the strategy is how companies end up with impressive-sounding AI initiatives that don't move anything that matters. Keep AI in its place — a powerful tool you reach for when it fits the problem — and you get the genuine value AI can deliver without the waste of technology-first theater. The same logic that makes right-sizing your model the smart move applies at the strategic level: match the tool to the actual need, and don't pay for capability the problem doesn't require. AI is the means; the solved problem is the strategy.
To use AI as a tool rather than mistaking it for a strategy:
The throughline: AI is a tool, not a strategy, and "we're adding AI" is technology in search of a problem — backwards by construction, because it commits to a tool before identifying the job. Problem-first thinking keeps AI in its proper place as a means evaluated by whether it solves a real problem better than the alternatives. Start with the problem, reach for AI when it fits, and refuse it when it doesn't. The solved problem is the strategy; AI is just one of the tools.
Q: Why isn't "we're adding AI" a strategy? Because it starts from the solution and hunts for a problem, which inverts how good decisions are made. A technology is only valuable when it solves a real problem better than alternatives; committing to AI before identifying the job means you're motivated to find problems that justify the tool rather than tools that solve your problems. The result is AI bolted onto things that didn't need it — features that exist to say "we have AI" rather than to make anything better. A strategy is about solving problems; AI is just one possible means.
Q: What does problem-first AI adoption look like? You identify a problem genuinely worth solving, consider the available tools, and reach for AI only when it's the best fit for that problem — and reach for something else when it isn't. The objective stays "solve the problem," with AI as one candidate means rather than the goal itself. This produces AI applications that matter because they address real needs, not because they wave a buzzword. The companies winning with AI didn't set out to use AI; they set out to solve problems and found AI fit some of them.
Q: Isn't ignoring AI risky when everyone's adopting it? The risk isn't in declining to use AI where it doesn't fit — it's in adopting AI for its own sake and spending resources on initiatives that don't move anything that matters. Problem-first thinking doesn't mean ignoring AI; it means using it with conviction where it's the right tool and refusing it where it isn't. That willingness to say "AI isn't right here" is the mark of a real strategy. You capture the genuine value AI offers without the waste of technology-first theater. Matching the tool to the problem is the opposite of risky.
AI is a tool, not a strategy. "We're adding AI" is technology in search of a problem — backwards by construction, because it commits to a tool before identifying the job it's meant to do. That produces AI features that exist to signal modernity rather than to solve anything, optimizing for using AI instead of for solving problems, which are different and often opposing goals.
The winners kept the order right: problem first, tool second. They identified problems worth solving, evaluated the options, and reached for AI when it was the best fit — and something else when it wasn't. Keep AI in its place as a means subordinate to the problem, be willing to decline it when it doesn't fit, and judge everything by whether the problem actually got solved. The solved problem is the strategy. AI is just one of the tools you reach for to get there.
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