Originally published on Ashdown Systems in August 2026, and revised for this blog. Read the original.
Every business is being pitched AI right now. New tools launch weekly, existing software adds an “AI-powered” feature to its marketing, and it’s hard to tell which of it deserves attention and which is noise. For many teams the result is a stuck feeling: aware that AI probably matters, unsure what to do about it, and wary of either falling behind or wasting money on hype.
The good news is that deciding what’s worth adopting doesn’t require becoming an AI expert. It requires treating it like any other architecture decision, because that’s exactly what it is.
Why it feels harder than it should
Most technology decisions have a fairly clear shape: a specific problem, a shortlist of tools that solve it, and a reasonably stable set of options to compare. AI adoption doesn’t have that shape yet:
- “Best in class” is a moving target. A tool that clearly leads today can be matched or overtaken within months, which makes normal due diligence feel less reliable.
- The marketing is louder than the substance. “AI-powered” is attached both to useful features and to little more than a chatbot bolted onto an existing product, and from the outside it isn’t always obvious which is which.
- Fear of missing out is doing a lot of the deciding. A quiet anxiety that competitors are pulling ahead pushes businesses to adopt quickly, without the scrutiny they’d apply to any other significant purchase.
- It touches everything at once. AI can plausibly show up in customer service, marketing, operations and product at the same time, which makes it hard to know where to start.
None of that means AI should be treated differently from other technology decisions. If anything, the normal discipline matters more, precisely because the noise is louder.
The question that cuts through
Strip away the hype and the question is the same as for any tool: what specific problem does this solve, and is AI actually the best way to solve it?
The second half matters more than it sounds. Plenty of AI tools solve real problems. Some solve problems a simpler, non-AI tool would handle just as well, at lower cost and lower risk. Adopting AI because it’s AI, rather than because it’s the best answer to a specific problem, is how a lot of wasted spend and abandoned pilots happen.
Six questions I’d ask of any AI tool
When a new tool crosses your desk, whether someone on the team is pushing for it or a vendor is pitching it, these questions consistently separate the useful adoptions from the ones that fizzle out:
What specific, recurring problem does it solve? Not “it could help with X” in the abstract, but a concrete, repeated task that is slow, expensive or error-prone today. Vague potential is a warning sign; a specific bottleneck is a good one.
What does it integrate with? A tool that sits apart from your existing systems creates exactly the kind of data silo and manual reconciliation that costs time elsewhere. Integration quality often matters more than the AI itself.
What will it really cost at the volume you’d use it? AI is often priced per use in ways that scale unpredictably. Look at the cost at realistic volume, not the entry-level price in the demo.
What happens when it’s wrong? Every AI tool makes mistakes some of the time. The question isn’t whether it’s ever wrong, but what the consequence is when it is, and whether there’s a sensible human check for anything high-stakes.
Who owns it, and who reviews whether it’s still worth it? A pilot nobody is responsible for tends either to become permanent with no oversight or to be abandoned without anyone noticing. Clear ownership from day one avoids both.
Does it reduce complexity, or add to it? A good adoption should make some part of the business simpler to run. If it adds another disconnected tool, another login and another system nobody fully understands, that’s a cost to weigh seriously against the benefit.
Fear of missing out isn’t a strategy
The pressure to “do something with AI” is real, but it’s no substitute for evaluating a specific tool against a specific problem. Adopting quickly and adopting well aren’t the same thing. In my view, the businesses getting real value from AI are mostly the ones applying ordinary due diligence, not the ones adopting the most tools the fastest.
Treated properly, an AI tool is just another architectural decision: does it solve a real problem, does it fit with what you already have, and do the cost and risk make sense at your actual scale? Getting that right, one tool at a time, tends to produce far more value than trying to write an overarching AI strategy before anything has been adopted at all.
