3 Dec 25 
AI tools are cheap to try and expensive to depend on. Before you add another subscription, run it through these seven questions. They are the test we use to decide what earns a place in our stack.
There is a new AI tool every week, each one demoing beautifully and promising to change how you work. The trial is free, the first result is impressive, and before long you have a drawer full of subscriptions you barely use and cannot quite bring yourself to cancel. The problem is not that AI tools are bad. It is that a great demo tells you almost nothing about whether a tool will actually earn its place in your daily work, and the only way to know is to evaluate deliberately before you commit. Here is the seven-point test we run on every tool before it gets a spot in our stack and a line in our budget.
1. Does It Solve a Problem I Actually Have?
The first question sounds obvious and is the one most often skipped, because impressive tools create a strange effect: you see what it can do and start inventing reasons to need it. That is backwards. Start from a real problem you have right now, a task that is slow, painful, or not getting done, and ask whether this tool solves that. A tool that solves a problem you do not have is not a bargain at any price, it is a distraction with a monthly fee. If you cannot name the specific job you are hiring it for, stop here.
2. Is It Meaningfully Better Than What I Already Do?
If it does address a real problem, the next question is whether it is enough of an improvement to justify the switch. Every new tool has a hidden cost: learning it, changing your workflow, maintaining it. So a tool that is marginally better than your current approach is often not worth adopting, because the switching cost eats the gain. The improvement has to clear a real bar, faster by a lot, better by a lot, or possible where it was not before. “Slightly nicer” is a trap, because slightly nicer rarely survives contact with the friction of actually changing how you work.
3. Where Does It Fit in a Workflow, Not Just a Demo?
A tool that shines in isolation can be useless in practice if it does not connect to the rest of your work. The demo shows the tool doing its one clever thing in a vacuum. Real work is a chain, and a tool has to slot into that chain: take input from where your input lives, hand output to where it needs to go, without a person copying and pasting around it. A brilliant tool that creates a manual step on either side often costs more attention than it saves. Ask how it fits the actual flow, because the difference between a clever tool and a useful one is whether it connects or just sits there being impressive on its own.
4. What Is the Real Cost, Not the Sticker Price?
The monthly price is the smallest part of what a tool costs, and judging by it alone is how stacks quietly bloat. The real cost of an AI tool includes tokens, seats, and workflow debt: usage-based charges that scale with how much you actually use it, per-person pricing that multiplies across a team, and the ongoing effort of keeping it in your workflow. A tool with a tempting headline price and expensive usage can cost more than one that looks pricier upfront. Work out the real number for how you would actually use it, because the sticker price is marketing, not budgeting.
5. How Hard Is It to Leave?
Before you commit, find out how hard it would be to walk away, because the answer shapes how much power the tool has over you later. A tool that holds your data hostage, that everything else comes to depend on, that would be painful to rip out, is a tool you should adopt with your eyes open. This is not a reason never to use sticky tools, some of the best are sticky, but you want to know the exit cost going in, so you are choosing dependence deliberately rather than discovering it when a price rise or a shutdown leaves you stuck. Ask how you would get your data out and how much would break if you did.
6. Does It Hold Up Beyond the First Impressive Result?
AI tools are exceptionally good at the impressive first result and often much weaker at the hundredth. The demo is the best case, curated to shine. Real use is the average case, day after day, on your actual messy inputs, and that is where tools quietly disappoint. So before paying, push past the first wow: run it on your real work, the awkward inputs and the edge cases, enough times to see the average rather than the highlight. A tool that dazzles once and frustrates on the mundane majority is worse than a plain one that is reliably fine, because you will live in the average, not the demo.
7. Will We Actually Use It in a Month?
The final question is the honest one, and it is where most tool decisions should die. Be brutally realistic about whether this becomes part of how you work or joins the graveyard of tools you were excited about for a week. The signal is usage during the trial: if you are not reaching for it naturally within a few days, a paid plan will not change that, it will just bill you for the intention. Most AI tools fail this test, and that is fine. The discipline is not adopting more tools, it is adopting the few that survive a month of real use and cancelling the rest without guilt.
How to Actually Run the Test
You do not need to score every tool formally against all seven points. The test is a filter, and most tools fall at the first or second question, does it solve a real problem, is it meaningfully better, so you rarely reach the rest. The ones that pass the early questions deserve the harder ones about cost, lock-in, and durability. Run the whole thing before the trial ends, while you can still walk away for free, because the worst time to evaluate a tool is after you are already paying for it and looking for reasons to justify the spend.
This is the same discipline behind keeping a lean, deliberate stack of tools you actually use rather than a sprawling one you feel vaguely guilty about. The goal is not to resist AI tools, it is to be the kind of buyer a great tool rewards and a mediocre one cannot fool. Seven questions, asked before the card is charged, are what stand between a stack that earns its keep and a subscription list that quietly drains it.
If you want help building an AI stack that earns its place rather than bloating your budget, that is a conversation away.