My company has been very supportive of us experimenting with AI and bringing it into our day‑to‑day testing workflow. But as AI usage (and pricing) keeps rising, we’re now being asked to justify our token consumption, especially when the numbers start looking large. For automated test generation, that’s easy: you can point to tangible outputs.
But for non‑automation work, it’s harder. I’m using Claude more and more as a testing assistant, particularly during exploratory testing. I bounce ideas off it, ask it to calculate sensible coverage permutations, generate test ideas, help me reason about risks, or summarise what I’ve explored so far. These interactions genuinely improve my thinking and speed, but they don’t produce a neat “deliverable” you can attach to a ticket. They’re more like having a second tester in the room. I imagine others are in the same situation.
Is anyone else using AI this way, and how are you justifying the token usage to your organisation?
i’d justify the spend by sampling decisions it changed, not counting prompts or generated text. for the next 10 exploratory sessions, log the risk you were testing, where Claude changed your coverage or next move, time spent, and whether it exposed a bug, ruled out a risk, or prevented duplicate exploration.
compare that with 10 similar sessions without it. if the assistant produces faster useful decisions or broader risk coverage after review, you have an economic case. if all it produces is nicer notes, you probably do not.
one guardrail: keep the tester responsible for the oracle. Claude can widen the search, but the browser behavior and resulting decision still need human verification.
Thanks, that seems like a really sensible approach that gets to the heart of what I’m trying to measure. I’ll give this a try, it will be an interesting experiment
Do you know what your token usage is? If not first try and establish what your token usage is, I’m no expert but those things do not sound like power using token activities.
If for example you were running it as agentic crawlers like a user running your app picking up common bugs on every CI run that would be a very different thing as it likely goes into process loops in many cases but you seem to be using it hands on, rapid back and forth loop in an interactive way which I doubt would be heavy usage.
Check if you have been flagged as a heavy user first of all, if so try and establish why, also compare to other users in the company just as an information only reference point. If you are part of a company group usage, a fixed monthly fee or if they you individual purchases or paying by usage that could make a difference.
I use it in a similar way though I also use playwright_cli for exploratory experiments, rapid short feedback loops and that back and forth interactions, I also find this model as a booster and much quicker at root causing at code level when I find something alongside recommended fixes.
Justifying a monthly capped licence may be easier than justifying token usage.
What metrics do the company care about? How do they judge the value of exploratory testing? If they have something, I’d aim to link the AI usage cost to an improvement in those metrics. If they don’t I think you’ll just have to argue your case - if you’re increasing your speed or coverage by x%, then anything less than x% of your salary seems like a reasonable cost.
Another thought - If your devs have used AI to increase their output if follows that QA will need to increase their output to match - that may be enough justification on it’s own, and you can just explain how AI makes you more efficient.