AI spend has a habit of growing quietly. A few subscriptions here, some API usage there, a team that uses the tools every day. Then the invoices start climbing month on month and nobody can point to exactly where the money is going or what it is producing.
Most of that waste happens one interaction at a time, which is why it goes unnoticed. Someone asks an AI tool to fix a problem, gets an answer that misses the mark, and asks again with slightly different wording. Then again. Five attempts later the problem is solved, but a properly framed request would have got there on the first try. Spread that across a team and a working week and it adds up quickly.
The way a lot of people now build with AI makes this worse. Describing what you want in loose terms and letting the tool regenerate everything until something works has become common practice, especially among developers leaning on AI assistants. It can produce results, but every regeneration reprocesses everything that came before it, and every "change that back" pays the same cost again. Someone hitting their usage limits by Wednesday is rarely doing more work than their colleagues. They are doing the same work less precisely.
Model choice is another leak. Teams default to the biggest, most capable model for everything because it feels like the safe option. For quality it usually is. For cost it often is not, because plenty of everyday tasks like summarising an email, sorting a support request, or pulling a date out of a document run just as well on smaller, cheaper models. Few teams have ever sat down and worked out which of their tasks genuinely need the expensive option.
The third leak is giving the tool far more information than the task needs. Pasting in an entire document to answer a question covered in one section, or carrying the full history of a long conversation into every new request, inflates the cost of every interaction without improving the result.
The training is put together for your specific business rather than delivered from a generic slide deck. We come in, look at the tools your team already uses and how they are actually being used, and build the sessions around that. Developers get practical guidance on writing requests that work the first time, asking for targeted changes instead of full regenerations, and choosing the right model for the job. Teams outside of development get the everyday habits that save time and money, like reusable prompts for recurring work and knowing when a task does not need AI at all.
Just as important as the techniques is the understanding behind them. Once people can see what an interaction actually costs and why, they make better decisions on their own, and the efficiency holds up long after the training is done.
If your AI usage is growing faster than the value it delivers, our two minute questionnaire at in4m.au is the place to start.