Written by Kiwi Dynamics Team 1What an AI Translator actually does
The role is straightforward to describe and rare to fill. An AI Translator can sit with a dispatcher for a morning, work out that the actual bottleneck is not the scheduling software but the twenty minutes spent every afternoon reconciling two spreadsheets, and then know whether that is a job for a model, a script, or a conversation with the office manager. It requires holding two things at once: how the work really happens, including the undocumented parts, and what the technology can and cannot reliably do. Most people have one half.
2Why the two obvious hires do not fill the gap
The two obvious hires each supply one half and neither supplies both. A data scientist or ML engineer knows the technology deeply and will build what you specify, which is a problem when the specification is the hard part. A consultant or account manager understands the business and will map your processes thoroughly, then hand the result to someone who has to guess at the technical constraints. The handoff between them is where the requirement quietly changes shape. This is not a criticism of either role. It is an observation that the gap sits precisely between them.
3The failure mode this creates
The failure mode is consistent enough to predict. A business buys a capable platform, runs a pilot on a workflow chosen because it was easy to access rather than because it was expensive, gets a demo that technically works, and then cannot articulate what it saved. Roughly a quarter of Australian businesses report lower operating costs from AI, which means most do not, and the difference is rarely the software. It is whether anyone picked the right workflow and rewired it properly. Calculating ROI on complex integrations is itself listed as a barrier, which is a polite way of saying nobody measured the before.
4Buying the role instead of hiring it
For a business under fifty people, hiring this role permanently is usually not sensible. The work is intense for a quarter and then intermittent. Buying it for the duration of a project is the better shape, provided the person doing the scoping is the same person accountable for the thing running afterwards. When those are two different people you have reinvented the handoff you were trying to avoid. That is the reason we stay small and why the engineer who scopes your build is the one who owns it in production, which is set out on our about page and in how we work.
5How to tell whether you are getting one
You can test for this in the first conversation. Ask what they would need to see before recommending anything, and be suspicious of an answer that arrives before the question. Ask what they would measure and when the before-measurement gets taken. Ask them to name a workflow they would decline to automate, because someone who cannot is selling rather than advising. And ask who will be on the call in month six.
The technology stopped being the constraint some time ago. What is scarce is the judgement about where to point it, and that is worth more than any particular model. If you want that judgement applied to your operation, tell us what is getting stuck. We will tell you if we think the answer is not to build anything.