The models are capable and getting more so. The failures we see are almost always upstream: a use case nobody needed, data that was never ready, or a pilot with no definition of success.
Our work begins with an assessment of where your organisation actually spends effort — the repetitive reading, writing, searching, checking and routing that consumes hours without producing judgement. Those are the places AI pays.
We then prioritise by value and feasibility, build one use case properly end-to-end, measure it against agreed success criteria, and only scale what earns it. Deployment includes the parts people skip: data governance, privacy, residency, evaluation and the training that gets it adopted.