Because sandbox success and enterprise-wide success are different problems. A pilot can work with clean, isolated data and a small user group; production requires the same model to run reliably against siloed, incomplete, or unstructured data across an entire organisation. Most AI initiatives stall at exactly this point, not because the model was wrong, but because the data foundations underneath it were never built to support scale.

At minimum, a data platform that can reliably feed models with accurate, structured, real-time information, rather than data that's siloed across departments or trapped in legacy systems. We assess your current infrastructure first and tell you exactly what's missing before recommending any AI deployment, because building AI on weak data foundations just moves the failure point further down the line.

We tie delivery to the commercial outcome the board was actually promised, not to technical milestones like "model deployed." For Jaguar Land Rover, that meant an estimated £30M bottom-line impact in year one against a £4-5M engagement. If your current AI reporting is all technical and no commercial, that's worth addressing before you scale further.

GenAI tools assist a person doing a task, like drafting content or summarising a document. Agentic workflows go further and automate the actual business process end to end, with AI agents completing tasks rather than just supporting a human through them. Most organisations are further along with GenAI adoption than agentic workflows, which is usually where the next real efficiency gain sits.

It depends on how far off your data foundations are, but the pattern we see consistently is that organisations underestimate this stage and overestimate the model-building stage. Fixing the foundations properly, rather than patching around them, is usually what determines whether the timeline is months or years. Talk to us and we'll give you an honest view of where you actually are.