Saturn was named for the rings. At the centre sits the foundation a business actually runs on; around it, the operating model, and around that, the AI layer. Every engagement works from the inside out, because AI layered onto a broken foundation is theatre, and there's no interest in selling theatre.
Data and the systems of record. The structure, cleanliness, ownership and integrity of the data estate, and the technology stack sitting on top of it: what exists, what's genuinely fit for purpose, and what should be kept, replaced or quietly retired.
How the business actually runs on its systems, day to day. The ERP and core process platforms (finance, supply chain, commerce), the integration between them, and the workflows people execute every day, including the internal tools and trackers quietly holding the whole thing together.
The tactical, reporting and actionable layer, where AI genuinely raises productivity on top of sound foundations. Agents, automation, AI-assisted reporting, and the practical business of making teams AI-native rather than AI-curious.
Every AI proposal, whether internal enthusiasm or vendor pitch, gets the same question: is this a genuine use case, or is it hype with a budget attached? Saying "not yet" is part of the job.
Practical, workflow-level change: which tools fit which specific jobs, and the habits that make them stick. Deliberately unglamorous. A team drowning in meeting notes needs call capture and summarisation, not a strategy deck.
Software can now be built quickly enough that the right answer to a broken internal workflow is often a small, purpose-made tool rather than another licence or a bigger spreadsheet. A tracker built around how a team actually works will beat a generic platform every time.
Deciding what to adopt is the easy half. The harder question is whether people actually change how they work, and that is where most AI adoption quietly fails. Who is genuinely affected, what they lose as well as gain, who owns the change once the enthusiasm fades, and what has to be true for a new way of working to survive its first busy week.
Change capacity is finite. A business midway through fixing its foundations cannot absorb an adoption programme at the same time, and sequencing those honestly is part of the work.
The order isn't a preference, it's a dependency. AI built on data nobody trusts produces confident answers that happen to be wrong. Automation layered over a broken process just makes the breakage faster. And a business that can't close its books in five days won't get value from an agent that reports on them.
Which means part of the work is being willing to say that AI isn't the next step yet, and to say it to someone who came looking for AI. That conversation is usually the most valuable one in the engagement.
Thirty minutes is usually enough for both sides to know whether there's a genuine fit.