Where AI helps, what it costs, and what isn’t worth doing.
Most AI efforts in a growing company fail on judgment, not technology: the wrong problem, an unpriced tool, no policy for what goes into it. Practical's AI advisory is led by a finance leader who uses AI on client work every day and governs what it costs. The advice comes from practice, and every assessment includes an explicit list of what not to do.
Readiness and opportunity assessment
A structured review of your finance and operations workflows to find where AI produces a measurable return, scoped and prioritized. Each opportunity comes with an estimated cost to run, a data and policy prerequisite, and a plain statement of the risk. The list of what's not worth doing is as useful as the list of what is.
Tool selection
Which tools, on what plan, with what usage controls. AI spend behaves like cloud spend: usage-based, easy to start, hard to see. Once the tools are in use, we manage the bill the way we manage cloud and SaaS, under Technology, Cloud & AI Financial Management.
Governance and policy
A working AI policy for the company: what data can be used, by whom, in which tools, with what review. Written for a founder-led company, not a bank, so people follow it.
Before a custom build
AI is not a side project at Practical; it is where the foundation work is headed. Once a company's data reconciles and its processes are defined, custom AI software can orchestrate the business across the tools already in place, built around the company's model, culture, and systems rather than the other way round. Built on anything less, it inherits the problem and the promised return never arrives. Before a company commissions a custom build, we assess the foundation it will sit on, name the prework that has to happen first, and say plainly whether the build is worth doing now, later, or not at all. The assessment, the baseline, and the prework are the client's whether or not a build follows.
The work, specifically.
AI readiness and opportunity assessment
Workflow review, prioritized opportunity list, cost and risk per item, and the explicit not-worth-doing list.
Tool selection
Requirements, shortlist, evaluation, and plan selection for finance and operations use cases.
AI governance and policy
A usable policy, an approval path, and the review cadence to keep it current.
Pre-build readiness assessment
For companies about to commission custom AI software: a review of the data, systems, and process the build depends on, the prework required, and a plain recommendation on whether to proceed.
Applied AI on your deliverables
Financial comparisons, policy drafts, roadmaps, and decision support produced with AI inside our own work, with a person accountable for every output.
Start with a thirty-minute call.
We’ll talk about where the company is headed and whether this is the right work to do first. If it isn’t, we’ll say so.