Services

AI readiness audit and enterprise adoption roadmap

Most AI projects stall not because the model was not clever enough, but because the use case was chosen from the technology side. We start from the opposite end: which process is most expensive to run by hand today.

What we usually find

Management has seen plenty of demos, but not one of them makes clear what it would be worth if it ran every day. Vendors offer a platform, not a resolved process. IT keeps a list of systems nobody may touch, and no one knows the state of the data until it is opened up.

Investment decisions then get made on how convincing the presentation was, rather than on which process actually weighs on operations.

What we do

  • Process mapping — documenting the flow as it runs today, including the steps that appear in no SOP but get done anyway. That is usually where the weight sits.
  • Data and systems audit — where the data comes from, what state it is in, where it lives, and how existing systems can be connected. Legacy systems without an open API included.
  • Use-case scoring — every candidate is judged on the same three questions: how often it runs, how long one cycle takes, and how expensive a wrong output is.
  • Staged roadmap — an implementation order that puts high-value, low-risk use cases first, so the team's confidence is built before the hard work starts.

What you receive

  • A process map showing where queues form and where the load sits
  • A ranked list of use cases by operational impact and difficulty
  • An assessment of data and system readiness, with integration obstacles already identified
  • A staged adoption roadmap with sequence, prerequisites, and measurable targets
  • A deployment architecture recommendation matching your data policy — on-premise, private cloud, or VPC

Why audit before building

Building first means betting the budget on a guess. An audit moves that bet forward, to a point where it still costs weeks of work rather than months of development already under way.

If the audit shows your processes are not ready to automate, that is a valuable answer too — and a far cheaper one to get now.

Typical duration: 2–3 weeks. Final scope is set after the audit.

Common questions

What people ask most.

Does our team need its own data scientist?

No. We handle strategy, development, and technical integration. Your internal team needs to be involved during discovery and adoption.

How long does the audit phase take?

Usually 2–3 weeks, depending on how many processes are mapped and how available your subject-matter contacts are.

Does the audit commit us to implementation?

No. The roadmap is yours and can be executed by your internal team or another vendor.

Other services

Tell us the process you want automated.

We reply within one business day. No cost, no commitment.

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