AI strategy & roadmaps
Two fixed-scope packages decide — honestly — whether and where AI is worth your money, then turn that into a plan your team can execute. Their output is a decision and a roadmap, not a demo.
From candidate use cases to a settled sequence
Illustrative concept- Candidate listEvery process that repeats, stalls or is re-keyed is written down before anything is ranked.
- Value against effortCandidates are placed on a value and effort grid — a placement is an opinion until it is checked against your data.
- Roadmap Now / Next / LaterThe surviving work is sequenced, so a first build does not depend on a fifth one shipping.
- Gate before each buildFor each entry: the outcome, the data it needs and the owner of the result are agreed in writing.
AI Clarity Session
What it produces: advice. A Clarity Session ends in a decision — worth doing, not worth doing, or build it — for each idea on your list. Nothing is built in the session itself.
The low-commitment entry point: a focused working session that tests the AI ideas on your list against the workflows and data you actually run. You leave with a straight answer on what is worth doing — and what is not.
What is included
- A short questionnaire beforehand covering the workflows, tools and pain points you want to examine.
- A live working session where we walk three to five candidate use cases against your real workflows and data.
- An honest per-candidate verdict — adopt now, automate, or not worth it — including explicit "do not spend here" calls where that is the right answer. No vendor pitch.
- A written one-page summary with a recommended next step.
What is not included
- Data-readiness or security review, deep process mapping, or a formal roadmap document (that is the audit).
- Any build, automation or integration work.
A good fit if
- You keep hearing that your industry should "do AI" and want an honest second opinion before spending.
- You have a handful of ideas and need them triaged against your actual operations.
- You want to meet the m00labs team before committing to a larger engagement.
Not a fit if
- The decision is already made and the only open question is who builds it — that is a sprint or an MVP Build.
- You need a formal ranked document to take to a board or a budget committee; that is the audit below.
- You want to see what AI can do in general rather than a verdict on the use cases you actually have.
What you bring to the session
- The candidate ideas in your own words, however rough. That list is the agenda.
- Someone who knows how the work really runs day to day, not only how it is described in the process document.
- Nothing else is required in advance. If examples involve real records, how they are shared, where they sit and who can access them is agreed and documented in scoping, before the session.
AI Opportunity Audit + roadmap
What it produces: a roadmap. An audit ends in a written, ranked plan — which opportunities are worth building, in what order, and what a build of each would have to cover. The builds themselves are separate packages; this one produces the decision and the sequencing.
The full picture: a fixed-scope audit that maps how your operations actually run, ranks where AI will pay off, and hands your team a build-ready roadmap with clear next steps for every recommended build.
What is included
- Stakeholder interviews plus mapping of your workflows, tooling and data flows.
- Screening of each shortlisted use case for data readiness and security fitness.
- Five to ten prioritised AI opportunities, each scored on business value, implementation effort and risk.
- A three-lane roadmap — quick-win automations, integration candidates and MVP candidates — sequenced for your business.
- A readout workshop and a written audit and roadmap document your team keeps.
What is not included
- Any implementation or build work (scoped separately from the automation, MVP and partner packages).
- Vendor selection or procurement, or organisational change management beyond the roadmap.
- More than a defined number of interviews; additional interviews are quoted separately.
A good fit if
- You need a ranked, sequenced answer to "where do we start and what is worth building first?"
- You already ran pilots and want the effort redirected to the workflows with real leverage.
- Leadership needs one document that justifies the next build investment.
Not a fit if
- One workflow is already the obvious answer and you want it fixed — a sprint is a more direct route than a ranked plan.
- The decision is already made and the only open question is delivery.
- Nobody inside the business can give interviews or confirm how the workflows actually run; the audit would only be describing itself.
What you bring to the audit
- Time with the people who run the workflows. The audit is only as good as what they tell it.
- A named sponsor who can confirm priorities and receive the readout.
- A list of the systems and data involved, even if it is only names. How data is shared, where it sits and who can access it is agreed and documented in scoping, before the audit begins.
Where this sits among the five packages
Neither package on this page builds anything. Both end in a decision, and those decisions feed the other three:
- AI Clarity Session — advice: a verdict on a short list of candidate use cases.
- AI Opportunity Audit + roadmap — this page: the ranked, sequenced plan for the wider operation.
- Automation & Integration Sprint — working software: one process automated and put into production.
- MVP Build — working software: a defined product built and deployed into your systems.
- AI Lab Partner — ongoing operation: keeping shipped work running and improving.
Compare the five packages to see what each one produces.
Start with the question, not the technology.
Tell us which workflows are on your mind. The request is recorded, there is no automated reply, and no reply time is promised. hi@m00labs.com is the published email address.