MuBra delivery shape
What the sprint leaves behind
The failure pattern is predictable: a business buys a tool, points it at messy process, then discovers nobody owns review, data quality, or adoption. This sprint is designed to prevent that.
AI setup
Set the business up for AI before you buy tools or automate the wrong process.
Most small businesses do not need a broad AI transformation programme first. They need rules, workflow clarity, data hygiene, staff guidance, and a narrow list of places where AI can safely help.
Practical setup
Human approval
Tool-agnostic
what staff can and cannot use AI for
what needs cleaning before automation is useful
where AI may draft, summarise, classify, or recommend
Best fit
Who this page is really for.
MuBra works best when the mandate is clear enough to act on, but still valuable enough to justify sharper sequencing and implementation discipline.
- Owner-managed businesses where staff are already experimenting with AI but there are no rules or review points.
- Teams considering AI tools, agents, or automations but unsure which workflow is safe or valuable enough to start with.
- Businesses with messy CRM, inbox, document, or handoff data that would make automation unreliable if left unresolved.
Outputs
What the sprint leaves behind
These are the artifacts and operating decisions the engagement is designed to leave behind.
AI safe-use policy written in plain operational language for staff and managers.
Workflow map showing where AI can assist, where a person must review, and what should stay manual.
Data readiness checklist covering CRM fields, inbox labels, documents, permissions, and source-of-truth gaps.
First automation shortlist ranked by value, risk, owner, and delivery effort.
Process
How the setup work runs
The work is sequenced to produce a cleaner decision path and a stronger operating outcome, not just a busier project footprint.
Map
List the real workflows: enquiries, quotes, documents, follow-up, reporting, handoffs, and recurring admin.
Control
Set practical rules for AI use, confidential data, review, approval, and external communication.
Clean
Identify the fields, files, inboxes, and ownership gaps that would break automation if ignored.
Prioritise
Choose the first sensible workflow to improve and define what should happen in a paid implementation sprint.
Commercial logic
Why this comes before tools
The failure pattern is predictable: a business buys a tool, points it at messy process, then discovers nobody owns review, data quality, or adoption. This sprint is designed to prevent that.
FAQ
Questions that usually come up before this engagement starts.
Is this an AI policy document or an implementation plan?
Both. The policy is useful only if it connects to real workflows, data, approval gates, and a shortlist of practical improvements.
Will this automate everything?
No. The point is to decide what should be automated, what should be assisted, and what should stay human-owned.