AI implementation
Start with a recurring process and a clear problem. Our method uses Qualify, Prove, Decide, Embed and Handover to establish the work, test uncertainty and agree the next decision.
Does my team need a fully defined AI project?
Getting started with an AI project
A fully defined AI project is not needed to begin. A recurring process and a clear description of what is difficult are a useful starting point. Muuvment can help assess whether an existing tool, configuration, process change, or bespoke AI implementation is the right next step.
Start with the workflow
Describe who performs the task, what information they use, and where the process becomes slow or difficult. Representative examples help make the discussion concrete, provided they are appropriate to share.
Choosing a next step
An existing tool, configuration, process change, or bespoke development may be appropriate. The aim is to address a specific uncertainty before committing to a broader implementation.
How does Muuvment decide whether to build?
How Muuvment decides whether to build
Muuvment starts with the workflow and the problem to solve, then considers available tools, information access, review needs, and likely value. A configuration or process change may be a better fit than custom development. We explain the options so your team can choose a proportionate next step.
Compare the options
We consider the workflow, available tools, permitted data access, review requirements, and likely value. A configuration or process change may be more suitable than bespoke development.
Test what is uncertain
The Qualify, Prove, and Decide stages help clarify the workflow, test key assumptions with appropriate examples, and support a decision to proceed, revise, or stop.
How long does implementation take?
AI implementation timelines
There is no single timeline for AI implementation because scope, data readiness, integrations, testing, and decision-making all affect delivery. We define the milestone, deliverables, and dependencies before proposing a schedule, so a prototype, pilot, and production rollout are clearly distinguished.
Milestones matter
A demonstration explains an idea; a prototype tests part of it; a pilot adds operational learning; and a production rollout requires agreed readiness and operating arrangements. Ask which milestone a proposed schedule covers.
Source availability, permitted access, integrations, decisions, and reviewer availability can all affect timing. A useful schedule states its assumptions and dependencies.
Can we stop after testing?
Stopping after an AI test
Testing can be a decision point: your sponsor can proceed, adjust the approach, or stop based on the evidence. We agree the scope, decision criteria, and exit deliverables before work begins, so your team knows what it will receive if it does not continue.
Agree the decision and exit
Testing should address an agreed uncertainty and give the sponsor useful evidence. Before the work starts, establish the decision criteria, what documentation or other deliverables will be provided, and the commercial terms if the work does not continue.