Practical AI
Use cases ranked by value, data connected and governed, and pilots on real data before rollout.
When this is the right fit
The board has asked what you are doing about AI
You need an answer with specific use cases, costs, and risks.
People are already using public AI tools with company data
The company needs governed tools that work at least as well, and a policy for the rest.
A vendor is promising AI on top of your ERP
An assessment of whether the data underneath can support the claim, from someone who knows the ERP.
What changes
A ranked list of use cases
Many AI ideas are reports or process fixes. The review separates the ones that need a model from the ones that do not, and ranks what is left by value and effort.
Data readiness and access control
Access controls, definitions, and connections settled before any model sees company data.
Pilots on real data
Each pilot runs on real data with a written definition of done. Rollout follows the way people already work.
What you get
- A review of where AI helps and where a report or a process fix would do
- Data readiness and access controls before any model sees company data
- A pilot on real data with a written definition of done
- Rollout that fits the way the team already works
How an engagement runs
Assessment, plan, build. Each phase ends with a written result you keep.
1Assessment
A few weeks of looking at what you have: systems, integrations, customizations, and the people who keep them running. You get a written picture of the current state and a ranked list of what to change first.
2Plan
A target architecture and a phased plan: the order of work, what each phase depends on, and a cost estimate. The first phase is sized to deliver something you can use.
3Build and hand off
We do the work alongside your team. Documentation and knowledge transfer are part of every phase. Your team owns the result.
Related work
Start with the review
Bring the list of AI ideas that have come up. We will review them and come back with which ones are worth a pilot.