AI strategy & AI CoE
Most enterprises have plenty of AI ideas. What they lack is a ranked portfolio tied to real processes, and an operating model that gets the first system into production and the next ones funded.
What it covers
- 01
Use-case discovery
We go through processes with the people who run them: volumes, cycle times, error rates and the systems involved. The result is evidence, not a brainstorm.
- 02
Value and feasibility ranking
Each opportunity is scored on measurable value, data readiness, integration effort and risk, so the portfolio holds up in front of finance and security.
- 03
Roadmap and business case
A sequenced plan that names the first production candidate, the measure it will be held to, and what it makes possible next.
- 04
AI Center of Excellence
Standards, reference architecture, intake, governance and enablement, so every business unit builds and approves AI the same way.
Where this sits
Every engagement follows Strategy, Build, Govern and Operate. This capability does most of its work in the highlighted stage.
- 01 · Strategy
Find where AI pays
We map processes, volumes and systems, then rank use cases by value and feasibility.
A funded roadmap and an AI CoE operating model
- 02 · Build
Embed engineers, ship systems
Forward deployed engineers work inside your teams and connect models to SAP, CRM, identity and data.
Production software in your environment, not a demo
- 03 · Govern
Control every AI action
Identity, policy, approvals, data controls and audit apply before anything executes.
A system your security and audit teams can sign off on
- 04 · Operate
Stay accountable after launch
Monitoring, evaluation, cost management and steady improvement against the original target.
Named owners, runbooks and measured outcomes