Composite Service Example

Turning an AI Pilot Into Approved Azure Scope

This is a composite example, not a real client story. It shows how AI and cloud work can move from open questions into assessment, internal owner actions, blueprinting, implementation, or governance.

Starting Point

The Team Had AI Momentum and Production Risk.

An AI pilot had business support, but production value and ownership were not yet clear.

Azure spend was increasing, but cost ownership was split across engineering and finance.

Data access, identity, model access, and approvals needed a safer production pattern.

Monitoring existed in pieces, but operational ownership and reporting were incomplete.

Findings

Ownership Came Before Tooling.

Business Case Before AI Production

The first issue was not model choice. The team needed a named owner, value hypothesis, adoption plan, and production success criteria.

Controls Before Production Access

Data boundaries, identity, model access, evaluation, and content safety needed a documented access and approval model.

Roadmap Before Implementation

The team needed to separate internal fixes, blueprint work, implementation scope, and governance before expanding delivery.

Actions

Assessment Kept Implementation Scope Bounded.

Start With Assessment: Confirm production blockers, owners, risk, and 90 day action plan before implementation.
Route Urgent Work Internally: Assign owners for cost, access, monitoring, and data-boundary decisions.
Use Blueprint for Architecture Approval: Define target architecture, security model, backlog, assumptions, and SOW.
Use Governance Only if Cadence Is Needed: Add monthly AI and cloud governance when risk, cost, and architecture decisions keep returning.

Start Here

Use the First Call to Check the Right Level of Help.

Bring the initiative, business risk, current owners, and target timeline. The first call checks which service level matches the initiative.

Book Azure Architecture Assessment Send Azure Scope