Enterprise AI · Workload and platform readiness
AI Readiness Assessment.
Decide which AI opportunity is ready to advance, what foundation it needs, and which risks or dependencies must be addressed first.
- Indicative timeline
- 3–5 business days for one or a small set of related use cases after stakeholders and source information are available.
- Investment approach
- Quoted after confirming the use-case count, data landscape and stakeholder involvement. Model usage, cloud resources and third-party services are separate.
01 · Business challenge
The problem this package addresses
AI ideas can move quickly into prototypes before teams understand data access, identity, deployment, evaluation, security and operating cost. This assessment connects a selected business opportunity to the technical and organizational work required for a responsible production path.
02 · What you get
Defined technical outcomes
- A prioritized view of candidate use cases and the assumptions or evidence behind each recommendation.
- A readiness summary for data, identity, platform, security, evaluation, operations and cost controls.
- A target architecture sketch and a staged roadmap with dependencies and decision gates.
- A readout that identifies a practical next step, which may be further discovery, a pilot or foundation work.
03 · Scope of work
Work included
- Clarify the business workflow, users, expected decision support or actions, and how success could be evaluated.
- Review selected data sources, access permissions, sensitivity, freshness, ownership and integration constraints.
- Assess model access, identity, network, application integration, evaluation, guardrails, observability and cost considerations.
- Compare options and sequence recommended work based on readiness, risk, dependencies and organizational fit.
04 · Acceptance
How completion is reviewed
Final acceptance checks are agreed in the statement of work before implementation. Typical checks include:
- Recommendations are tied to the agreed use case and stated constraints rather than generic AI maturity claims.
- The roadmap records key dependencies, unresolved decisions, risks and proposed evaluation measures.
- Stakeholders receive a walkthrough and can identify the next decision or validation activity.
05 · Delivery process
From discovery to handover
- DiscoverConfirm the target users, workload boundary, dependencies and acceptance conditions.
- DesignAgree the architecture decisions, delivery sequence, access model and implementation plan.
- Build and validateImplement the scoped work in the client environment and review it against the agreed checks.
- Hand overTransfer code, documentation, runbooks and ownership to the client team.
06 · Technical foundation
Tools and architecture
Cloud AI and model access patterns across Azure, AWS and Google Cloud; identity and service permissions; private connectivity; data permissions; retrieval and agent boundaries; evaluation, CI/CD, observability and AI cost management.