SME AI Strategy & Readiness Assessment

Executive Summary

A growing organization initiated an AI adoption initiative to explore generative AI for improving workflows, decision support, and operational efficiency.

While multiple use cases were identified, leadership lacked clarity on feasibility and how to scale beyond early experimentation.

The engagement reframed AI adoption into a structured strategy, focusing on feasibility, architecture readiness, and a governed foundation within Azure Public Cloud.

Environment

• Azure Public Cloud environment supporting business applications
• Multiple AI use cases across workflows and operations
• Early experimentation without standardized architecture
• Fragmented systems and integration patterns
• Limited governance for AI initiatives

The Challenge

• Lack of alignment between AI initiatives and business outcomes
• Uncertainty on viable use cases
• Fragmented pilots without scaling path
• Limited clarity across systems and cloud constraints
• No structured governance or adoption model

The Solution

• Defined use cases aligned to business value
• Assessed readiness across platform and integration layers
• Evaluated architecture feasibility and trade-offs
• Defined governed AI foundation in Azure Public Cloud
• Created structured decision framework and roadmap

Outcomes

• Completed a structured 3‑week AI assessment
• Evaluated multiple AI use cases for impact and feasibility
• Assessed readiness across platform and integration capabilities
• Defined a governed foundation aligned to Azure Public Cloud
• Identified key integration and governance gaps
• Delivered a scored recommendation and roadmap

Key Insight

AI success is driven by clarity of purpose and readiness—not tooling.

Organizations that structure AI as a capability—not experiments—are able to scale and realize measurable impact.