Technology & Architecture addresses how AI systems are deployed, secured, and integrated within your organisation’s technical infrastructure. This covers whether AI tools are sanctioned and enterprise-managed (rather than staff using uncontrolled consumer services), integration with identity and access management systems, alignment with security frameworks, scalability of the infrastructure, and architectural standards for AI deployment. It also includes considerations like multi-cloud strategies, sandboxed experimentation environments, and ensuring AI systems can be audited and monitored.
The risk here is “shadow AI”—staff using free or unsanctioned AI tools that bypass enterprise security, leak sensitive data, or create ungoverned dependencies. This dimension evaluates the security, scalability, and control of your AI technology infrastructure, and whether AI tools are enterprise-managed, integrated with identity and access management (IAM), and aligned with architectural standards.
Why It Matters
Uncontrolled AI tools expose organisations to data leakage, compliance risks, and operational vulnerabilities.
Cloud Infrastructure as a Foundation for AI
Many organisations face barriers to AI readiness related to ageing on-premises infrastructure: costly legacy systems, unreliable infrastructure that hinders AI initiatives, and limited scalability for data-intensive workloads. Migrating to cloud infrastructure can address these challenges by providing:
- Scalability — Infrastructure that grows with AI demands without over-provisioning
- Reliability — High availability and performance for mission-critical AI applications
- Cost efficiency — Pay-as-you-go models and flexible consumption options
- Integration — Seamless connection with identity management, security frameworks, and enterprise architecture
However, cloud migration alone is not sufficient for AI readiness. Organisations must still establish the governance, compliance, risk management, and operational frameworks covered by the other 14 dimensions in this assessment.
Further Reading: Cloud Infrastructure Planning
Microsoft’s white paper “Colocation: Build a Scalable Cloud Foundation for AI” provides detailed guidance on:
- Identifying infrastructure barriers to AI readiness (legacy systems, data silos, scalability constraints)
- Planning cloud migration strategies (infrastructure, databases, applications)
- Cost optimization approaches for AI workloads
- Security considerations for cloud-based AI systems
While Microsoft’s framework focuses on technical infrastructure prerequisites, the 15 dimensions in this AI Readiness Assessment address the broader governance, risk, ethics, and operational capabilities needed for responsible AI adoption.
Maturity Levels
| Basic | Standard | Advanced | Leading |
|---|---|---|---|
| Staff using free or unsanctioned tools with no enterprise oversight. | Enterprise-controlled AI platforms with basic security and access controls. | Multi-cloud AI infrastructure integrated with IAM and enterprise security frameworks. | Sandboxed environments for experimentation, with continuous updates aligned to Partner-in-the-Loop principles. |
📥 Related Resources & Templates
Downloadable templates, examples, and frameworks to help you implement this dimension.
AI Architecture Standards
Technical architecture standards and best practices for AI system design, integration, and infrastructure.
Identity & Access Management for AI
PremiumIAM policy and access control matrix specifically designed for AI systems, models, and data access.