Blueprint 2:
Assess your AI readiness
Before making any investment, you need to ensure your organization is ready to handle the security, data, infrastructure, and cultural changes AI requires.
Conducting a readiness assessment across these categories will highlight gaps and prepare your people and infrastructure to effectively adopt AI.
AI literacy: Assess your ability to use AI safely and effectively by conducting a survey based on the META AI Literacy Scale (MAILS).
Executive alignment: Assess the level of executive support for AI initiatives and alignment with business goals via a top-down steering committee.
Use case potential: Assess investment potential by grading use cases by business impact vs. implementation complexity.
Cost modeling: Use financial modeling tools to estimate model, infrastructure, and development costs and calculate ROI.
Data literacy and reporting: Meet with staff and review major reports to assess your ability to interpret and use data.
Data availability and accuracy: Review data storage practices and integrations to evaluate your ability to centralize and make data accessible to AI tools and APIs.
Data governance and management practices: Assess usage, governance, and data management policies for target data sets.
Overall security posture: Focus on mastering the basics. Conduct regular security posture reviews based on industry frameworks like CIS or NIST and include AI-specific security practices.
Threat awareness and reporting: Use AI security proxies and monitoring tools to evaluate your awareness of and ability to respond to AI security threats like model poisoning, data leaks, and more.
Governance, education, and risk management: Review responsible AI and risk management policies. Conduct literacy surveys to determine employees’ abilities to detect and use AI safely.
Current infrastructure: Evaluate your current infrastructure and its suitability for supporting AI workloads.
Workload location: Use infrastructure sizing tools to understand the cost, performance, manageability, and security implications of running AI workloads in the cloud vs. on-premise.
Deployment landing zones: Review existing deployment practices and assess your ability to create secure, production-grade environments for deploying AI workloads.
Edge and device-level AI: Assess the need and requirements of edge devices to run AI workloads locally.
Validated designs and reference architectures: Review validated designs that integrate hardware, orchestration layers, and MLOPs platforms.
SHI can perform comprehensive assessments of your environment and posture, including:
AI readiness assessment.
Security posture review (SPR).
AI literacy assessment.
Blueprint 2: Assess your AI readiness
