AI Infrastructure Readiness Framework

A practical checklist for evaluating whether your organization's infrastructure can support AI workloads.

The Problem

Most AI projects fail not because of bad models, but because the underlying infrastructure wasn't ready. Teams spin up GPU instances, train models, and deploy endpoints — only to discover their networking can't handle the throughput, their security posture has new gaps, and their ops team has no idea how to monitor any of it.

The Framework: 5 Pillars of AI Infrastructure Readiness

1. Compute & Storage

  • Do you have GPU/accelerator capacity (or a plan to provision it)?
  • Can your storage handle the I/O patterns of training data pipelines?
  • Is your infrastructure-as-code mature enough to manage AI-specific resources?
  • Do you have cost controls in place for on-demand AI compute?

2. Networking & Data Flow

  • Can your network handle large model artifacts and training data transfers?
  • Do you have low-latency paths between compute and storage?
  • Are your data pipelines reliable and observable?
  • Can you move data between environments (dev/staging/prod) securely?

3. Security & Governance

  • How will you secure model artifacts and training data?
  • Do you have controls for model access and versioning?
  • Is your supply chain secure (model dependencies, libraries, containers)?
  • Do you have a policy for AI-generated outputs and data privacy?

4. Operations & Observability

  • Can your monitoring stack track AI-specific metrics (inference latency, model drift, GPU utilization)?
  • Does your ops team know how to troubleshoot AI workloads?
  • Do you have runbooks for common AI failure modes?
  • Is your incident response process updated for AI-related incidents?

5. Team & Process

  • Does your infrastructure team understand AI workload patterns?
  • Is there a clear handoff between data science and infrastructure teams?
  • Do you have a process for promoting models from development to production?
  • Are cost responsibilities clearly assigned?

How to Use This Framework

Score each pillar on a 1-5 scale: - 1: Not started - 2: Awareness but no action - 3: Partial implementation - 4: Mostly ready with gaps - 5: Production-ready

An overall score below 15 means you should invest in infrastructure foundations before scaling AI initiatives. A score of 15-20 means you can pilot AI projects while strengthening weak areas. Above 20 means you're ready to scale.

Key Insight

The organizations that succeed with AI are the ones that treat it as an infrastructure challenge, not just a data science challenge. Getting the foundations right first saves months of rework later.