Free Resource

The AI Readiness Checklist

Most AI initiatives stall not because the model is wrong, but because the organization around it isn't ready. Use this checklist to find the gaps before you commit budget to a pilot.

Five dimensions of AI readiness

This checklist is built from the criteria that actually determine whether an AI initiative survives contact with production. Work through each section honestly. A handful of unchecked items in any one category is a normal starting point, not a disqualifier; it just tells you where to focus first.

Strategy and Vision

Clarity on why AI matters to your business before any technical work begins.

  • You can name specific business outcomes AI should improve, not just "we should be doing AI."
  • Leadership agrees on which 1-3 use cases to pursue first, rather than pursuing everything at once.
  • You have a rough sense of what success looks like and how you'll measure it.
  • Budget and timeline expectations are realistic for the use case's actual complexity.

Data Readiness

The foundation every AI system depends on, regardless of which model sits on top.

  • The data your use case needs actually exists and is accessible, not locked in disconnected systems.
  • You know roughly how clean, complete, and current that data is.
  • There's a clear owner for data quality issues when they surface.
  • Sensitive or regulated data is already classified and handled under existing policy.

Technology and Infrastructure

The systems and tooling that determine how fast you can move from prototype to production.

  • You have a cloud or on-prem environment capable of hosting AI workloads and their integrations.
  • Existing APIs or data pipelines can feed a model without a ground-up rebuild.
  • You've considered where inference will run and what that costs at expected usage volume.
  • Monitoring and logging exist, or are planned, for whatever you ship.

People and Skills

The team that will build, operate, and champion the system day to day.

  • At least one internal owner is accountable for the initiative beyond the initial pilot.
  • The team understands the difference between a demo and a production-grade system.
  • There's a plan for who maintains and retrains the system after launch, not just who builds it.
  • End users have been consulted about how the tool will change their workflow.

Governance and Risk

The guardrails that keep an AI system defensible once it's live.

  • You've considered accuracy, bias, and failure modes for this specific use case, not AI in the abstract.
  • There's a documented process for a human to review or override AI-driven decisions where it matters.
  • Relevant compliance requirements (GDPR, sector-specific rules, the EU AI Act) have been identified.
  • You have a plan for explaining how the system works to auditors, regulators, or customers if asked.

Next Step

Want a second opinion on your results?

Tell us a bit about where you landed and what you're trying to build. We'll reply with a short, honest read on your readiness and where we'd focus first, no sales pitch attached.