Is Your Business Ready for AI?
When an AI project disappoints, the cause is often the data underneath, not the model. Readiness means knowing what data you have, whether it can be trusted, and which problems are actually worth solving.
- 1First, the foundationData
What you have, where it lives, and whether you can trust it
- 2Governance
Who owns it, what it means, and who can use it
- 3Architecture
How it's stored, connected, and kept up to date
- 4Then, opportunityAI Opportunities
Where AI could genuinely help, and where it won't
- 5Prototype
A small, working test of one good idea
- 6Scale
Turn what works into something the business relies on
Each stage depends on the one before it. Sometimes the right next step is an AI prototype. Often it’s fixing the data first, and that’s a perfectly good outcome.
Data & AI Readiness Assessment
Four steps, ending with a written plan you can act on. Here's what's involved.
- Step 1
Understand
Review the systems, data sources, processes, and current challenges.
- Step 2
Assess
Look at data quality, architecture, integration, governance, accessibility, and AI readiness.
- Step 3
Identify
Find realistic opportunities where better data, analytics, automation, or AI could help.
- Step 4
Prioritize
Create a practical roadmap showing what to address first and what can wait.
What you receive
- Current-state overview
- Data and system inventory
- Key data gaps
- Architecture observations
- AI opportunity areas
- Prioritized recommendations
- Suggested next steps
A clear picture of where you are and a practical plan for what to do next.
Not ready to talk yet?
Eight questions, about three minutes. You'll get a rough score and a few suggestions by email. It's a starting point, not a substitute for looking at your actual data.