Resource · AI & Innovation

The AI-Readiness Assessment

Most AI projects stall on data and governance, not on the model. Run through this before you spend. It’s where we start every AI engagement, and it’s built to tell you the truth about your foundations.

Is your data actually usable?

Your core guest, operational and financial data is accessible and reasonably clean, not trapped in systems that won't share it.
You know where your data lives, who owns it, and what you are permitted to do with it.
There is a single source of truth for the questions AI would answer, or a realistic plan to get one.
You're not expecting AI to fix a data problem that better process would fix faster and cheaper.

Do you have a real use case, or a mandate to “do AI”?

You can name a specific, valuable problem AI would solve, not “explore AI.”
The value is measurable, and you'd know within a quarter whether it's working.
The use case survives the question “could a simpler tool or a fixed process do this?”
Someone senior owns the outcome, not just the initiative.

Governance, risk and trust

You have a clear view of where AI can, and cannot, make decisions in your business.
Data privacy, guest consent and regulatory exposure are understood for each use case.
You can explain, to a board or a guest, how an AI-driven decision was reached.
A human is accountable for every automated action that touches a customer.

Skills, ownership and change

Someone internal will own the capability once the consultants and vendors have left.
Your team can monitor, maintain and question the models, not just switch them on.
The people whose work changes have been brought in early, not surprised late.

Build, buy or partner

You've decided what to buy off the shelf, what to build, and what to leave alone, and why.
Vendor claims have been tested against your actual data reality, not a canned demo.
You're not paying enterprise-AI prices for something a product feature would solve.

Measuring value

Every initiative has a baseline and a target agreed before it starts.
You measure the result independently, not just accept the vendor's dashboard.
You've decided what you'll stop if it doesn't clear the bar, and when you'll make that call.

How to read it: score it honestly. If most of section one is red, you have a data project before you have an AI project, and that’s the cheaper place to win.

Wondering where AI is worth it for you?

Book a Strategy Call

Booking not loading? Email us directly.

We use only essential cookies to keep this site working, no advertising cookies. Anonymous, privacy-friendly analytics help us improve. See our Privacy Policy.