Free resource
Is your business ready for AI?
Fourteen questions to work through before you commit to an AI project. Steal them, share them, take them into your next board meeting. No download, no email required.
Most failed AI projects aren't failed because the technology didn't work. They're failed because the wrong question was asked at the start.
These are the questions we walk through in the first hour of a diagnostic. Working through them yourself, before any vendor is on the call, is the cheapest way to know whether you're ready and what to look out for.
Who this is for
Leadership teams thinking about a first AI project. Operations directors who've been asked to "look into AI." Founders working out whether to build something or buy something. Anyone who's been pitched and wants to ask better questions.
Part 1 of 4
Business fit
Most failed AI projects shouldn't have been AI projects. Start here.
- 01
What's the specific problem you're trying to solve, and what does it cost you today in money or time?
- 02
Is there a non-AI solution that would work — a process change, a script, a better-trained team — and be cheaper to run for the next three years?
- 03
Who in your business will use this system every day? Have they been in the conversation, or is this being designed for them rather than with them?
- 04
If this project succeeds, what do you do differently? If it fails, what do you actually lose?
- 05
Are you doing this because you've seen a clear opportunity, or because everyone else is and you don't want to be the one explaining why you didn't?
Part 2 of 4
Data readiness
No useful data, no useful AI. This is where most projects quietly die.
- 06
Does the data the system would need actually exist today, in a form you can extract and use?
- 07
Who owns that data, and what's the path to get permission to use it — internally and from any third parties?
- 08
If your data is messy or incomplete (it usually is), who's going to clean it — and is that work in scope and budget?
Part 3 of 4
Vendor and approach
Who you choose matters more than what you build. Especially for the boring questions.
- 09
Who owns the IP, the code, and the data when the project is over? Will you still have access if you stop working with the vendor?
- 10
Can the team you're hiring point to a system they built that's still in production today, not a demo from last year?
- 11
How is the price structured? Fixed price, time and materials, hourly? And what specifically happens if the scope changes mid-project?
Part 4 of 4
Success measurement
If you can't say what success looks like before you start, you won't recognise it when it arrives.
- 12
What does success look like in numbers? Pick one: money saved, time saved, error rate dropped, throughput increased.
- 13
When will you know? Three months in? Six? A year? Be specific so you can actually call the project finished or failing.
- 14
What's your exit if it's not working — and is that exit written into the contract before you sign it?
If this was useful
A diagnostic is what this looks like over two weeks, with our team in the room.
We work through these questions with your leadership team, look at your processes and your data, and write you a one-page report on where AI would help — and where it wouldn't. Fixed price. If there's nothing worth doing, we say so.
For board meetings, supplier calls, or your own working notes.
Want help working through these?
Twenty minutes on the phone. We'll talk through where you are, point out the questions that matter most for your situation, and tell you honestly whether a diagnostic is worth doing.