Why 80% of AI projects fail before they write a single line of code—and how to make sure yours isn’t one of them.
Let’s be honest. The pressure is on.
Somewhere in your inbox, there’s an email from a Director or a CEO. It probably arrived late on a Sunday, and it probably says something like: “What’s our Generative AI play?”
It’s the modern corporate equivalent of the Gold Rush. Everyone’s terrified that if they don’t start digging right now, they’ll be left with nothing.
But here’s the “Data Confession” that nobody wants to make in the boardroom: Most companies have champagne dreams built on Aldi’s own brand budgets.
The "Sh*t In, Sh*t Out" Problem
You can buy the most expensive, sophisticated AI model on the planet. You can hire a team of data scientists. You can promise the board that you’ll be “AI-First” by Q3.
But if your core customer data lives in six different spreadsheets, a legacy CRM, and Dave’s personal laptop?
The AI won’t make you smarter. It’ll just lie to you faster.
We call this Artificial Incompetence. When you feed an LLM (Large Language Model) dirty, unverified, or siloed data, it doesn’t say “I don’t know.” It confidently invents an answer that looks plausible but is factually wrong.
That is how you end up with a board report that says you made £4m profit last month, when the real number is closer to £400k.
The 10-Question Reality Check
Before you spend a single penny on AI licenses or consultants, you need to look at your foundations.
We built the AI Readiness Checklist to save you from the expensive embarrassment of a failed rollout. Don’t think of it as a sales pitch, think of it as an opportunity to not spend a fortune.
It consists of 10 simple statements. Things like:
“We have a clear ‘Data Owner’ for every department.”
“Our data is updated automatically, not manually refreshed once a month.”
“We have zero duplicate customer records.”
You simply read the statement and tick the box. But here is the rule: You have to be brutally honest. If you can’t tick it with 100% confidence, you leave it blank.
What Your Score Actually Means
When you finish, you’ll fall into one of three camps:
1. The Danger Zone (0–3 Ticks) Stop immediately. If you try to deploy AI now, you will fail. Your data is too messy, too siloed, or too risky. You don’t need an AI consultant; you need a Data Triage.
The Fix: We’ll connect you with Kevin Hurd, our CEO, to help you build a foundation that actually works.
2. High Risk (4–7 Ticks) Proceed with caution. You have some good systems, but there are gaps—maybe in privacy, maybe in history. If you deploy AI here, it will likely hallucinate or leak data you thought was secure.
The Fix: You need a roadmap to plug the gaps before you hit “Go.”
3. AI Ready (8–10 Ticks) Green light. Your house is in order. Your data is clean, governed, and accessible. You are ready to start building the future.
The Fix: You can book a session with David Stubbs, our Head of AI, to start designing the architecture that will transform your business.
Don't Guess. Know.
The worst thing you can do right now is pretend you’re ready when you aren’t.
Take two minutes. Take the test. Find out if you’re putting your time and money in the right areas, or whether you’re about to blame AI for something that it had zero control over.
