We Bought a Ferrari, But We Filled it with Sand.
You found your AI use case. Now, do you have the fuel to run it?
Last week, we talked about “Strategic Waste” and I shared the Use Case Validator to help you find your top AI opportunities.
(If you haven’t generated your report yet, you can still use the free tool here
But once you find that “winning” use case, you are going to hit a wall. It is the wall that kills more AI pilots than budget cuts or bad code.
The Data Wall.
Imagine that you are trying to deploy a “Customer Service AI Agent” (a classic high-value use case that saves time and improves customer experience).
You pick a great LLM. You hire smart engineers.
Two weeks later, the AI is hallucinating, quoting the wrong prices, and confusing customers.
The problem wasn’t the AI. The AI was a Ferrari.
The problem was that you filled the tank with sand.
You fed the model five years of contradictory PDF contracts, old email threads, and messy spreadsheets. You didn’t have a data strategy; you had a document pile.
The “Unstructured” Trap
In Chapter 3 of my book, AI First, I devote an entire section to this. I write: “data is the fuel that powers your digital engine—if it is of poor quality, you might as well be running your organisation on stale tea!
Most companies are drowning in Unstructured Data (text, images, emails). AI can process this, but only if it is curated, cleaned and governed.
If you look at the “Action Plan” inside your Use Case Validator Report,you’ll notice something about “Week 1” of almost every plan.
It doesn’t say “Build the model.” It says “Data Audit” or “Process Mapping.”
That’s not an accident.
Try out the use case validator here if you have not yet done so.
Your 3-Step Data Reality Check
Before you start building the use case you identified, ask three questions (from Chapter 3):
Is the data accurate? (Or are you feeding the AI old pricing manuals from 2019?).
Is the data accessible? (Or is it trapped in someone’s inbox or on a sales rep’s laptop?)
Is the data legal? (Are you accidentally feeding PII or client data into a public model?).
Don’t build the Ferrari until you’ve checked the fuel.
Data Governance: The “Tidy Filing Cabinet”
Good data governance is like having a tidy filing cabinet in the digital age. It ensures data is managed properly, with clear policies on who can access what, how it is stored, and how it is shared.
Key aspects include:
Standardisation: Setting uniform protocols for data entry. Without standardisation, your data will be as jumbled as a bowl of porridge.
Security: Implementing robust measures to protect sensitive data. In an era of cyber threats, ensuring data privacy is not just good practice — it is legally mandated.
Compliance: Adhering to regulations like GDPR. Failure to comply can lead to hefty fines and a damaged reputation faster than you can say “data breach” .
A lack of proper governance can turn a promising AI project into a logistical nightmare.
The Business Value of High-Quality Data
High-quality data is not just a technical nicety — it directly impacts the bottom line.
Reliable data leads to accurate insights, which drive better business decisions. When your data is robust, your AI models are more reliable, easier to scale, and far more likely to deliver real business value instead of expensive experiments.
If you want to know more, Chapter 3 of AI First (“Data, Technology & Infrastructure Readiness”) breaks this down in detail.
As a subscriber, you already have the exclusive 5-chapter preview in your welcome email.
Go back to Page 60. It might save your next project.
To your readiness,
Davies Bamigboye
P.S. If you’re wondering where to start more broadly, you can check your full AI Readiness Score here