There is a massive disconnect right now between what businesses think AI is, and what it actually looks like in a working P&L.
If you believe the LinkedIn hype, AI is about generating text or creating images. But for the businesses we work with, and for our own internal operation, AI is about something much more boring and much more profitable: optimisation.
It is about taking a process that is currently slow, expensive, or prone to human error, and building a mathematical model that does it faster and cheaper.
The "Toy Model" Problem
We see this happen constantly.
A company hires a data scientist or a graduate. They give them a static export of data (a CSV on a laptop). That person builds a model that predicts customer churn with 95% accuracy.
Everyone celebrates. Then they try to put it live.
And it fails.
It fails because a business doesn’t run on static CSVs. It runs on messy, real-time data streams. The data that comes out of the live ERP system looks different from the clean training data. The model “drifts”—it starts making bad predictions because the market has changed since it was trained.
The gap between a working prototype and a production system is huge. Crossing that gap doesn’t require more science; it requires hard-nosed engineering.
Real-World Application (How We Use It)
We don’t just sell this stuff; we use it. We have built internal tools using these exact principles to automate our own data ingestion.
For our clients, the value comes from specific, tangible wins:
Demand Forecasting: Moving away from “last year + 5%” to models that ingest weather data, economic indicators, and competitor pricing to predict stock levels with granular accuracy. This releases cash flow that was previously tied up in “safety stock.”
Churn Prevention: Most businesses report on churn after it happens. We build models that flag the behavioural patterns of a customer before they leave—allowing your sales team to intervene when it actually matters.
We're Tech Agnostic
A lot of agencies will try to sell you a Python Solution or an Azure Solution because that’s what they know.
We don’t care about the language. We care about the result. If your internal team runs on C#, we build in C#. If you’re an AWS shop, we build for AWS. We build solutions that fit your ecosystem, not ours.
The Takeaway
Stop running Proof of Concepts that never go anywhere.
If you want to see real ROI from AI, you need to stop treating it like a research project and start treating it like an engineering challenge.
Our Head of AI, David Stubbs, is offering free sessions where he helps you to figure out the best way to use AI in your business. Check out our Triage page to book your session today.
