Most AI projects do not fail because of AI. They fail because of Excel.
In almost every discussion about artificial intelligence, someone eventually asks which data the model is actually supposed to access. Then the room goes quiet.
The uncomfortable truth for many mid-sized businesses is that production data is on paper or in the shift supervisor’s head. Inventory is recorded in a spreadsheet maintained by exactly one person. The machines provide no data because they date from a time when nobody wanted any. And the ERP system was last seriously addressed when the predecessor’s predecessor was still in charge.
Nobody likes to discuss this because it sounds like the company has fallen behind. Yet it is simply the normal state of an economy whose substance lies in machinery and expertise rather than data architecture.
AI is not software that you simply install. It is the top layer of an infrastructure that must work beneath it: ERP, sensors, connected equipment, data quality and defined processes. Without that foundation, even the best model produces precise answers based on incorrect figures, and nobody notices because the answer is well written.
That is the real investment, and it is not glamorous. Nobody gives a keynote about barcode scanners. That is precisely why it is postponed: it is difficult to market as innovation and easy to classify as “later”.
The practical answer lies in sequencing, not abstention. These preliminary investments can be staged. Each stage must create value in its own right. Telematics saves kilometres before any algorithm joins the calculation. Financing with predictable instalments makes this staging budgetable in the first place because it avoids a new capital request every year.
A logistics SME leases telematics systems and modern scanners. Only then does AI-assisted route planning become a realistic option rather than a presentation slide.
An honest question for the room: how many AI workshops have you completed, and how many interfaces have you built in the same period?