Artificial intelligence in Algerian industry

In industry, what separates an AI project that lands from one that stalls is almost never the model: it is whether the history exists. The use cases that work today are those whose data is already recorded somewhere — a maintenance file, a production log, a control book. The rest has to be measured before it can be predicted.

Which use cases actually hold up in a plant?

Forecasting from production parameters. When a process depends on several settings and the result is measured afterwards, a model learned on the history gives an estimate before the run starts. It is the most profitable use because the error it avoids is physical.

Automatic reading of paper documents. Delivery notes, control reports, shift records. It is the most common case because manual retyping is everywhere, and it has its own article.

Search across technical documentation. Years of procedures, drawings and reports become searchable in plain language, with the source cited back. No specific training is needed: the documents are enough.

Visual inspection. Detecting a surface defect on a part demands annotated images in quantity and controlled lighting. It is feasible, it is the most demanding in preparation, and it is rarely where to begin.

What data does each one demand?

Use case	Data required	Does it already exist?
Result forecasting	Parameters and measured results, historised	Often, in spreadsheets
Document reading	Already-keyed archives	Almost always
Documentation search	The documents themselves	Always
Predictive maintenance	Sensor readings across several failure cycles	Rarely
Visual inspection	Annotated images, good and defective	Almost never

The table reads simply: the first three rows are projects of a few weeks, the last two are projects of several months — and the difference lies entirely in the middle column.

Why predictive maintenance so often disappoints

It is the most requested use case and the most poorly started. Predicting a failure assumes you have observed the same failure several times, with the measurements that preceded it. But a well-maintained critical machine rarely fails: the history that would let you anticipate it does not exist, precisely because the maintenance works.

The honest approach is to instrument first and predict later. Fit the sensors, build the history, and in the meantime handle the use cases whose data is already there. A supplier who promises you predictive maintenance with no history is selling you a dashboard, not a prediction.

Where should these models run?

On your servers, inside your building. Production data — throughput, scrap rates, process parameters — is among the most sensitive an industrial company holds, and often covered by customer confidentiality clauses. A model running locally makes no outbound network call, does not depend on the quality of the site's internet link, and does not bill by usage.

That is also what makes the approach workable on isolated sites — a poorly served industrial zone, a southern oil site. A link outage interrupts neither document reading, nor documentation search, nor forecasting.

Where to start

• Take stock of what is already recorded before choosing a use case. That inventory, not the wish list, decides what is feasible this year.
• Take the process that consumes the most retyping hours. The gain shows in the first month and is not open to argument.
• Insist on written scoping where every function carries a reference carried over into the acceptance test plan. A function is delivered when its test is accepted, not when it is demonstrated.

Our most technically demanding industrial work was delivered in France, for a forging manufacturer, and for an international oilfield services group. Those are the methods we apply in Algeria, where we work on site in Oran, Algiers, Constantine, Sétif, Annaba, Blida, Tlemcen, Tipaza, Sidi Bel Abbès and Hassi Messaoud. The assignments already delivered and what AI genuinely changes in an Algerian company.