Generative artificial intelligence is becoming a revolution in all fields, and the industrial sector is no exception. Long confined to automation and process optimization functions, AI is now taking on a new dimension, capable of generating content, assisting decision-making and exploring complex data.

But in concrete terms, what are the uses of this technology in industry?

Let’s see a key use case: the search and analysis of technical documents.

Use case: intelligent search in technical documents

In industry, teams – whether R&D engineers, maintenance technicians or quality managers – handle a huge mass of documents on a daily basis: Technical manuals, Industrial plans and diagrams, Standards and regulations, Test and test reports ,…

The difficulty? These documents are often heterogeneous, scattered in different formats (PDF, Word, proprietary databases) and written in very specific business jargon.

Generative AI now makes it possible to revolutionize this documentary research thanks to several capabilities:

First, natural language querying. No need to formulate complex queries or know the keywords precisely. An engineer can simply ask a question like:
“What is the maximum pressure supported by pump X in test protocol Y?” AI automatically scans documents and extracts relevant information, even if it is expressed in different forms.

Next comes the content synthesis capability. When an answer is not found in a single document, AI can aggregate data from multiple sources and provide a clear and contextualized summary. This saves valuable time in decision-making.

If needed, in an international context, AI can translate and rephrase complex technical passages, ensuring consistent understanding for multilingual teams.

 

Generative AI can also detect inconsistencies , by comparing multiple documents or versions of the same file (for example, different values ​​for the same technical specification) and alerting teams to these discrepancies.

Implementation challenges

While the promises of generative AI are immense, their realization in the industrial sector raises several challenges.

For AI to work effectively, it needs clean, well-structured and accessible data. However, technical documents are often scattered, poorly formatted and sometimes obsolete. The data preparation and cleaning phase is therefore crucial. Let us specify that this is heregenerally the same work as for a Technical Document Management project, where documents must be centralized,  

An important point, which comes back a priori to each discussion , is that information in general and industrial information in particular can be sensitive. Integrating a generative AI involves setting up protocols to protect the data and avoid the risks of leakage or misuse.

Generative AI, while powerful, often operates as a black box, producing results without always clearly explaining the reasoning behind them. 

For industries, it is essential to understand why information was extracted or a recommendation made. 

Implementing traceability mechanisms, such as mentioning the sources used, helps to strengthen user confidence and ensure that the final choices are based on solid foundations.

Finally , adopting these new technologies requires teams to upgrade their skills. It is not enough to deploy an AI solution: employees need to be supported so that they know how to interact effectively with these tools. But here too, AI can be an effective assistant for users!

Towards a new industrial era

This use case illustrates how generative AI goes beyond simple automation. It becomes a real co-pilot for industrial teams, offering them an augmented reading of information and accelerating their decision-making process.

As manufacturers strive to become more agile and precise, this technology opens the door to new opportunities: assisted design, automated writing of technical reports, etc. We are only at the beginning of this transformation.