AI for industry: which use cases actually create value?
Artificial intelligence is generating growing interest in the industrial sector. Yet, many companies still struggle to identify the use cases capable of delivering a concrete return on investment.
The success of an AI project does not depend solely on the technology used. It relies primarily on choosing the right use cases. The projects that generate the most value are generally those that enhance team expertise in processes with high operational or commercial impact.
So, what are the most relevant AI use cases in the industry today?
Why prioritize high-value use cases?
AI is not intended to replace subject matter experts. Its role is to enable them to access information faster, reduce low-value tasks, and improve the quality of decision-making.
The most successful projects are often those that address a clearly identified business problem: selling more effectively, resolving technical incidents faster, ensuring compliance, or transferring critical knowledge.
These are also the use cases where team adoption is generally strongest, as the benefits are quickly visible.
1. Sales performance and product consulting
Why expertise is critical
In the industrial sector, selling is not just about presenting a catalog. Sales teams must understand hundreds or even thousands of references, their technical specifications, their compatibility, and their application cases.
This expertise makes a direct difference in sales performance. According to a Clari study, the top 10% of salespeople generate 65% of total revenue on their own. Other research also shows that top-performing salespeople are up to 65% more likely to achieve upsells or cross-sells and 63% more likely to be referred to other companies by their clients.
Insufficient product expertise can therefore lead to missed opportunities, inappropriate recommendations, and lost revenue.
How AI can help
AI allows users to query product catalogs, technical data sheets, training materials, or internal feedback in natural language. It also facilitates connecting with the right experts when a question goes beyond the available documentation.
Concrete example
A sales representative is preparing for a meeting with a restaurant owner looking for a solution to clean a wooden deck. In seconds, the AI recommends the right products, the corresponding sales pitches, and complementary items to suggest.
Expected results
- increased average order value;
- reduced meeting preparation time;
- faster onboarding for new sales staff;
- fewer requests directed to product experts.
At SID, using Ask for the moon could boost revenue by up to 20% for some sales representatives. The solution is currently used by 120 reps across a catalog of over 800 products and more than 2,000 technical documents.
For a full case study on using AI for sales product expertise, watch the testimonial from Adrien Hubert, General Manager at SID, during our dedicated webinar: [https://youtu.be/bO4tSDb3TE0?si=QeY-e9PVa0cyI0po].
2. Technical support and maintenance
Why expertise is critical
Support and maintenance teams must master vast amounts of technical documentation, procedures, and regulations. They are often fielding recurring questions while needing to respond quickly to critical operational situations.
This pressure is particularly intense in industrial environments. At DEF, the National Technical Support team handles between 350 and 400 calls per month with a staff of only 6 to 7 people. According to their manager, at least 20% of these requests involve recurring questions that experts have to answer over and over again.
As product lines, regulations, and procedures multiply, teams must absorb ever-increasing amounts of knowledge while maintaining high responsiveness. This creates a double risk: unnecessarily tying up experts with repetitive questions and slowing down the resolution of more complex issues.
How AI can help
AI allows technicians to quickly find answers within existing documentation, while still retaining the ability to consult an expert when necessary. Validated knowledge then enriches the system and becomes accessible to the entire team.
Concrete example
During commissioning, a technician needs to check the compatibility of several pieces of equipment. They ask the AI assistant and immediately receive an answer based on product guides and previous interactions. If the information is unavailable, the request is automatically routed to the right expert.
Expected results
- faster resolution times;
- fewer interruptions for experts;
- greater autonomy for field teams;
- improved transfer of technical expertise.
In 2024, I was able to free up 508 hours of technical support workload thanks to Ask DEF, allowing me to focus on other innovation initiatives and new products. Rather than spending our time providing information to technicians.
Olivier Duhoux
Technique National
3. Compliance, quality, and production support
Why expertise is critical
Industrial processes rely on complex procedures, quality standards, and regulatory requirements. Misinterpretation can lead to production errors, costs associated with poor quality, or non-compliance risks.
According to AFNOR, costs related to non-compliance and rework can account for up to 10% of a company's revenue. Properly understanding and applying standards is therefore a major challenge for both operational performance and risk management.
How AI can help
AI makes it easier to access the right procedures, methods, and reference materials. It helps teams quickly find relevant information while relying on content validated by quality and methods experts.
Real-world example
A manufacturing engineer wants to modify a production process. Before implementing the change, they ask the assistant to identify technical constraints, applicable standards, and lessons learned already documented within the company.
Expected results
- reduction in production errors;
- more consistent practices;
- faster dissemination of best practices;
- facilitated continuous improvement.
4. Supporting expertise and knowledge capitalization
Why expertise is critical
In the industry, a significant portion of know-how remains tacit: it is held by a few experts and is not always documented. When an employee leaves the company or is unavailable, accessing this expertise becomes difficult.
How AI can help
AI transforms existing documentation and validated exchanges between experts into a living knowledge base. It makes it easier to identify the right people for a given subject and secures the transfer of knowledge over time.
Concrete example
A new employee asks a complex business question. AI provides an initial answer based on past documents and exchanges. If necessary, it automatically identifies the most relevant experts to supplement or validate the answer.
Expected results
- reduced dependency on experts;
- faster onboarding;
- preservation of know-how;
- better collaboration between teams.
How to identify the best AI use cases in your industry?
Not all AI projects produce the same impact. The most profitable initiatives are generally those that address a concrete business challenge and leverage the expertise already present within the company.
Before launching a project, it is essential to identify the processes with the greatest potential for gains: improving sales, reducing processing time, ensuring compliance, or transferring knowledge.
Discover our AI use cases for the industry and speak with our experts to identify the most relevant opportunities for your organization.
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