AI in B2B: 6 Use Cases Manufacturers Can Start With Today

"We should do something with AI." We hear this sentence in almost every strategy meeting. Almost always, the same question follows: Where do we start? Between chatbot hype and future visions, it's easy to lose sight of the fact that there are already very practical AI use cases in B2B. They save time, relieve skilled workers, and can be implemented in manageable steps.

In this article, we present six use cases that have proven themselves as entry points for manufacturers and B2B companies. For each one, you'll learn what AI actually does, what benefits you can expect, and what you need to get started.

Why Start Right Now?

AI functions are now built into many systems that companies already use:

• CMS
• PIM and DAM platforms,
• Shop systems
• Office applications.
      

This has significantly lowered the entry barrier. At the same time, pressure is growing: skilled workers are scarce, customers expect faster responses, and requirements for product data are increasing, for example due to the Digital Product Passport.

Companies that start with a well-chosen use case now gain experience before AI becomes standard and can build on this knowledge.

Automatically Create Product Texts and Translations

What AI does: Based on structured product data such as features, dimensions, and application areas, AI creates drafts for product descriptions, short texts for marketplaces, or catalog texts. Optionally in multiple languages and in the appropriate tone for each channel.

Benefits: New products go online faster, texts become more consistent, and marketing no longer has to describe each variant individually.

Requirements: Clean, structured product data, ideally in a PIM system. A human reviews and approves the texts before publication.

Getting started: Well-suited as a first project, especially if a PIM is already in use.
To learn why a central product data system is the foundation for this, read our article Ending Product Data Chaos

Knowledge at the Push of a Button: The Internal AI Assistant

What AI does: An AI assistant searches manuals, data sheets, guidelines, project documentation, and the intranet, answering questions in complete sentences, including source references.

Benefits: Inside sales, technical teams, and new employees find information in seconds instead of hours. Knowledge that was previously tied to individual people becomes accessible to everyone.

Requirements: Up-to-date, well-maintained documents and a clear access control concept: The assistant may only display what the respective person is authorized to see.

Getting started: Medium effort. Start with a clearly defined knowledge area, such as the technical documentation for a product line.

Extract and Validate Product Data from Documents

What AI does: AI reads technical data from PDFs, supplier catalogs, or data sheets and transfers it into structured fields. It also identifies missing values, contradictions, or duplicates in existing datasets.

Benefits: Less manual data maintenance, better data quality, and a solid foundation for shops, dealer portals, and regulatory requirements like the Digital Product Passport 

Requirements: A clearly defined data model, meaning an answer to the question of which attributes should be maintained per product group. Spot checks by experts remain important.

Getting started: Medium effort, but often with quickly measurable results, since the time saved on maintenance can be easily quantified.

Process Inquiries and Quotes Faster

What AI does: Incoming inquiries via email or form are automatically evaluated: Which product is being requested, in what quantity, with what requirements? AI suggests matching items and prepares a quote draft.

Benefits: Shorter response times in sales and less routine work for inside sales. This makes a noticeable difference, especially for complex products with many variants.

Requirements: Access to product data, pricing logic, and possibly customer history via interfaces to ERP, CRM, or PIM. Final approval remains with sales.

Getting started: Higher integration effort. A good second or third step once initial experience has been gained.

Relieve Customer Service

What AI does: AI sorts service requests by topic and urgency, routes them to the right place, and formulates response suggestions based on the knowledge base and previous cases. On the website, an assistant can answer frequently asked questions directly.

Benefits: Recurring questions tie up less specialized staff, customers receive faster responses, and the service team can focus on complex cases.

Requirements: A well-maintained knowledge base, clear rules on when to hand over to a human, and transparency with customers that they are interacting with an AI.

Getting started: Response suggestions for the internal team are a low-risk start. The public chatbot follows once quality is assured.

Automatically Tag Images and Media

What AI does: In DAM systems, AI recognizes image content, assigns keywords, suggests alt texts, and helps with routine tasks like cutouts or creating format variants.

Benefits: Marketing and sales find images faster, media is used more consistently, and alt texts improve accessibility and SEO.

Requirements: A central DAM or brand portal and a uniform keyword structure that the AI can follow.

Getting started: Low effort, as many DAM platforms already include such functions. Often, it's enough to activate and configure them sensibly.

Which Use Case Fits Your Company?

Not every use case is the right starting point for every company.

A simple rule of thumb helps with selection:

Evaluate each idea based on benefit and feasibility.

Benefit: How much time, money, or quality do you gain? How many people benefit?

Feasibility: Are the necessary data available and well-maintained? Can the systems be integrated? Is the risk of errors manageable?

The ideal first use case: Noticeable benefit, readily available data, low risk.
This builds internal confidence for the next steps.

Key Takeaways

• AI in B2B doesn't have to be complicated: Many use cases build on systems you already use.
      
• Good entry-level projects are product texts, media tagging, and response suggestions in service.
      
• Almost all use cases stand or fall with the quality of your data.
      
• Humans remain responsible: AI provides drafts and suggestions, experts give approval.
      
• Choose your starting point based on benefit and feasibility, not hype.   

Find Out Where You Stand

Have you discovered a use case that fits your company?

Then the next step is to check whether your company is ready for it.

With our AI Readiness Check, you'll receive an initial assessment of your data, processes, systems, and organization in just a few minutes.

Start AI Readiness Check Now

Would you like to discuss a specific use case directly?
Our team of AI development and automation experts looks forward to your inquiry.

Frequently Asked Questions About AI Use Cases in B2B

Which AI use cases are suitable for getting started?

Use cases with readily available data and low risk are particularly suitable, such as creating product text drafts, tagging images in the DAM, or response suggestions for customer service.

Do we need our own developers for AI applications?

Not necessarily. Many platforms like PIM, DAM, or CMS already include AI functions. For individual solutions, such as an internal knowledge assistant or integration with ERP and CRM, development know-how is useful, either internally or through a partner.

How long does it take for an AI use case to become productive?

This depends heavily on the use case. Functions already available in existing systems can often be used within a few weeks. Integration projects involving multiple systems take correspondingly longer. 

Is AI-generated content bad for SEO?

Not fundamentally. What matters is that content is accurate, helpful, and tailored to the target audience. Therefore, AI texts should always be reviewed for accuracy and edited editorially.

What happens to our data when we use AI?

This depends on the service and contract used. Pay attention to providers with clear data protection rules, optionally with hosting in Europe, and define internally which data may be entered into which tools.