Use casesWhere AI concretely helps in mid-sized companies

Anonymised application scenarios from service, sales, procurement, accounting, quality and administration. Every case names starting point, solution, possible benefit and the technology used. No invented figures, those only emerge in the pilot.

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Scenarios by solution

Sixteen cases, six solutions, one filter

Filter by solution and expand the cases closest to your daily work. Every case leads to the matching solution page.

16 scenarios

Service and maintenance

Search technical documentation with AI

Starting point: Service staff regularly search dozens of PDFs, manuals and maintenance logs for the right piece of information. For older machines, often only one colleague knows where to look.
Solution: The existing documents are loaded into a private AI knowledge base. Staff ask their question in plain language and get an answer with a reference to the source, including the page.
Possible benefit: Less time searching, faster answers in the field and less dependence on individual experts. The knowledge stays in-house because the system runs locally.
Possible technology: Open WebUI, local language model, RAG with ChromaDB

To the solution: Enterprise knowledge

Quality management

Query standards, work instructions and inspection specifications

Starting point: Work instructions, inspection plans and excerpts from standards exist in several versions on file shares. Before audits, days are spent finding out which version applies.
Solution: A knowledge system with version logic: only released documents enter the knowledge base, and every answer names document, version and section.
Possible benefit: Reliable answers for the departments, fewer questions to QM and a solid basis for audits.
Possible technology: Open WebUI, RAG with metadata filters, local language model

To the solution: Enterprise knowledge

HR and administration

Onboard new employees with an internal assistant

Starting point: New colleagues ask the same questions about processes, contacts and rules in their first weeks. The answers sit in intranet pages and handbooks that hardly anyone reads.
Solution: An internal assistant answers questions from the organisation manual, process descriptions and policies and points to the responsible contact.
Possible benefit: Faster onboarding, fewer interruptions for experienced colleagues and consistent answers.
Possible technology: Open WebUI with permissions, RAG, local language model

To the solution: Enterprise knowledge

Order processing

Read the inbox, classify and hand over to ERP and CRM

Starting point: Orders, complaints and shipping notices arrive as free-text emails with attachments. Staff read, retype and create cases in ERP and CRM by hand.
Solution: A language model reads the message, extracts customer number, case type, quantity and date and classifies the case. A workflow creates the record. Anything binding waits for approval.
Possible benefit: Less manual entry, fewer transcription errors, clear priorities in the inbox. Control stays with the staff.
Possible technology: n8n, local language model for extraction and classification, ERP and CRM APIs

To the solution: Process automation

Controlling and management

Compile recurring reports from several sources

Starting point: Every month, figures from ERP, spreadsheets and CRM are gathered, commented and turned into a presentation. Two days of work, often under time pressure at month end.
Solution: A workflow pulls the data together automatically, and a language model drafts the notable deviations. The professional judgement stays with the controller.
Possible benefit: The report is ready as a draft on the first working day. The time saved goes into analysis instead of collection.
Possible technology: n8n, database and spreadsheet connectors, local language model

To the solution: Process automation

Sales

Pre-qualify enquiries and prepare quotation drafts

Starting point: Enquiries from website, email and phone land unsorted in sales. For every quotation, master data, previous quotes and price lists are gathered by hand.
Solution: Incoming enquiries are captured in a structured way, checked against existing customer data and prepared as a quotation draft with matching text blocks and price references.
Possible benefit: Faster response time, consistent quotations and more time for the actual customer conversation.
Possible technology: n8n, CRM API, RAG on quotation history, local language model

To the solution: Process automation

Procurement and legal

Analyse contracts and tenders confidentially

Starting point: Supply contracts, framework agreements and tender documents contain prices and terms that do not belong in a cloud AI. Still, a quick summary and clause check would help.
Solution: A locally run language model summarises documents and flags deadlines, liability clauses and deviations from standard terms. The documents never leave the network.
Possible benefit: A faster overview of extensive documents with full data control. The legal assessment stays with the experts.
Possible technology: Local language model with large context window, Open WebUI, document pipeline

To the solution: Local AI

IT and management

Replace shadow AI with an approved internal offering

Starting point: Employees use private AI accounts for translations, texts and analyses. IT knows but cannot control it. A ban does not work in practice.
Solution: An internal AI portal with a local model, login via the company directory and clear rules on which data may go in. Plus a data classification that justifies the approval.
Possible benefit: Usage becomes visible and compliant. Employees get a tool they are actually allowed to use.
Possible technology: Open WebUI with directory integration, Ollama, local language model

To the solution: Local AI

Customer service

Process customer correspondence containing personal data

Starting point: Service wants AI to prepare answers to customer enquiries. The enquiries contain names, addresses and contract data that must not go to external services without a processing agreement under GDPR.
Solution: Answer drafts are created with a local model that draws on text blocks and product knowledge. No data transfer to third parties, no processing agreement needed.
Possible benefit: Shorter handling time per enquiry, a consistent tone and a solution the data protection officer can support.
Possible technology: Local language model, Open WebUI, RAG on product knowledge

To the solution: Local AI

Management, cross-functional

From a backlog of ideas to three prioritised use cases

Starting point: AI ideas come from every department, none gets implemented. What is missing is a criterion for what pays off, what is legally possible and where to start.
Solution: A workshop with business and IT: review processes, clarify data availability and data class, rank effort and benefit. The result is three assessed use cases with a first pilot proposal.
Possible benefit: A basis for decision that business, IT and management stand behind. The first pilot starts with a clear goal.
Possible technology: Method rather than tooling: process map, data classification, assessment grid

To the solution: AI assessment

IT and organisation

Check AI readiness before investing

Starting point: A proposal for an AI solution is on the table. It is unclear whether data, infrastructure and organisation are ready or whether the project will fizzle out after the pilot.
Solution: A structured review of data quality, system landscape, responsibilities and legal conditions. With a clear recommendation: start, prepare or take a different approach.
Possible benefit: An investment decision based on the actual prerequisites rather than on the vendor's presentation.
Possible technology: Assessment grid, interviews, review of the system landscape

To the solution: AI assessment

Management and IT

Test local AI with real documents in four weeks

Starting point: The company wants to know whether local AI delivers useful answers on its own documents, without buying servers first and spending months on setup.
Solution: A pre-configured appliance arrives, is connected to the network and loaded with a defined set of documents. A guided onboarding for IT and business, then four weeks of hands-on testing.
Possible benefit: A sound assessment based on your own data, a clear picture of the hardware needed and a recommendation for the next step.
Possible technology: AI Appliance with Open WebUI, Ollama, RAG, local language model

To the solution: AI Appliance

Leadership team

Make AI tangible for the leadership team

Starting point: The leadership team discusses AI on slides. Nobody has experienced the tool with their own questions and documents, so expectations stay vague.
Solution: A workshop at the appliance: your own documents, your own questions, visible limits. Followed by a shared assessment of what is realistic.
Possible benefit: A shared, experience-based picture of possibilities and limits. Decisions become more concrete.
Possible technology: AI Appliance, Open WebUI, RAG on the documents you bring

To the solution: AI Appliance

Customer service and quality

Record complaints, check them and present them for decision

Starting point: Complaints arrive by email and phone, are transferred into a form, matched against order data and passed on to quality assurance. Every handover costs a day.
Solution: An agent reads the complaint, pulls order and delivery records from the ERP, checks deadlines and warranty status and presents a prepared case with a recommendation. A person decides.
Possible benefit: Shorter lead time, complete cases on first contact with quality assurance, a traceable log of every step.
Possible technology: n8n as orchestration, local language model, ERP API, approval step before every change

To the solution: AI agents

Procurement

Monitor supplier notices and flag the need for action

Starting point: Delivery date changes, price adjustments and quality notices from suppliers arrive via portals and emails. Procurement often notices deviations only when production asks.
Solution: An agent collects notices, compares them with orders and requirements and raises alerts by urgency. Read and prepare only, no independent changes to orders.
Possible benefit: Deviations become visible early, procurement acts instead of reacting. The agent's permissions stay deliberately narrow.
Possible technology: n8n, portal and email integration, local language model, ERP read access

To the solution: AI agents

Accounting

Check incoming invoices and prepare them for approval

Starting point: Invoices arrive as PDFs by email, are printed or retyped, matched with order and delivery note and forwarded for approval. Missing order numbers cause follow-up questions.
Solution: An agent extracts the invoice data, matches it with orders, detects deviations and presents the invoice with a verification note for approval.
Possible benefit: Less manual entry, faster approvals and documented verification steps. The approval itself stays with the responsible people.
Possible technology: n8n, document extraction with a local language model, ERP API

To the solution: AI agents

Next step

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