Put experience to work. Help customers more effectively.
Technical service connects people, equipment and information. AI can support preparation, make existing solutions easier to find and turn service information into useful insights for other functions.
How quickly can your service team turn a customer report into the right information?
USE CASE EXAMPLES
How AI could help in everyday work.
Illustrative scenarios. Requirements and value are assessed for your specific process.
01
TICKET ASSISTANCE
Structure and route service enquiries
THE STARTING POINT
Customer enquiries vary in language, length and completeness. Product references, issue categories and missing details have to be identified manually.
A POSSIBLE APPROACH
AI supports classification and prepares a structured summary. Missing information is highlighted for follow-up with the customer.
WHAT YOU NEED
Historical tickets, agreed categories, routing rules and reliable product or equipment identifiers.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
Text classification · Entity extraction · Routing rules
What does the AI actually do?
AI identifies intent, language, product references and missing information in incoming service text.
How the solution works
01
Structure the request
A language model or classifier extracts facts and proposes a category.
02
Check the match
Product master data and fixed escalation rules validate the proposal.
03
Suggest a team
Routing software proposes the responsible group; uncertain or critical cases go to manual triage.
What value can it deliver?
Less intake effort and fewer transfers, with a more complete case from the start.
A concrete example
An asset ID and message are extracted from a long email; the missing software version becomes a proposed follow-up.
Technology, setup & operation+
A pretrained LLM can start a pilot; a trained classifier is an alternative for high-volume stable categories. Evaluate routing per category using confirmed assignments.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Trial frequent enquiry categories for one product line with the responsible service team.
HOW TO ASSESS VALUE
Evaluate routing quality, the number of transfers and time to a complete initial case record.
Urgent or safety-related reports follow defined escalation rules and are assessed by qualified personnel.
02
ASSISTANCE FOR SERVICE AGENTS
Bring service knowledge into the conversation
THE STARTING POINT
The answer is often known, but sits in manuals, service articles or previous cases. During a customer conversation, there is little time to search.
A POSSIBLE APPROACH
A source-based assistant finds relevant information and prepares suggested answers. The service team checks each answer in the context of the equipment and customer.
AI retrieves service knowledge during case handling and drafts a response for the specific customer context.
How the solution works
01
Set context
Asset revision, customer relationship and question constrain retrieval.
02
Find evidence
Hybrid search retrieves valid articles and manual passages.
03
Draft a response
An LLM turns passages into a proposed answer with sources and open questions.
What value can it deliver?
Less searching during conversations and more consistent responses; useful evidence can reduce handling time.
A concrete example
A configuration question returns the article for the installed software revision and a reviewable draft.
Technology, setup & operation+
Combine a pretrained LLM, maintained index and service interface. Experienced staff assess source quality, revision accuracy and correction effort.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Start with a defined knowledge base and a small group of experienced service staff as reviewers.
HOW TO ASSESS VALUE
Use typical enquiries to assess search effort, technical accuracy and usefulness during conversations.
Customer communications and technical recommendations are reviewed by service staff before being shared.
03
VOICE OF CUSTOMER
Identify recurring themes in service cases
THE STARTING POINT
Valuable feedback remains in individual tickets and reports. Product management and quality often only see isolated cases.
A POSSIBLE APPROACH
AI can group descriptions by theme and present recurring issues with traceable example cases.
WHAT YOU NEED
Suitable ticket and report data, agreed use of customer information and clearly defined topic categories.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
NLP · Embeddings · Topic clustering
What does the AI actually do?
AI groups differently worded service descriptions by meaning and proposes readable topic labels.
How the solution works
01
Prepare cases
Link repeated contacts to their case where possible; retain product and time context.
02
Group similar text
Embeddings and clustering form topic groups. An LLM can name them from representative passages.
03
Compare themes
Show counts, trends and source cases together for expert review.
What value can it deliver?
Less manual analysis and earlier visibility of recurring themes for product and quality teams.
A concrete example
“Will not start after update” and “stuck during boot” become a reviewable topic group.
Technology, setup & operation+
Clustering does not inherently require prelabelled cases. Evaluate group coherence and stability. Ticket counts without a denominator are not reliable failure rates.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Analyse one product area over a defined period with service and product management.
HOW TO ASSESS VALUE
Compare the usefulness of the overview, classification quality and time spent on analysis.
Frequent reports are a signal for investigation. They do not replace root cause analysis or a representative customer survey.
MY CONTRIBUTION
Work out together what is worth pursuing.
I help you turn an interesting use case into a clear, assessable business task.
Structure needs and objectives with business functions and IT
Clarify value, requirements and open questions
Prepare a basis for decisions and define a possible pilot scope
Support alignment with suitable implementation partners
FREQUENTLY ASKED QUESTIONS
Before taking the first step.
Does a chatbot have to speak directly to customers? +
No. An internal assistant for the service team can be a sensible starting point. Experienced staff can then evaluate knowledge quality, suggested answers and workflows together.
How does Customer Success fit in? +
Information from product usage, service and customer conversations can help teams understand needs and unresolved issues. Suitable data and responsibility for follow-up are defined within the customer process.
FROM A USE CASE TO THE NEXT STEP
Which idea would you like to make concrete?
Together, we clarify the starting point, possible use cases and expected value, creating a sound basis for your next decisions.