AI in business practice
Which use cases create real value? How do data become reliable answers, and prototypes become securely integrated systems? Find concrete examples and informed answers to plan your AI project.
Three application areas
A good case study starts with a clear task. These examples show how AI can help employees day to day without promising more than can be reliably verified.
Knowledge management · RAG
Image recognition · Service process
AI agent · Support
Questions & answers
Key questions about value, feasibility, data, security and production operations.
An AI case study documents a concrete business challenge, the chosen solution, integration into processes and systems and verifiable results, beyond a model or demo.
It is meaningful when prerequisites, quality criteria and limitations are visible, helping you assess what applies to your business.
Start with clearly bounded, frequent, knowledge-intensive tasks: document search and review, service enquiries, classification, image recognition or preparing steps across systems.
Technology alone is not decisive. Assess expected value, data quality, frequency, error risk and integration effort.
Metrics come from the process and are set before development. Depending on the application, they may include handling time, accuracy, answer quality, resolution rate, error rate, adoption or the share of correctly prepared cases.
A baseline, representative tests and expert acceptance make assessment reliable. For generative AI, “sounds good” is not enough.
It mainly depends on data access, process scope, interfaces and protection needs. A bounded prototype can often demonstrate viability within a few weeks.
Production also needs testing, roles and permissions, robust interfaces, monitoring, documentation and employee involvement. Planning should distinguish prototyping from production rollout.
Protection is designed for the use case, including data minimisation, controlled access, suitable deployment, encryption, logging and clear retention rules.
We select models and providers by task and protection requirements. Where errors could have serious consequences, we add safeguards and human approvals.
Yes. Through APIs and secure interfaces, AI can use system information, return results or prepare defined work steps.
AI receives only necessary permissions. Existing roles, access rules and checks remain authoritative; critical actions can require human confirmation.
A prototype tests feasibility: does the approach work with realistic data and defined test cases? Its scope may deliberately be limited.
Operations add reliable interfaces, identities, permissions, quality monitoring, error handling, privacy, documentation and clear responsibilities. These unseen elements turn a demo into a dependable system.
RAG suits answers based on approved, regularly changing company sources. Before answering, the system retrieves relevant content and provides it as context to the language model.
That does not automatically make answers correct. Sources, access controls, good documents and systematic evaluation remain essential.
Agents suit goals requiring several traceable steps, such as finding information, checking a case and preparing an action. Tools, rights and stopping rules must be narrowly defined.
For fixed, fully rule-based workflows, conventional automation is often simpler and more reliable. Combining workflows, software logic and AI may be best.
Employees know exceptions, quality standards and real bottlenecks. Their experience helps select use cases, build tests and assess results.
Early involvement improves usability and adoption. The aim is a sensible division of tasks between people and systems.
Use case selection
The best projects combine a real bottleneck with accessible data, measurable quality and manageable risk.
The task regularly consumes time, causes errors or prevents effective use of knowledge.
Required documents, images or case data are available, lawfully usable and sufficiently representative.
Quality and business value can be assessed with concrete tests and metrics.
Error consequences are understood and controlled through roles, safeguards and human approvals.
The path to reliable results
Clarify the problem, participants and goals together.
Evaluate with realistic data and measurable criteria.
Make interfaces, permissions and approvals production-ready.
Monitor usage and results and improve continuously.
Your use case
You do not need to know the technology yet. Bring your process: together, we assess value, feasibility and a secure path to production.
