Usable AI @ Business

The AI Potential Workshop helps you identify where AI can create real added value in your organization—and how to get started in a meaningful, human-centered way. Together, we identify opportunities, evaluate benefits and feasibility, and develop prioritized recommendations for the next steps.

Phase 1: Recognizing potential

We provide guidance, analyze processes and tasks, and examine where AI can provide meaningful support.

Process:

• Brief introduction to trends and practical examples
• Analysis of processes, data flows, and pain points
• Evaluation and prioritization of initial ideas

Result:

A clearly documented overview with initial areas of potential and a cost-benefit assessment.

Phase 2: Concrete ideas

We explore selected approaches in greater depth and examine their technical and organizational feasibility.

Procedure:

• Process and pain point analysis
• Development of realistic AI scenarios
• Examination of the data situation and prioritization

Result:

A structured report with prioritized use cases and an assessment of the effort, benefits, and feasibility.

Your benefits

• Clear, structured introduction to the topic of AI
• Well-founded basis for investment decisions
• Early involvement of employees
• Identification of key productivity and knowledge potential
• Scientifically sound, human-centered approach

From impulse to implementation

After the potential workshop, you will know exactly

• where AI makes sense
• which processes offer the greatest potential
• and how to plan the next steps in a targeted manner.

This will lay the foundation for an effective, human-centered AI strategy.

To determine what your AI solution should achieve in a specific area of application, we begin with a precise analysis of activities and tasks. In doing so, we identify which work steps are suitable for AI support and how humans can remain meaningfully involved—whether as actors, supervisors, or completely relieved of their duties.

On this basis, we create an AI requirements specification: a clearly structured document that describes what the AI should do, how automated it can be, what quality requirements apply, and which interfaces need to be taken into account.

You receive an actionable blueprint for technical teams, enabling targeted, efficient, and high-quality implementation.

We develop and implement AI solutions that fit your systems, your data, and your economic conditions. In doing so, we make sure to make optimal use of existing hardware and set up solutions in a cost-efficient and resource-saving manner.

Our focus

• Data sovereignty and data protection
• IT security and reliable system stability
• Efficient use of existing infrastructure
• Future-proof, scalable architectures

We provide independent advice on whether a local installation (on premise), a cloud setup, or a hybrid architecture makes the most sense for your application—and accompany you from technical planning to productive implementation.

We evaluate AI solutions based on a scientifically developed, user-centered quality model that we ourselves researched and validated at Fraunhofer. Traditional usability methods are not sufficient for generative, probabilistic AI systems – that's why we have developed our own approach that takes the special features of GenAI into account.

Our focus

• Qualitative analysis of real-world usage situations and work contexts
• Quantitative measurement using our empirically validated quality model
• Evaluation of key quality dimensions
  • Productivity of responses
  • Trustworthiness
  • Contextual fit
  • Technical appropriateness
  • Operational usability
• Identification of strengths, weaknesses, and potential for optimization
• Concrete recommendations for action to improve response quality, interaction, and human-AI collaboration

To do this, we use a mixed-methods approach developed in-house that is based on extensive research – including over 2,000 employee surveys, interviews, content analyses, and benchmarking validation studies (EFA, KMO, Cronbach's Alpha).

The result is a practical, scientifically sound quality diagnosis that can be used to improve existing AI systems in a targeted manner, make them more secure, and optimally adapt them to user requirements.