Recognizing Potential in Your Own Processes
It’s long been known that artificial intelligence can take over tasks and make processes more efficient. But where does it pay off most in your company? Together with your business departments and leadership, we analyze existing business processes, alongside the people who live them every day. This way, we identify and prioritize concrete AI use cases that enable noticeable efficiency gains or create new possibilities. No theoretical scenarios. No generic hype lists. Instead, AI solutions with real value for your company.
AI Thinking Workshops at a Glance
What?
In structured workshops, we bring together business users, process owners and leadership. Together we look at your actual business processes and specifically identify where AI reduces effort, improves quality or opens up new possibilities. The result: a prioritized selection of concrete use cases with clear business value.
When?
AI Thinking Workshops are the ideal starting point when your company wants to move beyond initial AI experiments but it’s not yet clear where the biggest levers are. They also help align different stakeholders around a shared goal or back new initiatives with a convincing business case.
Why?
AI transformation succeeds when the right use cases are recognized and consistently prioritized. To achieve this, we combine our methodological expertise in process analysis and AI with the domain knowledge of your teams. This creates concrete initiatives with real value and high internal acceptance, because the people who will later work with it are involved from the start.
Workshop Flow
A typical AI Thinking Workshop lasts one to two days and follows a clear, field-tested process. At the same time, the format leaves enough room to address the specific questions and constraints of your company.
00
Preparation & Process Selection
In advance, we jointly define the content focus and the right group of participants. We then select two to four central business processes to be analyzed in detail during the workshop.
01
Process Mapping
In a structured kick-off, we look at today’s workflows together with the process owners: What already works well? Which steps cause excessive effort? Where do errors occur or information get lost? This creates a shared understanding of the current state as a solid basis for the subsequent potential analysis.
02
Use-Case Development
Based on the process analysis, we jointly develop concrete AI use cases. We clearly distinguish between automation potential, support scenarios and new capabilities. We assess each use case by effort, impact and technical feasibility.
03
Prioritization & Business Case
We then prioritize the developed use cases and back them with a solid business case. Together we formulate realistic assumptions about benefit, effort and risk that can be communicated and defended internally, without overblown promises or underestimated complexity.
04
Change Roadmap
Finally, we develop a concrete roadmap for implementation. Which use cases are tackled in which order? Who takes responsibility? And which steps can start right away? On request, we also support you in implementing the first initiatives as part of an AI Engineering or Agentic Engineering coaching.
AI Transformation Journey
Placement in the Journey
This service is part of the embarc AI Transformation Framework. The highlighted tile shows where in the journey it fits in – and which other building blocks complement it well.
01
Orientation & Exploration
Outcome: Shared vocabulary, first experience, prioritized use cases
Building Knowledge
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Value Hypotheses, Risk Identification
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Safe Experimentation Spaces, Creating Guardrails
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02
Team Pilots & Validation
Outcome: proven usage patterns and a solid state of practice for scaling
Measurable Value
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Evaluating Development Practices, Agentic SDLC
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Reference Use Cases, Deployment in Real-World Environments
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03
Platformization & Controlled Scaling
Outcome: A "Golden Path" for teams, on which agents can work within clear boundaries.
Architecture, Quality & Observability
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Professionalization, Security, Governance & Compliance, AI-FinOps
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AI Development Platform (Context, Harness, Hooks and Processes)
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04
AI-Native Product Organization
Outcome: A new "AI Engineering Operating Model" for the learning, AI-native product organization.
New Role Models, Skills & Career Paths
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New Organizational Structures: Teams, Communities, AI Hubs
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Intent-Orientation and Outcome-Based Steering (incl. Continuous Validation)
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