From use case to a deliverable AI initiative.
We help you select the right initiatives from a wide range of AI ideas. In the AI Transformation Lab, we assess value, feasibility, data availability, and risks—and develop a prioritized roadmap with a business case and concrete implementation steps.
What the lab delivers
Which AI ideas truly have priority right now?
Before evaluating technology, we create transparency: Which use cases are realistic? What data is required? What risks exist—and where is measurable value created?
The AI Transformation Lab is a structured way of working that enables organizations to identify, from a wide range of AI ideas, those that can be meaningfully prioritized and realistically implemented.
Lab results
- Prioritized use-case roadmap with value assessment
- Technical feasibility assessment per use case
- Data quality and requirements analysis
- Shared target vision for IT and business units
- Recommendation for platform, governance, and initial implementation steps
- Robust business case as a basis for decision-making
Target audience
The lab is particularly suitable for you if…
You want to evaluate use cases in a structured manner
Initial in-house AI ideas exist, but there is not yet a clear prioritization or implementation strategy.
IT and business departments should agree
Different expectations of AI between technology and business require a shared foundation.
You need a business case for AI investments
Decision-makers need reliable figures before budget is approved for building the platform.
You want to establish a robust AI platform
The lab lays the foundation for Sovereign AI, governance, and automation—as the first structured step.
Lab phases
Four phases—from idea to implementation
In the first step, we capture all existing AI ideas across the organization—structured by business unit, process, and problem type. Together with you, we create a complete overview of potentials and starting points.
- Stakeholder interviews in business units and IT
- Process map and use-case catalog
- Initial assessment by effort and potential
- Identification of critical data situations
Sample output
USE CASE #12 – HR
USE CASE #3 – Purchasing
Contract summarization & review
Potential: high · Complexity: medium · Data: available
Evaluate Use Cases by Potential
Not all ideas are equally valuable. In Phase 2, we assess each use case based on benefit potential, technical feasibility, data availability, and risk level—and recommend clear prioritization.
- Scoring by Effort, Benefit, and Risk
- Data and System Requirements Analysis
- AI Model Type Assessment per Use Case
- Prioritized Shortlist for Prototyping
Prioritization matrix
Proposal Preparation
Contract Review
Applicant Screening
High
High
Medium
1
2
3
Build and test prototypes
For the prioritized use cases, we develop rapid prototypes—using real business data and real users. The goal is not perfection, but reliable insights into feasibility and value.
- Rapid prototyping with suitable models
- Testing with real data and users from the business department
- Quality assessment and hallucination checks
- Feedback loop and iteration cycles
Prototype sprint
Example result
Sales quote preparation
Based on the prototypes, we develop a concrete implementation plan including technology recommendations, resource requirements, governance requirements, and a cost-effectiveness analysis.
- Technology selection and make-or-buy recommendation
- Resource and timeline planning
- Governance requirements and role model
- Investment appraisal and ROI scenarios
Deliverables
- Use-case roadmap (prioritized)
- Technology recommendation (including justification)
- Business case including ROI model
- Governance requirements (basis for AI governance)
- Handover package for implementation project
Practical examples
Typical use cases in the mid-market
The lab works with real-world application areas. These examples show where organizations typically start.
- Sales & Marketing
Proposal and presentation preparation
AI-supported creation of initial proposals, presentation structures, and product descriptions based on existing documents and CRM data.
- Legal & Procurement
Contract summarization & risk review
Automated extraction of relevant clauses, deadline detection, and risk flagging in supplier and customer contracts.
- Human Resources
Application screening
Structured screening of application documents with relevance scoring and short summaries for recruiters—with clear human-in-the-loop logic.
- Finance & Controlling
Report creation & commentary
Automated generation of report narratives, variance comments, and executive summaries based on structured database queries.
- IT & Service
Internal knowledge management
AI-supported search and retrieval of internal documentation, SOPs, and knowledge articles—with reliable source citations.
- M&A & Strategy
Due diligence document analysis
Structured analysis of large volumes of documents in the data room—extracting relevant KPIs, risks, and key information in a short time.
Start with structured use-case prioritization
We will discuss with you which AI initiatives in your organization have the greatest potential—and how you can reach robust implementation decisions within 8–12 weeks.
Competent Advice at Your Side
Our Expert for Your Concerns
Thomas Pietrzykowski supports organizations in not only positioning AI strategically, but also making it productively usable. His focus is on developing pragmatic AI architectures, evaluating relevant use cases, and implementing secure, scalable solutions across existing business processes.
With 25 years of experience in software engineering, enterprise architecture, cloud, DevOps, and digital transformation, he combines technological depth with operational implementation experience. He is familiar with modern AI platforms, automation tools, and integration approaches not just from consulting, but from direct practical application—from prototyping and system integration to governance, operations, and scaling.
His strength lies in translating business requirements into actionable technical solutions. In doing so, he brings international leadership experience, experience in regulated environments, and a deep understanding of data, interfaces, security, and operating models.
