GenAI Integration: From Use Case to Implementation
Generative AI is transforming business processes. The challenge isn't the technology itself but the structured evaluation of use cases, integration into existing workflows, and governance for AI workloads. I guide organizations from use case identification through feasibility assessment to an implementation roadmap.
When to Engage
- GenAI potential identified, but no structured use case evaluation in place
- Pilot project planned, but unclear requirements around data, compliance, and operations
- AI tool already in use, but missing governance for costs, quality, and risks
- Business side wants to adopt GenAI, IT sees open questions on data privacy and integration
Deliverables
Governance & Controls
Use Case Prioritization: Structured evaluation of GenAI use cases by feasibility, benefit, and risk. Decision basis for investment.
AI FinOps: Transparency on token costs, API usage, and infrastructure spend. Budget control and optimization opportunities.
Responsible AI: Documented decisions on model selection, data privacy, and quality controls. Traceable for audits and compliance.
Compliance & Data Privacy Alignment
GenAI projects touch data privacy, IT security, and regulatory requirements. I work closely with compliance and data privacy teams to:
- Clarify data processing and privacy requirements early
- Document model selection and vendor governance traceably
- Assess AI-specific risks (hallucinations, bias, vendor lock-in) systematically
Project contexts are anonymized. Roles and results are truthful; details available under NDA.
Project Example (Anonymized)
GenAI Pilot with Use Case Evaluation and Roadmap
Mid-sized Enterprise (Germany) - Process Optimization
Challenge: Multiple business units wanted to adopt GenAI, but there was no structured evaluation of use cases and no clear path from pilot to production.
Role: Business Analyst and Project Manager for use case evaluation, requirements specification, and implementation roadmap
Results:
- Use case portfolio with prioritized applications and feasibility assessment created
- Requirements specification for the first pilot defined and agreed
- FinOps framework for AI cost governance established
- Implementation roadmap with three phases and clear decision gates handed over
Note: The project examples presented are drawn from previous roles in consulting and industry. All contexts have been anonymized; roles and outcomes are described accurately.
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Last updated: February 2026