SAP IS-U Experts in Munich

• Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
• Implementation of automated end-to-end cloud processes
• Development of LLM and NLP models
• Creation of interactive reports
• Support for national and international large corporations as well as medium-sized companies in implementing ML projects

• Lead AI readiness and process assessments across HR and Finance, identify automation opportunities, develop ROI business cases and translate findings into prioritized recommendations for executive decision-making.
• Built CloudMcFly's internal AI operating system in self-hosted n8n, with a 22-strong digital workforce across seven business functions and an estimated delivery capacity equivalent to three to five people. A separate document-classification workflow reduced manual HR ticket routing by 60%.
• Translate EU AI Act requirements into practical governance roadmaps and deliver "AI Driving License" workshops, strengthening leadership understanding of AI adoption and governance responsibilities.

AI Assistance and Target Vision for Partially Autonomous Energy Portfolio Management

Building an AI control layer directly on the up-to-date daily live portfolio of a large energy provider — not as an isolated pilot, but as an operational extension of the existing DB1 and portfolio management. The goal is the gradual development from assistance through monitoring/alerting to analysis agents with Human-in-the-Loop approvals, supplemented by a role-specific System of Engagement alongside the BI System of Record. At the same time, the business case, target vision and management pitch compared with static monthly reporting are being developed.

• AI control layer: Design and build on the existing live portfolio data product (several million contracts) — development stages assistance → monitoring/alerting → agents with Human-in-the-Loop approvals.
• LLM-supported data analysis: Semantic queries, SQL/tool integration and additional RAG components based on portfolio, plan-versus-actual and data quality data (Azure OpenAI, Snowflake), with drill-downs to individual contract level.
• Analysis agents: Multi-stage agents for variance and driver analyses of churn, price adjustments, volumes and procurement costs.
• Views concept: Role-specific interfaces for business units, management and C-level as a System of Engagement alongside the BI System of Record.
• Business case & pitch: Target vision and cost-benefit argumentation compared with static monthly reporting.
• LLM setup (privacy & security): Coordination with IT Security and Data Protection — EU region, data separation and approval processes.
• Agent architecture: Multi-stage agent pipelines (analysis → validation → summary) with documented data sources, tool calls, review steps and source references for each statement.
• Data foundation: Built on the up-to-date daily DB1 data product (Snowflake, dbt) — portfolio, plan-versus-actual and data quality metrics as the common basis for all AI analyses.
• Guardrails & evaluation: Evaluation and approval processes for LLM responses relating to management-relevant statements — test sets, metrics and human review.
• Prototyping & validation: Iterative validation of agent responses with the business unit — test question catalogue, feedback loops and response quality for each release.
• Roadmap & development stages: Detailed stages from assistance → monitoring/alerting → partially autonomous management, including transition criteria and governance for each stage.
• Integration: Integration into the existing BI and data landscape — BI remains the System of Record, while the AI layer provides interactive drill-down paths as the System of Engagement.
• Enablement: Enablement of business users — prompting guides, training and an operating model for ongoing use.
• Management: Coordination of business units, Data Engineering, IT Security and Data Protection.
• Change Management: Communication and expectation management with business units and management throughout the development stages.

Results:

• Built on an existing up-to-date daily data product with several million contracts
• Established an LLM setup coordinated with Data Protection and IT Security in the EU region, including data separation and approvals
• Defined three development stages through to partially autonomous management
• Designed role-specific views for business units, management and C-level
• Developed the business case and management pitch for the development stages
• Established an iterative response-quality validation process with the business unit
• Designed the operating model for assistance operations and initiated validation

Stack: Azure OpenAI, Azure AI Foundry, Snowflake, dbt, React, TypeScript, Entra ID, RAG, Agentic AI, analysis agents, Human-in-the-Loop, Prompt Engineering, LLM Evaluation, LLMOps, GDPR / EU region, Azure DevOps, Python, SQL, Change Management

• Overall process definition, planning, training, tools, effective execution, and progress and quality measurement of test activities at the release level
• Defining and improving the test process
• Scheduling of releases
• Coordinating alignment with release management for test setup and data provisioning
• Coordinating migration test planning for Westnetz, Mitnetz, and EON Grid
• Monitoring test progress, audit-proof documentation, progress tracking via defects, OSS reports, and incidents
• Designing and coordinating potential test automation

IT environment: Jira Zephyr, Confluence, SIGMA, PowerBI

Streamline – implementation of S/4 HANA Utilities in cooperation with Accenture

• Consulting and coaching of program management (OPL)

• Planning and support in executing all organizational and system technical activities to migrate from SAP IS-U to SAP S/4 HANA Utilities

• Communication and coordination with involved stakeholders and committees (core team, program management, steering committee)

• Project and process management

• Coaching of the stream leads in the main project

• Requirements management for interface connection of ancillary systems (non-SAP systems)

• Risk management

• Resource and capacity planning

• Consulting the existing migration, test, interface/ancillary system, and training teams

• Consulting on managing the SAP systems cut-over

• Planning and management including necessary post-migration activities (e.g., verification, documentation, adjustments)

• Team coaching & servant leadership: supporting one or more Scrum teams as coach, facilitator, and servant leader

• Promoting self-organization, personal responsibility, and continuous improvement

• Facilitation and continuous improvement of Scrum events (daily, planning, review, retrospective)

• Ensuring adherence to Scrum values, principles, and practices

• Identifying and actively managing impediments to ensure team productivity

• Working with product owners, agile coaches, and leaders to resolve structural hurdles

• Supporting agile scaling in frameworks such as SAFe, LeSS, or Nexus

• Co-designing agile transitions as part of MVV's digital strategy

• Building an agile mindset in the organization through training, workshops, and active role modeling of agile principles

• Collaborating with other Scrum Masters and agile coaches in the company-wide agile chapter

• IT systems: ERP: SAP IS-U, SAP S/4 HANA Utilities

• Cleaning up the SOD’s
• SAP checks for the audit