AI Agents Experts in Austria

Organizational Change Management for the strategic setup of a hybrid GBS rollout as part of a global, AI-driven Finance Transformation. End-to-end management of the OCM architecture in a highly complex on-, near-, and offshore partner setup [IN, PL, RO]. Successful completion of Phase 1 (Initial Strategy) and strategic pausing by the Executive Board after the early disclosure of critical vendor-side OPEX risks

• Systemic Baseline Assessment & Stakeholder Diagnostics: Evaluation of the global organization and analysis of the process landscape across 21 countries [AT, DE, CH, FR, UK, SI, RO, UA, RU, US, CD, CN, AR, MX, ZA, IN, JP] to precisely determine change requirements

• Architecting the OCM Strategy & Master Roadmap: Independent development of a comprehensive, AI-centered Change Roadmap for end-to-end management of all change initiatives across every project phase

• Data Quality & AI Agent Risk Mitigation: Auditing of Process Data Repositories (Celonis analysis); prevention of the ingestion of 58% synthetic training data to safeguard AI model reliability amid geopolitical supply chain tensions

• GBS Vendor BAFO Triage & Contractual Commitment: Critical review of Tier-1 vendor pitches and contractual commitment to a reduced Non-STP rate (from 7% to 4%) to avoid extreme manual rework with a planned FTE reduction (-63%)

• Executive Enablement & Board Alignment: Design of the GBS Transformation Executive Board Deck to strategically enable C-level executives and transparently present completely overlooked AI tokenization costs

• Targeted Change Communications & Engagement: Design and development of audience-specific communication measures to align the highly diverse, globally distributed stakeholder network

• Adoption Velocity & Resistance Measurement: Engineering of quantitative measurement tools (Pulse Check Survey) using Behavioral Dyads to identify resistance based on data and ensure sustainable success measurement

Results:

• Avoidance of disruptive misallocations: Stopping the ingestion of 58% synthetic AI-agent training data after discovering only 42% compatibility with existing processes, preventing critical GBS cascade errors
• Significant OPEX & effort reduction: Reduction of the Non-STP rate in Tier-1 vendor BAFOs from 7% to 4% alongside an impending 63% Finance-FTE reduction; avoidance of thousands of time-consuming manual correction processes
• 100% Delivery of the OCM architecture & ROI protection: Delivery of all OCM artifacts (Change Roadmap, Comms Plan, Executive Board Deck, Pulse Check Survey) and protection of ROI by identifying incalculable LLM runtime token costs

Independent practice serving clients in SAC-P and AI-enabled FP&A transformation, including financial model development for PE firm engagements, data cleansing with PowerQuery, workflow automation Claude Cowork, SAC-P scoping task-based assignments (Micro1.ai & Mercor.ai), and SAP partner network development.

• 2026 – Completed significant upgrade training for Positioning Business AI and SAP Cloud ALM Workshop (In-Person). Live Demos • SAC-P & Excel-Based Reports • Claude Partner • SAP Partner#: 1961209 (OE – Build)
• Fall 2026 – New effort to re-train/update past FI/CO, SAP BPC, SAP Platform to S/4 Finance & Core G/L Accounting
• 2026 – Upskilling for Transformation Toolchain. In-Person Workshops: Cloud ALM & SAP BTP • Joule Demo(s)
• 2025 – FP&A processes • Datasphere & Business Data Cloud • Team Workshops for AI • Toolchain & SAP Cloud ALM
• 2024 – Built key credentials on Deloitte S4 Experience • Group Reporting and SAC-P

I build. I advise. I think in systems. After years leading digital transformation at scale — Accenture, BP, Lidl — I made a deliberate choice: trade platform dependency for structural independence. My focus is at the intersection of AI systems, product strategy, and venture thinking. I work with operators and founders who want to move fast without losing control — of their data, their stack, their direction.

• AI strategy concept and implementation roadmap for an energy consulting company.
• Use-case matrix with nine application scenarios (including funding research, chatbot, initial AI consulting, topic radar), implemented as reusable Claude Code skills.
• AI strategy, use-case analysis / Claude Code Skills / n8n.

• Analysis of existing work, information and decision-making processes.

• Identification and prioritization of suitable use cases for AI agents.

• Elicitation of functional and non-functional requirements.

• Design of collaboration between users and AI agents, including control and escalation mechanisms.

• Support for the implementation of the AI agents and adapted keyboard hardware.

• Business target vision for the AI-first transformation.

• Structured and prioritized requirements for implementation.

• Defined roles, interactions and control points between people and AI agents.

• Preparation for the gradual automation of productive work processes.

Techstack: AI agents, LLMs, agent orchestration, RAG, APIs, automation, hardware

• Digital Product Owner & Manager at a large transport company south of Vienna (2023 – 2025)
• Agent Boss | Implementation, further development & management of autonomous AI agents
• Requirements Engineering | From the idea ... to success! (E2E)
• Strategic business development (transformation into a „Frontier Firm“)
• Change management | Digital Transformation · Reorganization & restructuring · Cultural change
• Process management | Analysis · KPI management · Implementation
• Project / initiative management | agile (Scrum) & classic (waterfall), or hybrid PM (method: The Race)
• Facilitation & presentations (teams, C-level, stakeholders, large groups)
• Functional leadership and mentoring of (Junior) Consultants and Business Analysts

• Designed and built a next-generation NLP platform to accelerate sales-driven customer service through intelligent request analysis and routing, reducing average customer query response time by 30%.
• Architected a hybrid NLP system combining Large Language Models (LLMs) with traditional NLP pipelines for robust, explainable results.
• Developed request classification and routing mechanisms to accelerate customer support teams in handling customer queries faster and more accurately.
• Optimized LLM based data extraction and classification with context engineering.
• Integrated the platform into customer service processes, reducing response times and enhancing workforce efficiency.

Complete build of a B2B SaaS platform for recruitment agencies

• Multi-source job aggregation via ATS APIs
• AI-assisted career page scraping
• Rule-based and AI-assisted matching
• Credit-based monetization model with Stripe integration
• Live in production since July 2026

Tech stack

• NestJS
• React
• PostgreSQL
• pgvector
• Claude AI
• Prisma

• Develop AWS-based components connecting Python backend services to React/TypeScript frontends.
• Improve database queries and application workflows for document processing and screening.
• Evaluate LLM retrieval and document-review workflows as part of the product stack.

• Took Dawnguard from initial product concept to a launched MVP within two months, building the backend platform and reducing cloud-environment scan times from approximately 40 minutes to under two minutes across 70 accounts.
• Designed and built the core resource discovery engine for Azure and AWS, enabling the frontend to visualise complex cloud environments and the AI to evaluate architectural patterns.
• Engineered distributed APIs and dynamic Cosmos DB schemas to support rapidly evolving product requirements and variable cloud-environment structures.
• Built the architecture-generation engine to support clients with 100+ cloud accounts and dynamically generate complete architectures containing approximately 25,000 resources.
• Developed custom data-generation tools that enabled the AI and frontend teams to simulate and test large-scale cloud architectures.
• Delivered backend features end to end by working with AI, frontend, and business stakeholders from technical design through implementation and testing.

• Project management for the introduction of an AI-supported email automation system for a logistics service provider
• Requirements analysis, stakeholder coordination, consulting, implementation support

Project Tech Stack: Python, AWS, Azure, FastAPI, openai, pandas, unittest/pymock

Achievements:

• Engineered automated data extraction pipelines to transform complex Excel datasets into structured formats via LLM-driven workflows.
• Architected a generative slide-deck engine that translates natural language prompts into formatted presentation assets.
• Integrated advanced LLM capabilities with the OpenAI Response API, implementing sophisticated tool-calling and structured output logic.
• Developed and containerized scalable backend microservice using FastAPI, Docker, and OpenShift to host and serve agentic skills.

• Design and deploy production multilingual AI voice-agent solutions for customer service, lead capture, after-hours support and front-office automation use cases.
• Delivered three client implementations spanning Europe, Asia and North America, including multilingual support, sales/lead-capture and privacy-sensitive intake workflows.
• Translate business requirements into conversation flows, prompt architecture, knowledge grounding, fallback and handoff logic, guardrails, consent gates and structured data capture; production support deployments include knowledge bases of approximately 1,000 grounded Q&A entries per language.
• Build scenario-based evaluation and regression QA covering normal flows, edge cases, failure modes, handoff behaviour and acceptance criteria; coordinate fixes through production readiness and rollout.
• Work hands-on across Retell, ElevenLabs Agents, Twilio, Supabase/PostgreSQL, Vercel, APIs/webhooks and CRM workflows; lead discovery, demos, solution shaping and cross-functional delivery.

Sector: AI voice automation / customer workflow design