2023 to present · Wuppertal / Düsseldorf

Riedel Communications GmbH.

Data Scientist / AI Product Lead

Core responsibility

Turn scattered enterprise data into decisions, and GenAI potential into shipped products, across seven+ departments.

AI Product ManagementSolution ArchitectureGenAIStakeholder OrchestrationAI Governance

Riedel builds real-time video, audio and communications technology for the world's biggest live events, from the Olympics to Formula 1. I joined the Digital Transformation team as a Data Scientist, and the role grew into something broader: part product manager, part solution architect, part tech lead for the company's digital and AI journey.

The work moved through three phases. It began with making the business visible: building the BI layer across multiple ERP systems, covering the full order-to-delivery chain, and lifting SAP master data quality from 70% error rate to 2% so that every decision downstream ran on data the company could trust. From there it moved to digitising core processes, most notably orchestrating the pricelist transformation across seven departments, taking a 3+ month Excel-driven cycle down to under a month.

The current phase is generative AI. I built the company's GenAI portfolio from zero, collecting use cases across departments, prioritising with leadership, writing the PRDs, and owning delivery through epics and user stories. Three products have shipped so far, including a multi-agent IT helpdesk on AWS serving 1,500 employees. Alongside the products, I built what makes them durable: the AI governance layer covering GDPR and the EU AI Act, and the engineering standards our team now delivers on.

€500K+

Procurement savings via BI

3 mo → <1 mo

Pricelist cycle time

40%

IT ticket deflection, year one

Measured impact

Master data errors
Twelve months of governance work, month by month. Hover for the value.
Error rate today: 70%
0%20%40%60%80%Month 1Month 12
Before / After, flagship outcomes
Simplified processes and multi-agent AI cut cycle times and lifted deflection across the board. Hover a group for details.
0255075100PricelistAddendumsIT deflectionIT resolutionTender
AfterBefore
Performance metrics

Notes from work

01

BI Foundation & Master Data Quality

BI ArchitectureData GovernanceSAP Master DataPower BI
Challenge

Data sat across multiple ERP systems, every department saw only its own slice, and 70% of SAP master data records carried issues that compounded into every report and process downstream.

What I did

Built the BI layer across the ERP landscape covering the full order-to-delivery chain, and drove SAP master data (product and business partner) from 70% error rate to 2% in a year through systematic audits and governance rules at the point of entry. Product management, operations and procurement finally decided on shared, reliable data, saving €500K+, most visibly in procurement.

02

Product Lifecycle Reporting

Reporting DesignCross-Functional AlignmentProduct Lifecycle
Challenge

Seven functions, from product management to controlling, each held a different picture of where a product stood in its lifecycle, so decisions waited on email chains and suffered from version confusion.

What I did

Built a single shared lifecycle view used across all seven functions. Cut decision delays and eliminated the communication errors that come from everyone working off a different version of the truth.

03

Pricelist Transformation

Stakeholder OrchestrationProcess DesignSolution ArchitectureAzure AD
Challenge

Creating a pricelist took 3+ months in Excel, with seven departments working in silos, routine communication errors, and no structured way to reflect market shifts like trade taxes in the multiplier calculations.

What I did

Orchestrated the change end to end: business case, alignment across all seven departments, technical architecture, delivery with IT and data science, and the new operating process around the tool. Azure AD role-based workspaces let each department own its part, with changes flowing transparently in a clean cycle. Pricelists now take under a month, addendums days, with zero manual uploads.

04

GenAI Portfolio (3 products)

PRD WritingMulti-Agent ArchitectureAWS BedrockHuman-in-the-Loop Design
Challenge

The company wanted generative AI but had no roadmap, no prioritised use cases, and no delivery framework. Just potential.

What I did

Collected use cases across every department, prioritised with leadership, wrote the PRDs, owned delivery through epics and user stories. Internal knowledge assistant (Copilot Studio): instant answers from company policy and training docs; 'ask around and wait' became a ten-second conversation. Multi-agent IT helpdesk (AWS Bedrock, Lambda, Strands, S3): Azure AD routing to an employee self-service bot and an IT-support bot. 1,500 employees, 40% deflection in year one, resolution from a week to two days, ~€100K/yr saved. Tender and product bot for Sales: product specs, prices and features answered in seconds; human-in-the-loop tender creation cut end-to-end time from one month to two weeks.

05

AI Governance & Team Standards

AI GovernanceGDPREU AI ActCI/CDEngineering Standards
Challenge

Shipping one product is an achievement; shipping repeatedly needs structure. And in the EU, AI without a governance path never reaches production at all.

What I did

Built the governance layer covering GDPR compliance, EU AI Act risk classification and works council alignment, and established GitHub, CI/CD and structured project management as the team's delivery standard. AI that legal signs off on, shipped by a team that can repeat it.

Skills applied

Product StrategyPRD WritingStakeholder ManagementSolution ArchitectureGenAI / LLM ProductsAWS BedrockMulti-Agent SystemsPower BISAP DataData GovernanceAI Governance (GDPR, EU AI Act)Agile Project Management

Key learnings

Enterprise AI succeeds or fails before any model is chosen. Clean data, aligned stakeholders and a governance path decide more than the technology does. And the fastest way to earn an organisation's trust in AI is to ship something small that people use every day. The helpdesk bot did more for AI adoption here than any strategy deck could have.

What this role proves

That I can take a business from "our data is scattered" to "we ship governed AI products": the business case, the stakeholders, the architecture, the delivery, and the process that makes it stick.