A hybrid governance model for data and AI at scale
Mercedes-Benz Customer Assistance Center (CAC)
A strong foundation, ready to scale
The Customer Assistance Center (CAC) of Mercedes-Benz had invested in modern platforms and demonstrated value through AI initiatives like agent assist, multilingual routing, and automated classification. The ambition was to evolve from a reporting-driven operation toward a data- and AI-enabled organisation that could serve as a global aftersales data domain within the Mercedes-Benz Group. To get there, the organisation needed clarity on three fronts: how to structure ownership and governance as the operation scales, how to align the technical architecture with corporate standards, and how to embed data quality and compliance into the way teams work day to day.
Mapping current state, defining the path forward
We ran a series of targeted deep-dive sessions with business, IT, operations, and the Intelligence & AI teams. The goal was to map the current state across people, process, and technology — and to identify what needed to change to support the next phase of growth. The engagement covered the full spectrum: data governance, data quality, technical architecture, AI readiness, and compliance (GDPR, EU AI Act). Each domain built on the previous one, with validation workshops to ensure findings reflected operational reality. We delivered a unified strategic alignment canvas for Data & AI, a gap analysis of current versus target state, and a hybrid governance model that combines SAFe delivery practices with Data Mesh principles. The output was not a theoretical framework, but a set of concrete recommendations, quick wins, and a phased implementation roadmap.
Three priorities for scaling
The strategy centred on three priorities that together form the structural foundation for scaling data and AI capabilities across the organisation.
Strengthen the organisational foundation
Define clear ownership structures and governance responsibilities that can scale with the organisation. Ensure that accountability is distributed across domains rather than concentrated in a few roles, and that governance is embedded into how teams plan and deliver.
Harmonise the technical architecture
Consolidate the data platform into a unified structure with shared standards for ingestion, transformation, lineage, and monitoring. Align the local data catalogue with the corporate metadata platform to ensure end-to-end governance and traceability.
Embed governance into the delivery process
Make data quality and compliance part of the planning and delivery lifecycle — not something that is bolted on afterwards. Introduce measurement-driven feature approval and automated quality gates within the existing SAFe cadence.
A strategy ready for implementation
The engagement produced a pragmatic strategy and a roadmap ready to put into action.
Core deliverables
We handed over a connected set of artefacts that together give the CAC everything it needs to move from planning to execution. These include a gap analysis across people, process, and technology; an alignment strategy between business and IT with clear ownership structures; a hybrid governance model combining SAFe and Data Mesh principles; a 12–24 month implementation roadmap with quick wins in the first six months; and a compliance readiness assessment for GDPR and the EU AI Act. Together, these give the CAC a clear path to scale its data and AI capabilities, with the structural foundations needed to do so sustainably across 40+ markets.
