We are proud of what we have already been able to help shape.

How do first AI pilots become an enterprise-wide strategy that creates measurable business value instead of remaining stuck in pilot status?

We operationalised an enterprise-wide data & AI strategy: through a hub-and-spoke model, a portfolio management function as the link between corporate strategy and execution, and transformation concepts across five fields of action: organisation, people, data, technology and compliance. Assessed by an interdisciplinary team of management consultants and technology experts, geared towards maximum ROI.

How can distributed GenAI initiatives be scaled company-wide when a shared vision, binding governance and a consistent technological target picture are missing?

A shared GenAI vision and strategy creates the binding framework for the entire organisation. Building on this, we established a central programme organisation with clear governance, consolidated existing technology initiatives and translated business requirements into a shared technology blueprint as well as an aligned architecture and implementation roadmap.

How can end-to-end processes be realised when systems interpret the same business objects differently and do not represent relationships and lifecycles consistently?

Starting from the relevant business objects, we developed a shared domain data understanding. Entities, lifecycles and dependencies were modelled graph-based and transferred into a technical ontology. The architecture artefact provided in Turtle format can be integrated directly into existing systems and data platforms.

How can prioritised GenAI initiatives be realised in the short term without fragmented tool decisions and missing integration capability blocking the long-term agentic architecture?

We validated the prioritised GenAI initiatives against the existing IT, application and data landscape. Dependencies, risks and success factors were translated into concrete short-term measures. In parallel, a long-term target picture for the Agentic Enterprise emerged, along with an experimental approach to investigate a semantic layer as a future architecture foundation.

How can relevant AI potential in complex finance processes be identified and prioritised without investing resources into unclear or economically unattractive use cases?

We analysed finance processes down to activity level and identified suitable starting points for AI and automation. The expected efficiency potential was quantified, translated into concrete use cases and business cases and prioritised by impact and feasibility. This resulted in a robust roadmap for targeted implementation.

How does corporate IT move from cost block to enabler: agile, product-oriented and tightly meshed with the business?

We established an agile, product-oriented IT organisation, structured along value streams instead of functional silos. Through cross-departmental project teams, a scaled agile framework (SAFe) and modern collaboration tools, we addressed the requirements of a complex corporate IT and positioned IT as a central driver of the digital transformation.

How do you give employees one central working cockpit without having to replace the entire legacy world for many millions?

We developed a central, cloud-based platform that virtualises the data from all legacy systems, complemented by a frontend for ML-based analyses. From the technical concept through to the architecture, we delivered every component in BizDevOps mode. The result is consistent information across system boundaries — information that enables data-driven decisions and end-to-end process control.

How do fragmented analytics initiatives become one powerful organisation that responds quickly and reliably when acute field issues arise?

We consolidated the distributed analytics initiatives into one integrated, agile organisation. Through a comprehensive data-flow analysis, we captured the existing use cases, harmonised objectives across all teams and set up cross-functional AI, analytics and data teams. Scalable prioritisation processes and a sustainable funding structure ensure that data is firmly anchored in the decision-making structures.

How does fragmented vehicle knowledge become usable company-wide when inconsistent data models require individual pipelines and complex integrations for every application?

A central ontology represents vehicle types, configurations, components and attributes in a unified semantic model. Existing data structures are harmonised in a knowledge graph and made accessible for domains, applications and AI through automated data pipelines, standardised interfaces and flexible queries.

How can quality deviations be detected early when relevant process data is spread across isolated systems and not semantically comparable?

An ontology-based process and data model unifies the meaning of distributed quality data. Building on this, we developed a scalable cloud platform and a central user interface that integrates data across systems, makes quality processes transparent and enables AI-supported anomaly detection as well as future automation.

How does a historically grown ERP landscape, outdated and overly complex, become a modern, manageable foundation?

We set up the ERP transition holistically: from analysing the business processes and aligning them with the corporate strategy through to evaluating different landscape scenarios. Taking users and data into account, we defined the approach, timeline and budget and drastically reduced the landscape of systems and interfaces.

How does a DAX-listed group become truly data-driven when clear responsibilities, uniform processes and governance are missing?

We built a scalable data governance & management organisation. Based on the core Data Mesh concept, we established group-wide data domains, defined domain boundaries and responsibilities in cross-functional workshops with business and IT, and anchored data-owner roles in the structure. In parallel, we standardised data management processes, documentation, approval and governance, and prepared the organisation for the new data architecture through enablement.

How does the data potential in After Sales become a measurable economic contribution, rather than scattered individual ideas without prioritisation?

We developed a specific data strategy that supports the long-term business-field strategy in After Sales. To do so, we assessed the strategic goals for how well data, analytics and AI can support them, derived concrete and measurable objectives from them, and defined suitable use cases for each objective. We then prioritised these by business value, described them functionally and technically, and translated them into an implementation plan with concrete measures.

How does an automotive manufacturer comply with the EU Data Act when transparency across vehicle usage data, clear responsibilities and processes are missing?

We took over project management for the data-governance stream and implemented a multi-stage approach. First, we identified all relevant usage-data sources from vehicles and connected services, anchored data ownership at management level and secured budgets for maintenance and classification. In parallel, we assessed data flows in backend systems for EU Data Act relevance together with the responsible owners and the legal department, and developed a standardised metadata model that we integrated into the company-wide inventory approach.

How does a company use data in AI applications compliantly when the EU AI Act demands transparency and traceability, but the roles and processes are missing?

From the legal requirements of the EU AI Act, we derived specific governance requirements for handling AI-relevant data. In cross-functional workshops with business, IT and legal departments, we defined roles and responsibilities, in particular for data use in AI applications. Building on that, we developed a target process for the documentation, assessment and approval of data, handed it over to the development team and interlocked it closely with the existing data-management processes.

How do data stay usable for business and AI when unclear responsibilities, missing documentation and new regulatory requirements create significant compliance risks?

Together with business, IT and legal, we translated regulatory requirements into operationally applicable governance principles. Roles and responsibilities were bindingly defined and integrated into an end-to-end process for data inventory, review and approval. This anchors compliance traceably without unnecessarily slowing down the use of data.

How can a business-critical legacy system be fundamentally modernised without endangering central functions, overlooking dependencies or taking on uncontrollable migration risks?

Starting from the existing value streams and core functions, we developed a cloud-native and AI-ready target architecture. Technical components were consistently aligned to the business logic and transferred into a concrete migration plan that transparently brings together implementation steps, dependencies and required resources.