We are proud of what we have already been able to help shape.
A selection of our completed consulting engagements. Mostly unnamed, because confidentiality is part of our client relationships.
- The question
How do first AI pilots become an enterprise-wide strategy that creates measurable business value instead of remaining stuck in pilot status?
Our answerWe 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.
What it achieved- AI strategy anchored across the entire enterprise, with clear target states.
- Action fields backed with concrete roadmaps for implementation.
- An ROI-maximised, strategically aligned portfolio of AI use cases established.
- Data and AI initiatives steered with purpose - business value sustainably increased.
- The question
How can distributed GenAI initiatives be scaled company-wide when a shared vision, binding governance and a consistent technological target picture are missing?
Our answerA 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.
What it achieved- A shared GenAI target picture creates direction for all initiatives
- A technology blueprint prevents isolated platform and architecture decisions
- Central programme governance reduces duplicate effort and accelerates decisions
- An aligned roadmap enables scalable GenAI implementation
- The question
How can end-to-end processes be realised when systems interpret the same business objects differently and do not represent relationships and lifecycles consistently?
Our answerStarting 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.
What it achieved- Shared data understanding across domains and systems
- Unambiguous object relationships improve data quality and reusability
- A technically usable ontology accelerates downstream data integrations
- A semantic foundation enables scalable automation and AI applications
- The question
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?
Our answerWe 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.
What it achieved- Robust decisions on the feasibility of strategic GenAI initiatives
- A long-term target picture prevents isolated technology and architecture decisions
- Prioritised immediate measures reduce risks in implementation
- A semantic layer concretely investigated as a future architecture foundation
- The question
How can relevant AI potential in complex finance processes be identified and prioritised without investing resources into unclear or economically unattractive use cases?
Our answerWe 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.
What it achieved- A transparent potential map shows AI levers along the finance processes
- A prioritised roadmap focuses resources on value-creating AI use cases
- Quantified business cases create a robust basis for investment decisions
- Identified automation potential opens up concrete efficiency gains
- The question
Almost every sales question ends in the bill of materials — the most complex structure a vehicle has. How does a sales organisation answer it when order type, vehicle structure and importer data are joined by hand every single time?
Our answerWe brought the three data worlds into one information model and made it available through d.AP as a knowledge layer. Order types and configuration options from the sales systems, vehicle structure from the bill of materials, and importer data on prices, texts and sales areas now exist as a single model, with concepts declared and the mappings between systems written down. Domain teams query that model themselves; applications and agents consume the same statements through API and chat. Views are derived from the model instead of being built per request.
What it achieved- A question about a model, its options and a market is answered inside sales instead of being collected across three departments
- Bill-of-materials information is available through self-service instead of being delivered as a separate view per use case
- GenAI initiatives in sales build on shared knowledge instead of wiring up their own data connections
- Point-to-point links between the systems involved fall away wherever the model carries the connection
- The question
How does corporate IT move from cost block to enabler: agile, product-oriented and tightly meshed with the business?
Our answerWe 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.
What it achieved- Clear end-to-end ownership accelerates decisions and operational execution
- Product-oriented teams reduce handovers between business and IT
- Business priorities steer resources, products and technological advancement
- A scalable operating model increases speed, quality and adaptability
- The question
How do you give employees one central working cockpit without having to replace the entire legacy world for many millions?
Our answerWe 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.
What it achieved- Harmonisation and integration of legacy data into one central platform.
- Cloud-based architecture for scalability and flexibility.
- Embedded ML analyses for optimising the processes.
- BizDevOps approach for continuous improvement and efficient delivery of features.
- The question
How do fragmented analytics initiatives become one powerful organisation that responds quickly and reliably when acute field issues arise?
Our answerWe 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.
What it achieved- A high-performing, fully funded organisation for vehicle data analysis built on all relevant data sources.
- Cross-functional structure with experts from engineering, production and after-sales.
- Significant financial value from data-based product decisions and series production support.
- Faster, reliable answers to acute field issues instead of contradictory insights.
- The question
How does fragmented vehicle knowledge become usable company-wide when inconsistent data models require individual pipelines and complex integrations for every application?
Our answerA 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.
What it achieved- Unified vehicle knowledge replaces application-specific data preparation and integrations
- Standardised interfaces accelerate new applications and data products
- Consistent product data reduces contradictions between systems and domains
- Semantic access creates a robust basis for AI applications
- The question
How can quality deviations be detected early when relevant process data is spread across isolated systems and not semantically comparable?
Our answerAn 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.
What it achieved- Unified access to distributed quality and process data
- Central process transparency improves well-founded decisions in quality management
- Earlier detection of quality deviations supports faster countermeasures
- A scalable data foundation enables further AI and automation cases
- The question
How does a historically grown ERP landscape, outdated and overly complex, become a modern, manageable foundation?
Our answerWe 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.
What it achieved- A modern, integrated and manageable enterprise IT landscape.
- Systems and interfaces drastically reduced.
- An ERP landscape tailored precisely to needs and capabilities.
- A scalable, low-maintenance and cost-efficient foundation laid.
- The question
How does a DAX-listed group become truly data-driven when clear responsibilities, uniform processes and governance are missing?
Our answerWe 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.
What it achieved- Tailor-made data management and governance framework implemented.
- Cross-functional data domains established group-wide as the central organisational element.
- System-supported processes for data documentation and approval introduced.
- Data-owner roles successfully anchored across the entire organisation.
- The question
How does the data potential in After Sales become a measurable economic contribution, rather than scattered individual ideas without prioritisation?
Our answerWe 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.
What it achieved- Clear strategic objectives defined for the effective use of data and AI.
- Relevant use cases with high business value identified and prioritised.
- Concrete measures derived for implementing the data strategy.
- Implementation plan created for a step-by-step rollout.
- The question
How does an automotive manufacturer comply with the EU Data Act when transparency across vehicle usage data, clear responsibilities and processes are missing?
Our answerWe 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.
What it achieved- Developed a standardised metadata model to comply with the EU Data Act.
- Anchored clear responsibilities for data assessment and upkeep.
- Identified the relevant backend systems for connecting to outbound data-sharing technologies.
- Created a governance foundation that lowers regulatory risk and makes data-sharing processes efficient.
- The question
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?
Our answerFrom 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.
What it achieved- Governance framework established to satisfy the EU AI Act.
- Defined roles and processes for data approval and data use.
- Requirements derived for implementing the target process in the central governance tooling.
- The new processes integrated into existing data-management structures.
- The question
How do data stay usable for business and AI when unclear responsibilities, missing documentation and new regulatory requirements create significant compliance risks?
Our answerTogether 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.
What it achieved- Regulatory requirements translated into operationally usable processes
- Integrated compliance processes reduce risks and manual coordination effort
- Clear responsibilities accelerate data review and approval across the company
- A usable data inventory creates transparency and reliable auditability
- The question
How can a business-critical legacy system be fundamentally modernised without endangering central functions, overlooking dependencies or taking on uncontrollable migration risks?
Our answerStarting 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.
What it achieved- A cloud-native target architecture creates scalability and long-term technological flexibility
- Integrated AI capabilities enable future intelligent services
- A value-stream-oriented system structure reduces complexity and eases further development
- A concrete migration plan creates planning certainty for resources and implementation
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