Strategy & AI
Our Services
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Operating Model & Governance
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Portfolio & Priorisierung
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Transformation & Enablement
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Execution-Oriented Delivery
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Understand
We clarify the starting point, objectives, and expectations. Points of friction become visible, and a shared picture of the challenge takes shape.
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Focus
We sharpen priorities for the business. Decisions are translated into clear structures, and resources are aligned to what matters most.
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Embed
We support implementation in day-to-day operations. Responsibilities and next steps are clearly defined, and new ways of working become stable and effective.
Our Framework for Effective Execution: PAICE®
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Reduce Process Costs
AI initiatives cut manual effort in core processes.
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Accelerate Time to Market
Faster execution through focused roadmaps and clear accountability.
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Unlock Revenue Potential
Additional revenue streams through AI driven products, services, and customer interactions.
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Improve Quality and Stability
Better data and automated workflows increase reliability in day-to-day operations.
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Control Risk
Clear governance and policies reduce exposure across compliance, security, and reputation.
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Demonstrate Business Value
KPIs and value tracking make it visible which initiatives are delivering economic impact and where course corrections are needed.
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Energy
280+ Million EUR Potential Unlocked Through AI Strategy at a Leading Energy Utility
- 7 Focus Areas
- 280+ Million EUR Potential
- 10,000+ Hours/Year Efficiency Gains
No company-wide AI strategy through 2035; isolated initiatives without clear business prioritisation, governance, or a scalable platform.
Insufficient AI enablement across the workforce; target vision, architecture, and data governance were missing to move from pilots into productive operations.
A specific AI target vision and roadmap across 7 focus areas to realise more than 280 million EUR in business potential.
A structured use case portfolio with prioritised PoCs and MVPs, aligned to measurable business value and scalability.
Definition of AI maturity level, training needs, and an AI target operating model including roles, processes, and governance structures.
Design of platform and data architecture as the foundation for company-wide, sustainable AI adoption.
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Insurance
AI-Driven Code Migration for Predictable Legacy Modernisation
- Transparent Dependencies
- Automated Test Generation
- Foundation for Scalable Modernisation
A core application needed to be modernised, but complex dependencies and historically grown structures made reliable migration planning difficult.
At the same time, it was essential to ensure that existing business logic remained technically traceable and functionally stable throughout the transformation.
The goal was a controlled approach to using AI in legacy modernisation with measurable, demonstrable benefit.
Structured analysis of the application using knowledge graphs and additional context from Jira and Confluence.
A modular migration approach using AI agents for test generation, functional validation, and rule-based translation into the target architecture.
Proof of technical feasibility and a solid foundation for evaluating efficiency potential and the scalability of the modernisation effort at an early stage.
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Healthcare
Strategic Alignment of Modernisation and Data Strategy in the Statutory Health Insurance Sector
- Transparency for Governance
- Data Ownership and Lineage
- Foundation for Data-Driven Use Cases
No overarching view of the modernisation landscape, and limited transparency into the potential and added value of centralised LeanIX usage.
Unclear modernisation dependencies, and a lack of transparency around data ownership, data lineage, and data responsibilities.
Data systems and processes needed to be structured and mapped to build a reliable foundation for the future data strategy.
Development of a methodical approach to align IT modernisation and data strategy on the basis of LeanIX.
Support in designing and implementing data lineage and data governance to establish clear accountability.
Structuring of data objects, data quality, and usage across downstream processes as the foundation for data catalogues and new data-driven use cases.
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AI · Interne Reference
Scalable AI Adoption Through Centralised Platform and Access Logic
- Central Model Access
- Governance and Cost Control
- Foundation for AI Scaling
No central platform for simple, controlled access to different AI models.
AI usage needed to be enabled without additional friction, while maintaining transparency over access, resources, and costs.
A reliable foundation was also needed to develop further internal AI applications securely and at scale.
Development of a central AI platform with unified access to various models via API endpoints.
Build-out of an integrated authentication and authorisation system for controlled usage and straightforward management of access rights.
Establishment of a platform foundation for further AI applications, along with greater transparency over usage and costs.
Get in Touch
Johannes Schmidt
Chief Business Builder
+49 30 99 404-0970
FAQ
Exxeta helps companies align AI strategically and translate it into production-ready solutions. This includes target vision, use case prioritisation, operating model design, governance, and implementation into real workflows and system processes.
No. We don't stop at the target vision. Our teams connect strategy, technology, and transformation so that an AI strategy leads to specific roadmaps, clear accountability, and production-ready solutions.
Both. What matters is the contribution AI is expected to make to the business. From there, we determine which data, technologies, processes, and roles are needed.
As soon as AI is meant to be more than just an experiment. When multiple use cases are emerging, budgets are being allocated, or governance questions are coming up, a clear approach and reliable priorities become essential.
We don't separate strategy from execution. Strategists, data scientists, AI engineers, and transformation experts work together to ensure that concepts actually work, technically, organisationally, and commercially.
For any company looking to embed AI purposefully into their business. This applies to mid-sized companies, large enterprises, and public sector organisations, especially when multiple initiatives are running in parallel and a shared framework is missing.
We assess use cases based on business value, feasibility, data availability, risks, and scalability. The result is a portfolio that not only sounds promising but can realistically be delivered.
An AI operating model defines how AI is governed within the organisation. This covers roles, responsibilities, decision-making paths, governance, technical standards, and processes for operations and continuous development.
We assess from the outset where AI can create real value. That logic is built directly into target visions, processes, products, and systems, rather than being added as an afterthought.
Agentic AI refers to AI systems that can handle tasks with greater autonomy. They plan steps, use tools, access systems, and prepare decisions or actions, all within clearly defined guardrails.
We define success criteria at the start of every project. Depending on the objective, these can include efficiency gains, faster decision-making, improved process quality, revenue contribution, reduced risk, or greater system stability.
We typically begin with a shared assessment of the current situation. From there, we sharpen objectives, identify friction points, define use cases, and clarify decision-making structures. The output is a specific action plan for the next steps.