AI& Data
What it covers
Where does AI create dependable value in our operations?
We connect concrete tasks with suitable AI, automation and data solutions, from assistants to agents that work within processes using tools. Technical feasibility is only one consideration; value, data quality, clear accountability and control in day-to-day operation matter just as much. The result is prioritised use cases, evaluated pilots and a control layer that keeps AI manageable and auditable in operation. We establish the data foundation for automation and compare AI models by quality, running costs and operational effort. This gives management, CFOs and controlling teams a sound basis for AI decisions.
When companies bring us in
- There are many AI ideas and first pilots, but no clear priorities and no reliable path into operations.
- AI agents are meant to act within processes, but permissions, approvals and monitoring are not settled.
- Data is inconsistent, AI costs remain unclear and model selection lacks a sound decision basis.
From task to operation
We start with the task, not the model. Control and operations are part of the plan from the outset because they decide whether a solution outlasts its pilot.
Define the task
We describe the workflow, its inputs, its output and today's sources of error. This becomes the baseline against which every solution is measured.
Check the data
We establish which data is available, in what quality, and who approves access. Gaps are named before anything is built.
Set autonomy
For each process step, we decide what an assistant or agent may do on its own and where a person approves. The result is a model of roles and approvals.
Pilot with criteria
A limited pilot works with real cases and success and stop criteria agreed in advance. It ends with a decision, not a presentation.
Harness and operation
Guardrails, evaluations, logging and monitoring become part of operations. Owners, metrics and change procedures are defined.
Services in this area
Explore all servicesArtificial Intelligence & Agentic AI
Which AI use case deserves the next step?
- AI readiness and use-case prioritisation
- AI assistants, agentic AI and pilot projects
- Integration with existing tools and data
- AI governance and practical adoption
Enterprise AI Agents
Where can AI agents reliably take on work?
- Process analysis and suitability assessment per step
- Autonomy and approval model
- Target process with tools, permissions and fallback paths
- Pilot planning and evaluation against success and stop criteria
AI Governance & AI Harness
How does AI stay under control in operation?
- AI inventory and risk classification
- Roles, policies and approval process
- Harness architecture with guardrails, evaluation and monitoring
- Logging and evidence concept for operation
Operational Intelligence & AIOps
How can operational data lead to better decisions?
- Map of operational decisions and data
- Process mining or AIOps use case
- Target design for data platform and event processing
- Data quality rules and data ownership
Data & Automation
How can scattered data support reliable workflows?
- Data sources, flows and ownership
- Quality rules and data classification
- Interfaces and workflow automation
- Exception handling, monitoring and operational handover
AI Controlling & Model Selection
Which AI model delivers the required value at a sustainable cost?
- Model comparison using quality and cost criteria
- Cost per use case and usage scenario
- Comparison of hosted and self-hosted options
- Budgets, ownership and ongoing cost monitoring
