One logical journey: from insight to lasting control

Boards and management teams who want to know where they stand on data, reporting and AI — and the logical order of investment.

Uncertainty about bottlenecks, risks and opportunities. You know it can be better, but not where to start — and investing without overview leads to scattered initiatives.

• Inventory of systems, Excel files and reports
• Analysis of bottlenecks in finance, operations and management information
• Assessment of AI opportunities and AI risks
• Initial assessment of data quality and dependencies
• A concrete advisory report with priorities and a roadmap

1. Intake conversation: your situation, questions and ambition
2. Focused analysis of systems, reporting and AI use, with a few short interviews
3. Advisory report with priorities and a roadmap
4. Review of the findings and the follow-up decision

Organisations steering on scattered reports and Excel, ready for one reliable view for management, finance and operations.

Reporting takes manual work, figures contradict each other because definitions differ, and no one fully trusts the numbers.

• Shared definitions and a clear KPI structure, with ownership per KPI
• A management cockpit (Power BI) on reliable data sources
• An underlying, extensible data model
• Connection to source systems or existing exports
• Validation with finance/operations, handover and documentation

1. Definition workshop: what the core figures mean, and who owns them
2. Data model and connection to your sources
3. Cockpit build in short iterations, together with the users
4. Validation with finance and operations, then handover

Teams already using AI informally — or wanting to start deliberately — who now want to apply it under control, safely and with agreements, in one concrete workflow.

AI is used ad hoc, without agreements on quality, safety and privacy — or pilots stall without demonstrable business value.

• One clearly scoped workflow with a baseline and an intended result
• Working AI support with human decision points — a person confirms
• Safe work instructions and a prompt library, fitting privacy and security
• Evaluation of quality and errors, with adoption and training
• Workable agreements on ownership and use

1. Process selection and baseline: what it costs now, what it should deliver
2. Design with explicit human decision points
3. Build and test, together with the people who will use it
4. Working agreements, training and an evaluation moment

Organisations that need structural direction on AI and data — course, priorities and accountability — without hiring a full-time specialist.

Initiatives stall, knowledge sits with a few people, no one structurally weighs costs, benefits and risks — and management lacks a mandated point of contact.

• An AI and data portfolio: priorities and business cases in one overview
• Governance and policy: working agreements, an AI register and supplier assessment
• AI literacy: guidance and formats for your staff
• Monitoring of costs and benefits, risks and incidents
• Quarterly reporting to management or the board, plus continued development of cockpits and workflows

1. Starting point: establish the portfolio and quarterly agenda
2. A fixed monthly rhythm with management and the teams involved
3. Getting and keeping the register, policy and supplier agreements in order
4. Every quarter: reporting, evaluation and updated priorities