Services that make AI work. Safe, human-centred and practical.

What this involves

To truly benefit from AI, it’s important to first understand whether your organisation is sufficiently prepared. This is called AI readiness and includes general components (e.g., regulation and AI literacy) and organisation-specific components (e.g., governance). A strong foundation and governance make implementing and integrating AI successful in the short and long term.

What you can expect

• Evidence-based, tailored support with joint alignment and periodic evaluations.
• Human-centred, holistic approach by involving relevant stakeholders and strengthening collaboration.
• More self-reliance by bringing you and key internal players along, building future readiness for AI.

My approach

1. Assess current AI readiness
   • Interviews and workflow observation.
   • Mapping your internal AI landscape and stakeholders.
2. Define the target state & what it takes
   • Clarify goals, vision, scope and risks.
3. Roadmap to the target state
   • Realistic steps to progress towards the target state.
   • A checklist to monitor progress.

Deliverables

Deliverables depend on your needs and wishes. We align on what they should be and what “good” looks like. Examples:

• A roadmap with or without an implementation plan.
• An advisory report with implementation strategy/plan.
• A roadmap including strategy and a progress monitoring checklist.

What this involves

AI is complex and can deliver significant value, but correct implementation is crucial. It’s essential to involve all relevant stakeholders—especially end users—early in the process. This drives effective, efficient and sustainable adoption and integration of AI into daily work.

What you can expect

• Support aligned to where you are: from selecting the right tool(s) and running a pilot to scaling, implementing and integrating.
• Human-centred, holistic approach by involving stakeholders and strengthening collaboration.
• I act as a linking pin between parties to improve alignment and delivery.
• Goal: increase your organisation’s self-reliance for future AI implementations.

My approach

1. Assess current state & preparation needs (incl. AI readiness).
2. Evaluate and validate the intended AI tool(s).
3. Create implementation plan/strategy and AI governance (incl. privacy, KPIs for evaluation & monitoring).
4. Execute, evaluate and define next steps / integration options.

Deliverables

Deliverables depend on your needs and scope. Examples:

• Business case.
• Implementation plan and/or implementation strategy.
• AI governance proposal.

What this involves

Choosing the right AI tool(s) for your problem and organisation can be complex. The market is crowded with different goals, features, vendors and usage modes. You may wonder what to consider (e.g., regulation), which tools are safe, how to decide objectively, who to involve, what risks exist and which KPIs matter. A clear, holistic evaluation/validation/monitoring framework aligned to both regulation and your organisation is essential.

What you can expect

A practical approach that helps you not only choose or use AI, but also continuously assess and adjust. This supports control over performance, risk, compliance and organisational fit—with clear ownership and follow-up.

My approach

• Set up an evaluation framework (what to measure, why, and how often).
• Define validation criteria (quality, reliability, safety, organisational fit).
• Agree monitoring responsibilities (signals, escalation, ownership).
• Build an improvement loop (feedback, updates, documentation).

Deliverables

Deliverables depend on your needs and scope. Examples:

• Holistic, multidisciplinary evaluation & validation framework (digital support system or protocol/work instruction).
• Advisory report.
• Business case.

What this involves

Scientific research on AI—measuring impact, effectiveness, functioning and applicability objectively—is increasingly important. Strong validity and reliability are crucial for regulation (e.g., EU AI Act and EHDS) and can influence adoption. This requires a rigorous approach spanning research methods, domain context and AI-specific considerations (data, model types, regulation).

My approach

The approach depends on research type (qualitative, quantitative, experimental), scope and sector (e.g., healthcare, public, commercial), AI type (e.g., anomaly detection) and where you want support (from proposal to execution and follow-up). Many stakeholders may be involved (researchers, participants, ethics boards). We align these aspects and define a fitting approach together.

Areas I can support

• Research proposal: integrating regulation into data management/processing; selecting and justifying training/testing approaches.
• Research preparation: informing study populations for data collection.
• Execution: monitoring agreed data governance and policies.
• Strengthening collaboration among direct and indirect stakeholders.
• Post-research next steps: translating TRLs into practical steps towards market readiness.

What this involves

If your organisation uses AI in daily work, people need the right knowledge and skills—AI literacy. This is a legal requirement under the EU AI Regulation: since February 2025, organisations must set up and run an internal AI literacy programme. Supervisory bodies may require evidence that staff have adequate AI literacy for the relevant AI systems and sector. Designing and delivering a compliant, inclusive programme requires tailored work.

What you can expect

• You don’t need to figure out all legal requirements alone.
• An evidence-based, auditable approach aligned with regulation and practice.
• A programme that matches your organisation, roles, workflows and required literacy levels.

My approach

First, I get to know your organisation. Then I systematically determine which AI literacy requirements apply and how to translate them into practice:

• Assess the current situation.
• Define required AI literacy levels.
• Design the training programme and self-assessments.
• Maintain and grow: a tailored monitoring strategy.

Deliverables

• Tailored AI literacy self-assessment (for evaluation & monitoring).
• Slides/materials from training sessions.
• Tailored AI curriculum (programme + rationale + strategies).
• Tailored e-learning modules for different AI literacy levels (beginner to expert).