The 6 Dimensions of AI Readiness

The Strategy dimension examines whether AI is strategically embedded in your organisation — not just as an experiment by individual teams, but as an explicit business initiative with resources, responsibilities, and measurable goals.

It measures whether leadership actively drives AI, whether a documented AI vision exists, and whether that vision is linked to business objectives. Organisations with a high Strategy score don't just have AI on the agenda — they know specifically which processes they want to transform, by when, and who is responsible.

Without strategic anchoring, AI initiatives often fizzle out: they start as pilots, deliver isolated results, but never scale. A low score here typically means lots of interest but little commitment at leadership level.

AI is only as good as the data it works with. The Data dimension assesses whether your organisation has the data foundation needed for meaningful AI applications — in terms of availability, quality, accessibility, and governance.

Many organisations already collect substantial volumes of data but cannot make it usable for AI: because it sits in silos, is inconsistently structured, lacks clear ownership, or faces data protection constraints. The score measures data maturity, not data volume.

Without sufficient data quality and accessibility, AI projects don't fail because of the model — they fail at the foundation. This dimension is often the most important early indicator of whether an organisation can deploy AI productively within six months or needs to invest in data infrastructure first.

The Tech Stack dimension evaluates your organisation's technical infrastructure: what systems are in place, how well they are integrated, and to what extent they support AI solutions.

This is not about whether you already use AI-specific tools. It is about whether your existing system landscape — from ERP and CRM to communication and collaboration tools — is modern enough and sufficiently connected to integrate AI applications meaningfully. Legacy systems without APIs, fragmented tool landscapes, or a lack of cloud infrastructure significantly increase integration effort.

A high score does not mean everything needs to be new. It means the infrastructure is open, integrable, and extensible — a necessary prerequisite for AI solutions to become part of workflows rather than ending up as isolated tools.

The Processes dimension analyses how clear, repeatable, and documented your organisation's workflows are. AI can only automate where a process is sufficiently standardised — undefined or highly variable workflows are difficult to support meaningfully with AI.

High process maturity does not mean rigid bureaucracy. It means your people know how a process works, that it is measurable, and that exceptions are documented — not improvised. Organisations with mature processes can deploy AI in a targeted way: as decision support, as an automation layer, or for quality assurance.

A low score in this dimension is not a failure — it is an indicator of where optimisation work is needed before AI adoption. A poorly defined process that gets automated remains a poor process.

Technology alone is not enough. The People & Skills dimension assesses whether your team has the capabilities, knowledge, and willingness to use and develop AI systems productively — across all levels of the organisation.

It starts with basic AI literacy: do employees understand what AI can and cannot do? Do leaders know how to evaluate and steer AI projects? Does the organisation have the technical competence to implement AI solutions or at least procure them effectively? And perhaps most importantly: how high is the readiness for change?

Resistance to AI often stems not from ignorance but from legitimate uncertainty about consequences. Organisations with a high score here have built trust: through transparency, involvement, and genuine skills development rather than mandatory training.

The Governance dimension assesses whether your organisation has established a structured approach to the risks, obligations, and ethical questions surrounding AI. It is the newest of the six dimensions — and at the same time the one gaining increasing regulatory relevance through the EU AI Act.

Governance spans multiple layers: what policies exist for AI use in the organisation? How is it ensured that AI decisions are traceable and explainable? Who is responsible for AI risks? Are suppliers and external AI services selected according to ethical and data protection criteria?

A low Governance score does not automatically mean mistakes are happening — but it does mean the organisation is poorly prepared for incidents, regulatory enquiries, or compliance audits. With the EU AI Act, organisations will be explicitly accountable in an increasing number of use cases from 2025/26.