AI readiness assessment — score your enterprise across nine dimensions

An AI readiness assessment quantifies how prepared your organization is to adopt AI at scale. AI-Koutsi scores nine dimensions, benchmarks your results against industry leaders, and feeds the gaps directly into a prioritized AI adoption roadmap.

The nine dimensions of AI readiness

Strategy, data foundations, technology stack, talent and skills, process maturity, governance and risk, organizational culture, measurement discipline, and responsible AI. Each dimension is scored on a five-stage maturity curve from Ad Hoc to Self-Optimizing.

Step 1 — Evidence-based scoring, not self-assessment sentiment

Each of the nine dimensions is scored 0–100 from structured evidence: which data products exist and at what quality tier, which controls are documented, how decisions are governed, and how value is measured today. Every score carries the evidence behind it, so a low score is a specific finding — "no owned customer master, no monitored model in production" — rather than an opinion. Parallel assessments across executives and middle management surface the alignment gap, which is usually the largest single source of stalled AI programmes.

Step 2 — Benchmark against your industry, not a global average

Your nine scores are compared against reference profiles for your own industry across all twelve sectors AI-Koutsi covers — banking, insurance, retail, manufacturing, telecom, energy, logistics, hospitality, media, construction, public sector and healthcare. The benchmark shows both the gap to industry median and the gap to the full potential of your industry, which is the number that matters for an investment case.

Step 3 — What the assessment produces

The output is four artefacts, not a score card: a maturity report with per-dimension evidence and benchmark, a prioritized use case portfolio scored on value, complexity and risk, the data products each priority use case depends on with their current readiness tier, and the EU AI Act risk classification plus required controls for every high-risk candidate.

Step 4 — Next steps after the assessment

Every low-scoring dimension is linked to the specific use cases it blocks or accelerates. From there the roadmap sequences waves so data, governance and talent investments unlock dependent use cases in the right order: close the blocking data and control gaps in wave one, run the highest-value low-complexity use cases in parallel, and re-score quarterly so the roadmap stays current instead of ageing into a deck.

Frequently asked questions

How long does an AI readiness assessment take?
The structured assessment takes 2–3 hours of input from your team. AI-Koutsi processes the results and delivers the scored report, benchmark, and prioritized roadmap within 1–2 weeks.
Who should complete the AI readiness assessment?
A cross-functional group: CDO or Head of AI, CIO/CTO, Head of Data, a business unit lead, and a governance or risk owner. AI-Koutsi also supports parallel assessments to surface the executive-to-middle-management alignment gap.
What do you get at the end of the assessment?
Four artefacts: a nine-dimension maturity report with the evidence behind each score, an industry benchmark against median and full potential, a prioritized use case portfolio with the data products each use case depends on, and EU AI Act risk classifications with the controls required for high-risk candidates.
How often should readiness be re-scored?
Quarterly. Scores move when data products change tier, controls are documented or use cases reach production, and the roadmap is regenerated from the current scores rather than from the original baseline.