AI readiness assessment
Evaluate your organisation across data maturity, infrastructure, team skills, and governance to build a prioritised AI adoption roadmap.
AI readiness is an organisation's capacity to successfully adopt and scale artificial intelligence. It spans five dimensions: data maturity (quality, accessibility, governance), technology infrastructure (compute, APIs, MLOps tooling), talent and skills (data engineers, ML practitioners, AI-literate business users), organisational culture (executive sponsorship, willingness to change processes), and strategy alignment (clear use cases tied to business outcomes). Companies that score highly across all five dimensions typically move from proof-of-concept to production 3–5x faster than those that invest in models before addressing foundational gaps.
Assessment
AI Readiness Assessment
1. Data Infrastructure
How centralized and accessible is your data?
2. Data Quality
How clean and consistent is your data?
3. Technical Talent
What AI/ML expertise does your team have?
4. Process Maturity
How standardized are the processes you want to automate?
5. Leadership Buy-in
How committed is leadership to AI investment?
How scoring works
Results
Answer all five questions, then click “Calculate Results” to see your AI readiness score.
Frequently Asked Questions
What makes a company AI ready?+
AI readiness requires strength across five pillars: (1) clean, accessible, and well-governed data; (2) scalable cloud or on-premise infrastructure with MLOps tooling; (3) a team that includes data engineers, ML practitioners, and AI-literate business stakeholders; (4) executive sponsorship and a culture open to process change; and (5) clearly defined use cases tied to measurable business outcomes. Weakness in any one pillar can stall an otherwise promising AI programme.
What data requirements must be met before starting an AI project?+
At minimum you need: sufficient volume of relevant historical data (typically 6–12 months), consistent data formats and schemas, documented data-quality baselines, access controls and privacy compliance (GDPR, HIPAA, etc.), and a data pipeline that can deliver fresh data to models in production. Many organisations underestimate the effort required — data preparation often accounts for 60–80% of a project's timeline.
What team skills are needed for AI adoption?+
A balanced AI team includes data engineers (to build and maintain pipelines), ML engineers or data scientists (to develop and tune models), MLOps or platform engineers (to deploy and monitor models in production), a product owner (to prioritise use cases and define success metrics), and AI-literate domain experts (to validate outputs and manage change). Early-stage teams can start with 3–5 people; enterprise-scale programmes typically need 8–15.
What are the most common blockers to AI adoption?+
The top blockers are: (1) poor data quality or siloed data systems, (2) lack of executive sponsorship, (3) unclear ROI expectations leading to premature project cancellation, (4) shortage of ML engineering talent, (5) regulatory uncertainty around AI governance, and (6) cultural resistance to changing established workflows. Addressing these before selecting a model or vendor dramatically increases the probability of success.
Where should a company start its AI journey?+
Start with a high-impact, low-complexity use case — one that uses data you already collect, solves a well-understood business problem, and can show measurable results within 8–12 weeks. Common starting points include document classification, demand forecasting, customer-ticket triage, and anomaly detection. Use this first project to build internal capability and demonstrate value before scaling to more ambitious initiatives.