AI Readiness Scorer
Assess your organisation’s readiness for AI adoption across six key dimensions. Get a readiness score out of 100, identify gaps, and receive a prioritised action plan.
Assess Your Organisation
Select the option that best describes your current state across six AI readiness dimensions. Be honest - this assessment is most useful when it reflects reality.
AI Readiness Levels by Industry
| Industry | Avg Score | Common Strengths | Common Gaps | Typical First AI Use Case |
|---|---|---|---|---|
| Financial Services | 62/100 | Data, Governance | Skills, Infra | Fraud detection, risk scoring |
| Professional Services | 48/100 | Leadership, Budget | Data, Skills | Document automation, analytics |
| Retail / E-commerce | 52/100 | Data, Infrastructure | Governance, Skills | Customer personalisation |
| Manufacturing | 38/100 | Budget, Leadership | Data, Infrastructure | Quality control, predictive maintenance |
| Healthcare | 42/100 | Governance, Data | Skills, Infrastructure | Diagnostics, patient scheduling |
| Technology | 71/100 | Skills, Infrastructure | Governance, Budget | Product intelligence, DevOps |
Scores represent UK industry averages based on 2025 benchmarks. Individual organisations vary significantly within each sector. Talk to Cloudswitched for a personalised AI readiness assessment.
More Free Tools
Try our other free AI and IT calculators.
Frequently asked questions
AI readiness depends on data quality and accessibility, existing technical infrastructure, staff digital skills, governance around data privacy, and organisational appetite for change. This scorer assesses each area and flags where gaps are most likely to derail an AI project before it delivers value.
Poor data quality and fragmented data spread across disconnected systems is typically the biggest barrier UK SMEs face, since most AI tools need clean, accessible, well-structured data to work reliably. Staff skills gaps and unclear governance policies around data use are the next most common blockers.
Generally yes - AI tools amplify existing data problems rather than fixing them, so inconsistent, duplicated, or poorly structured data typically leads to unreliable AI outputs. Most UK SMEs need some data cleanup and consolidation work before an AI implementation delivers trustworthy results, which is why this scorer weighs data quality heavily.
At minimum, a governance policy should cover which data can be shared with AI tools, staff approval processes for new AI tools, data privacy and GDPR compliance, and accuracy-checking for AI-generated outputs. Most UK SMEs lack any formal policy, which this readiness score flags as a gap to address before wider rollout.
