Free Assessment

How AI-ready is your organisation?

17 questions across 5 dimensions. 15 minutes. A scored baseline, with specific next steps, before you commit any AI budget.

15 minutes 17 questions 5 dimensions Scored results

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The Five Dimensions of AI Readiness

0:00

Data Foundation

4 questions

Process Maturity

3 questions

Technology & Infrastructure

3 questions

People, Culture & Leadership

4 questions

Governance & Risk

3 questions

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Data FoundationProcess MaturityTechnology & InfrastructurePeople, Culture & LeadershipGovernance & Risk

Dimension 1 of 5

Data Foundation

4 questions in this section

1. How is your organisation's core business data currently managed?

1Scattered across emails, spreadsheets, and individual files: no central system2Centralised in systems, but data is inconsistent, outdated, or poorly labelled3Mostly clean and centralised; some gaps and quality issues remain4Clean, structured, and consistently maintained with documented data standards5Formal data governance programme with ownership, lineage, and regular audits

2. How much relevant historical data does your organisation hold for its core operations?

1Less than 12 months of structured data21–2 years, but incomplete or stored in legacy formats32–5 years of reasonably complete operational data45+ years, partially digitised and accessible55+ years, fully digitised, structured, and accessible on demand

3. How quickly can your team access the data needed for a specific business decision?

1Manual extraction required: takes days or requires specialist support2Some dashboards exist but limited; most data requires manual pull3Most data accessible via reports or dashboards within hours4Real-time or near-real-time access to most operational data5Self-service data platform with governed access for all relevant teams

4. Does your organisation have documented data ownership: named individuals responsible for specific datasets?

1No: it is unclear who owns which data2Informally understood but not documented or enforced3Partial: some key datasets have named owners, others do not4Most datasets have documented owners with clear accountability5Full data ownership catalogue, enforced through policy and reviewed regularly

Process Maturity

3 questions in this section

1. How well-documented are your organisation's core operational processes?

1Undocumented: processes exist in people's heads2Some informal documentation, but outdated or inconsistently followed3Most key processes are documented and broadly followed4Documented, measured, and reviewed: with clear owners and metrics5Full process library with version control, performance metrics, and continuous improvement cycles

2. Has your organisation identified and prioritised specific AI use cases?

1No: we have not systematically identified where AI could apply2Some informal discussions, but no structured analysis3We have a shortlist of potential use cases but have not validated them4Use cases are documented, prioritised, and assessed against data and process readiness5Use cases are validated, sequenced, and tied to specific business outcomes with ROI estimates

3. How consistently are your business outcomes measured and reported?

1We track basic financials but have no operational KPIs2Some KPIs exist but measurement is inconsistent or manually compiled3Core business areas have defined KPIs measured monthly or quarterly4Leading and lagging indicators tracked in real time across major functions5Full performance management framework: KPIs linked to strategy, reviewed by leadership

Technology & Infrastructure

3 questions in this section

1. How would you describe your organisation's current IT infrastructure?

1Primarily legacy systems with limited integration capability2Mix of legacy and modern tools: some integration, mostly manual workarounds3Modern core systems with some API capability and partial cloud adoption4Cloud-first environment with strong API integration across core systems5Cloud-native architecture with microservices, strong data infrastructure, and modern tooling

2. What is your organisation's level of cloud adoption?

1On-premises only: no cloud usage2Some SaaS applications (e.g. email, accounting), but core systems are on-premise3Some workloads migrated to cloud; hybrid environment in progress4Majority of operations in cloud; active cloud strategy in place5Cloud-first strategy fully implemented; on-premise is the exception

3. How capable is your organisation at deploying and integrating new software tools?

1No in-house capability: fully dependent on vendors or consultants2Limited: basic IT support only; integration projects require external support3Some capability: can configure tools but complex integrations need partners4Strong internal IT team with proven integration and deployment experience5Dedicated engineering capability with modern DevOps, API, and integration tooling

People, Culture & Leadership

4 questions in this section

1. How does your senior leadership team currently engage with AI?

1Unaware or sceptical: AI is not discussed at leadership level2Aware and curious but no formal commitment or budget allocated3Supportive: exploring opportunities, some informal pilots approved4Actively sponsoring AI initiatives: budget committed, progress reviewed5AI is a board-level strategic priority with named accountability and governance

2. What best describes your organisation's current AI and data analytical capability?

1No staff with AI or data skills; no plans to build them2One or two individuals with basic data skills; no dedicated function3Small analytics team; limited AI/ML capability, primarily descriptive analytics4Multiple staff with AI/data science skills; some machine learning in production5Dedicated AI/data function with advanced engineering, modelling, and deployment capability

3. How does your workforce typically respond to significant technology change?

1Strong resistance: previous change initiatives have failed or stalled2Mixed: some teams resist, change requires significant management effort3Neutral: staff comply but do not actively engage4Generally receptive when change is well-communicated and supported5Change-ready culture: staff actively seek new tools and approaches

4. Does your organisation have a formal AI literacy or upskilling programme?

1No structured programme and no planned investment in AI skills2Ad hoc training when requested; no organisation-wide approach3Some structured training for selected roles or teams4Organisation-wide AI literacy programme underway or recently completed5Continuous learning culture with AI as a core component of professional development

Governance & Risk

3 questions in this section

1. Does your organisation have a formal AI policy or governance framework?

1No AI-specific policies: general IT policy is applied loosely2AI usage guidelines are being developed but not yet in place3A basic AI policy exists but is not consistently enforced4A formal AI governance policy is in place and communicated to all staff5Comprehensive AI governance framework: includes model oversight, ethics, and regular review cycles

2. How mature is your organisation's approach to data privacy and information security?

1Basic compliance only: reactive to incidents, no dedicated programme2Compliance-focused: meets regulatory minimums, limited proactive work3Dedicated privacy and security function; some proactive risk management4Privacy by design embedded in processes; regular audits and staff training5Leading programme: certified, continuously tested, integrated into product design

3. How does your organisation currently manage the risks associated with AI?

1AI risk has not been formally considered2Risk discussed informally but no structured approach or named owner3AI risk assessment performed on a case-by-case basis for some projects4Formal AI risk framework exists: risks assessed, documented, and reviewed5Integrated AI risk management: embedded in project governance, reviewed at board level

Your AI Readiness Score

/ 85

Score by dimension

Data Foundation–

Process Maturity–

Technology & Infrastructure–

People, Culture & Leadership–

Governance & Risk–

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What This Measures

Five dimensions that determine AI deployment success.

Data Foundation

The quality, accessibility, and governance of your data. AI is only as reliable as the data it processes. Poor data quality is the single most common deployment failure.

Process Maturity

Whether your processes are documented, measured, and consistent enough to automate. AI cannot reliably improve a process that varies every time it is performed.

Technology & Infrastructure

Your cloud adoption, integration capability, and infrastructure modernity. Legacy systems drive up AI deployment cost and reduce reliability.

People, Culture & Leadership

Leadership commitment, staff capability, and organisational change readiness. Technical readiness without cultural readiness produces shelfware.

Governance & Risk

Your AI policy, risk management approach, and data privacy maturity. Governance gaps create liability, and slow every subsequent deployment while you retrofit controls.

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