AI & Intelligent Automation

How ready is your organisation for AI?

This assessment measures your organisation’s readiness across five dimensions — from strategy and data to technology, people and governance. Answer 18 short questions and get a clear, evidence-based picture of where you stand today and where to focus next.

Strategy & Direction

Data Foundations

Technology & Infrastructure

People & Adoption

Governance & Risk

18 Questions · Under 8 Minutes · Results Delivered Instantly

How would you describe your organisation's current AI direction?

We haven't formally discussed AI at leadership level yet We've had conversations but haven't agreed on priorities or a plan We have some agreed priorities but no formal strategy or roadmap We have a defined AI strategy with clear priorities and board-level ownership

When your board or executive team discusses AI, what typically drives the conversation?

External pressure — competitors or media coverage General awareness that we should be doing something Specific business problems we believe AI could help solve A clear set of use cases with expected outcomes and success metrics

How are AI initiatives currently managed in your organisation?

Individual teams or departments run their own experiments independently There's informal coordination but no central oversight There's a designated owner or team, but it's not well resourced AI initiatives are governed centrally with clear accountability and budget

How confident is your leadership team in making AI investment decisions today?

Not confident — we lack the knowledge to evaluate options critically Somewhat confident for simple use cases, but not for complex ones Reasonably confident, though we rely heavily on vendor input Very confident — we have the internal expertise to evaluate and decide independently

When your teams need data to make a decision, how would you describe the typical experience?

Finding the right data is difficult and time-consuming Data is available but often inconsistent or requires significant cleaning Data is mostly accessible and reliable, with some gaps Data is trusted, well-documented, and readily accessible across the business

How well does your organisation know what data it holds and where it lives?

We have a rough idea but no formal inventory or catalogue We've mapped some of it, but coverage is incomplete We have a data catalogue that covers most key datasets We have a comprehensive, actively maintained data catalogue

How would you describe the quality of your data for use in AI or machine learning?

Data lives in the systems that produced it — we query operational databases or export to spreadsheets when we need it We have a central data warehouse or data lake, but it's largely a copy of source data with limited transformation We have a layered data structure (e.g. raw → cleansed → analytics-ready) but coverage is partial or inconsistent We have a mature layered data architecture with curated, analytics-ready datasets that feed our reporting and any AI use cases

Does your organisation have a data governance framework in place?

No — data management is largely ad hoc There are some informal processes but no formal framework We have a framework but it's not consistently applied We have a mature, consistently applied data governance framework

How would you describe your current technology environment in relation to AI adoption?

Legacy systems that would need significant work before AI could be layered on Newer systems on top, but the old systems underneath still do the heavy lifting — integrating the two is where things get complicated Mostly modern systems with APIs available, though integration still requires effort A cloud-native, API-first environment well-suited to AI integration

How does your organisation currently handle integrations between systems?

Integrations are largely manual or point-to-point with no central approach We have some integrations but they're fragile and hard to maintain We have a defined integration approach, though not all systems are connected We have a robust integration layer (API gateway, middleware, or similar) that connects our key systems reliably

How would you best describe your organisation's current use of AI tools?

We don't use AI tools in any meaningful way Staff use general-purpose AI tools (ChatGPT, Claude, etc.) informally We've rolled out AI features within our existing software (Copilot, Gemini in Workspace, vendor AI features) We have custom AI or ML solutions running in production, beyond what comes built into our tools

If you deployed a new AI tool to your team today, what would happen?

Most people would ignore it or revert to existing processes within weeks A small number of enthusiasts would use it; the majority would not Adoption would be patchy — good in some teams, poor in others Most teams would adopt it if given adequate onboarding and support

How would you describe AI literacy across your organisation?

Very limited — most staff have little understanding of what AI can and can't do Awareness is growing but capability is low outside a few individuals A reasonable proportion of staff are comfortable using AI tools in their work AI fluency is well-distributed and actively developed as an organisational capability

Does your organisation have dedicated internal capability to lead AI initiatives?

No — we would be entirely dependent on external vendors We have interested individuals but no dedicated roles or formal capability We have some capability, though it's stretched across other responsibilities We have dedicated people with the skills to own AI programmes end to end

How does your organisation typically approach change when new technology is introduced?

Adoption is slow and often incomplete — people stick to old habits We push tools out but don't invest much in change management We do some change management but don't always sustain it long enough We invest in structured adoption programmes and measure behaviour change

Does your organisation have a policy that governs how AI tools can be used by staff?

No — staff use AI tools at their own discretion with no guidance There's informal guidance but nothing documented or enforced We have a policy but it hasn't been widely communicated or adopted We have a clear, communicated AI use policy that staff are aware of and follow

How does your organisation manage the risk of AI producing incorrect or biased outputs?

We haven't formally considered this We rely on individual judgement to catch errors We have some review processes in place for high-stakes outputs We have defined human-in-the-loop processes and quality checks for AI outputs

Has your legal, compliance, or risk function engaged with your AI programme?

No — AI has not been on their radar There's been some conversation but no formal involvement They're aware and have flagged some concerns, but haven't formally reviewed our approach Legal/compliance/risk are active participants in AI governance decisions

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