Resource
AI Readiness Hub
Everything that makes an organisation ready for AI, in one place. Five readiness areas, each broken into the practical things you can measure, fix and improve — starting with the free AI Readiness Assessment.
Leadership Readiness
Nothing moves without direction from the top. This measures how ready your leadership team is to back AI with decisions, budget and accountability.
Executive understanding
Leaders know what AI can and cannot do for their business, and can separate genuine opportunity from hype.
Sponsorship
At least one senior leader owns AI adoption, champions it internally and is accountable for results.
Risk appetite
Leadership has agreed, in principle, what the business is willing to use AI for — and what it is not.
Workforce Readiness
Adoption is carried by people, not plans. This measures whether your team has the skills, awareness and training to use AI well and safely.
Skills
Staff have the practical ability to use AI tools in their day-to-day work, not just awareness that they exist.
Awareness
Everyone understands where AI is already being used — including the shadow AI that sits outside official IT.
Training
There is a structured way for people to learn and keep up to date, rather than one-off sessions.
Data Readiness
AI is only as good as the data it works with. This measures whether your data is ready to support AI tools today.
Data quality
The information AI would draw on is accurate, consistent and current — not scattered across spreadsheets.
Ownership
Every important dataset has a named owner responsible for keeping it correct, secure and documented.
Accessibility
The people and tools that need data can actually reach it, without fragile manual workarounds.
Governance Readiness
Guardrails make scaling safe. This measures whether the rules around AI use exist, have owners and are actually enforced.
Policies
A written AI use policy covers what tools staff may use, what data they may share and what is off-limits.
Ownership
Someone is named as responsible for AI governance, so accountability does not fall between departments.
Oversight
AI use is reviewed on a regular loop — vendor choices, outputs and risks — not adopted silently.
Operational Readiness
AI has to work inside real processes. This measures whether operations can run and monitor AI day to day.
Monitoring
AI-assisted processes are tracked, so quality and performance are measured rather than assumed.
Incident response
There is a clear way to spot, escalate and fix problems when an AI tool goes wrong.
Reporting
Results feed back to leadership, so AI investment is judged on evidence.
See where you stand
The free AI Readiness Assessment scores you across all of these areas, classifies your maturity and gives you a roadmap that starts with your weakest links.
Start the free assessment