AI Readiness Checklist

Senior leadership can articulate what AI means for your industry

Not just buzzwords — a genuine understanding of how AI will reshape competitive dynamics, customer expectations, and operational models in your sector over the next 2-3 years.

There is an executive sponsor or champion for AI initiatives

A named individual at C-suite or board level who owns the AI agenda and has the authority to allocate resources and remove blockers.

AI is part of the strategic plan, not just an IT initiative

AI appears in your strategic roadmap as a business priority, with defined outcomes tied to revenue, efficiency, or competitive advantage — not buried in the IT department's backlog.

The board has discussed AI risk and opportunity in the past 6 months

Board-level conversations about AI should cover both the upside (opportunities for growth and efficiency) and the downside (regulatory risk, ethical concerns, competitive threats from AI-native competitors).

There is a budget allocated (or being planned) for AI initiatives

Even if modest, a dedicated budget signals organisational commitment. This includes training, tools, consulting, and pilot projects.

Staff across the organisation understand what AI can (and cannot) do

Beyond the hype — your people should have a realistic, grounded understanding of AI capabilities and limitations relevant to their roles.

Teams have received role-specific AI training (not just a generic overview)

Marketing teams learning prompt engineering for content; finance teams exploring AI for forecasting; HR teams understanding AI in recruitment. One-size-fits-all training doesn't stick.

There are internal AI champions or early adopters who share knowledge

Peer-to-peer learning is powerful. These champions run informal demos, share tips, and build grassroots enthusiasm across departments.

Staff feel psychologically safe to experiment with AI tools

People should feel comfortable trying AI tools, making mistakes, and asking questions without fear of judgment or job-threat anxiety.

There is a learning pathway or development plan for AI skills

A structured approach to upskilling — not just a one-off workshop, but an ongoing programme with clear progression from awareness to proficiency.

There is a clear AI usage policy that staff know about and follow

Written guidelines on what AI tools can be used, for what purposes, and with what data. Crucially, staff must actually know about the policy — not just its existence in a SharePoint folder.

Data handling practices are defined for AI tool usage

Clear rules about what data can be input into AI tools, how outputs should be validated, and how to handle sensitive or personal data in AI workflows.

There is an approval process for adopting new AI tools

A lightweight but effective process for evaluating new AI tools against security, privacy, accuracy, and commercial criteria before they're used in production.

You have considered regulatory requirements (UK AI Act, GDPR, sector-specific rules)

Awareness of the evolving regulatory landscape and how it applies to your specific use of AI — including data protection, transparency requirements, and sector-specific regulations.

There is a process for human review of AI-generated outputs in critical areas

For decisions that affect people, finances, or reputation, there should be a defined human-in-the-loop review process before AI outputs are acted upon.

You have an inventory of the AI tools already in use across the organisation

Shadow AI is real — many teams are already using ChatGPT, Copilot, and other tools. An audit reveals what's happening, where the risks are, and what's working well.

Your data is organised, accessible, and of sufficient quality for AI use

AI is only as good as its data. Are your key datasets clean, structured, up-to-date, and accessible to the tools and teams that need them?

You have evaluated which workflows would benefit most from AI augmentation

A prioritised list of processes where AI could save time, improve quality, or enable new capabilities — based on actual workflow analysis, not just assumptions.

IT and security teams are involved in AI tool evaluation and deployment

Security, privacy, and infrastructure implications should be assessed before, not after, AI tools are adopted. IT teams need to be partners, not gatekeepers.

You have a method for measuring the impact of AI implementations

Before/after metrics, time savings, quality improvements, cost reduction — you should know how to demonstrate ROI for AI investments.

The organisation has a track record of successfully adopting new technology

Past experience with digital transformation, cloud migration, or new tool adoption gives insight into how well the organisation handles change.

Communication about AI is proactive, honest, and addresses concerns

Leadership communicates openly about AI — why it matters, what it means for roles, and how the organisation will support people through the transition.

There is willingness to redesign workflows and processes around AI capabilities

Not just bolting AI onto existing processes, but genuinely rethinking how work gets done to take full advantage of AI augmentation.

Experimentation and pilot projects are encouraged and resourced

A culture of testing and learning, where small-scale pilots can prove value before committing to full-scale rollout.

There is a clear change management plan for AI adoption

A structured approach to managing the people side of AI transformation — communication plans, training schedules, feedback loops, and support mechanisms.