Our previous newsletter looked at the AI Diffusion Matrix Tool and what an organisation can learn from its assessment. But knowing where you stand is only one part of the solution. What do you do next?
For many organisations, the challenge is no longer getting access to AI. Employees are already trying different tools, teams are running pilots and leaders are investing in new systems. What’s more difficult is turning this activity into improvements that can be seen in everyday work.
It’s not about buying more AI tools
Using AI does not automatically create business value. The AI Diffusion Matrix report makes a distinction between the two and looks at whether a use case changes a workflow and improves a measurable outcome, such as processing time, cost, customer experience, error rates or product development. AI readiness goes beyond just mere adoption. It involves how an organisation structures its work, supports its technology, manages risk and helps its people use AI effectively.
The McKinsey ‘State of AI in 2026’ report also points to this gap. While 80% of respondents say AI has improved their individual productivity and half say it helps them make better decisions, only 37% report a positive impact on their organisation’s EBIT, a figure that remains largely unchanged from the previous year. This suggests that the benefits employees see from using AI do not automatically translate into broader business results.
What will AI-ready organisations do differently?
Measure what changes: AI projects will start with a clear question: what is this expected to improve? Organisations will track measures such as cycle time, cost per transaction, customer satisfaction, error rates, revenue or other relevant business outcomes. The Matrix recommends defining these measures before a pilot begins and assigning clear owners for the use case, data, technology, risk and benefits.
Change the way work is organised: Adding AI to an existing process does not necessarily improve the process. AI-ready organisations will examine the whole workflow and decide what should change. This may bring business, technology, data and risk teams together rather than leaving AI as a project owned by one department.
Get the basics of data and technology right: An AI system is only as dependable as the information and infrastructure supporting it. Organisations will need clearer ownership of data, better data quality, access controls and visibility into the models, cloud services and other systems their AI depends on. The Matrix also stresses that leaders need to understand these dependencies rather than seeing AI only through the application or interface.
Make responsibility clear: As AI becomes part of important decisions and workflows, someone needs to be accountable for what happens when things go wrong. Governance will therefore need to cover security, data use, human review, risk, vendor contracts and escalation. The Matrix recommends assigning specific roles across business, technology, data, risk, procurement and AI value management.
Help people work with AI: Readiness also depends on culture. Employees need room to experiment, ask questions and learn, while leaders need to remove barriers and support responsible use. The Matrix links AI readiness with open communication, cross-functional knowledge sharing, leadership support and learning from experimentation.
What should leaders look for?
A useful way to think about AI readiness is to look at what has changed because of AI. Are teams spending less time on repetitive work? Are processes becoming easier to manage? Are successful pilots actually making their way into everyday operations? These changes matter more than the number of AI tools an organisation has adopted.
The organisations that build lasting value from AI will be the ones that turn individual experiments and investments into a capability that is part of how the organisation works. AI is meant to become more than another technology initiative. It meant to be part of how work is improved over time.
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