New research suggests employees are adapting to AI faster than the organisations around them. For smaller businesses, that readiness gap may be easier to close than it first appears.
If your people are already using AI to draft emails, summarise meetings, prepare documents or analyse information, you are in good company. The more useful question is what happens after that.
For many organisations, the answer is still: not enough.
McKinsey’s latest research on AI transformation, published in July 2026, puts numbers around something many leaders have probably already sensed. Around 70% of respondents said they felt personally ready to adopt and use AI, while only 27% of leaders believed their organisations were ready to make the people and culture shifts needed for an agentic future.
Employees are adapting faster than the organisations around them.
That gap matters because McKinsey found organisational readiness was much more strongly associated with reported enterprise value than personal readiness. Organisational readiness accounted for 48% of the difference between leaders reporting value and those who did not, compared with 25% for personal readiness.
The tools can work. People can be willing to use them. But if workflows, expectations and operating models stay broadly the same, individual productivity gains can remain exactly that: individual.
Three stages of AI maturity
McKinsey groups organisations into three useful horizons of AI maturity.
The first is enablement: giving employees access to general-purpose AI tools that help with parts of their existing jobs.
The second is automation: using AI at scale to automate and improve end-to-end workflows across teams or functions.
The third is reinvention: redesigning roles, workflows and operating models around what AI now makes possible.
Most leaders in the research still placed their organisations in the first two horizons. Only 11% reported reaching reinvention. That is useful context for businesses that feel as though they are still working out the basics. Most organisations are.
The important part is what happens next.
The further organisations progressed, the more likely leaders were to report meaningful enterprise value from AI: 13% in enablement, 24% in automation and 48% in reinvention.
One of the most practical findings sits inside the earliest stage. Among organisations focused on enablement, leaders were 5.3 times more likely to report enterprise value when workflows had been redesigned than when they remained unchanged, 32% versus 6%.
That is a far more useful lesson than simply buying more AI.
Access to the technology matters. Changing how the work itself gets done is what turns that access into something the business can measure.
Why smaller businesses may have an advantage
Enterprise research can be easy for a smaller organisation to dismiss as something designed for companies with transformation teams, programme boards and enormous technology budgets. The underlying lesson here applies at a much more practical level.
Smaller businesses feel wasted spend quickly. A 60-person organisation paying for AI licences that save individuals a few minutes but make no measurable difference to customer response times, turnaround, capacity or cost is still spending real money for convenience.
The advantage is that smaller organisations can often change the workflow around the technology much faster.
Redesigning a process inside a multinational may involve multiple functions, governance layers and months of consultation. In a smaller business, it may mean getting the right people together, mapping how a quote, customer query or onboarding process actually works, deciding where AI helps, and changing the process while everyone involved can still fit around one table.
The opportunity is not to recreate an enterprise AI transformation programme on a smaller budget. It is to use the agility you already have.
Trust still matters
The research also makes an important point about people. Trust in the organisation emerged as a critical readiness factor across all three maturity horizons.
Employees need to understand what is changing, what is expected of them and what support is available as AI affects the way work is done. McKinsey found anxiety about AI-related change at every job level, with one in four middle managers expressing concern compared with one in five individual contributors.
Some uncertainty is inevitable. Clear communication, realistic expectations, useful training and follow-through matter more than reassurance that promises nothing will change.
That matters for smaller organisations too. Introducing an AI tool is easy. Building confidence around where it should be used, what good use looks like, what stays human-led and how new capacity should be used requires deliberate management.
AI activity and AI value are different things
McKinsey’s research ends with a simple message: “Don’t wait. Iterate.” The organisations creating an advantage are learning by trying something specific, measuring what happened, redesigning the workflow and improving it again.
That is a useful approach for a smaller business because it keeps AI adoption tied to practical outcomes.
Start with one or two workflows where improvement would genuinely matter. Understand how the work happens today. Identify the parts AI can improve. Redesign the process around that capability, support the people involved and agree how success will be measured.
That measure might be quote turnaround time, customer response time, administrative hours removed, fewer errors, more capacity or better conversion.
The specific metric will vary. The principle is the same: AI activity and AI value are not the same thing.
The VitrX view
For VitrX, this research reinforces a pattern that is becoming increasingly familiar. The gap is often less about access to AI and more about what the organisation does around it.
Employees may already be experimenting. Licences may already be in place. But workflows have not changed, expectations are unclear, and time saved by one person does not automatically become capacity gained by the business.
Closing that gap does not require every organisation to launch a major transformation programme. A better starting point is a clear business outcome, a realistic view of the existing workflow and a decision about where technology can make a measurable difference.
VitrX can support organisations with the technology side of that journey, from AI-enabled workplace solutions and Microsoft technologies through to the cloud, infrastructure, networking and security that sit around them. The aim is to make sure AI adoption is supported by an environment that is practical, secure and aligned with how the business actually needs to work.
If you want a clearer picture of where your organisation sits, what your people are already doing with AI, and which workflows may be worth changing first, that is a useful place to start the conversation.
A readiness conversation costs an hour. Licences that change nothing cost rather more.
References
McKinsey & Company.
“From adoption to impact: Three horizons of AI transformation.” 8 July 2026.
