Are we falling behind on AI?

What the official numbers say

Headlines can make it sound as if every company has already transformed itself with AI. National statistics give a calmer picture.

• Europe: Eurostat reports that 19.95% of EU enterprises with 10 or more employees used AI technologies in 2025, up 6.47 percentage points on 2024. Size matters: 17% of small, 30.36% of medium and 55.03% of large enterprises used AI.
• United States: the Census Bureau's Business Trends and Outlook Survey found overall AI use hovered between 17% and 20% from December 2025 to May 2026. By early May 2026, 32% of firms with 100 to 249 employees and 37% of firms with 250 or more reported using AI.

Adoption is growing quickly, but a mid-sized company that is only starting is in the majority, not behind.

Why do some reports show much higher figures? Many surveys ask only large companies, or only people already using AI, and some count any trial at all. Questions also change: in November 2025 the Census Bureau widened its question from AI used in producing goods or services to AI used in any business function. Check who was asked before comparing yourself.

How many of the five self-check questions can your organisation answer yes to today? Book a free AI Value Briefing and we will look at it with you.

Experimenting is not the same as getting value

A 2025 report from MIT's Project NANDA, based on interviews, a survey of senior leaders and a review of over 300 public AI initiatives, found that over 80% of organisations had explored or piloted general tools such as ChatGPT or Copilot, and nearly 40% reported deploying them. For custom enterprise AI tools, only 5% reached production. It is a preliminary study with a modest sample, but it matches a common pattern: many pilots, fewer working changes to how work is done.

So the real question is not whether you use AI, but whether any of your use has changed a piece of work in a way you can measure.

Why second, with a plan, can beat first

Being first brings learning, but also the cost of early mistakes: tools that change, pilots that stall, budgets spent before anyone knows what works. A company that moves second, with a clear task and a way to measure it, can borrow the lessons and avoid some of the cost.

Size is not the barrier it seems. The same MIT NANDA report found that mid-market companies moved faster than large enterprises: top performers reported about 90 days from pilot to full implementation, against nine months or longer for large enterprises.

A simple self-check

Answer yes or no:

1. Do you, personally, use an approved AI assistant on your own work at least weekly?
2. Do your people know which AI tools are approved and what data must never be pasted in?
3. Has at least one team changed a real, frequent task with AI?
4. Was that change measured against a baseline taken before?
5. Is someone named to decide whether to scale, change or stop?

Three or more yeses suggests the foundations are in place. Fewer means you have clear, small next steps, not a crisis.

What keeping pace means in practice

Keeping pace is a steady rhythm, not a big bet: leaders who use AI themselves, people trained on their own work, simple guardrails, and one measured improvement at a time that is shared across teams (Field Guides 29 and 32). Expect results to dip before they rise (Field Guide 30), and judge progress by measured hours given back, not by the number of tools bought.

It also means saying no. Not every process needs AI, and choosing to wait on a task where the risk is high or the gain is small is a sound decision, provided it is made deliberately and reviewed, rather than left to drift.

Goes deeper in our free AI Field Guide: 32. Leading AI adoption: what leaders do differently, 16. Choosing the right level: use what you have before you build, 28. Measuring the value: hours given back, 30. The AI J-curve: why results dip before they rise, 29. Sharing what works across a team.