A year ago the adoption story was one number, the MIT report's 95% of pilots with no return, whose methodology was narrower than its headline, as we set out at the time. The surveys published since are larger, more consistent with each other, and tell a less dramatic story that is more useful to a business deciding what to do next. This briefing reads them together as of the last week of January 2026, with the Irish figures wherever an Irish figure exists.
How much, and where
- 88%
- Organisations using AI in at least one business function, up from 78% a year earlier (McKinsey, 1,993 respondents, 105 countries) [1]
- 20.0%
- EU enterprises with ten or more employees using AI in 2025, up from 13.5% (Eurostat) [2]
- $37bn
- Enterprise spending on generative AI applications and models in 2025, from $11.5bn in 2024 (Menlo Ventures) [3]
| Survey | Fieldwork | Who | Headline |
|---|---|---|---|
| McKinsey State of AI | Jun–Jul 2025 | 1,993 participants, 105 countries | 88% use AI in at least one function; 39% report any EBIT impact; 6% high performers |
| Eurostat | 2025 reference year | EU enterprises, 10+ employees | 20.0% use AI, up from 13.5%; Denmark 42.0%, Finland 37.8% |
| Menlo Ventures | Published Dec 2025 | Enterprise spend analysis | $37bn spend; API share Anthropic 40%, OpenAI 27%, Google 21% |
| Ibec | Jul 2025 | Irish employees | 40% use AI in their work, up from 19%; 81% want more training; 27% have had none |
| Deloitte | Aug–Sep 2025 | 1,854 executives, 14 countries | 1 in 4 Irish firms has a chief AI officer, against 19% elsewhere |
| PwC CEO Survey | Published Jan 2026 | 4,454 chief executives, 95 countries | 17% of Irish CEOs saw AI revenue in the past year, against 29% globally |
| Gartner Hype Cycle | Aug 2025 | Analyst assessment | Generative AI in the trough of disillusionment; agents at the peak |
Two readings survive contact with all of those numbers. The first is that use is now near-universal among large organisations and roughly one in five among all firms above ten staff.[1][2] The second is that use is shallow. McKinsey finds a third of organisations scaling AI across the enterprise and only 6% capturing more than 5% of earnings from it; 62% were experimenting with agents and 23% had scaled one anywhere.[1] The distance between trying a tool and running part of the business on it has not closed, and the surveys are now precise enough to measure it.
Ireland, specifically
Ireland sits a little above the European average in Eurostat's census and a long way behind the Nordic leaders.[2] The executive surveys add texture. Deloitte's fourteen-country study found one in four Irish firms had appointed a chief AI officer against 19% elsewhere, which suggests governance ahead of delivery.[5] PwC's survey of chief executives, published at Davos in January, found only 17% of Irish respondents had realised additional revenue from AI in the past year against 29% globally, and Irish confidence in revenue growth at its lowest in the survey's history.[6] Ibec's survey of employees found usage had doubled in a year to 40%, that 81% of workers said they could do more with training, and that 27% had received none.[4] Adoption in Ireland is being pulled from below by staff and set from above by governance, with the middle, where a workflow gets changed, still thin.
Where the value showed up
Software development was the runaway case. Coding tools took $7.3bn of enterprise generative-AI spending in 2025, more than IT, marketing, customer service, design and human resources combined, and Anthropic's share of that market drove its 40% of API spend, up from 12% two years earlier.[3] Customer service and document-heavy work came next, with smaller and more contested gains. OpenAI reported a 40% average reduction in ticket handling time across its enterprise customers, which is a vendor's number.[8] JPMorgan's internal assistant reached 200,000 daily users who reported saving three to six hours a week on document work.[9] Klarna, the most-cited automation story of 2024, spent 2025 walking part of it back: its chief executive told Bloomberg in May that cost had been too dominant a factor and quality had suffered, and the company began hiring human agents again while the assistant kept most routine conversations.[10][11]
| Company | What | Reported |
|---|---|---|
| JPMorgan Chase | Firm-wide assistant on OpenAI and Anthropic models | 200,000 daily users; 3–6 hours a week saved; 450+ use cases in production |
| Klarna | AI customer-service agent, then partial reversal | Two-thirds of chats automated in 2024; human agents rehired from May 2025 after quality fell |
| AIB | Microsoft 365 Copilot | Rolled out to most of about 10,000 staff from July 2025 |
| Shopify | Company-wide mandate | AI use a baseline expectation; teams must show AI cannot do a job before hiring |
| Accenture | Reskilling and AI-linked restructuring | 550,000 staff reskilled; about 11,000 exited in September 2025 as part of an $865m programme |
The workforce
There was no evidence of economy-wide displacement in 2025, and clear evidence of an effect at the entry level. Stanford's study of payroll records from ADP, published in August, found employment of 22 to 25 year olds in the most AI-exposed occupations about 13% below where it would otherwise be since late 2022, while older workers in the same occupations were unaffected; the effect ran through reduced hiring rather than redundancies.[15] Employers cited AI for 54,836 announced US job cuts in 2025, about 5% of the total.[16] The company-level stories were mixed: Accenture tied cuts to staff who could not be reskilled while reporting half a million reskilled, and Shopify's chief executive made AI use a condition of asking for headcount.[13][14] In Ireland the constraint ran the other way, with four in five workers asking for more training than they had had.[4]
Why the pilots still died
The MIT figure measured the absence of a financial baseline in a non-random sample, and it has held up as a description of a management problem rather than a technology one; its own explanation was that tools which do not learn from feedback stall after the demo, and that purchased tools reached production about twice as often as internal builds.[17] Gartner's placement of generative AI in the trough of disillusionment in August was the same finding from the analyst side: pilots that impress and do not scale.[7] The shadow numbers explain part of it. In the MIT sample 90% of workers used personal AI tools while 40% of employers had bought any; the adoption was happening from the bottom, on personal accounts, outside any policy.[17]
