AI Adoption: What the Evidence Measures

A figure describing AI adoption is useful only when its coverage is clear. It may measure individual use of an assistant, an organisation's use in one business function, or deployment within a controlled production process. These are different activities.

Start with the population

The OECD reports that 20.2% of firms used AI in 2025 across countries with available data. Stanford's 2026 AI Index reports an 88% adoption rate among surveyed organisations for the same year. The figures come from different evidence bases and should not be presented as competing estimates of the same population. [1] [2]

A leadership team needs to establish which organisations were represented, what respondents were asked and what counted as use. A high adoption rate does not establish broad deployment within each organisation. It also does not establish a return on investment.

Adoption is not an operating model

An employee drafting a document with an assistant may be counted as a user. A process connected to customer records, approved data sources and a review workflow is a different proposition. The second requires decisions about access, acceptance criteria, responsibility and exceptions.

Those decisions are best examined at task level. What work is being done? What standard is required? Who checks the result? Where does it enter the next process? What happens when the output is wrong?

Tiph's assessment

Adoption statistics provide context for an investment decision. They do not replace an assessment of the proposed process. A useful pilot records the existing workload and compares the revised process against it, including review time, correction work and ongoing system costs.

The distinction matters because the objective is an operating improvement that can be examined, rather than an increase in reported AI use.