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AI Adoption Trends

Original field guide · October 2026 · Free PDF

The AI Adoption Operating Report

From access to accepted work: the evidence, economics, and decisions that turn a promising AI trial into something a company can actually rely on.

Most reports tell you how many organizations say they use AI. This one helps you decide what to try, how to measure the whole job, when to give a system more authority, and when to stop. It combines primary research with worksheets you can use in a real planning meeting.

Open the free 20-page PDF No form, email address, or payment required. Opens in a new tab.
A person holds the beginning of a paper pathway that becomes sturdy steps toward an open door
AI-generated editorial illustration. The route from an idea to useful work must be built.

The useful distinction

A seat is access. It is not adoption.

The report tracks four different questions: Who has an approved tool? Does it get used on eligible work? Does the output survive review? Does the finished job improve a business outcome? A program can look healthy on the first measure and fail on the fourth.

01

Access

The right people can reach an approved tool.

02

Activity

They use it on eligible work, not only in demos.

03

Accepted work

Outputs withstand review and correction.

04

Result

Service, quality, speed, or economics improve.

Evidence without a false funnel

Three numbers worth reading carefully

A U.S. Census working paper estimates that 18% of firms used AI in a business function in its Nov 2025-Jan 2026 reference period. Deloitte surveyed 3,235 leaders in 24 countries and found that 25% said their organization had moved at least 40% of AI experiments into production. In that same Deloitte survey, 84% reported that their organization had not redesigned jobs around AI.

These are different populations and questions. They do not describe a single conversion funnel. The Census paper also changed the core question from earlier waves, so its 18% cannot be treated as a direct rise from the old 5.4% measure. The PDF puts source, sample, and limitation next to each finding.

18%
of U.S. firmsAI in a business function; Census working paper.
25%
of Deloitte respondents40% or more of AI experiments in production.
84%
of Deloitte respondentsNo jobs or nature of work redesigned around AI.
An illustrated pathway carries a human-led task through source records and review toward a completed result, while a disconnected loop goes nowhere
AI-generated editorial illustration of the operating path.

Inside the report

A plan you can put to work

  • Four testable jobs in service, finance, marketing, and operations, each with a first boundary and stop rule.
  • A 30-day pilot and a 60-day expansion sequence that earns permissions gradually.
  • A measurement sheet for accepted work, review load, errors, outcome, and full cost.
  • A six-dimension readiness scorecard, a one-page pilot charter, and explicit expand/revise/stop gates.

The financial discipline

Freed time is not automatically saved money

If AI saves three net minutes on 1,200 cases, that releases 60 hours. It may be valuable capacity, but it becomes cash savings only when a budget line changes or spending is avoided. The report shows the arithmetic and keeps capacity, cash, growth, and quality separate.

It also includes the less exciting work that often determines success: source cleanup, expert review, integration, training, monitoring, and recovery. Stanford's study of 51 selected successful deployments is useful here precisely because it describes those implementation costs; it is not a random sample of all AI projects.

Model your own workflow in the AI ROI calculator →
An illustrated approved route passes through an open gate while a tempting shortcut curves toward a dead end
AI-generated editorial illustration. Useful controls include a workable approved route.

Research basis

Source notes you can follow

This is an editorial synthesis, not a new survey. The report distinguishes representative firm data, executive self-report, field experiments, a set of success-selected cases, and voluntary risk guidance. Every consequential figure is tied to its original source and what that source can support.

U.S. Census Bureau research working paper ↗

Firm-level AI use, Nov 2025-Jan 2026; the question differs from earlier Census measures.

Deloitte State of AI in the Enterprise ↗

Survey of 3,235 senior leaders in 24 countries, fielded Aug-Sep 2025.

Generative AI at Work ↗

Original study of 5,179 customer-support agents and task-specific productivity effects.

Shifting Work Patterns with Generative AI ↗

Randomized field experiment involving 6,000 workers; email time changed more than meetings.

The Enterprise AI Playbook ↗

Stanford Digital Economy Lab study of 51 selected successful deployments and their implementation work.

NIST AI Risk Management Framework ↗

Voluntary guidance for mapping, measuring, managing, and governing AI risk.

The PDF also cites a software-development field experiment and a 2026 human-AI collaboration experiment. Its scorecard and pilot plan are AI Adoption Trends tools, not independently validated benchmarks.

Free to read and share

Take the report into your next adoption meeting

Twenty pages. No form. Includes a reusable charter and scorecard. The PDF opens in a new tab, where you can save or print it.

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