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Evidence before enthusiasm

Practical AI, without the fog.

Short, source-led notes for leaders deciding where AI is useful, where human judgment belongs, and what a team needs to learn next.

Research reviewed August 2026. Statistics retain the scope and caveats of their original sources.

17.7%of small firms in JPMorganChase's dataset had paid for an AI service by December 2025

Brief 01 / United States

The access problem is shrinking. The integration problem is not.

JPMorganChase Institute analyzed payment behavior across 4.6 million small businesses. Paid AI adoption rose from 5.2% in 2023 to 17.7% by the end of 2025, while the share of generative-AI users paying monthly reached 30%. The report's implication is pointed: as tools become cheap and accessible, advantage depends less on buying AI and more on integrating it into useful operations.

Read the JPMorganChase Institute research

Published April 14, 2026. Transaction-based measure of paid AI services; it excludes free tools, custom development, and AI embedded inside other software.

Brief 02 / United Kingdom60%

named limited AI skills, expertise, and knowledge as a barrier

Use cases and skills move together

Teams need help finding the work before learning the tool.

The UK Department for Science, Innovation and Technology surveyed 3,500 private-sector businesses with at least five employees and conducted 100 follow-up interviews. Limited AI skills were a barrier for 60% of businesses, while 71% cited a lack of identified need. Among businesses using or planning to use AI, creative and content creation was the most common use case at 77%.

The same research found that 84% of AI-using businesses applied at least some human input or checking. Training, use-case discovery, and oversight are not separate purchases. They are one adoption problem.

Read the DSIT AI Adoption Research

Updated February 13, 2026. Telephone fieldwork ran from February to May 2025; results were weighted by business size and sector. The study does not capture shadow AI use.

Brief 03 / Global knowledge work47%

of leaders listed upskilling existing employees as a top workforce strategy

Training is operating capacity

AI literacy is becoming part of how teams manage work.

Microsoft's 2025 Work Trend Index surveyed 31,000 knowledge workers in 31 markets. It found upskilling existing employees was leaders' top workforce strategy for the next 12 to 18 months, named by 47%. More than half of managers, 51%, expected AI training or upskilling to become a key team responsibility within five years.

The commercial lesson is not that every team needs an agent strategy immediately. It is that leaders need a shared way to delegate, provide context, inspect output, and decide where human judgment remains controlling.

Read the 2025 Work Trend Index

Published April 23, 2025. Microsoft's online survey covered full-time employed or self-employed knowledge workers and was supplemented by Microsoft 365 telemetry and qualitative interviews.

Brief 04 / United States workforce37.4%

of adults ages 18 to 64 reported using generative AI for work in August 2025

Time saved is only an input

Productivity depends on what happens after the shortcut.

The Federal Reserve Bank of St. Louis reported that workplace use of generative AI rose from 33.3% in August 2024 to 37.4% one year later. Across users and nonusers, respondents reported time savings equal to 1.6% of total work hours. A standard production model translated that into a possible productivity increase of up to 1.3% since ChatGPT's release.

The authors are explicit about the limits: reported time saved may be redirected to lower-value work, and higher-adoption industries may differ for reasons unrelated to AI. The industry correlation is not causal. Saved minutes create capacity; workflow choices determine whether that capacity becomes value.

Read the St. Louis Fed analysis

Published November 13, 2025. Based on the nationally representative Real-Time Population Survey and pooled February, May, and August 2025 responses for the time-savings estimate.

What the evidence supports

A smaller, more disciplined first move.

None of these studies proves a particular workshop will create a particular return. Together, they do show where adoption repeatedly stalls and what a sensible experiment should address.

  1. 01

    Choose one recurring task

    Start where the team can recognize quality and compare before-and-after effort.

  2. 02

    Make review explicit

    Name the person, source, and standard required before an AI-assisted output can be used.

  3. 03

    Measure the workflow

    Track time, rework, error rate, or throughput instead of counting prompts or tool logins.

  4. 04

    Expand after proof

    Keep what produces a better result. Stop or redesign what merely produces more output.

From evidence to practice

Put one real need through a disciplined test.

Explore custom work