BCG Is Watching AI Adoption Closely. Two of Their Findings Match What I See Every Week.
Employees with a clear strategy but limited access to AI tools outperform employees with strong tool access and no direction.
Read moreNotes on AI, Business Transformation, and what ships.
Employees with a clear strategy but limited access to AI tools outperform employees with strong tool access and no direction.
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I asked Claude to help me open a password-protected Excel file, and it worked. I had a near-miss password. I knew the general structure, the relevant words, and the kind of year I probably used. Claude generated plausible variants, tested them against the file, and found the one that opened it.
ReadAnthropic published its latest [Economic Index report](https://www.anthropic.com/research/economic-index-june-2026-report) on June 26th. It's the first one built on a data pipeline that can see what a Claude session actually *produces*, not just what was said in it — a new classifier that labels the output of every conversation, higher-rate sampling, and the first results from a survey linked to real usage data across roughly 9,700 respondents.
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As a codebase grows, Claude Code can start to feel less sharp. Early prototypes fly; then complexity sets in, and suggestions begin missing context or editing the wrong file.
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Most AI rollouts don't fail because the models underperform. They fail because the humans around the models keep doing what they've always done. The tool gets deployed, dashboards light up with usage, and six months later leadership quietly concludes that AI "didn't really move the needle." Output is the same. Headcount is the same. The same people are working the same hours on the same kinds of work.
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For most of modern history, value came from producing the thing itself. You wrote the memo, built the spreadsheet, designed the interface, analyzed the market, or wrote the code. Execution was the work.
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