AI is spreading through businesses faster than it is changing how they work, according to Fortune commentary by Ming Lu. The gap is striking: 88% of organisations reportedly use AI in at least one function, but only 6% qualify as high performers with more than 5% EBIT impact.
Watch Desk analysis
What happened
Lu argues that AI’s business impact depends less on how quickly companies adopt it than on whether they redesign their operations around it. The figures he cites from McKinsey’s 2026 survey put 88% of organisations at some level of AI use, while 6% meet the survey’s high-performer measure. Lu says that share is unchanged from 2025, and that the high performers are three times more likely to have redesigned workflows end to end. Only 21% of adopters have done so, he writes.
He also cites a separate survey he conducted with Workplace Intelligence, in which 79% of organisations reported AI-adoption challenges. Those figures come from different surveys and should not be treated as one combined measurement. Lu’s article is commentary, and its wider claims about productivity and organisational complexity are his argument, not a settled verdict on AI’s economic impact.
Why it matters
For workers and businesses, installing AI is not the same as making it useful. If a tool is bolted onto an old process, it may speed up individual tasks without changing the work that limits productivity. The practical question is whether companies are redesigning workflows, or simply adding AI to the existing machinery and hoping for a different result.
Our read
The adoption-versus-impact gap is a useful corrective to both the “AI is everywhere” victory lap and the “nothing has changed” shrug. Lu makes a case for organisational redesign, but the cited figures show a gap, not proof that redesign alone causes better results. Companies should be judged by what changes in their operations and outcomes, not by how many AI tools they have switched on.
What to watch
- Whether later surveys show a rise in end-to-end workflow redesign.
- Whether the reported high-performer share changes from its 2025 level.
- Whether companies publish clearer measures of business impact beyond adoption rates.
Discussion spark: Should companies redesign whole workflows around AI before expecting productivity gains, or is that asking too much before the technology has proved itself?
Sources and evidence
- Source update (11 October 2026, 22:00 UTC)
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