--- title: "Measured on Different Clocks" canonical: "https://current.tinyfish.ai/issue/45/practitioners-corner/article/37150/measured-on-different-clocks" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" author: "Sevda Polat" author_url: "https://current.tinyfish.ai/team/200" issue: 45 section: "practitioners-corner" section_name: "Practitioner's Corner" published: 2026-09-03 word_count: 280 source_links: 1 --- # Measured on Different Clocks *By [Sevda Polat]()* *September 3, 2026* [Read on the website]() In McKinsey's [2026 State of AI survey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), 80% of respondents say AI has improved their personal productivity. Only 37% attribute any positive impact to their organization's earnings. That figure is flat from last year, even as adoption has grown. The optimistic assumption is that financial results will eventually follow. But a person finishing tasks faster and a firm's earnings improving are different phenomena, tracked by different instruments, on different time horizons. A completed task registers as a gain for the individual while generating an escalation, a retry, or a cleanup cost that surfaces in someone else's queue entirely. Speed at the task level and value at the organizational level are not connected by a simple pipeline. The 6% of organizations reporting meaningful earnings impact share a telling pattern: three-quarters have redesigned workflows around AI, and they are twice as likely to have built processes for measuring its effects. They invested in the institutional plumbing that connects individual output to organizational outcomes. Most organizations haven't. Which means the gap between reported productivity and reported earnings isn't closing because nobody has built the instrumentation that would reveal where the costs actually land. **80%** report personal productivity gains from AI **37%** attribute any positive earnings impact — unchanged from 2025 **6%** qualify as high performers (meaningful earnings from AI) — also flat **74%** of high performers have redesigned workflows, vs. ~25% of others **2x** more likely among high performers: defined processes to measure AI impact **1 in 5** organizations say AI operating costs now constrain usage **32% vs. 14%** expected vs. actual workforce reductions last year; 39% expect them again this year *All figures self-reported; McKinsey, May–June 2026, 1,719 respondents across 97 countries* ## More - [Practitioner's Corner — Issue 45]() - [Issue 45]() - [Publication guide]()