๐จ๐ป๐ฝ๐ผ๐ฝ๐๐น๐ฎ๐ฟ ๐ผ๐ฝ๐ถ๐ป๐ถ๐ผ๐ป: ๐ ๐ผ๐๐ ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐ฐ๐ผ๐ป๐ณ๐๐๐ฒ ๐๐ ๐ฎ๐ฑ๐ผ๐ฝ๐๐ถ๐ผ๐ป ๐๐ถ๐๐ต ๐๐ ๐ถ๐บ๐ฝ๐ฎ๐ฐ๐.
They measure the easy stuff.
They just track usage.
โ How many employees logged in.
โ How many prompts were written.
โ How many tokens were consumed.
โ How many Copilot licenses were activated.
That is not impact.
That is activity.
The real question is different:
โ Did AI increase revenue?
โ Did it shorten time to value?
โ Did it reduce manual handoffs?
โ Did it free up workforce capacity?
โ Did it improve customer satisfaction?
Most companies do not have an AI adoption problem. They have an AI accounting problem. They cannot clearly show where value is created, who owns it, how it compounds, and when it becomes measurable.
You do not scale AI because people are excited. And you do not scale AI because people โuseโ it. Adoption matters (still), of course. But adoption is only the starting point. The much more important questions is whether AI creates value that is measurable, repeatable, and attributable.
Clare Kitching nicely summarized below a practical set of KPIs to start measuring AI impact more seriously.
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๐๐ณ ๐๐ต๐ถ๐ ๐๐ฎ๐ ๐๐๐ฒ๐ณ๐๐น, ๐๐ผ๐ ๐บ๐ฎ๐ ๐ฎ๐น๐๐ผ ๐ฒ๐ป๐ท๐ผ๐ ๐บ๐ ๐ณ๐ฟ๐ฒ๐ฒ ๐ป๐ฒ๐๐๐น๐ฒ๐๐๐ฒ๐ฟ: https://lnkd.in/dbf74Y9E