๐ ๐ฒ๐๐๐ซ ๐๐ ๐จ, ๐ ๐๐ซ๐๐ฐ ๐ฎ๐ฉ ๐ ๐ฉ๐ฅ๐๐ง ๐๐จ๐ซ ๐ก๐จ๐ฐ ๐๐๐ง๐๐ ๐๐ง๐ ๐๐ฎ๐ญ๐จ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ฅ๐ ๐ฌ๐๐๐ฆ๐ฅ๐๐ฌ๐ฌ๐ฅ๐ฒ ๐๐ข๐ญ ๐ฐ๐ข๐ญ๐ก๐ข๐ง ๐๐ง๐ญ๐๐ซ๐ฉ๐ซ๐ข๐ฌ๐ ๐๐๐จ๐ฌ๐ฒ๐ฌ๐ญ๐๐ฆ๐ฌ. The industry buzz was at an all-time high, promising 100% value and immediate transformation. But hereโs the catch: many were building on what I call โregenerative data loops.โ These were models designed to analyze and generate insights, yet they often fell short in delivering measurable business outcomes.
๐๐ก๐ข๐ฅ๐ ๐๐ฏ๐๐ซ๐ฒ๐จ๐ง๐ ๐ฐ๐๐ฌ ๐ญ๐๐ฅ๐ค๐ข๐ง๐ ๐๐๐จ๐ฎ๐ญ ๐๐ ๐ฌ๐ฎ๐ฆ๐ฆ๐๐ซ๐ข๐ณ๐ข๐ง๐ ๐ฆ๐๐ฌ๐ฌ๐ข๐ฏ๐ ๐๐๐ญ๐๐ฌ๐๐ญ๐ฌ ๐๐ง๐ ๐๐ซ๐๐๐ญ๐ข๐ง๐ ๐ค๐ง๐จ๐ฐ๐ฅ๐๐๐ ๐ ๐๐๐ฌ๐๐ฌ, ๐ญ๐ก๐๐ฒ ๐ฆ๐ข๐ฌ๐ฌ๐๐ ๐ ๐๐ซ๐ข๐ญ๐ข๐๐๐ฅ ๐ฉ๐ข๐๐๐: defining real, trackable value. Itโs easy to assume that if people are using AI, theyโre benefiting from it, but proving that value? Thatโs a different story.
๐๐ก๐ ๐๐๐๐ฅ๐ข๐ญ๐ฒ ๐๐ก๐๐๐ค:
Automation is the unsung hero, it doesnโt just pull data but orchestrates it, feeding it into your AI systems so that insights can flow downstream efficiently. Much like how the role of a data scientist evolved, we now need to take an automation-first approach to business intelligence.
โ๐๐ซ๐ฎ๐ ๐๐ง๐ญ๐๐ซ๐ฉ๐ซ๐ข๐ฌ๐ ๐ญ๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐ข๐ฌ๐งโ๐ญ ๐๐๐จ๐ฎ๐ญ ๐ซ๐๐ฉ๐ฅ๐๐๐ข๐ง๐ ๐จ๐ง๐ ๐ญ๐๐๐ก๐ง๐จ๐ฅ๐จ๐ ๐ฒ ๐ฐ๐ข๐ญ๐ก ๐๐ง๐จ๐ญ๐ก๐๐ซ, ๐ข๐ญโ๐ฌ ๐๐๐จ๐ฎ๐ญ ๐ฅ๐๐ฏ๐๐ซ๐๐ ๐ข๐ง๐ ๐ญ๐ก๐ ๐๐ฑ๐ข๐ฌ๐ญ๐ข๐ง๐ ๐๐ฑ๐ฉ๐๐ซ๐ข๐๐ง๐๐ ๐๐ง๐ ๐๐ฆ๐ฉ๐ฅ๐ข๐๐ฒ ๐ญ๐ก๐ ๐ฌ๐ญ๐ซ๐๐ง๐ ๐ญ๐ก ๐จ๐ ๐ข๐ง๐ง๐จ๐ฏ๐๐ญ๐ข๐จ๐ง.โ -๐๐จ๐ฎ๐ ๐๐ก๐๐ง๐ง๐จ๐ง
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