Aug 15 • General Discussion 💬
How I Actually Use AI (And Why Data Mining Isn't Enough)
Someone posted about AI drafting content while you lead it, asking which part of workflow to delegate.
This sparked something important.
Here's my take: I'm going to expose myself here - I use AI for research and extrapolating technical components.
I do this to solely focus on high-level stuff.
The reality is, for research, the data mining from AI is useful.
But for extrapolating technical stuff, I don't like to rely on data mining.
Instead, I insert validated data and principles - because I fear AI bias will screw it up.
Let me be honest: The publisher responded that he's also wary of the data AI presents.
He uses validated data and principles to create reports and dashboards that keep teams aligned and stakeholders informed.
Here's the strategic framework: AI tools improved his existing work, freeing up time rather than replacing his thinking.
This is the difference between using AI as a crutch versus using it as leverage.
The goal isn't to let AI think for you - it's to handle the grunt work so you can focus on strategy.
The key insight: For research purposes, AI data mining works well for speed.
For decision-making and strategic thinking, you need to input your own validated frameworks and principles.
This prevents AI bias from leading you down the wrong path.
Bottom line: Use AI to amplify your existing capabilities, not replace your judgment.
Research acceleration: yes. Strategic thinking replacement: no.
The winners will be those who maintain control over the high-level decisions while automating the low-level execution.
Hope you found this valuable! :)
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How I Actually Use AI (And Why Data Mining Isn't Enough)
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