AI Analytics: How Far Is Too Far?
Is AI Going to eat us all?
In my previous post - I discussed how AI should strive to make data simple, not simplistic.
Today, I want to focus on another topic: how far do we collectively want AI to go? As the AI analytics ecosystem evolves, many companies have envisioned AI making strategic decisions based on data. We're just a few steps away from functional text-to-SQL bots, but what’s next? Text-to-insights, text-to-decision, and even text-to-action. And what does “text” mean in this context? What kinds of questions can we pose to Analytics Agents? "What was the revenue last year?" might become "What can I learn from last year’s revenue?" or even "How should I act based on last year’s revenue?" But how far can this go? Could we ask, "How do I triple my revenue next year?” And what if you program the bot to always follow its own recommendation to triple the revenue every quarter?
AI is pushing the boundaries in analytics. But where's the line?
Imagine this: An AI tells your Head of Sales, "Stop investing in these channels. Focus on outbound with this talk track." Then it dumps a pile of data to back it up.
Efficient? Sure. But is this what we want?
The advantage is clear. More data-informed decisions. However, it raises big questions about human expertise and creativity.
As we get closer to this reality, we need to think hard about a few things:
Oversimplification is a real danger. AI crunches numbers well, but business is messy. Human context matters.
We'll always need human oversight. But how do we keep that expertise sharp if AI does all the heavy lifting?
What happens to data teams? They can't just be AI babysitters. Their role needs to evolve.
Should AI make the final call? Or should we keep humans in charge of big decisions?
Ethics matter. If an AI-recommended strategy tanks, who's to blame? How do we keep things fair?
The idea of AI running the whole show is thrilling, but it gets scarier as we get closer to this reality. Efficiency is great, but at what cost to human intuition?
Maybe the sweet spot is somewhere in the middle. AI crunches numbers and spots patterns. Humans make the final call, considering the full picture.
As we push forward, the goal should be to make humans better decision-makers, not to replace them. It's about finding the right mix of AI smarts and human wisdom.
So, how far should AI go in analytics?