AI can deliver enormous amounts of data in seconds. Charts, trends, summaries, and insights faster than any human ever could. That’s impressive. And useful. But it’s also where the trouble starts because having the information doesn’t mean you know what to do with it.
Chip Conley wrote in Harvard Business Review that we must move beyond our obsession with “knowledge work.” The term was introduced by Peter Drucker in 1959, when he made the case that the true investment in a modern economy wasn’t in machines, it was in the brainpower of people. That idea defined business for more than half a century. But today, nearly anyone with a phone or laptop can access the world’s knowledge. And with AI now handling more and more of the tasks we used to think could only be done by a human, the real value has shifted. Conley’s point is clear: the future belongs less to knowledge workers and more to wisdom workers. The ones who know how to turn insight into impact.
Here’s how I’ve come to understand the difference.
Data
Data is the starting point. It’s just facts. Billions of them. When you ask AI questions, it sweeps through a massive universe of sources: research papers, websites, reports, blogs, transcripts, spreadsheets. In seconds, it identifies patterns and pulls what it believes is relevant. That speed and scope are hard to fathom. But at this stage, you’re not looking at insight. You’re looking at output.
Information
Information comes next. This is where the system starts giving structure to the noise. It sorts the raw facts into something you can actually look at: tables, timelines, trend lines, and summaries. The confusion lifts a little. But don’t mistake clarity for meaning. The machine hasn’t decided what matters. It hasn’t drawn any conclusions. It’s just rearranged the puzzle pieces. You still have to figure out what picture they create.
Knowledge
Knowledge is when the system explains the shape. “Here’s what we’re seeing. Here’s what this might mean. Here’s what you should consider.” The recommendations feel smart. Logical. Well-organized. But don’t be fooled, this isn’t expertise, it is an algorithm. The model pulls from what others have said, not what it has learned.
Wisdom
Wisdom is the intersection of knowledge and human insight. AI has become an invaluable asset in diagnostics, capable of analyzing vast datasets and identifying patterns with remarkable precision. For instance, AI algorithms have demonstrated a 99.1% sensitivity rate in detecting abnormalities on chest X-rays, outperforming radiologists’ 72.3% sensitivity rate in certain studies.
However, AI’s capabilities are confined to data analysis. It doesn’t account for the patient’s unique situation, nor does it bear decision-making responsibility. That’s where human wisdom comes into play, evaluating AI’s findings, considering the broader clinical picture, and making informed decisions that align with the patient’s best interests. If I were the patient, I would want the doctor to use AI. But I do not want a robot to roll into my hospital room and tell me I have cancer.
Here are two examples from my own experience:
I can’t imagine returning to working without artificial intelligence’s assistance.
It’s hard to believe that ChatGPT became available to the public in November of 2022, a little more than two years ago. A strategic analysis that used to take me a week to research can now be completed in a single afternoon. AI can generate an incredibly impressive list of data and recommendations. However, and this is the key point, without 30 years of doing strategy work, it’s just a list of information. Then, because I have so much experience in this area, I can turn the output into a superb strategy report.
In stark contrast, a client asked me to customize a keynote and several workshops in an industry I knew nothing about.
I did a massive amount of research. I created a custom GPT specifically to help me put the program together. I sent a preliminary overview to the client, thinking they would be highly impressed. Instead, they asked me to verify the numbers. They told me they had never seen this sort of information before. So, I double, triple, and quadruple checked the data. It was all wrong. It was a hallucination. Since I had no experience in this area, I did not realize that the information was flawed. The AI delivered the knowledge, but I did not have the wisdom to understand that it was not correct.
So, you don’t need to be afraid of AI. But you do need to learn how to work with it. Use the technology to take care of the tasks that wear you out. Let it do the sorting, the summarizing, the scanning. Then use that time to think deeper, reflect, increase your expertise, and build stronger human connections. These are things that no computer will ever be able to do.
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