CSE: Francis Bacon once said, “Knowledge is power,” but that’s not really true anymore, right? We now have all of the answers with AI to a lot of commonly asked questions and some uncommon questions.

We need to make sure we’re defining the problem that we’re solving, that we are understanding and communicating the motivation and providing the context, because you don’t want all the information. Otherwise, we’re going to end up with analysis paralysis.

One thing people can use is what I call the “vision of success” question. This question asks you to time travel, to jump over the decision to see that the decision has been made well. What’s happening? What are those few things that if they don’t succeed, the decision fails? It’s actually very powerful, because it allows you to clearly identify so that you can articulate those factors that you absolutely need to be able to share with AI. These become the constraints and boundaries that set the perimeter around the decision, so that it can give you research that’s targeted and focused on those things.

For example, Max deciding where to spend his next vacation. Asking AI for
help directly—without turning to himself first—he got a list of destinations, airline options, and local attractions, complete with ratings, reviews, and price comparisons. On paper, that sounds ideal. But in reality, Max quickly found himself paralyzed by the sheer amount of information. What if he picked a less-than-perfect hotel? What if he overlooked a hidden gem? AI has handed him a mountain of data, but it didn’t necessarily make the decision any easier.

However, when he used the “vision of success” question, he was able to realize and to share with his AI tool: “I want to return home feeling genuinely rested, having spent quality time with my family, without overspending or feeling like I missed the experience I was hoping for.”

Only then does AI have a meaningful problem to solve. Instead of searching for the “best” vacation—a question with no objective answer—it can help Max find the destination most likely to produce the outcome he actually wants. AI can’t define success but it can optimize for the success you define.

KA: How does the AREA method help people make wiser decisions, especially when AI is involved?


CSE: AREA is more than an acronym—it’s a framework for challenging our assumptions and thinking mistakes, and actively includes the perspectives of others. It stands for Absolute, Relative, Exploration and Exploitation, and Analysis.

The first “A,” Absolute, focuses on primary, unfiltered information straight from primary sources. “R,” Relative, shifts to external viewpoints—secondary sources connected to the subject. “E,” Exploration and Exploitation, fuels creativity: Exploration broadens research through interviews and unconventional sources, while Exploitation turns inward, challenging personal biases and assumptions. The final “A,” Analysis, synthesizes everything, bringing clarity and structure to complex decisions.

When using AI, a system like AREA becomes even more important because AI doesn’t eliminate bias—it can reflect and sometimes amplify biases in its training data, design, and the way we use it. Good decisions require a process that helps us recognize and challenge both AI’s biases and our own. 

For AI, so much of the information, for example, is from Reddit. Information can be incomplete or outdated, and you also know that you’re interacting with a liar. It’s a machine that just gives you answers that are wrong sometimes.

So you need to be able to have a quality due diligence process to not only be able to use the tool effectively, but to be able to evaluate its outcome. Too often, people look at an answer with AI and think that it’s a transaction, and they can just take it. AREA is going to help you make sure that it’s a conversation. So you have an evidence-based perspective that has looked at a variety of stakeholder viewpoints, and made sure that you have pried open that cognitive space to challenge the natural, well-worn mental pathways that we all lean on every day.

KA: How can we use AI in ways that strengthen—rather than undermine—our values and agency?

CSE: It’s a great question. I think the new competitive advantage is going to be Self-awareness.

This is the first tool that actually asks for some of our cognitive load; it wants us to offload some of our decisions to it. And this is a machine fundamentally about decision making, because we ask it for something and it gives us an answer. But there’s a very interesting invitation here, which is to each of us to get to know ourselves better.

We each have a special sauce, which is the way that we make our decisions, and we make thousands of them every single day. But most of us have not sat down and said to themselves, What does that process actually look like? Can I actually name those pieces and put language to it?

There is this invitation to get to know our thinking so that we can translate it better, and not only for the tool. How helpful would it be in conversation with our colleagues to be able to say clearly, This is how I’m actually going through the steps, this is how I’m thinking about it, these are the variables that I’m prioritizing, and here’s what I think is the most diagnostic.

You can think about your thinking skills as muscles. If we turn over our cognitive load, we will also weaken it. But if we actually take this invitation to understand our Self-awareness and our metacognition, we can make sure that we strengthen those tools.

AI is a tool that has so many implications for our environment and for our world, in addition to the fact that it has implications for our actual thinking skills.

So I would say that the invitation is to turn to yourself first. Ask yourself why you’re using the tool, and whether or not you should be turning to yourself instead. And either way, bring your own thinking forward before sitting down, so that you can feel far more comfortable that you can problem-solve both with it and beyond it.