by Cheryl Strauss Einhorn, author of “The Human Edge: Smarter Decisions in the Age of AI“
When generative AI first appeared, people rushed to download it. Much of the conversation focused on how to use the tools: how to write better prompts, work faster, generate ideas, and automate tasks.
But I knew the tools would keep changing. The more enduring question, I thought, was not simply, “What can AI do?” It was, “What is my role when AI is in the room?”
Which parts of decision-making remain uniquely human? At a moment when answers have suddenly become easy to produce, what thinking do we still need to do for ourselves? And how do we maintain control of the decisions that shape our lives while benefiting from what AI can offer?
I have spent my career thinking about decision-making—first as an investigative journalist, later as an educator at Columbia Business School and Columbia’s Graduate School of Journalism, and now at Cornell, as well as through my work coaching and consulting with companies and nonprofits facing high-stakes decisions. I believed I could help people identify where human judgment lives and which parts of complex problem-solving we should not give away.
For the first time, we had a machine that seemed to participate directly in decision-making. You could bring it a question or a gap in your knowledge and, with the click of a button, receive a polished, plausible answer—even when that answer was wrong.
AI’s speed outpaces human thinking. It can answer before we have brought forward our own ideas. And because our thoughts often arrive slowly and messily, an AI response can appear more complete and confident than anything we might initially produce ourselves.
I saw several dangers in that. We could mistake speed for accuracy and fluency for understanding. We could weaken critical-thinking skills that, like muscles, atrophy without use. And we could forget that AI and humans are not solving the same problem.
AI is pattern-based. Humans are purpose-based.
A model can tell us how similar problems have been solved, but our decisions are shaped by our particular motivations, values, beliefs, emotions, relationships, and responsibilities. Sometimes a seemingly good outcome can even result from a poor decision, while a thoughtful decision can lead to a disappointing outcome. AI does not have values, experience emotions, or live with consequences. We do.
Long before generative AI, during my years as an investigative journalist, I developed a problem-solving and decision-making system called the AREA Method. Because the articles I wrote affected people’s lives, I wanted to be certain I was telling stories that were both true and worthy of being told. I needed a disciplined way to evaluate information, check my assumptions, seek perspectives different from my own, and distinguish what I knew from what I merely believed.
I eventually wrote my first book, Problem Solved, to share that method with others.
When AI arrived, I saw that the same habits could help people benefit from these tools without surrendering their judgment. The need to challenge information, understand context, examine bias, consider stakeholders, and test our confidence had not disappeared. It had become more urgent.
That urgency became especially visible in my teaching.
I began to see students use AI to complete a project without recognizing that the process was where the learning happened. In one assignment, I asked students to read an article about the Gates Foundation’s decision to wind down and to generate questions that the story raised. I wanted them to ask: Why was this happening now? What might have precipitated the decision? Was the lengthy wind-down period significant?
One student submitted a paper that was organized and clearly written, but it read like a general explanation of why organizations close. It was a competent summary rather than an analysis. The student had offloaded the learning to AI. The tool produced an acceptable paper, but it did not help the student achieve the actual objective: understanding how context shapes an organization’s decision.
That experience crystallized something important for me: AI can help us complete the work while allowing us to miss the point of the work.
As I wrote The Human Edge, I drew on my journalism, teaching, decision frameworks, and conversations with leaders. I kept returning to one question: What must a person still locate, protect, and direct when working with AI?
The book ultimately centers on eight human steps, including defining the problem, understanding motivation and context, directing research, analyzing evidence, recognizing bias, considering stakeholders, and testing conviction. AI can assist with every one of them. But it cannot decide why the problem matters, whose interests deserve priority, which risks are acceptable, or what kind of person we want to be after the decision is made.
The hardest part of writing the book was resisting the temptation to write about the technology itself. Models, features, and products would change before the book reached readers. I wanted to write something durable: a guide to the human responsibilities that remain even as the tools become more powerful.
Writing The Human Edge strengthened my belief that AI is not simply asking us to learn a new technology. It is asking us to become more conscious of our own thinking.
Answers are now abundant so judgment is more precious. The human edge does not come from refusing AI or competing with it—we have different strengths. It comes from knowing what the tool can contribute, recognizing what it cannot understand, and taking responsibility for the choices that are still ours to make.
Cheryl Strauss Einhorn is the creator of the AREA Method, a decision-making system for individuals, companies, and nonprofits to solve complex problems. She is the founder of the decision-sciences company Decisive, offering leadership training, curriculum, coaching, and professional development services, teaches complex decision making for Cornell University. An award-winning author, her new book is “The Human Edge: Smarter Decisions in the Age of AI” (Cornell Publishing, May 15, 2026). To learn more, check out her TED talk and visit areamethod.com.
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