The systems skills that can’t be replaced by AI
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Almost 10,000 U.S. tech sector employees lost their jobs the week of July 9. The irony shouldn’t be lost on us. Some of those laid off helped build the AI that rendered their jobs obsolete.
Amazon CEO Andrew Jassey confirmed this trend in statements he made in a memo to employees this past March. “As we roll out more Generative AI and agents, it should change the way our work is done,” he said .”We expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.”
The very skills that once separated MBAs from the pack—data analysis, process optimization, strategic planning—are now performed more efficiently, and often more effectively, by machines.
This disruption has shattered a long-standing belief: success comes from mastering models, frameworks, and quantifiable insight. For decades, business schools have been teaching MBA students to build discounted cash flows, run regression analyses, and optimize supply chains—all based on the premise that smarter analysis equals better decisions.
But as AI takes over the heavy lifting of analysis, a new set of business skills are needed. These are skills that can’t be codified into algorithms or outsourced to large language models. Some of them aren’t even the skills currently taught in most MBA programs.
These are the skills demanded in our hyperconnected, systems-driven age – one in which all the technical analysis in the world cannot yield the ‘right’ answer or even a ‘best’ answer. They are skills needed in a world of uncertainty, ambiguity, and change, not one of stability. They are the skills that help executives and students not just manage what can be measured, but also what they can’t even see.
Three capabilities build this edge: the relational, the cognitive, and the behavioral. These capabilities aren’t just AI-resistant—they’re uniquely irreducibly human.
1. Listening Deeply And Building Empathy
Executives are encouraged to speak up often, precisely, and assertively. But this focus on speaking overlooks something more transformative: the power of deep listening. In systems thinking, listening isn’t passive. Listening is deep and active, so the listener can hear not only what is said, but what is not said and to build a stronger relationship with the listener.
Deep listening is the discipline of being fully present with another person—suspending judgment, setting aside distractions, and resisting the urge to formulate a response. It means tuning into tone, body language, and emotional undercurrents as much as words, creating space for others to surface thoughts they themselves may not yet fully understand. Deep listeners catch the unspoken concerns and subtle cues that others miss.
Deep listening
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But deep listening does not only apply in social situations. It applies in any situation, whether it’s watching your children play or sitting alone in nature. The ability to listen—really listen—requires being willing to be truly present in the moment.
In systems thinking, this skill is essential because it helps the listener build empathy and trust with the speaker. It also helps sense the broader relational dynamics at play across teams, departments, and ecosystems—relational dynamics that are not always visible and yet shape how people act and react.
A meta-analysis published in the Journal of Business Psychology in 2023 examined the effects of perceived listening. The researchers found that in the 144 studies they reviewed, that speakers that felt heard would have stronger workplace relationships and job performance.
Reed Hastings, co-founder of Netflix, said on “The Tim Ferriss Show” podcast that he practices deep listening by “farming dissent.” By encouraging managers to speak up when they disagree with him and listening deeply, he can avoid mistakes and see new opportunities. He claims that Netflix’s 2011 failed attempt to rebrand the company’s DVD-by-mail service through the new company Qwickster was because he did not listen. He was hell-bent on the new company, even though many people raised serious doubts. As Hastings wrote in his book “No Rules Rules: Netflix and the Culture of Reinvention,” leaders should “be humble, you have to be curious, and you have to remember to listen before you speak and to learn before you teach.”
This relational edge extends beyond human dynamics to natural ecosystems. Marine biologist Rachel Carson exemplified systems thinking through deep listening. She didn’t just study chemical data on nature—she listened to the silence. The absence of birdsong became her signal that something fundamental had shifted in the ecosystem’s interconnected web. In her 1962 book, “Silent Spring,” Carson wove disparate observations about nature and industrial chemicals like DDT into a powerful narrative of environmental collapse. By listening to what the system wasn’t telling her—the missing sounds—she detected patterns that traditional analysis had missed. Her insight motivated John F. Kennedy to strike a panel that ultimately led to the formation of the EPA. In short, there are many ways that leaders can benefit from active listening.
Systems thinkers listen with more than their ears. They listen with their bodies, detecting signals from their emotions and their senses. This is embodied listening. Fire fighters do not simply see and speak about the flames, they sense them. They can detect weak signals in the business environment and are attentive of their meanings.
Whereas AI can process data, it can’t feel or sense the weak signals.
2. Thinking Critically And Recognizing Patterns
Executives and MBA students love frameworks. They rely on ratios, spreadsheets, and tools like the 2×2 matrices that simplify complex problems but seduce executives into thinking that complex problems can be cleanly dissected or solved. Yet these ratios and spreadsheets mask assumptions that frame the answers. This is why critical thinking is so important.
Critical thinking is the capacity to step outside habitual models and mental shortcuts. It involves asking fundamental questions, reframing problems, and resisting the pressure to reduce complexity prematurely. While AI may be much faster at recognizing patterns, only people can think critically about them.
By thinking critically, executives can recognize patterns that others may miss; detecting recurring structures, relationships, or feedback loops across time and domains. Systems thinkers develop this skill by scanning broadly, reflecting deeply, and continually integrating new insights into evolving mental models.
Systems thinkers resist quick simplifications evoked by questions like “What’s the solution?” Instead, they first ask “what is the right question?” And, in thinking critically, they can see patterns that an AI might miss. AI can only answer the question it is asked, while people can reframe the questions themselves.
Consider what happened when traditional analysis failed spectacularly. During the 2008 financial crisis, Wall Street’s finest analysts missed the systemic risk building under their feet. In testimony to a Congressional panel, Former Citigroup’s CEO, Charles Prince, admitted after the crisis: “We did not foresee what lay before us.”
Hedge fund manager, Michael Burry did. His insight came not from standard models but from noticing strange patterns: rising home prices, lax lending practices, investor complacency. He didn’t just analyze mortgage bonds. He saw the system—the interactions among incentives, behavior, and structures. That’s the cognitive edge: applying insight across domains to spot what others miss.
Massachusetts Institute of Technology’s Erik Brynjolfsson and Andrew McAfee argue in their book, “The Second Machine Age,” that while AI excels within defined tasks, humans remain better at more creative tasks. Analogical reasoning—seeing connections across seemingly unrelated areas—is particularly salient to a complex world. Whereas machines can sift through vast amounts of data to make connections, they cannot make the meaningful connections that humans can make.
This is the cognitive edge: the ability to identify patterns among weak cues across disciplines, question dominant narratives, and reason beyond the data. While AI can find patterns in past data, human thinkers glimpse possibilities at the edge of what’s known.
3. Embracing Uncertainty And Adapting To Change
Business leaders often seek clarity through defined goals and the stepwise actions that can take them there. Executives and business students alike are encouraged to make decisions about next steps with the best available data and the most sophisticated data analysis.
This encourages people into fearing uncertainty rather than embracing it. The pursuit of more or better data to achieve well-defined goals delays action. In a world defined by constant disruption, waiting often means missing the window.
Embracing uncertainty doesn’t mean swimming in chaos and being paralyzed by the need for more and better data—it means becoming comfortable making decisions when clarity is elusive. This mindset requires executives to build psychological flexibility—a trait that requires the ability to maintain focus on one’s goals, while adjusting to shifting conditions. This is about seeing the short-term situation while maintaining focus on long-term goals.
The behavioral response to embracing uncertainty is adaptability. When the business environment changes, systems thinkers don’t panic. They pause and reflect. These executives are comfortable with change, because they experiment and adjust. They do not over-plan.
These systems thinkers, though, are not skittish. They do not change direction with every piece of new information. Instead, systems thinkers are anchored with a strong sense of purpose and personal values. They maintain a general direction, and yet adapt when they learn something important and new.
Reid Hoffman, co-founder of LinkedIn, stressed the need for continuous learning and adaptability, especially for founders and entrepreneurs navigating rapidly changing markets, when he appeared on the “Masters of Scale” podcast. For Hoffman, this ability to adapt quickly is a key marker of success, which he calls “permanent beta” – the idea that nothing is ever truly finished and that leaders need to stay alert to how things are shifting. As he put it during the podcast: “You know things but don’t know the whole game, and you are alert to how the game is changing.” The key, he says, is to “never stop starting.”
How To Develop Systems Thinking Skills
Together, these three edges create a new leadership paradigm. Historically, business skills have been grounded in strong data analysis. Better data meant better answers. Today, AI can analyze data.
Business leaders need to focus on uniquely human qualities, especially the skills that are required in a highly chaotic environment. Rigor and reasoning are now replaced with sensing, interpreting, and adapting. The best way to build these skills is through practice, leading with intention.
Business leaders need to hear not just what’s said, but what’s not said, ask good questions, see connections among seemingly disparate ideas, and remain adaptable to new salient information, while remaining anchored on a strong values and sense of purpose.
AI has commoditized technical skills. The uniquely human edge is needed to manage within messy, nonlinear, often chaotic, human systems. These aren’t just generic ‘soft’ leadership skills, but the survival skills for a world in flux.
Leading institutions are catching on. Stanford’s d.school emphasizes human-centered design. MIT teaches managers to map interdependencies as part of business courses. Companies like Unilever and Google now train leaders in mindfulness and emotional intelligence, not just spreadsheets and strategy decks.
Business now operates in a system. The skills to navigate complex systems are not only technical, which can be performed by AI, but uniquely human. To navigate complexity requires navigating uncertainty not with fear, but with curiosity.
The leaders who thrive in the age of AI won’t out-analyze machines. They’ll out-sense them and build strong relational networks.