History has shown that every major wave of innovation—whether it be electricity, the automobile, or the internet—has followed a familiar pattern: early job losses followed by adaptation and the eventual creation of more new jobs. Yet with the rise of modern AI, many are wondering if this time will be different. Unlike previous waves of creative destruction, AI feels uniquely disruptive to knowledge workers—those who generate, analyze and communicate information.
The long-term impact is debatable, but one thing is clear: AI is already causing significant disruptions in knowledge work, displacing many roles that require critical thinking, problem-solving and expertise. As the transformation unfolds, key questions are: Which knowledge worker jobs are most at risk? Which roles will disappear, and which will remain? For the roles that remain, how must they evolve in response to AI’s growing capabilities?
The Anatomy of Knowledge Work
To understand how AI is reshaping knowledge work, we first need to break down its core components. At its essence, knowledge work has three stages:
1. The Question – Knowledge work inherently anchors on strategic questions: What products should we launch? How should we pivot? Where should we invest to grow? Questions like these are the lifeblood of knowledge work.
2. The Answer – Finding answers to strategic questions is often the most resource-intensive phase. Teams in strategy, R&D, marketing, finance and innovation (to name a few) are steeped in the pursuit of answers. This is where the bulk of knowledge work—and competitive advantage—has resided.
3. The Action – Once answers are found, the final step is to take action: launch a product, enter a new market, adjust operations based on the insights, etc. Once answers are known, there’s a presumption that action steps will follow. This is the transition from “strategy” to “execution.”
For many knowledge workers, answer-finding has been the most demanding in terms of time, effort and cost. It’s home base for legions of knowledge workers and the basis of countless industries. That's because companies with the most effective answer-finding capabilities have historically enjoyed a competitive edge, justifying the tremendous investments that have been made.
AI and the Commoditization of Answers
AI is upending this paradigm by commoditizing answers. AI is dramatically reducing the time, effort and cost associated with finding answers, and it's taking on progressively more difficult questions at a rate that's existentially threatening to knowledge workers. Tasks that once required teams of experts—be they managers, coders, scientists, industry analysts, marketers, paralegals, diagnosticians, or others—are now being automated byte by byte, as AI delivers increasingly better insights at unprecedented speed. In this new environment, answers are migrating from scarce resources to becoming accessible with the click of a button.
As a result, the historical advantage conferred by answer-finding is eroding. While there will always be answers that are hard, unknown or unknowable, every day AI is doing its best to shrink that list. To the extent that it does, the competitive edge moves - like an air bubble - from finding answers, to asking better questions and proficiency in taking action.
Historically, advantage stemmed from the capacity to find difficult answers.
As answers become more accessible, advantage shifts to the questions we ask, and our capacity to act on the answers.
The New Competitive Advantage: Better Questions and Taking Action
Asking Better Questions
In a world where AI can generate answers quickly and accurately, a true differentiator becomes the ability to ask better questions. This isn’t a subjective platitude about curiosity or creativity; it’s about an objective skill set. Successful knowledge workers must master the art of framing and evolving strategic questions in ways that lead to differentiated insights.
This shift involves more than just prompt engineering. It can often require the ability to gather and interpret new types of data that AI may not have access to or hasn’t been trained on yet. In this AI-driven world, skilled knowledge workers must be able to identify, source and integrate data, both with and without AI. They also need to ask higher-value questions that others haven’t thought of, or that competitors don't know enough to ask in the first place.
In this new landscape, scientific rigor—once applied primarily to finding answers—must now be applied with renewed enthusiasm to formulating questions. Knowledge workers will need to focus on identifying the right problems to solve and selecting the most important variables to measure. If you ask the same questions as your competitors, you’ll get the same answers and miss a valuable opportunity to gain an edge.
Taking Action
Once AI provides a useful answer, the true test is a company’s ability (or inability) to act. Here, many organizations stumble. There's often a gap between knowing and doing—between understanding what needs to be done and actually doing anything with that information.
For example, a few years ago, a friend who was an executive at a materials company developed one of the most advanced processes for analyzing markets and identifying acquisition targets that I'd ever seen. Yet two years later, not a single acquisition had come from this process. Instead, deals came from the CEO’s pet projects—ideas that often arose spontaneously, such as on the golf course.
This frustrated my friend. The real issue wasn’t finding the right answers—it was the organization’s inability to act on them. Whether rational or not, the CEO wasn’t listening to his team. If they had presented deals that aligned with his true (perhaps unspoken) priorities, he would have acted. Some attribute this to personality or ego, which is fair, but I also see it as an organizational capability gap. If the team had understood the CEO’s true criteria, they could have delivered deals that moved ahead instead of spending a fortune and working their guts out, only to go nowhere. In this case, I think the CEO fell short (not the team), but the underlying issue is the very real gap that can exist, in all its forms, between answer and action.
As AI speeds up the process of finding better questions and answers, it increases the frequency of calls to action; this tends to reveal or amplify dysfunctions between answer and action. More pressure builds up around the bottlenecks. As a result, those people and businesses who can better identify, shorten, or remove the gaps between strategy and action will thrive, while rivals remain comfortable in their invisible inadequacies and largely unaware of their organization's capability deficits.
The Future of Knowledge Work in the Age of AI
To the extent that AI commoditize answers in a role or domain, competitive advantage shifts to:
1. Asking Better Questions – In a world where answers are easily accessible, the ability to frame and refine key questions in unique ways will increasingly distinguish knowledge workers and companies that thrive in the AI era from those that lose relevance.
2. Ability to Act – Similarly, competitive advantage will increasingly come from a company’s ability to act on differentiated insights swiftly, effectively and often. To optimize this alongside modern AI, many companies will realize that various knowledge processes, decision-making rules and governance systems need to be reimagined and redesigned. In some cases, this will require fundamentally new organizational structures, teams, delegations of authority and—often—cultural and political reshuffling.
I won’t lie; it won’t be easy. The upside is that identifying and addressing these types of problems (many of which are very 'human') creates opportunities for knowledge workers. As AI exposes and intensifies capability gaps, companies will need more assistance from knowledge workers with the necessary skills to close them.
Ask yourself – is your career largely based on answer-finding skills that AI is commoditizing? Alternatively, are you building the skills to ask better questions or to close the gap between insight and action? How will this affect your next career decision? How will it impact your business? How might it change what you'd advise a teenager to study in college? These questions may delineate success and failure for knowledge workers in today's era of AI. Now that you know this, can you act on it?