待翻譯：AI Is Revolutionizing Strategic Decision-Making

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Idea in Brief The Problem Strategic decision-making has long been constrained by bounded rationality. Leaders can generate, evaluate, and debate only a limited number of strategic options because humans have finite time, attention spans, and cognitive capacity. The Solution Generative AI can expand the bounds of strategic thinking by generating and evaluating thousands of options, replacing static frameworks with dynamic, data-rich models and enabling more-structured deliberation. The Payoff Firms that integrate gen AI into strategy processes won’t just move faster—they’ll make higher-quality decisions, uncover hidden opportunities, and build a durable competitive advantage using proprietary data, processes, and speed. Think about your last strategy offsite. Your team spent weeks preparing. You flew people in, booked the conference room, hired a facilitator. And after two days of debate, how many truly different strategic options did you walk out with? Three? Four? Now ask yourself: Was that because only three or four good options existed, or because that’s all your team had the time and mental bandwidth to develop and evaluate? For most companies, the honest answer is the latter. The bottleneck in strategic decision-making has never been a shortage of possible directions. It has been the limited capacity of the human minds doing the work. We can hold only so much information in our heads, evaluate only so many alternatives in a strategic-planning cycle, and process only so many perspectives in a meeting before fatigue, politics, or the clock forces a decision. Scholars call this dynamic bounded rationality—the idea that human decision-makers, however capable, are constrained by finite attention, memory, and processing power. These constraints are so fundamental that we rarely notice them, but they have quietly shaped every tool in the standard strategy playbook. The reason a SWOT analysis has four quadrants, a growth share matrix is a 2×2, and Michael Porter’s most famous framework has exactly five forces is not that the competitive world is actually so simple. It’s that the frameworks had to be simple enough for a human team to map them out on a whiteboard in a few hours. For decades, those frameworks were the best we had. That’s no longer the case. The current generation of artificial intelligence tools—particularly large language models (LLMs) and the multiagent systems being built on top of them—are not just additions to the planning tool kit. They’re technologies that directly relax the cognitive constraints that have shaped how companies make their most important decisions. AI can generate and screen thousands of strategic alternatives where a human team might be able to consider a mere handful. It can build and continuously update models of markets, customers, and competitors that are far richer and more dynamic than any static framework. And it can test ideas through simulated deliberation—synthesizing diverse perspectives and challenging assumptions without the groupthink, hierarchy, and time pressure that distort real-world strategy meetings. In short, AI offers a path to unbounding the strategy process. The limits are not removed, of course, but they’re pushed outward—and where they end up matters enormously, because the implications go well beyond efficiency. When you can explore more options, you find better paths forward. When you can model your environment in higher resolution, you see more opportunities and threats. When you can stress-test a plan by simulating competitors, skeptical customers, and a devil’s advocate who never gets tired, you make decisions that are more resilient. The companies that master this new way of working won’t just do strategy faster. They’ll do it better—and build the next generation of competitive advantage. I study strategic decision-making and have spent considerable time examining how AI performs on core strategy tasks. In recent experiments my colleagues and I found that business plans generated by an LLM were rated more favorably by experienced investors than were plans written by entrepreneurs in a startup accelerator and business plan competition. Perhaps more striking, when the business plans were evaluated, AI’s assessments were more aligned with the investor panel’s average judgment than individual investors’ assessments were—which suggests that AI can both generate strategic options at scale and evaluate them with a consistency exceeding that of human experts. Such findings don’t mean AI is ready to replace your strategy team. But they do suggest that the cognitive work at the heart of strategy is no longer exclusively human territory. In this article I’ll lay out what this shift means in practice. First, I’ll describe three ways AI can transform the strategy process, with examples from companies already using it to do so. Then I’ll address the skeptic’s natural question—If every company has access to the same AI, how can it be a source of advantage?—and explain how firms can build durable competitive moats around strategy capabilities augmented by AI. Finally, I’ll offer a practical playbook for leaders who want to start redesigning how their organizations make strategic decisions. Three Ways AI Expands Strategic Thinking To understand what changes with AI, it helps to think about what your organization has to do when making strategy. At its core, any strategic decision involves three cognitive tasks: searching for possible courses of action, representing the environment in which those actions will play out, and aggregating the judgments of the people involved in the decision. Let’s look at each in turn. From a handful of options to thousands. The most immediate constraint on any strategy process is in the search for options: How many alternatives can your team generate and evaluate? For most organizations the answer is surprisingly low. A typical strategic-planning cycle might surface a dozen ideas and then narrow the field to three or four, with team members spending most of their energy debating those finalists. The vast majority of the possibility space is never explored—not because it lacks promise but because human teams lack the time and capacity to take on that job. Dimitris Ladopoulos combines art, math, and algorithms to create a study of forms and variations. As my research suggests, the breakthrough with AI is that the cost of generating and screening options falls sharply, helping organizations explore far more of the possibility space. The shift is already visible in M&A. In one case, documented by McKinsey, a software company used a generative AI scouting system that combined semantic search with a database of more than 40 million public and private companies. Drawing on patents, filings, expert transcripts, and other structured and unstructured data, the system surfaced and scored more than 500 acquisition targets in less than a day, narrowed the list to 15 serious leads, and supported three completed acquisitions within months. The point here is not just speed. It is a qualitative shift in what “looking for options” means. Instead of asking a team to brainstorm plausible targets, a firm can now scan nearly the entirety of the relevant universe, using multiple strategic lenses simultaneously, and create a short list that no earlier process would have produced. The implication for leaders is straightforward: Before any major strategic choice—an acquisition, a new market entry, a product portfolio decision—you should task an AI system with generating a far larger set of alternatives than your team would normally consider, filtered by your specific criteria. Use your team’s judgment on the short list, not the long list. AI expands the search; humans choose. From static frameworks to living models. The second constraint is representation: How does your organization model the environment it operates in? For most companies the answer is some combination of frameworks, spreadsheets, and quarterly reports—tools that are useful but inherently static and simplified. They capture the most important dynamics but necessarily leave out enormous amounts of nuance, context, and real-time change. AI makes it possible to work with far richer and more dynamic representations. MYbank, the digital lender within Ant Group, offers a striking example. Traditional banks evaluate small businesses’ creditworthiness using just a handful of variables—revenue, collateral, credit history. Many small businesses lack one or more of these, leaving millions effectively “unscorable” and therefore unfundable. MYbank replaced that basic model with an AI system that draws on more than 3,000 variables, integrating transaction data, supply chain relationships, business network data, and even satellite imagery. The result is what the company calls its 3-1-0 model: three minutes to apply, one second for approval, zero human intervention. MYbank has extended credit to more than 53 million small businesses—72% of which were first-time borrowers—with a default rate around 1%. The strategic insight here is about what happens when you replace a low-resolution model of your environment with a high-resolution one. Using its AI system, MYbank didn’t just serve existing customers faster. It became able to see an entirely new customer segment—tens of millions of viable borrowers—that the old model had rendered invisible. More HBR Resources How People Are Really Using AI in 2026 AI Adoption Is Overloading Your Middle Managers AI-Generated “Workslop” Is Destroying Productivity The Hidden Realities of AI Adoption AI Doesn’t Reduce Work-It Intensifies It When Using AI Leads to “Brain Fry” The same logic applies to any company whose models of demand, supply, or competitive dynamics were built for an earlier era. Consider Unilever’s ice-cream business. Rather than relying on lagging quarterly forecasts, the company created an AI-driven model that integrates weather probabilities, demand signals, and telemetry from roughly 3 million connected freezers across 35 factories. The result is a continuously refreshed picture of where demand is forming and where supply may fall short—a living representation of the market that lets managers intervene in real time rather than react after the fact. Unilever reports a 10% improvement in forecast accuracy in Sweden and sales increases of up to 30% in some regions from insights generated by AI-enabled freezers. For leaders, the practical step is this: Identify the one or two strategic models your organization uses most heavily (your market map, your customer segmentation, your competitive positioning) and ask whether AI could make them richer, more current, and more granular. In most cases the answer will be yes. And the payoff won’t just be better information. It will be the ability to see opportunities and risks that your current models are incapable of revealing. From groupthink to structured challenge. The third constraint is aggregation: How does your organization combine the knowledge and judgment of multiple people into a decision? In theory, diverse teams should produce better decisions than any individual could alone. In practice, strategy meetings are shaped by hierarchy, politics, personality, and time limits. The most senior person’s view carries disproportionate weight. Dissent can be risky. The group converges too quickly. AI offers a different approach. Instead of depending on a roomful of people with uneven incentives to speak up, organizations can now orchestrate what might be called synthetic deliberation: AI-driven processes that pressure-test ideas without the social frictions of group dynamics. The mechanism can be simple or sophisticated. McKinsey, for example, has described multiagent workflows in which a “creator” agent drafts an analysis and a “critic” agent systematically challenges it—a process that automates the rigorous back-and-forth that strong teams try to [truncated for AI cost control]