5 Lessons from Teaching Strategy with AI

Teaching strategy has always been about equipping leaders to navigate uncertainty and make bold decisions. But in an age defined by rapid technological advancements, traditional methods alone no longer suffice. AI is reshaping the strategic landscape, offering both opportunities and challenges that demand a fresh approach. Over the past few months, I’ve led workshops at 英国伦敦商学院 to explore how AI can transform strategy-making. These sessions became a fascinating blend of discovery and reflection—not just for the participants but for me as well.

AI attracts considerable attention, yet its role in strategy-making remains a work in progress. Beyond generating forecasts or analyzing data, I’ve seen AI prompt executives to rethink their assumptions, challenge entrenched views, and approach strategic problems from new angles. It is a powerful enabler, but it comes with risks and limitations that require thoughtful navigation.

As both an educator and a scholar, I’ve observed how AI can spark innovation and foster collaboration while raising critical questions about the pace of competition and decision-making. Reflecting on these experiences, I’ve distilled five lessons—not definitive answers, but insights worth considering for executives steering their organizations through complexity and for educators preparing tomorrow’s leaders.

In this article, I’ll share these lessons, not as an endorsement of AI as a panacea, but as an invitation to explore its potential and constraints. My aim is to encourage thoughtful dialogue about integrating AI meaningfully into strategy-making while acknowledging its inherent challenges.

The Workshops?

These workshops were designed to help senior executives navigate the complexities of strategy-making in a world increasingly shaped by AI. The program unfolded in three distinct phases: identifying trends, constructing business models, and conducting a gap analysis to address critical needs. In the first phase, participants leveraged AI tools to uncover major trends influencing their industries and assess their strategic implications. This set the foundation for scenario planning, where participants explored potential futures and their associated challenges and opportunities.

The second phase focused on building business models tailored to these scenarios. Using structured prompts and AI-generated insights, participants experimented with various strategic approaches. This phase demanded critical reflection as participants weighed trade-offs, tested assumptions, and evaluated the resilience of their strategies under different conditions. Throughout, I emphasized that while AI is a valuable tool, human judgment must remain central to decision-making.

In the final phase, participants conducted a gap analysis to compare their current capabilities with the demands of their proposed strategies. This exercise revealed areas requiring investment in resources, skills, or technology. Collaboration was a cornerstone of the process. Participants presented their findings to peers, sparking dynamic discussions enriched by clear prompts and structured guidelines.

Each workshop concluded with a reflective session where participants examined the strengths and limitations of using AI in strategy-making. These discussions often yielded important insights, highlighting how AI complements human expertise and where its use requires cautious oversight. These reflections formed the basis for the five key lessons I’ve identified.?

Lesson 1: AI as a Catalyst for Accelerated Strategic Insight?

AI's capacity to accelerate strategic analysis is transformative. Tasks that traditionally took hours—such as identifying trends or evaluating market conditions—can now be completed in minutes. This capability allowed workshop participants to focus less on routine analysis and more on interpreting insights and debating their implications.

For instance, in one session, participants used AI to assess sustainability trends in their industry. The AI highlighted key patterns, including shifts in consumer preferences for greener products and tightening regulatory pressures on carbon emissions. These insights enabled the group to rapidly evaluate how these trends aligned with their strategic goals and identify areas requiring immediate action. However, this speed introduced risks. Participants noted that rushing through analysis could lead to overconfidence in AI outputs or missed nuances. The ability to produce rapid insights must be coupled with rigorous scrutiny to avoid superficial conclusions.

The broader implications of this acceleration were also evident. Participants recognized that AI’s efficiency doesn’t just save time—it reshapes how time is allocated in strategic processes. With less time spent on groundwork, teams could devote more energy to higher-order tasks such as scenario planning, and hypothesis testing. This dynamic reallocation of effort is potentially where the true potential of AI lies.

Perhaps most provocatively, participants realized their competitors were likely going to use similar tools to accelerate their own decision-making. This prompted reflections on how to maintain a competitive edge when others have access to the same technological advantages. AI can deliver speed and insight, but achieving meaningful outcomes demands careful oversight and intentional strategy.

Lesson 2: Building a Shared Foundation for Strategic Debate?

AI proved invaluable in providing a consistent starting point for strategic discussions. By generating structured outputs, it allowed participants to quickly align on key issues and move into deeper, more substantive debates. This shared framework minimized initial disagreements over data and freed participants to focus on solving complex problems. For example, one group used AI to model the potential impacts of regulatory changes. The scenarios presented by the AI highlighted trade-offs and possible outcomes, framing the conversation without dictating solutions. This approach enabled participants to focus on the most pressing strategic questions, fostering targeted discussions.

Despite this value, some participants cautioned against treating AI outputs as definitive. The most productive teams critically evaluated AI’s contributions, integrating them with their own expertise to uncover deeper insights. This interplay between AI analysis and human judgment enriched the discussions. Beyond offering a structured starting point, AI facilitated greater inclusivity in strategic debates. Teams with varying levels of expertise could engage on more equal footing, as AI outputs demystified complex data and framed issues in accessible ways. This leveling effect not only fostered collaboration but also encouraged more diverse perspectives to emerge, enhancing the richness of strategic deliberations.

AI’s value as a facilitator of debate lies not in its conclusions but in its ability to catalyze meaningful conversations. It provides a shared foundation upon which diverse perspectives can build, underscoring its role as an enabler of collaboration rather than a prescriptive tool.

Lesson 3: Harnessing Iterative Collaboration with AI?

One of AI’s most distinctive contributions is its support for iterative collaboration. Participants often began with an initial AI output and refined it through questions, new data inputs, and human insights. This iterative back-and-forth sharpened strategic options, revealed risks, and uncovered opportunities that might have been overlooked. For instance, during one session, a team used AI to explore potential market entry strategies. Initial outputs were broad and generic, but through iterative refinement—posing targeted questions and incorporating specific variables—the insights became increasingly actionable. This process turned AI from a static tool into a dynamic collaborator.

However, this approach also revealed pitfalls. Teams that relied too heavily on AI’s iterative outputs risked narrowing their focus, repeatedly refining a single idea without stepping back to consider alternatives. Without facilitation, this iterative process could stifle divergent thinking and creativity. The iterative process highlighted an important truth about human-AI collaboration: the questions we ask matter as much as the answers AI provides. Teams that excelled in these workshops approached AI outputs not as fixed solutions but as starting points for exploration, constantly probing and reframing their assumptions. This mindset of curiosity and adaptability proved essential in extracting the full value of AI while preserving the broader vision necessary for strategic innovation.

The key takeaway is that iterative collaboration with AI works best when paired with deliberate efforts to maintain a broad perspective and challenge initial assumptions. When treated as a sparring partner rather than an oracle, AI can enhance creativity and strategic rigor.

Lesson 4: Expanding the Boundaries of Strategic Innovation?

AI’s ability to spark unconventional thinking and explore uncharted possibilities is one of its most exciting attributes. Participants frequently used AI to model scenarios that pushed the boundaries of traditional strategic thinking, enabling them to consider long-term risks and opportunities in novel ways. In one workshop, participants worked with AI-generated sustainability scenarios. The tool highlighted interconnections among environmental regulations, shifting consumer preferences, and technological advancements. By prompting participants to explore how these factors might converge, the AI facilitated a reimagining of strategic priorities.

Nonetheless, the allure of speculative scenarios carried risks. Some teams found themselves captivated by bold but impractical ideas. The most effective groups balanced AI’s exploratory potential with disciplined evaluation, ensuring their insights remained actionable. What became evident was AI’s ability to visualize complexity in ways that stretched participants’ thinking. It allowed teams to model scenarios that combined variables—such as economic, environmental, and geopolitical trends—in ways that would be nearly impossible manually. This capacity to simulate multifaceted interactions enabled a level of strategic creativity that felt both structured and expansive, opening doors to ideas that were ambitious yet grounded in data-driven logic.

AI complements human creativity by amplifying our ability to imagine alternative futures. Its role in expanding strategic horizons is profound, but it requires careful anchoring to practical realities to deliver true innovation.

Lesson 5: Navigating Competitive Dynamics in an AI-Driven World?

Perhaps the most thought-provoking realization was how AI is reshaping the competitive landscape. Participants understood that the advantages AI offers their organizations are not unique—competitors are likely leveraging similar tools, potentially at equal or greater speeds. This recognition raised urgent questions about differentiation in an AI-enabled environment. For example, one group used AI to analyze supply chain vulnerabilities amid geopolitical tensions. While AI-generated insights allowed them to propose mitigation strategies, the broader discussion revealed deeper concerns: If competitors could adapt just as quickly, how could their organization maintain an edge? Would faster strategy cycles lead to industry-wide acceleration, and how should leaders respond? These reflections highlighted a dual-edged reality. While AI accelerates decision-making, it also heightens the intensity of competition. Participants grappled with the tension between speed and depth, recognizing the need to balance swift action with comprehensive strategic thinking.

Beyond the immediate competitive implications, AI also prompted participants to consider how industry norms might shift over time. If AI tools become ubiquitous, would competitive advantage depend more on how organizations integrate and interpret these tools rather than on the tools themselves? This evolving dynamic underscores the importance of investing in complementary capabilities—such as talent development, organizational agility, and cross-industry partnerships—to sustain differentiation in a rapidly changing landscape.

AI doesn’t just transform internal processes; it redefines the rules of competition. To stay ahead, leaders must not only adopt AI but also rethink how their industries operate and uncover new ways to differentiate in a crowded field.?

Looking Forward?

These lessons are not definitive answers but rather reflections from my recent experiences teaching strategy workshops with AI. They underscore AI’s potential to accelerate processes, foster collaboration, and unlock new avenues for innovation. At the same time, they highlight challenges, such as the risk of over-reliance on AI or the pressure to keep pace in an increasingly competitive environment.

What became clear throughout these sessions is that we are only beginning to understand AI’s role in strategy-making. Teaching with AI has shown me that its impact extends beyond efficiency; it challenges us to rethink the processes through which strategies are crafted. Yet, much remains to be learned—not just about integrating AI into teaching, but about how it will shape the practice of strategy in the years ahead.

As AI evolves, so too must our approaches to leveraging it responsibly and effectively. For educators, this means fostering environments that encourage critical engagement with AI. For executives, it means balancing AI’s opportunities with thoughtful oversight and a long-term perspective. The journey is ongoing, and the most transformative lessons may still lie ahead.