Strategy in the Classical (pre-AI) Era

To set the stage, I shared a simple view of how strategy has traditionally worked – a model that has served organizations well for decades:
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This model’s defining characteristics include:

• Top-down – Strategy is set at the top and cascades downward
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• Periodic – Strategic reviews happen annually or quarterly, not continuously
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• Human-driven – Insights rely heavily on executive intuition and experience
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• Centralized – Decision-making is concentrated in leadership teams
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• Near-term focused – Strategy is built around current or near-term market conditions rather than longer term, anticipated transformations in the environment or the company itself
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This approach made sense in a world where market intelligence took time to gather, and strategic shifts were measured in months or years, not weeks.
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But it was clear from our dialogue that, while these assumptions about how strategy should operate aren’t obsolete, AI is challenging all of them. The main question was not ‘will AI change strategy?’ but ‘how far will it go?’

Strategy in the AI-Augmented Era

To this point, there are already many examples of AI creating value in new ways across all the strategy function:
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Some of the more interesting real-world examples we discussed included:
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• J.P. Morgan’s Real-time Market Intelligence. AI scans massive financial and economic data to detect early trends invisible to humans, giving the bank a competitive edge in investment decisions before the rest of the market reacts
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• Unilever’s Executive Sentiment Analysis. Unilever has developed an AI-powered way to track leadership alignment. Their AI tools analyze executive communications, internal data, and external sentiment to help leadership teams anticipate potential misalignments before they become problems
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• Workday’s AI-Driven Workforce Planning. Instead of relying on static headcount planning, Workday’s AI uses real-time workforce modeling to predict skill gaps and future workforce needs - so HR leaders can proactively hire, promote, and develop employees before talent shortages impact performance
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• Amazon's Dynamic Capital Allocation. Rather than rigid annual budgeting cycles, Amazon’s AI dynamically reallocates capital in real-time based on shifting consumer demand, supply chain constraints, and emerging opportunities, making sure every dollar is working at peak efficiency
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These examples show how AI is already reshaping strategy by making existing processes smarter and more adaptive. But the real opportunity is about redefining what’s possible.
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AI’s Superpowers: The Next Level of Strategy

When a new technology arises, humans tend to focus on how it might be used to make the existing way of doing things better (as Paul Graham points out). To think beyond those constraints, we asked the questions 'what are AI's unique superpowers?' and 'what do those enable?'

Here is our initial list:
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Using these superpowers as a foundation, we explored ideas for how AI could fundamentally change the strategy process itself. Some of the more provocative ones:

• AI-Powered ‘Ghost Competitors.’ Instead of analyzing existing competitors, AI creates thousands of hypothetical competitors, testing different strategies against them. This allows companies to anticipate and outmaneuver competitive threats before they even emerge
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• AI ‘Shadow Boards’ for Leadership Decisions. AI runs every major executive decision through millions of alternative scenarios before implementation. This helps counteract leadership biases, surface unseen risks, and ensure the best course of action
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• Sentient Investment Funds & Dynamic Budgeting. Instead of static annual budgets, AI dynamically reallocates resources in real-time based on live market conditions. This makes capital allocation fluid, responsive, and more efficient.
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These ideas only scratched the surface (and our 90-minute time allotment flew by), but there was great energy in the room to explore and build on them further.

Final Thought: Are We Moving Fast Enough?

While no one in the room doubted AI’s potential to fundamentally change strategy, everyone agreed the main bottleneck isn’t the technology itself—it’s organizations’ ability to adopt, adapt, and integrate it effectively.

I’ll share a deep dive on organizational barriers - and ways to overcome them - in future posts, but for now would love to hear from others: How is AI shaping strategy in your company? Are you using it to refine the old way of doing things—or to create something entirely new?

A big thanks to Kaihan Krippendorff for inviting us to speak, and to everyone who joined the conversation. Special shout-out to Monique Nolk Patricia Miron Paul Mendelsohn David Zapata Ivan Rapin-Smith Darcy Dement (Pedetti) Eric Woodard Tamas Hevizi Michael Drexler Kevin Ilcisin Jairo Riveros Rudy Olivo Philipp Willigmann Alex Tserelov Amelia Fox Vincent Atallah June Barrage for their thought-provoking comments during our session.

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