Juan Carlos Bustamante: Strategy in the age of AI

A year ago, I wrote a column titled “Data- and AI-driven organizations: More analytical or more strategic?”, where I warned that the general view of AI in strategy is centered on the idea that AI alone could define the right strategy.

However, this futuristic conception takes us away from its real application in strategic design. Even today, AI does not replace human intuition or creativity, but it already allows us to experiment with predictive and prescriptive models to improve decision-making.

Since then, the debate has evolved and the use of generative AI in strategy raises a critical question: Are we using AI to design the future or are we simply using it to optimize processes and make faster decisions?

AI and strategy: The border between optimization and differentiation

Michael Porter, in his classic article What is Strategy? , distinguishes between operational effectiveness and strategy. While operational effectiveness enables the same thing to be done more efficiently, strategy involves creating a unique and sustainable position.

In this context, advances in generative AI have been celebrated for their ability to optimize processes and reduce costs. However, this does not necessarily contribute to strategic differentiation, as many organizations are implementing it without a clear strategic framework, limiting themselves to improving efficiency without rethinking their value proposition.

Recent reports from McKinsey and the Boston Consulting Group (BCG) reveal an interesting paradox: 75% of executives consider generative AI a strategic priority, but only 25% perceive that they derive real value from its implementation. This suggests that many companies adopt AI without managing to align it with their business strategy.

This gap may be due to the lack of a clear vision on how to integrate AI into the value proposition, the dependence on standardized solutions that do not generate differentiation, and the underestimation of human leadership in the interpretation and application of the results generated by AI.

Technology alone cannot define strategy; it is its adaptation to the company's structure and objectives that truly generates sustainable competitive advantages.

Companies must ask themselves what questions their AI answers and in what aspects they can develop their own models to obtain a differential advantage. The implementation of AI must be aligned with the organizational structure, avoiding replicating generic approaches adopted by the competition.

AI and strategic decision-making: the role of trade-offs

A key aspect of strategy, according to Porter, is making clear choices and assuming trade-offs.

However, generative AI is redefining these trade-offs by enabling greater flexibility and lower costs to operate simultaneously, which can lead to the misperception that all decisions can be automated without compromising strategic coherence.

Companies must therefore ask themselves which parts of the business are worth automating to the point where AI can make operational decisions autonomously, and which ones remain essential for human intervention. It is not just a question of what AI can do, but what it should do within the company's strategic framework.

Some decisions require deep understanding of context, interpretation of nuances, and creativity in problem solving, which remains a uniquely human strength. Assuming that AI can completely replace human judgment can lead to a loss of strategic differentiation. While AI improves efficiency in marketing, production, and financial analysis, strategic decision making still requires contextual interpretation that only leaders can provide.

Towards a hybrid strategy: integrating AI without losing differentiation

To prevent AI from leading to homogeneous competition, companies must integrate it into their strategy without losing what makes them unique. This means:

1. Create proprietary AI models : Develop proprietary models instead of relying on generic solutions. One example is BloombergGPT, a specialized financial language model that enables Bloomberg to offer advanced analytics and strategic automation in financial markets, differentiating itself from more general AI solutions.
2. Define clear trade-offs : AI should complement existing strategy rather than trying to do everything. It is critical to decide where AI adds value without compromising human judgment, ensuring that automation does not dilute strategic vision or the ability to adapt to unforeseen changes.
3. Avoid competitive convergence : Ensure that AI reinforces differentiation rather than homogenizing strategy.
5. Aligning AI with competitive advantage : Its implementation must strengthen the company's business model and value proposition.

In conclusion, we can say that generative AI represents one of the biggest transformations in business strategy since the emergence of Porter's models, but its impact will depend on how companies use it: as a support tool or as the center of their strategy.

It's not about adopting AI because it's trendy, but rather about integrating it to boost a sustainable and differentiated competitive advantage. The key question is: Are we using AI to design the future of our company or just to optimize what we already do? The answer will define who will lead the market in the coming years.

The author is Professor of the Department of Marketing and Business Intelligence at EGADE Business School of the Tecnológico de Monterrey.