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Yet the dawn of any new disruptive technology brings tremendous uncertainty as companies struggle to identify and prioritize use cases that offer the greatest potential for their business model and industry. For instance, one recent survey of C-suite executives found that 70% said that, while their company has an AI strategy, they believe it is currently misaligned with their overall business strategy.1
This environment recalls what our colleague Patrick Viguerie and his co-authors wrote in “Strategy Under Uncertainty,” a 1997 Harvard Business Review article that defined four levels of uncertainty, the highest being level 4 where “multiple dimensions of uncertainty interact to create an environment that is virtually impossible to predict.”
The challenge is to identify a framework to proactively manage such uncertainty, creating proprietary insights and making bold moves in the absence of data about the future. We call this dilemma the information-action paradox, as shown in Fig. 1, where companies either act early to acquire capabilities and customers even in the face of insufficient data or face constraints as competitors move faster and build barriers to entry.
There are strategies that can be used to overcome this paradox, confronting ambiguity by framing threats as opportunities and being both decisive and scientific in developing a robust AI strategy. We explore five of these strategies here.
1. Identify key drivers of uncertainty and build a fact base.
The future of AI is shaped by a complex web of factors. One of the key drivers of uncertainty is AI itself, with its inherent characteristics and emergent capabilities making the technology uncertain and difficult to understand, such as the evolution of proprietary models or the pace at which technologies will progress toward artificial general intelligence (AGI). Others include regulatory changes and the evolving ethical landscape; market and industry disruptions, such as shifts in consumer behavior; and global economic and political volatility.
Companies should seek to pinpoint both the broad and industry-specific variables that will influence their AI strategies. It’s crucial to distinguish between what is known, what can be discovered, and what remains unknowable at present. Regularly updating this understanding will ensure decision-making is informed and grounded.
Waymo, a leader in the AI-driven autonomous vehicle industry, has been proactively tracking and managing the forces influencing self-driving technology adoption. For example, it has been shaping and anticipating growing consumer and public acceptance of self-driving technology as it expands operations with extensive simulated tests. In addition to partnering with car manufacturers and investing in flexibility by targeting both passenger services and goods delivery, it has been documenting and publicly sharing research into industry safety to engage with regulators and public officials.2
2. Develop competing scenarios around critical uncertainties.
Since many variables shaping AI’s future are still undefined, it is impossible to craft a set of scenarios that are comprehensive and individually complete. However, maintaining a few plausible, competing scenarios reflecting the current state of knowledge, and conducting war games based on them, can help with identifying actions that maximize opportunities while minimizing risks.
As we explored in our e-book Leading Into the Age of AI, organizations can stress test strategies to ensure they’re resilient and adaptable. For instance, they can simulate scenarios where AI adoption accelerates at different rates across regions or industries, prioritizing investments and pivoting as new information emerges. This fosters a culture of continuous learning as teams revisit and revise scenarios to reflect developments in AI.
A case in point is education, where the power and personalization of AI-driven curriculum is met by skepticism from some students and questions about its effectiveness. This creates competing scenarios on the pace and extent to which AI-driven curriculums will be adopted in education. Pearson, a global leader in education publishing, has adopted a strategy that seeks to address this uncertainty, investing in research validating AI effectiveness, emphasizing transparency in how it uses the technology, and embedding it in products and services across its portfolio.3
3. Adopt an emergent strategy.
AI challenges how companies pursue strategic goals. For guidance, they can look to startups that leverage an emergent strategy, embracing flexibility and discovery rather than pre-planning the path to success. By encouraging employees and managers to look to the periphery for signals on the direction of their market, and not just in the direction of end goals, they foster an adaptive capacity that is crucial given AI’s potential to cause disruptive systemic shocks.
By embracing an emergent strategy, companies can develop more flexible strategic choices, regularly reviewing and adjusting them based on internal learnings, such as customer engagement with AI products, and external developments like technological or regulatory changes. This can help reduce the risk of becoming stagnant or taking a scattershot approach to innovation.
JPMorgan, an early adopter of AI with over 400 use cases in production, is taking a phased approach to AI that emphasizes flexibility and pragmatism. For instance, it developed a gateway that connects its workforce to external large language models and is designed to switch seamlessly between different models based on specific needs. This will ensure it isn’t tied to a single AI provider as it seeks to weave the technology into every aspect of the bank’s operations, both internally and customer facing.4
4. Institutionalize innovation and learning.
The most effective way for organizations to grasp AI’s capabilities and implications is through hands-on experimentation. Learning from past disruptive technologies and staying informed about AI developments – both within and beyond one’s industry – will also help foster a rigorous learning culture.
To institutionalize learning, organizations can create dedicated structures and processes that encourage continuous exploration and adoption of AI technologies. They can also establish cross-functional teams and centers of excellence while fostering partnerships with AI experts, universities, and other external innovators to accelerate the learning process.
Proctor & Gamble, the world’s largest consumer goods company, has developed what it calls an AI factory to improve operational efficiency and product development while also personalizing marketing efforts. The factory enables employees to share learnings, building a base of understanding and insight into the technology, which has led to a more efficient use of inputs and increased the pace of innovation.5
5. Establish conviction through leadership.
Organizations that cultivate a shared sense of purpose among all employees are more resilient and adaptable during times of change. By fostering a common understanding of their organization’s strategic AI goals, leaders can build conviction at all levels to experiment and move forward, despite uncertainty.
Clear and frequent communication and transparency about objectives can overcome fear of the unknown and build the trust needed to take risks. In addition, by investing in AI literacy, companies can enable cross-collaboration, leading to better decision making and a more agile response to emerging challenges.
Microsoft CEO Satya Nadella is an example of a leader fostering innovation and AI-driven growth. His strategic focus on AI has positioned Microsoft as a leading player, driving substantial growth and maintaining its competitive edge in the tech industry. His emphasis on culture – he has said the “C” in CEO represents culture – has helped it move with agility and make big bets.
“At the end of the day, companies will have to take a process, simplify the process, automate the process, and apply these solutions,” Nadella said. “And so, that requires not just technology, but in fact, companies to go do the hard work of culturally changing how they adopt technology.”
As a result, Microsoft has made significant advances in its suite of software and applications, including the development of tools such as Copilot, which is being embedded across its broader suite of software offerings.
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By strategically navigating the complexities of AI, organizations can unlock its transformative potential and secure their future in an evolving landscape. A crucial element in this journey is how companies tackle the uncertainty this technology creates, cultivating a strong leadership that drives a culture of innovation and collaboration while embracing adaptability and continuous learning.
About the Authors
Shari Parvarandeh is a Senior Director at Innosight based in London. sparvarandeh@innosight.com
Ned Calder is a Managing Director at Innosight based in Boston. ncalder@innosight.com
Freddy Solis is a Senior Director of Innosight based in Boston. fsolis@innosight.com
Endnotes
- “40% of executives say AI not ready to achieve accurate outcomes: report,” CFO magazine, May 29, 2024.
- “Waymo is taking on the task of writing a safety case for the entire AV industry,” The Verge, May 22, 2023.
- “Pearson: Generative AI Strategy Update,” FT Markets Data, May 9, 2023.
- “JPMorgan Chase is giving its employees an AI assistant powered by ChatGPT maker OpenAI,” CNBC, Aug. 9, 2024.
- “P&G’s AI Factory Scaling Data Science Innovations,” Consumer Goods Technology, Oct. 20, 2023.
