In the gold rush of artificial intelligence, companies are scrambling to stake their claims. Yet according to Gregory La Blanc, a Distinguished Teaching Fellow at California’s Berkeley Haas, many are digging in the wrong places. La Blanc, who brings a multidisciplinary perspective spanning finance, strategy, law, and data science, argues that the mother lode in the AI era isn’t found in algorithms, but in unique data assets and a culture of continuous learning.

“Companies have been using artificial intelligence since day one,” La Blanc notes, challenging the notion that AI is a recent phenomenon. “The real differentiator is an organization’s ability to integrate new tools seamlessly into their operations.” This perspective sets the tone for La Blanc’s nuanced view of AI’s role in business transformation, a topic he’ll be exploring further at the upcoming Emerging Tech Management Week: Silicon Valley program at UC Berkeley in early November.

Redefining AI: More Than Just Algorithms

In a conversation with CKGSB Knowledge, La Blanc begins by expanding our understanding of AI itself. Rather than limiting the definition to the latest buzzwords or cutting-edge algorithms, he proposes a broader view: “any kind of use of computation for decisioning.” This inclusive approach allows for a more comprehensive understanding of how AI can create value across various business functions and industries.

This broader definition also highlights a crucial point: the effectiveness of AI implementation often depends more on a company’s existing culture of innovation and adaptability than on the sophistication of its algorithms. La Blanc traces the evolution of AI technologies from early rule-based systems to modern machine learning algorithms and generative AI, emphasizing that each iteration brings both opportunities and challenges for businesses.

AI is not just about technology,” he notes. “It’s about how you reshape your business processes, your organizational structure, and your overall strategy to take advantage of these new capabilities.”

Importantly, La Blanc cautions against viewing AI as a panacea for all business problems. “AI is not just about technology,” he notes. “It’s about how you reshape your business processes, your organizational structure, and your overall strategy to take advantage of these new capabilities.” This holistic view underscores the need for AI to be integrated into broader digital transformation initiatives rather than treated as a standalone solution.

Data: The New Oil, but Not Quite

La Blanc emphasizes that in the age of AI, data has become the new frontier of competitive advantage. “At the end of the day, the algorithms are commodified,” he argues. “Anybody can have access to these tools. If you’re really trying to create a competitive advantage, you’re not going to be able to do it on the basis of the algorithms.”

Instead, La Blanc advises companies to focus on their unique data assets. He cites Texas-based Caterpillar as a prime example of this strategy in action. The industrial equipment maker has reinvented itself as a data company by instrumenting its products to collect valuable data about customers, landscapes, and soil conditions. This shift has created a new source of competitive advantage that transcends Caterpillar’s traditional manufacturing expertise.

Drawing historical parallels, La Blanc compares data to traditional sources of competitive advantage: “There was a point in time in history where your competitive advantage came from access to some natural resource, or some exclusive monopoly access to trade. The East India Company had a monopoly on trading.” In the modern era, he argues, data has become the new frontier of competitive advantage.

However, La Blanc cautions that data’s value lies not just in its quantity but in its uniqueness. “The thing about data is that it’s not fungible,” he points out. While more data is generally better, the real power comes from possessing idiosyncratic information that allows for unique insights.

La Blanc believes challenges are inherent in developing a robust data strategy, including issues of data quality, privacy, and governance. He stresses the need for companies to develop sophisticated systems for data collection, storage, and analysis, as well as clear policies for data use and sharing.

Moreover, La Blanc highlights the potential for data-driven network effects, where the value of a company’s data assets can increase exponentially as more users or customers interact with their systems. Digital platform companies have leveraged this principle to create powerful competitive moats.

Emphasizing the dynamic nature of data strategy, La Blanc notes,

The landscape is constantly evolving. Companies need to think about new sources of data, ways to analyze it, and applications for the insights they derive.”

Navigating the Bias Minefield

On the thorny issue of AI bias, La Blanc distinguishes between statistical bias and ethical considerations, using Google’s 2015 image recognition fiasco—where the system mistakenly tagged African American faces as gorillas—as an example. “There’s absolutely no way that’s purely a statistical problem,” La Blanc explains. “It’s a business problem; it’s a sociological problem; it’s an ethical problem; there’s no way that you could spot the problem with just math.” This underscores the interplay between technical performance and societal impact in AI systems.

Interestingly, La Blanc argues that in many cases, AI systems can actually reduce sociological bias compared to human decision-makers, particularly in areas like lending. However, he warns that the design of the model is crucial. “If all you’re trying to do is predict what a human’s going to do… if the humans are racist or biased, then the model will be racist and biased.”

La Blanc’s insight highlights the importance of carefully considering the training data and objectives of AI systems to avoid perpetuating or exacerbating existing biases. La Blanc recommends a thoughtful approach to AI development that goes beyond mere prediction to actually improve decision-making processes.

Implementation Strategies

When it comes to AI implementation strategies, La Blanc suggests a pragmatic approach. Most companies, he notes, will likely start with existing foundation models and then add their own proprietary data to improve performance. This strategy allows organizations to leverage advanced AI capabilities while maintaining a competitive edge through their unique data assets.

La Blanc also stresses the importance of human reinforcement in training these models for specific needs. This human-in-the-loop approach ensures that AI systems are aligned with the company’s goals and values, and can adapt to the nuances of specific business contexts.

Looking ahead, La Blanc predicts that AI tools will become as ubiquitous in the workplace as Excel and PowerPoint are today. “I think every single employee needs to be empowered to use these tools in the same way that every single employee currently is empowered to use Excel or PowerPoint,” he says. This democratization of AI tools has significant implications for workforce development and organizational structure.

La Blanc also foresees a reshuffling of data permissioning and licensing agreements as companies navigate the balance between protecting proprietary information and benefiting from shared insights. This will require organizations to carefully consider their data sharing policies and partnerships in the AI ecosystem.

Culture: The Hidden Differentiator

La Blanc’s most emphatic point is the critical importance of creating a learning culture within organizations to maximize the benefits of AI. He argues that this cultural shift must begin with performance evaluation: “You have to include in your performance evaluation something about teaching and learning. You have to reward people explicitly and implicitly.”

La Blanc cites Microsoft’s transformation under Satya Nadella as a prime example of this principle in action. “Microsoft underwent fundamental transformation under Satya Nadella,” he notes. “I did not think you could take a company with a strong culture, which was very competitive, and turn it into one that was deeply about learning.”

This cultural reorientation, La Blanc believes, has positioned Microsoft at the forefront of AI integration and innovation. It serves as a reminder that even large, established companies can reinvent their cultures to thrive in the AI era. La Blanc argues that this kind of cultural transformation is essential for organizations hoping to fully leverage the power of AI and other emerging technologies. It’s not enough to simply adopt new tools; companies must foster an environment where continuous learning and adaptation are part of the organizational DNA.

The Human Factor

While much of the discourse around AI focuses on its technical capabilities, La Blanc stresses the enduring importance of human skills and judgment. As AI takes over more routine tasks, he argues, uniquely human capabilities such as creativity, emotional intelligence, and complex problem-solving will become even more valuable.

Touching on the ethical implications of AI adoption, La Blanc stresses the need for human oversight and decision-making in critical areas. He recommends a balanced approach that leverages the strengths of both AI and human intelligence, rather than viewing them as competing forces.

Moreover, La Blanc discusses the potential of AI to augment human capabilities, particularly in fields like healthcare and education. He shares examples of how AI is being used to assist doctors in diagnosis and treatment planning, and to provide personalized learning experiences for students.

La Blanc offers a compelling vision for how organizations can prepare themselves for an AI-powered future. His central message is one of resilience and adaptability. “One mistake that we can make is we can think that every one of these new technology trends is unique, and that we need a unique strategy for surfing this latest wave,” La Blanc warns. “My view is that the technological waves are just going to keep coming, and they’re going to come faster and faster. The goal of strategy is to create organizations that are resilient and that can seamlessly incorporate new technologies and trends.”

La Blanc’s perspective underscores the need for companies to develop flexible, adaptable strategies that can evolve alongside technological advancements. Rather than trying to predict and prepare for specific technologies, he advocates building organizational capabilities that allow for rapid learning and integration of new tools and approaches.

The Road Ahead

As AI continues to reshape business, La Blanc’s insights offer a valuable roadmap for companies seeking to harness its power while navigating the ethical and strategic challenges it presents. His multifaceted approach—emphasizing the importance of unique data assets, addressing bias thoughtfully, and fostering a culture of continuous learning—provides a framework for organizations to not just survive but thrive in an AI-powered future.

La Blanc’s upcoming participation in the Emerging Tech Management Week: Silicon Valley program at UC Berkeley in early November offers an opportunity for business leaders to dig deeper into these critical issues. As companies around the world grapple with the implications of AI, La Blanc’s guidance serves as a reminder that success in the age of AI involves reimagining how we learn, work, and create value in a rapidly evolving digital landscape.

In the end, La Blanc’s message is one of cautious optimism. While the challenges posed by AI are significant, so too are the opportunities for innovation, growth, and positive societal impact. By approaching AI with a strategic mindset, a commitment to ethical practices, and a culture of continuous learning, organizations can position themselves at the forefront of the AI revolution, ready to shape the future rather than merely react to it.