AI is changing the rules of business, see how to adapt from a Harvard Business School professor

Key Concepts

• Generative AI as a democratizing force, similar to the 1992-1993 browser moment for the internet.
• Abundant expertise due to generative AI lowering the cost of expertise.
• AI as a "drug" requiring randomized controlled trials to understand its effects.
• Activation of leadership through personal use of AI tools.
• AI-native leaders and the shift from "MBAs with Excel spreadsheets" to "MBAs with AI."
• The importance of "you with AI" versus AI alone.
• "Falling asleep at the wheel" phenomenon: over-reliance on AI output without critical evaluation.
• Focus on solving real customer problems with AI and scaling successful pilots.
• The increasing exponential gap between technology improvement and company adoption.
• AI agents as team technologies and the evolution of work management.
• Strategic shifts required at the company level to avoid the "Barnes & Noble - Borders strategy."
• Learning, adoption, and transformation gaps at the leader level.
• AI as the "bicycle of the mind" requiring practice and investment.

Generative AI: A Democratizing Force

Karim Lakhani likens the rise of generative AI to the introduction of the web browser in 1992-1993. Just as the browser democratized the internet, generative AI democratizes AI, making it accessible to individuals without specialized technical skills. He notes that while the democratization of AI was anticipated, the scale, speed, and scope of its impact were not.

The Impact on Business Education

Lakhani describes a new course at Harvard Business School, "Data Science and AI for Leaders," designed to be "AI-native." The course utilizes AI bots to explain concepts and a service that allows students to perform machine learning and statistical analysis using natural language, removing the barrier of programming in R or Python.

The Cost of Expertise and Strategy

Lakhani argues that generative AI is lowering the cost of expertise, similar to how the internet lowered the marginal cost of information transmission. This shift necessitates a re-evaluation of the core of the firm, as companies are essentially "bundles of expertise." He co-authored a Harvard Business Review article, "Strategy in an Era of Abundant Expertise," exploring this concept.

AI as a Drug: The Need for Experimentation

Lakhani compares generative AI to a drug, emphasizing that its optimal "dose," efficacy, and potential side effects in business are still unknown. He advocates for randomized controlled trials and a scientific approach to understanding AI's effects, both for advising companies and guiding the development of AI tools.

Leadership Activation and Bold Strokes

Lakhani stresses the importance of leadership "activation," meaning that leaders must personally use AI tools to understand their power and make informed decisions. He criticizes leaders who are happy to talk about AI but don't use it themselves. He highlights an exercise developed by his colleague, Iavor Bojinov, where executives create a snack food company in 90 minutes using AI prompts, demonstrating the technology's potential. Leaders need to identify a "bold stroke" – a significant initiative leveraging AI to reduce the cost of expertise.

The AI-Native Generation

Lakhani expresses optimism that the next generation of leaders will be "AI-native," comfortable using AI tools as a natural part of their workflow. He envisions a future where "MBAs with AI" replace "MBAs with Excel spreadsheets." He quotes the saying, "Machines aren't going to replace humans, but humans with machines are going to replace humans without machines." He notes that AI tools may empower founders to build MVPs without needing technical co-founders immediately.

The Value of Expertise in the Age of AI

Lakhani emphasizes that individuals and companies must focus on "you with AI" versus AI alone. As AI continues to improve, the value lies in the combination of human expertise and AI capabilities. He warns against blindly trusting AI output, citing a study by his postdoc, Fabrizio Dell'Acqua, on the "falling asleep at the wheel" phenomenon, where people become over-reliant on AI and fail to critically evaluate its results.

Customer-Centric AI Implementation

Lakhani advises focusing on solving real customer problems with AI and scaling successful pilots. He highlights opportunities in new product development, customer experience, and customer service. He emphasizes that if a pilot works, the decision to scale should be immediate, rather than subject to further delays.

The Exponential Gap and Change Management

Lakhani points out the increasing "exponential gap" between the rapid improvement of AI technology and the linear adoption rate of companies. He argues that this is not simply a technology adoption issue but a work transformation issue. Companies must adapt their work processes, teams, and organizational culture to keep pace with AI advancements. He emphasizes the difficulty of change management and the need to build "change fitness" within organizations.

AI Agents as Team Technologies

Lakhani views AI agents as team technologies that will transform the way work is managed. He draws a parallel to existing services like Uber and Amazon warehouses, where algorithms already direct the work of humans. He envisions a future where workers have their own AI agents, teammates that are agents, and even bosses that are agents.

Strategic Shifts and the Barnes & Noble - Borders Analogy

Lakhani's biggest worry is that companies will adopt a "Barnes & Noble - Borders strategy," implementing superficial AI solutions without fundamentally rethinking their business models. He urges companies to reimagine their businesses from the core up, recognizing that the cost of expertise has dropped.

Gaps at the Leader Level

Lakhani identifies three key gaps at the leader level:

• Learning Gap: Leaders lack sufficient knowledge and experience with AI.
• Adoption Gap: Leaders are not adopting AI quickly, fiercely, or widely enough.
• Transformation Gap: Leaders view AI as a technology play rather than a culture, work, and team play.

He emphasizes that HR and Data/AI officers should be closely aligned, and adoption should be considered in terms of technology, change, and process.

AI as the Bicycle of the Mind: Practice and Investment

Lakhani concludes by comparing AI to the "bicycle of the mind," emphasizing the need for practice and investment to master the technology. He encourages individuals to actively use AI tools and not just talk about them.