On a recent visit to Barcelona, I sat down with Marco Bertini, one of the world’s foremost experts on dynamic pricing. Bertini is a professor of marketing at Esade University and the author of the book, ‘The Ends Game: How Smart Companies Stop Selling Products and Start Delivering Value,’ which examines how technology and society stimulate accountability, prompting companies to profit from the outcomes they deliver rather than the offerings they bring to market.

We spoke about the origins of fixed pricing, the impact of AI on dynamic pricing, and the psychological aspects of consumer behavior related to pricing decisions. Bertini shared his insights on how companies can leverage pricing as a mechanism to create value for both businesses and consumers, emphasizing the importance of transparency and ethical practices in the process.

Our discussion also touched upon the concept of access versus ownership, exploring how subscription-based models and outcome-based pricing are transforming various industries. Bertini spoke about the need for companies to focus on customer outcomes and to allocate risk to the party best equipped to handle it.

5 Key Insights:

1. Embrace dynamic pricing strategies that leverage technology to offer personalized prices based on individual consumer preferences and behaviors.

2. Focus on customer outcomes and align pricing models with the value delivered to consumers.

3. Be transparent in communicating pricing strategies to customers and frame them in a way that highlights the benefits for both parties.

4. Consider shifting from ownership-based to access-based models, such as subscriptions or usage-based pricing, to improve asset utilization and customer satisfaction.

5. Use pricing as a mechanism to facilitate mutually beneficial transactions, ensuring that both the company and the consumer derive value from the exchange.

Ben Waber is one of the world’s leading experts on people analytics and organizational culture. He is the founder of Humanyze and a visiting scientist at the MIT Media Lab. We caught up in Tokyo to discuss the future of work and the impact of large language models (LLMs) on productivity. 

In our discussion, Ben emphasizes the importance of considering systemic issues and inherent technological limitations when measuring productivity, arguing that companies often lack proper metrics for assessing the performance of knowledge workers. Despite the current hype around AI, we talked about the fundamental flaws of LLMs, such as their inability to reliably perform sequential reasoning and their lack of coherence, raising concerns about their application in critical areas like HR and legal departments.

Ben suggests that the most valuable work stems from interactions between people with diverse mindsets and experiences, as friction between different perspectives often leads to interesting ideas. He believes that technology should be used to shape these interactions creatively, both for individuals and organizations. While computational tools can assist in this process, humans will remain the driving force behind innovation for the foreseeable future.

5 Key Insights:

1. Task-level analysis of productivity is insufficient; consider systemic issues and the long-term impact of decisions on organizational outcomes.

2. LLMs have inherent limitations, such as the inability to reliably perform sequential reasoning and maintain coherence, making them unsuitable for tasks where truth and accuracy are critical.

3. Firing workers based on short-term productivity gains from LLMs may lead to long-term issues, as domain experts are best equipped to identify the tool's limitations.

4. Valuable work is probabilistic and often driven by interactions between people with diverse perspectives and experiences.

5. Technology should be used to foster creative interactions among employees, but humans will remain the primary source of innovation.

I sat down with Pierre-Yves Calloc'h, Chief Digital Officer at Pernod Ricard, to discuss the company's journey in embracing AI, algorithms and automation to drive marketing effectiveness, sales performance, and consumer engagement. Pernod Ricard, known for its extensive portfolio of brands such as Absolut Vodka, Jameson, and Ballantine's, has been at the forefront of leveraging AI to make data-driven decisions and personalize consumer experiences.

Pierre-Yves shares insights into how AI has transformed various aspects of the business, from optimizing marketing investments and generating personalized content to empowering sales teams with tailored recommendations for each outlet. He emphasizes the importance of transparency, internal capability building, and the role of human judgment in challenging and interpreting AI-driven insights.

Looking ahead, Pierre-Yves envisions a future where AI will enable even greater personalization and adaptation to individual consumer preferences and moments. He highlights the potential for AI to enhance consumer experiences by ensuring that people find the right product at the right time, tailored to their specific desires and the social context of their consumption.

5 Key Insights:

1. Embrace AI and automation to drive marketing effectiveness, sales performance, and consumer engagement.

2. Foster a data-driven culture and empower teams to make decisions based on AI-generated insights while encouraging them to challenge and interpret the recommendations.

3. Invest in building internal AI capabilities to ensure transparency, cost-effectiveness, and alignment with business needs.

4. Prepare for a future where AI enables hyper-personalization and adaptation to individual consumer preferences and moments.

5. Ensure responsible and transparent use of AI, addressing consumer concerns and adhering to ethical guidelines.

In this episode, I speak with Byron Reese, an acclaimed author, futurist, and entrepreneur, who shares his insights on the concept of ‘Agora’, a superorganism formed by the collective interactions of human beings. Drawing parallels complex adaptive systems like beehives, Reese argues that just as individual bees come together to create an entirely new organism with emergent properties, humans too form a higher-order entity through their differentiated roles and interactions. 

Reese delves into the idea of life spreading through the universe via panspermia and the role of natural selection in the evolution of intelligent life on planets. He posits that the purpose of Agora is to protect life on Earth from existential threats, such as asteroids, and that the optimal number of intelligent species on a planet is one, as too many would lead to self-destruction, while too few might fail to protect the planet.

In our discussion, we also touch upon the potential co-evolution of humans and artificial intelligence, the consolidation of human knowledge through language models, and the importance of individual actions in contributing to the well-being of the superorganism. 

5 Key Takeaways

1. Agora, a superorganism formed by human interactions, emerges from the differentiated roles and specialized interactions of individuals, much like a beehive.

2. Life may have spread throughout the universe via panspermia, and natural selection favors planets with a single intelligent species capable of protecting it from existential threats.

3. The purpose of Agora could be to safeguard life on Earth from catastrophic events, such as asteroid impacts, highlighting the importance of collective action.

4. The co-evolution of humans and artificial intelligence, along with the consolidation of human knowledge through language models, may significantly shape the future of society and decision-making.

5. Individual actions, such as kindness and personal growth, play a crucial role in contributing to the well-being and progress of the superorganism, potentially leading to a utopian future.

What are the new challenges that generative AI is creating for copyright law? In this episode, I speak with Matt Hervey, the head of AI law at Gowling, to explore the legal intricacies surrounding the creation of content and ideas by large language models. In our discussion, Matt sheds light on the complex landscape of AI ownership, highlighting the differences in legal frameworks across various jurisdictions, such as the UK, EU, and US. 

In Matt’s view, a key issue for leaders to consider in determining the ownership and protectability of AI-generated works, is the degree of human involvement and the level of effort contributed. We also touch upon the controversial issue of training data and the potential liabilities arising from the use of scraped content from the internet.

In this episode, we further explore the transformative potential of AI in industries like life sciences, where AI-powered drug discovery and clinical trials are already making significant strides. Matt discusses the importance of documenting the AI development process to mitigate risks and the need for organizations to develop proprietary models to safeguard their intellectual property.

The 5 Key Takeaways:

1. Understand the legal implications of AI ownership and the varying levels of protection across different jurisdictions to inform strategic decisions and mitigate risks.

2. Assess the potential liabilities associated with training data and implement measures to ensure the ethical and responsible use of AI within the organization.

3. Recognize the transformative potential of AI in industry-specific applications and invest in proprietary models to leverage unique data assets and gain a competitive edge.

4. Foster collaboration between legal, technical, and domain experts to develop a comprehensive understanding of AI's impact and potential within the organization.

5. Prepare for the future of AI-powered organizations by building interdisciplinary teams and upskilling employees to effectively navigate the complexities of AI implementation and governance.