We recently had the privilege of hosting a session with Peter Cohan, a distinguished voice in the world of business and technology. As a renowned reporter for Forbes and Inc., an Associate Professor of Management Practice at Babson College, and author of 17 books, Cohan brings a wealth of experience and insight to the rapidly evolving field of generative AI.
In our session, Cohan shared valuable perspectives from his latest book, “Brain Rush: How to Invest and Compete in the Real World of Generative AI.” Drawing on his extensive background research in AI, strategy, and high-tech companies, he introduced us to a compelling framework he called “The Value Pyramid,” for understanding the potential value and impact of generative AI in business.
The Value Pyramid: A Framework for Generative AI Applications
The Value Pyramid is a three-tiered model that illustrates the increasing levels of value that generative AI can bring to businesses. Each level represents a different way companies can leverage AI, with the potential for impact and competitive advantage growing as we move up the pyramid.
1. Overcoming Creator’s Block (Base Level)
At the foundation of the pyramid, we find the most common and current uses of generative AI, or what Cohan referred to as the level for overcoming creator’s block. This encompasses various creative processes, from writing and drawing to coding, where AI can help define a starting point for the creative juices to begin flowing. While it’s widely accessible and can boost individual productivity, it offers limited competitive advantage for businesses.
Due to the widespread availability of tools like ChatGPT, this level of application, while useful, may not provide a significant return on investment for companies.
2. Boosting Productivity (Middle Level)
Moving up the pyramid, we encounter applications that boost broader productivity in specific areas of business. To be more specific, getting more work done with the same number of people in the organization, introducing additional areas for impact like sales, marketing and even onboarding new hires.
To further explain this level of application, Cohan shared a fascinating example from one company he spoke with, Bullhorn, a software provider for talent-recruiting firms, whose AI solution (in part) is being used to increase upskilling time for its customer’s employees.
Trained on its proprietary industry-specific data, Bullhorn’s AI analyzes successful recruiters’ actions and provides recommendations throughout the recruitment process. This AI, called Copilot, significantly boosts productivity by helping recruiters make informed decisions quickly, while transferring knowledge from experienced recruiters to new hires at the same time. This effectively has shown to “multiply productivity of recruiters by 100,” according to some of their customers currently using it, illustrating the substantial productivity gains possible at this level of the Value Pyramid.
By training AI models on the language and approaches of their most successful team members, Bullhorn is making it possible for their customers to quickly bring new hires up to speed. This not only accelerates the onboarding process but also enhances overall team performance and impact on both the customer and candidate experience.
Cohan further mentioned that from his research, he learned customer service AI implementations have shown the potential to increase productivity by up to 30% – a substantial improvement compared to the typical 3-5% gains seen with most new technologies.
3. Enabling Growth (Peak Level)
At the peak of the pyramid, we find the highest-value applications of generative AI, where it helps increase revenue. And one way for it to increase revenue is for it to increase your customers’ revenue. If the customers are making more money, then they will view it as worth paying for—a win-win. This is where Cohan believes executives and companies should be focusing their time and efforts.
A prime example comes from the retail sector. Cohan described how AI can revolutionize customer engagement through hyper-personalized marketing. Imagine an AI system that generates individualized weekly flyers for each customer, based on their unique purchasing history, preferences, and location-specific deals. These tailored promotions, delivered directly to customers’ preferred platforms (be it a mobile app, social media, or email), not only enhance the shopping experience but also drive sales by offering relevant deals on products customers actually want to buy.
This level of customization, updated weekly to reflect current inventory and trending items, can significantly boost a retailer’s revenues. In fact, companies implementing such AI-driven personalization have seen average revenue increases of 3-5%—a substantial uplift in the typically lower-margin retail industry. This approach exemplifies how AI can create win-win scenarios, benefiting both the business and the consumer.
Things to Consider
While Cohan’s Value Pyramid provides a robust framework for understanding the current applications of generative AI, he also reiterated the points related to the known challenges, opportunities, and strategic considerations that companies must navigate as they explore AI integration and development into broader company operations and offerings.
1. Competing Business Fears
There is an intriguing tension in the business world: the fear of missing out (FOMO) on AI advancements versus the fear of reputational damage from AI mistakes or misuse. This dichotomy is driving both rapid adoption and cautious implementation across industries. So, why is this important? Because AI is becoming more table stakes vs. a nice to have to be able to maintain competitive pace across nearly every industry—a “when” vs. “if” reckoning if you will. So, make sure you are executing due diligence and taking the proper steps to vet AI models and go about its implementation methodically. What’s the saying: “Fail to plan, plan to fail.” AI is not something to rush into, but rather an enhancement that commands very intentional considerations about where, how and when it comes into play across your workflows and experiences—internally and externally.
2. Revenue Growth is Key
For AI service providers or businesses contemplating AI adoption or service offerings, a simple yet crucial factor to ROI must be considered: to justify the substantial investment costs of paid AI models, it must drive revenue growth, not merely offer cost and time savings. This principle underscores the importance of targeting applications at the peak of the Value Pyramid, where AI can directly contribute to top-line growth. It emphasizes the need for strategic, intentional selection of AI use cases that can demonstrably enhance profitability and create tangible value for both the business and its customers.
3. Human Oversight
Despite AI’s advancing capabilities, Cohan stressed the importance of keeping a “human in the loop” to check AI outputs and mitigate ethical concerns. This is not a “set it and forget it” kind of technology. Human oversight is crucial for maintaining quality control and trust in AI-driven processes. Remember, it’s easy to lose trust and extremely hard to regain it, so make sure that you have the proper people and processes in place to keep your tech and its outputs in check.
4. Ethical Considerations
The ethical landscape of AI extends far beyond bias and accuracy concerns. It encompasses complex issues such as copyright infringement in AI training, as highlighted by the New York Times lawsuit against OpenAI. The potential for AI to generate convincing misinformation raises significant concerns, particularly during critical periods like elections. In academia, AI tools challenge traditional notions of academic integrity, forcing educators to adapt their policies. Media organizations grapple with the ethical implications of AI-generated content, while companies worry about their proprietary information being absorbed into public AI models. These multifaceted challenges call for comprehensive policy development and regulation to balance innovation with responsible AI use. As AI continues to integrate into various aspects of society, there’s a growing need for ongoing dialogue and adaptive strategies across all sectors to address these evolving ethical considerations.
——–
Overall, the session with Cohan underscored the transformative potential of generative AI across nearly every industry and job out there. However, it also highlights the need for careful implementation, ethical and use case policies, and a focus on creating genuine value for customers and businesses alike.
As we navigate an increasingly AI-powered landscape, the Value Pyramid serves as a crucial framework for evaluating and prioritizing AI initiatives. By moving beyond basic applications and focusing on growth-enabling AI solutions, organizations can position themselves to thrive in this new era of technological innovation.
If you’d like to hear the full session with Peter Cohan, please listen below.
