Feb 21, 2024

The economic impacts of generative AI: Separating hype from reality

Caroline Hao
Ryan Mahtab

Caroline Hao and Ryan Mahtab

The economic impacts of generative AI: Separating hype from reality

Generative artificial intelligence (AI) is making waves, promising to reshape our economy and change the way we operate our businesses. In a recent episode of the Technology Tangents podcast, “The generative AI revolution in business,” Credera’s Chief Technology Officer Jason Goth and Chief Data Scientist Vincent Yates give us a peek into the economic potential of generative AI, its impact to the workspace, and strategies for competing in the new generative AI landscape.

The following  is a summary of their podcast conversation. 

Economic potential of Generative AI

Recent reports estimate generative AI could add roughly $2.6 to $4.4 trillion annually across studied applications. To put that into perspective, that is roughly the size of the United Kingdom's 2021 gross domestic product.  

The rapid development of generative AI models has also been a significant factor in its growth. In just a few years, these models have gone from being relatively unreliable and limited to achieving remarkable accuracy and generalizability. This change has unlocked a massive economic opportunity, seemingly overnight. 75% of its predicted value comes from just four use cases: customer operations, marketing and sales, software engineering, and research and development. Forward-thinking organizations are scrambling to identify how these use cases can be applied to generate value for their businesses.  

However, while generative AI is predicted to generate significant economic activity, traditional AI applications, such as computer vision and natural language processing, are expected to contribute even more—around $13 trillion (about $40,000 per person in the U.S.). Generative AI is only a piece of the pie organizations should consider in context of the value AI can generate. 

While large language model (LLM) technology has large potential for value generation, without strategic planning and investment, machine learning (ML) AI projects will likely fail. To stay ahead of the game, corporations must define an AI/ML roadmap to outline a clear path toward maturity in this space. 

Workspace impact of Generative AI  

Generative AI’s capacity to automate tasks core to a wide range of industries has led to a general fear that it will have a depressive effect on jobs. We believe it will actually do the opposite.   

Based on data from U.S. Census Bureau, there has only been one job throughout all time that has been fully automated—the elevator operator. In fact, the pattern of generative AI may be reminiscent of the impact of ATMs. While initially seen as job-killers for bank tellers, the adoption of ATMs led to more bank branches opening and even more roles for bank tellers in a phenomenon known as Jevon’s paradox. This is because the cost for opening a single branch dropped substantially due to automation. 

While generative AI will likely not lead to a decrease in jobs, it will change the employment landscape. There will be less need for certain human input as tasks that once took hours and many hands can be completed nearly instantaneously. Managing through the generative AI revolution will involve re-skilling the workforce to match changing demand. 

One example of this disruptive change in marketing and customer operations is in the heightened potential for tailored customer experiences. By personalizing content, AI can help businesses increase revenue by capturing and retaining customers. Furthermore, AI can be used to enhance customer service by providing more cost-effective and scalable touchpoints for customers than traditional customer service solutions. Specifically, AI-powered personal assistants like Siri, Alexa, and Google Assistant can now understand and respond to complex requests, making it possible to deliver on the promise of truly personalized assistance. As AI continues to advance, these personal assistants will become even more sophisticated, learning individual preferences and providing tailored recommendations, while also generalizing to a broader set of tasks. This shift in interaction modality has significant implications for businesses, as it means consumers may no longer be directly influenced by traditional advertising methods. Instead, advertisers may need to target the AI agents themselves, which will be responsible for surfacing brands to their users.  

As AI continues to evolve and becomes more integrated into our daily lives, businesses must adapt and invest in the technology to stay competitive. By prioritizing AI-driven initiatives such as in marketing and customer service, companies can improve their customer experience, increase revenue, and ultimately, position themselves for success in the rapidly changing business landscape. 

Three strategies for competing in the new AI landscape 

As AI continues to advance, businesses must be prepared to navigate the challenges and opportunities that come with this transformative technology. 

1. Niche AI 

One potential strategy for businesses is to develop narrow AI applications that solve specific problems within their domain. By doing so, they can prevent being disintermediated by larger tech companies like Apple, Google, and Amazon, which may be better equipped to develop more general AI solutions. By focusing on their unique strengths and capabilities, businesses can maintain control over their customer relationships and continue to deliver value in the face of increasing competition from mainstream AI-driven solutions. 

2. AI partnerships 

Another approach for businesses would be to scale up by collaborating with other industry players and create a consortium or third-party platform that is not directly aligned with any single company. This would allow them to pool resources and develop a more competitive AI-driven interface that can cater to a wide range of consumer preferences. By working together, businesses can create a more level playing field and ensure they are not left at the mercy of a single dominant player in the market. 

3. Adjacent industries 

Another interesting aspect of generative AI is its potential to create new opportunities for businesses in adjacent industries. For example, home automation and energy management systems could benefit from AI-driven interfaces that can optimize energy consumption and save consumers money. By enhancing preexisting products with AI features, these companies can offer a more personalized and efficient service that appeals to their customers. 

Looking ahead 

As AI continues to advance, organizations must adapt and invest in the technology to stay competitive. Managing through the generative AI revolution will involve diving into the most relevant use cases, evaluating a strategic approach to leveraging AI tools, and re-skilling the workforce to match changing demand. Overall, generative AI presents both challenges and opportunities, and organizations must be prepared to navigate the changing landscape to stay ahead of the game. 

To learn more about how to make AI work for your organization, explore our data insights page or reach out to us at

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