February 7, 2024
The evolution of Generative AI has indeed been a significant milestone in the field of artificial intelligence.Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in a way AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and create—and preternatural ability to have a conversation with a user. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. As we've entered 2024, let's break down this concept and its implications in more detail:
Generative AI is a step change in the evolution of artificial intelligence. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions. We have used two complementary lenses to determine where generative AI with its current capabilities could deliver the biggest value and how big that value could be
Source (McKinsey & Company)
The McKinsey & Company report evaluates the economic impact of generative AI through two lenses. The first examines 63 use cases across 16 business functions, predicting an annual economic benefit of $2.6 to $4.4 trillion. The second lens assesses the impact on labor productivity across approximately 2,100 work activities in 850 occupations, contributing to an estimated total annual economic benefit of $6.1 to $7.9 trillion when accounting for overlaps in cost reductions and productivity improvements.
Using generative AI in just a few functions could drive most of the technology’s impact across potential corporate use cases.
Generative AI holds significant potential for transforming customer operations, enhancing both customer experience and agent productivity. It has shown effectiveness in automating customer interactions through natural language, with a notable impact on efficiency and quality of service. For example, in a company with 5,000 customer service agents, generative AI increased issue resolution by 14% per hour and reduced handling time by 9%. Additionally, it lowered agent attrition and manager contact requests by 25%. Notably, it improved productivity and service quality especially among less-experienced agents, as AI assistance allowed them to employ communication techniques akin to more skilled agents.
Key operational improvements through generative AI include:
The application of generative AI in customer care functions is estimated to potentially increase productivity by 30 to 45% of current function costs. However, this analysis only considers the direct impact on productivity and does not include potential indirect benefits such as improved customer satisfaction and retention, which could arise from enhanced personalized support by human agents aided by AI.
Generative AI is rapidly transforming marketing and sales functions by enabling personalized communication and content creation at scale. Its applications range from crafting personalized messages, generating brand advertising, creating headlines, slogans, social media posts, to drafting product descriptions. This technology is particularly effective in areas where text-based communications and personalization are key.
However, the integration of generative AI into marketing requires careful consideration due to potential risks, such as plagiarism, copyright violations, and biased representations stemming from limited or biased training data. These concerns necessitate significant human oversight for strategic and conceptual thinking tailored to each company's needs.
Key operational benefits of generative AI in marketing include:
The productivity increase in marketing functions due to generative AI is estimated to be between 5 and 15 percent of total marketing spending. This analysis does not account for potential indirect benefits such as higher-quality data insights, which could lead to more effective marketing campaigns and better-targeted customer segments. Additionally, it could enable a shift in resources towards creating higher-quality content for owned channels, potentially reducing dependency on external channels and agencies.
Generative AI is significantly transforming the field of software engineering by treating computer languages as just another language. This approach opens up new possibilities for software engineers, such as:
In summary, generative AI is a game-changer in software engineering, enhancing productivity, improving work experiences for engineers, and potentially shifting the focus toward more advanced aspects of code and architecture design.
The advent of AI-generated avatars in multilingual videos, facilitated by tools like Heygen and Synthesia, is transforming education by offering personalized and accessible learning experiences in multiple languages. This technology enables the creation of avatars for well-known educators, broadening educational reach across language and geographical barriers, and allows for content tailored to diverse learning styles and levels. However, it also poses significant risks, including the potential for spreading misinformation, ethical concerns around impersonation, and a decrease in human interaction in education, which could impact the authenticity and social aspects of learning. Balancing these opportunities and risks is crucial for the responsible integration of this technology in education.
Generative AI tools like ChatGPT, Bing chat, and ChatPDF are increasingly supporting teachers in course analysis and design, offering a range of opportunities from aiding in initial course preparation, idea generation for transferable skills, enhancing teaching methodologies, to finding online resources, developing activities, and assisting in planning and scheduling. However, this comes with risks such as potential over-reliance on AI by teachers, the risk of oversimplification in assessments, and concerns about the accuracy and bias of information provided by AI algorithms. Balancing these tools' benefits with mindful usage and critical pedagogical judgment is essential for educators.
While 2023 was filled with hype and discussion around generative AI, few health systems had developed definitive strategies for the emerging technology, and even fewer implemented applications outside of isolated pilot projects with highly targeted use cases.
Most health systems, as is common in healthcare technology adoption, are playing the wait-and-watch game before taking the first step. For example, health systems will likely monitor their peers’ actions and those of the largest vendors in the industry until they feel safe enough to dive in.
The few early provider adopters, however, will start to see clear benefits from their implementation of generative AI applications. Primarily, early ROI will be felt in areas such as ambient documentation, data solutions, revenue cycle management and other administrative tasks that are predominantly lower risk.
Although slower than some other industries, healthcare’s interest in generative AI is astonishing given that it has not even been one year since the launch of ChatGPT by OpenAI. The pace of innovation inside and outside of healthcare in this past year has also been astounding, and I expect it will continue in 2024.
Health systems that are thoughtful about the right infrastructure and use cases will start to see benefits accrue and accelerate in the coming year as the technology progresses.
As businesses experiment with the possibilities of generative AI and the technology develops rapidly, how can teams make sure that their staff have the right skills?
“The AI universe is moving so fast that only those really immersed in the industry are truly au fait with developments,”. “Everyone else needs training on specific systems, and that training needs to be updated every time the stack is altered.”
However, Researchers have some practical advice to offer for marketers, who have the advantage of mostly using low-cost AI tools or tools built into existing tech stacks such as Salesforce and Microsoft (as opposed to more elaborate AI setups for back-end operations or business processes). She recommends that marketers “dive in from a personal point of view and start using the tools and experiment with how these could be applied for work use.”
This approach means defining the “jobs to be done”, “testing and learning by channel” and noting “productivity and quality improvements”, but also managing expectations as you go.
History has shown that new technologies have the potential to reshape societies. Artificial intelligence has already changed the way we live and work—for example, it can help our phones (mostly) understand what we say, or draft emails. Mostly, however, AI has remained behind the scenes, optimizing business processes or making recommendations about the next product to buy. The rapid development of generative AI is likely to significantly augment the impact of AI overall, generating trillions of dollars of additional value each year and transforming the nature of work.
But the technology could also deliver new and significant challenges. Stakeholders must act—and quickly, given the pace at which generative AI could be adopted—to prepare to address both the opportunities and the risks. Risks have already surfaced, including concerns about the content that generative AI systems produce: Will they infringe upon intellectual property due to “plagiarism” in the training data used to create foundation models? Will the answers that LLMs produce when questioned be accurate, and can they be explained? Will the content generative AI creates be fair or biased in ways that users do not want by, say, producing content that reflects harmful stereotypes?
Technological innovation often sparks a mix of awe and concern, particularly when it seems to emerge suddenly and spread rapidly. This was the case with the advent of generative AI in the fall of 2022, marked by its unexpectedly swift adoption and the rush among companies and consumers to utilize, integrate, and experiment with it. This phase represents just the beginning of a journey to comprehend the full extent of this technology’s power, scope, and capabilities. Reflecting on the past eight months suggests that the upcoming years will be a roller-coaster of rapid innovation and technological advancements, continually reshaping our understanding of AI's impact on our work and lives. Understanding and anticipating the effects of this phenomenon is crucial, especially considering the rapid pace of generative AI’s deployment. This situation underscores the urgency to accelerate digital transformation and reskill the workforce.
Generative AI holds the promise of creating substantial value for the global economy, particularly at a time when addressing and adapting to climate change is a pressing concern. However, it also poses the potential to be more destabilizing than previous iterations of artificial intelligence. Its proficiency in human language, a key aspect of many work activities tied to expertise and knowledge, adds a complex dimension. This skill can be a double-edged sword, capable of causing emotional harm, creating misunderstandings, obscuring truths, and even inciting violence and wars. The dual nature of generative AI, as a tool of immense potential and a source of significant risks, necessitates a balanced and thoughtful approach to its development and application.