In September of 2018, McKinsey published a report on the impact of AI on the world economy. The report’s overriding finding was that AI has the potential to incrementally add 16 percent or up to $15.4 trillion by 2030 to annual global economic output, — an average contribution to productivity growth of about 1.2 percent. McKinsey’s 2018 report was published before the advent of generative AI and related technologies lik large language models (LLMs) and chatbots like ChatGPT that have been taking AI to a whole new level in the past few years.
In June of 2023, McKinsey published, “The Economic Potential of Generative AI: The Next Productivity Frontier.” This new research study aims to take a first look at generative AI’s economic impact over and above the projections in its 2018 report. The 2023 report study evaluated the potential economic impact of generative AI by identifying 63 concrete use cases of generative AI that would lead to measurable outcomes. In addition, the study estimated how generative AI would affect the productivity of the global workforce by analyzing its potential impact on each of more that 2,100 detailed work activities across 850 occupations.
Not surprisingly given that generative AI is still in its early stages, the new report explains that “While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation.”
To estimate generative AI’s business value, McKinsey first revised its 2018 estimate of the potential annual contribution of traditional machine learning and advanced analytics to the global economy to up to $17.7 trillion. Generative AI use cases and worker productivity could then add an additional $6.1 trillion to $7.9 trillion annually, increasing the overall impact of all artificial intelligence to the yearly global economy by $17.1 trillion to $25.6 trillion, — a highly significant 35% to 70%.
While generative AI use cases could have an impact on most business functions, four particularly stand out: customer operations, marketing and sales, software engineering, and research and development. Let me discuss a few of the use cases in each of these functions.
In customer operations and marketing, generative AI plays the role of a virtual expert to help augment employees performance.
Customer operations. “Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills.” McKinsey estimates that generative AI customer operations use cases could increase productivity from 30% to 45%. Such customer operations use cases include:
- Customer self-service. Generative AI can significantly improve the quality and effectiveness of automated responses to customer inquiries, thus enabling the customer care teams to only have to get involved in inquires that can only be resolved by a human agent. Generative AI could reduce the volume of human-serviced contacts by up to 50% depending on their existing level of automation.
- Prompt issue resolution. Generative AI can instantly retrieve the data necessary to handle the inquiry of a specific customer, which helps the customer service personnel to successfully answer questions, provide assistance, and resolve issues during an initial interaction.
- Increased sales. Because of its ability to rapidly analyze data on customer preferences, generative AI can identify product suggestions tailored to each specific customer.
Marketing. Generative AI could increase the productivity of the marketing function by between 5% and 15% of total marketing spending. But, introducing generative AI to marketing functions requires careful consideration. “A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data. Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs.” Marketing use cases include:
- Efficient and effective content creation. Generative AI could significantly reduce the time required for marketing ideation and content creation, helping team members integrate their ideas into a single cohesive piece. Its ability to produce content with varying specifications can increase customer value, attractions and retention.
- Enhanced use of data. Generative AI can help marketers better use data such as territory performance, synthesized customer feedback, and customer behavior to generate data-informed marketing strategies.
- Search engine optimization. Generative AI can help marketers achieve higher conversion and lower cost through search engine optimization for marketing and sales technical components such as page titles, image tags, and URLs.
- Product discovery and search personalization. Generative AI can leverage individual user preferences, behaviors, and purchase history to help customers discover the most relevant products and generate personalized product descriptions.
In software engineering and research and development, generative AI plays the role of an expert assistant to help speed up the work of engineers and scientists.
Software engineering. “Treating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do. Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services.” McKinsey estimates that generative AI could improve the productivity of software engineering by between 20% and 45% of the current annual spending on the function.
Research and Development (R&D). “Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs. While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of ‘traditional’ machine learning, can limit their application. Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task. They can therefore accelerate time to market and broaden the types of products to which generative design can be applied.”
Let me end by summarizing McKinsey’s key findings on the impact of generative AI on worker productivity.
“Current generative AI and other technologies have the potential to automate work activities that absorb 60 to 70 percent of employees’ time today. The acceleration in the potential for technical automation is largely due to generative AI’s increased ability to understand natural language, which is required for work activities comprising 25 percent of total work time.”
“Generative AI could increase labor productivity by 0.1 to 0.6 percent annually over the next 10 to 20 years, depending on the rate of technology adoption and redeployment of worker time into other activities. Combined with all other technologies, work automation could add 0.2 to 3.3 percent annually to productivity growth. However, workers will need support in learning new skills, and other risks associated with generative AI also need to be mitigated and controlled.”
“The era of generative AI is just beginning,” notes the McKinsey report in conclusion. “Excitement over this technology is palpable, and early pilots are compelling. But a full realization of the technology benefits will take time, and business and society still need to address considerable questions. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills.”
I am a software engineer for IBM, and my personal feeling about generative AI for software is that is hard and time consuming enough already to understand,revise for bug removal and feature implementation, and build appropriate test cases, and this will only get more time consuming if an AI comes along and effortlessly multiplies the lines of code count by a factor of 10 or so. And if the software is intended for use by a global business audience... as IBM software is ... all the more so.
My sort-of.nightmare has been that I would be presented with a million lines of code written at Chinese universities and given to the world as
open source. And my manager would give me the task of fixing a bug.
All the comments and variable names would be in Chinese, and I neither read nor write Chinese.
To this you can add a new nightmare that an AI will mak it 10 million lines ... should only take it a few seconds to do. That would be job security until retirement if my manager really wanted me to diagnose and fix a bug for the sorr of customers that IBM has.
Posted by: Chris Ward | July 08, 2023 at 04:28 PM