The transformative tech will enormously impact productivity, and 75% of the value will be in four key areas—including engineering.
2023 has been the year of generative AI. Tools like ChatGPT have led the news cycle, and many predict generative AI will revolutionize nearly every industry. Now, analyst McKinsey has quantified that revolution with a new report, The economic potential of generative AI. It’s gigantic: generative AI could add up to $4.4 trillion in value to the global economy, according to McKinsey. Maybe even twice that, depending on how you count.
One key finding is that, unlike traditional AI, generative AI will likely have the greatest impact on high-income jobs that require more educational training, like engineering. The good news is that this impact could drive economic benefits and improve time management for most engineers.
Generative AI will drive trillions of dollars in economic value
Contrary to some inflammatory news pieces, most companies don’t view generative AI as a chance to replace the workforce but as an opportunity to refocus human time, attention and skill where it is most beneficial, especially in engineering. Instead of spending half the workday completing menial tasks, like answering e-mails, engineers can focus on the more complex jobs that actually require human intervention and decision-making.
Importantly, these benefits would be in addition to what has already been predicted for the impact of non-generative AI. The report outlines anywhere from about two to eight trillion dollars of additional economic value from generative AI, with a total economic impact of AI-based technologies predicted to be upwards of 17 trillion dollars.
The report was based on a McKinsey database of potential AI use cases and included survey data of more than 100 experts in diverse industries. A big focus was evaluating use cases of generative AI to solve problems not addressed by current technologies. In assessing these use cases, the report found the finance, tech and life sciences industries stand to gain the biggest increase in their revenue from generative AI.
Generative AI will transform four key areas
Independent of specific industries, the report identified four essential business functions that stand to benefit the most from generative AI and account for approximately 75% of the total annual value derived from adopting the technology. The big four are software engineering, research and development, customer operations, and marketing and sales.
Software Engineering
Generative AI stands to accelerate the coding process and assist with paired programming, which McKinsey predicts could increase productivity in software engineering from 20 to 45 percent based on current annual spending. The report indicates that most of this value would be generated through more efficient use of time when it comes to programming, allowing engineers to focus on more complex tasks and quality control measures. Human software engineers will still be critical for software architecture, but programming time will be more efficient using generative AI coding tools.
Research and Development
An emerging application of generative AI is its potential use in research and development. One of these emerging applications is in experimental drug design for pharmaceutical companies. Although the technology is still developing, generative AI could assist with the initial discovery of drug candidates in a high-throughput, low-cost setting. The McKinsey report found that generative AI could increase productivity with a value of 10 to 15 percent of overall costs in research. When it comes to pharmaceutical and medical-product industries, the report indicates that generative AI could increase revenue by $60 to $110 billion annually, due in large part to accelerating these early time and money-intensive stages of drug discovery.
Customer Operations
One key area where generative AI stands to provide economic value is in customer relations and operations. On a small scale, this is already being observed with digital AI-powered assistants that provide help through chat bot functions on many websites. These generative AI tools can improve customer self-service, reduce response times for customer service, and increase sales by improving the overall customer experience. These applications have enormous utility, as automated issue resolution can decrease wait times and ensure human agents are only assisting with problems that truly require human intervention. The McKinsey report indicates that generative AI applied in customer care settings could increase productivity from 30 to 45 percent based on current operation costs.
Marketing and Sales
Generative AI can greatly improve personalization at scale for marketing and sales purposes. Not only can generative AI be used to create personalized messages and advertisements for target groups, but it can also be used to draft advertising campaigns, blog posts, product descriptions, and more. Generative AI can even combine multiple peoples’ ideas or drafts into one cohesive message in little to no time. Many people view generative AI tools as brainstorming devices for inspiring new ideas, which can be very useful for personalized marketing strategies. The McKinsey report indicates that generative AI use in marketing could generate between a 5 and 15 percent increase in value based on total spending.
Day-to-day changes for the average engineer
Beyond specific applications in business processes, generative AI stands to have the most significant impact on day-to-day life for the average engineer. In 2012, the McKinsey Global Institute released a study that found about one-fifth of workers’ time, or about one day per week, was spent gathering information. If generative AI can increase the speed required to search for something either on the internet or in internal systems, it stands to save engineers an incredible amount of time.
One of the most noteworthy economic impacts of generative AI is how it will modify the availability of staff. Generative AI stands to automate many day-to-day tasks, freeing engineers’ time for increasingly complex tasks or areas that require human ingenuity. Based on the report, 60 – 70% of employees’ current working time stands to be automated by generative AI-based tools.
Although early adoption might be slow, generative AI continues to improve at a remarkable pace. As tools increase in their utility and accuracy, the report predicts that up to half of today’s working activities could be automated between 2030 and 2060. So, engineers can focus on engineering-related tasks, and less on day-to-day menial tasks. In essence, generative AI can make a personal assistant automated, and accessible, to anyone.
The generative AI revolution is only just beginning
One of the report’s main takeaways is just how rapidly our understanding of the impact of generative AI is evolving. Referencing one of their own previous reports, McKinsey highlighted that the predicted adoption of generative AI and the time to return on investment are all consistently increasing.
Additionally, where traditional AI analytics stand to impact more data-driven fields like manufacturing and supply chain optimization, generative AI will have a greater impact on areas that require natural language processing and creativity. Where traditional AI will see application in areas that require quantitative data and technical expertise, the report finds that the most significant impact of generative AI will likely be in less quantitative business functions, like marketing and customer relations. This suggests that traditional AI will likely have the greatest impact on the technical aspects of engineering jobs, whereas generative AI will transform the less technical, but nevertheless time-consuming, tasks that take up the day-to-day.
Another interesting finding in the study is that despite often being lumped together, not all generative AI applications are created equally. Over the next few decades, there will be a significant deviation in how well generative AI can recapitulate human success in different areas, such as creativity or emotional reasoning. Where average human language processing and creativity might be reached by 2040, it could take several additional decades before generative AI can compete with the top quartile of engineers. Many of these timelines also remain uncertain as we don’t know precisely how long it will take for the technology to advance and for the tools to be adopted by the broad workforce.
One caution is that the technology is still in development and continues to improve with each use of the tool. Over time, the accuracy and utility of generative AI will improve and further extend economic benefits. However, like any new technology, it will take some time to realize the full potential of generative AI, and we don’t yet fully understand how it will impact our lives.
