How to use data to fuel generative AI
Across a majority of occupations (employing 75 percent of the workforce), the pandemic accelerated trends that could persist through the end of the decade. Occupations that took a hit during the downturn are likely to continue shrinking over time. These include customer-facing roles affected by the shift to e-commerce and office support roles that could be eliminated either by automation or by fewer people coming into physical offices. Declines in food services, customer service and sales, office support, and production work could account for almost ten million (more than 84 percent) of the 12 million occupational shifts expected by 2030.
Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI Yakov Livshits could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities. Combining generative AI with all other technologies, work automation could add 0.2 to 3.3 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations.
This period of change can be an opportunity for more inclusive growth
Others may want to exercise caution, experimenting with a few use cases and learning more before making any large investments. Companies will also have to assess whether they have the necessary technical expertise, technology and data architecture, operating model, and risk management processes that some of the more transformative implementations of generative AI will require. The preceding example demonstrates the implications of the technology on one job role. But nearly every knowledge worker can likely benefit from teaming up with generative AI. In fact, while generative AI may eventually be used to automate some tasks, much of its value could derive from how software vendors embed the technology into everyday tools (for example, email or word-processing software) used by knowledge workers.
Global economic growth was slower from 2012 to 2022 than in the two preceding decades.8Global economic prospects, World Bank, January 2023. Although the COVID-19 pandemic was a significant factor, long-term structural challenges—including declining birth rates and aging populations—are ongoing obstacles to growth. AI high performers are much more likely than others to use AI in product and service development.
The future is now
With the rise of the direct-to-consumer model, revenue increasingly comes from online rather than traditional channels. More than 500 million people interact with the Nike brand across its apps.1“Nike, Inc. FY 2023 Q4 earnings release conference call official transcript,” Nike, June 29, 2023. The Starbucks app is the second-most-popular mobile payment platform in the United States for point-of-sale transactions, trailing only Apple.2“US proximity mobile payment users, by platform, 2019–2026 (millions),” Insider Intelligence, March 1, 2022. As digital experiences carry the weight of revenue, consumer-facing organizations have to make effective digital investments.
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.
- For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor.
- 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.
- Research from Goldman Sachs suggests that gen AI has the potential to automate 26% of work tasks in the arts, design, entertainment, media and sports sectors.
- Installing ChatGPT in the car is one step, very iteratively done, and will continue to evolve from there over many, many increments.
This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms. We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts. These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves. Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data.
Use generative AI to help you manage data
They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases Yakov Livshits in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.
The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. It also reduced agent attrition and requests to speak to a manager by 25 percent. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents. This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report, we define generative AI as applications typically built using foundation models.
“Forward-thinking C-suite leaders are considering how to adjust to this new landscape.”
Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent. DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation. Although it remains possible that another AI winter could loom (where the tech fails to live up to the hype and falters), it is increasingly looking like an AI tsunami is inevitable. Thus, it is important to be prepared for change on both personal and societal levels. This means that we will need to be willing to learn new things, including how to use the latest gen AI tools — and to adapt to new ways of doing things.
Could decrease by 1.6 million jobs, in addition to losses of 830,000 for retail salespersons, 710,000 for administrative assistants, and 630,000 for cashiers. These jobs involve a high share of repetitive tasks, data collection, and elementary data processing, all activities that automated systems can handle efficiently. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors.