McKinsey: gen AI could add $4 4T annually to global economy

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. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8). The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth.

And notably, it was trained on just two regular gaming GPUs, which is unheard of by modern standards. Over the last six years, I’ve seen generative AI (GenAI) evolve from a niche idea to a major industry. It’s been an eye-opening ride from the early days when AI breakthroughs were big news until now when AI is changing how we do business.

China economy in 2024

Generative AI can instantly retrieve data a company has on a specific customer, which can help a human customer service representative more successfully answer questions and resolve issues during an initial interaction. Generative AI can cut the time a human sales representative spends responding to a customer by providing assistance in real-time and recommending the next steps. Because of its ability to rapidly process data on customers and their browsing histories, the technology can identify product suggestions and deals tailored to customer preferences. Additionally, generative AI can enhance quality assurance and coaching by gathering insights from customer conversations, determining what could be done better, and coaching agents. We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs. The technology could generate value for the retail and consumer packaged goods (CPG) industry by increasing productivity by 1.2 to 2.0 percent of annual revenues, or an additional $400 billion to $660 billion.1Vehicular retail is included as part of our overall retail analysis.

AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. My hope is that the changed conversation of this year and the initial investments we’re seeing companies make mean that we don’t need 2024 to be another year of the manager to remind us that managers are important. The other thing you’re seeing from a talent perspective is software coding—in product development, for instance. In this episode of McKinsey Talks Talent, join McKinsey partners Bryan Hancock and Brooke Weddle, in conversation with global editorial director Lucia Rahilly, as they speak about the trends that shaped last year’s talent landscape—and those poised to redefine its contours yet again in 2024. In general, workers can spend more time on the human connection and interaction aspects of their roles, which is something AI can’t take away. While generative AI is off to a blazing start, we know from experience that gaining the full value from a technology and implementing it across an organization takes time, talent, and hard work.

How can government agencies address the potential risks of gen AI?

Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year. It would create a long-term 3 percent economy (again), instead of a 1.5 percent economy, with productivity growth at the highest levels since the boomy late 1990s and early 2000s. Key caveats here include how good generative AI gets (the difficulty level of the tasks it can automate) and how fast the evolving technology is adopted throughout the economy.

To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management. Traditional AI and advanced analytics solutions have helped companies manage vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence. For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. 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.

Turbo-charging productivity in Asia: the economic benefits of generative AI

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 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. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world. But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. 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.

The model answers complex questions based on a prompt, identifying the source of each answer and extracting information from pictures and tables.

The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice. The raison d’être of this newsletter is building a better world — wealthier, healthier, more opportunity, more resilience, more fun — through faster technological progress and economic growth. And while big advances in artificial intelligence are likely going to be a big part of making that goal a reality, I’m not banking on human-level AI, much less superhuman machine intelligence.

Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. This analysis may not fully account for additional revenue that generative AI could bring to sales functions. For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover the economic potential of generative ai new leads and facilitate more effective outreach that would bring in additional revenue. Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success. We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending.