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How Generative AI can spur economic growth
Generative AI, which has been in the news almost daily for the last six months or so because of its ability to transform business by generating or creating new content (text, images, audio, video, etc.), could put about 300 million full-time jobs at risk due to automation, Goldman Sachs researchers estimated in their March report titled ‘The Potentially Large Effects of Artificial Intelligence on Economic Growth’.
McKinsey researchers, too, estimate that 50% of today’s work activities could be automated between 2030 and 2060, with a midpoint in 2045. This prediction is roughly a decade earlier than in their previous estimates.
Given that the potential loss of jobs is a very sensitive issue around the world, and understandably so, these predictions get a lot of press.
Productivity boost What, however, gets ignored is the productivity boost that AI can provide. For instance, in the above-cited report, the Goldman Sachs researchers had also concluded that generative AI could also boost global labour productivity in many countries, including India, and eventually increase annual global GDP by 7%, or almost a $7 trillion increase in annual global GDP over 10 years “if it delivers on its promise”.
Similarly, McKinsey researchers underscored that generative AI could increase labour productivity by 0.1-0.6% annually through 2040. They added that combining generative AI with all other technologies, work automation could add 0.2-3.3 percentage points annually to productivity growth. They did quality, though, that workers will need support in learning new skills, and some will change occupations.
Goldman Sachs analysts believe that most workers employed in occupations partially exposed to AI automation may use at least some of their freed-up capacity toward productive activities that increase output when AI is applied. The researchers also anticipate that many workers displaced by AI automation will eventually be reemployed in new occupations that emerge either directly from AI adoption or in response to the higher level of aggregate and labour demand generated by the productivity boost from non-displaced workers.
They cited a comforting study by economist David Autor and co-authors, who used Census data to discover that 60% of workers today are employed in occupations that did not exist in 1940, implying that over 85% of employment growth over the last 80 years is explained by the technology-driven creation of new positions.
Goldman Sachs researchers point out that current generative AI technologies, such as ChatGPT, DALL-E, and LaMDA, have three primary features that set them apart. To begin with, their usage is not limited to specific domains but instead encompasses a wider range of applications. Second, these technologies can produce unique outputs that closely resemble human-generated content. Last but not least, these AI systems offer user-friendly interfaces that comprehend and interact using natural language, images, audio, and video. The researchers add that the importance of the first two advancements lies in their potential to expand the scope of tasks that AI can undertake, while the third characteristic has helped in popularising generative AI. For instance, ChatGPT had more than 100 million users in the first two months of its launch.
McKinsey researchers corroborate this line of thinking. They predict that generative AI could add the equivalent of $2.6-$4.4 trillion annually across 63 use cases it analyzed. It adds that the number could almost double if the impact of embedding generative AI into software currently used for tasks beyond the stated use cases is accounted for.
Banks, for instance, could generate an additional $200-$340 billion from increased productivity while reducing fraud and increasing customer satisfaction, improved decision making and employee experience. Retail could get a $310 billion from generative AI boost by automating aspects of key functions such as customer service, marketing and sales, and inventory and supply chain management. Likewise, pharmaceuticals and medical products industries could unlock $61-$110 billion annually through generative AI’s potential to expedite the 10 to 15-year cycle that it takes a drug to get to market. Further, drug compound quality could be improved, and the cost of R&D lowered.
The report (https://t.ly/3zob) also highlights that about 75% of the value that generative AI use cases could deliver covers four areas: Customer operations, marketing and sales, software engineering, and R&D.
Personalizing, automating customer operations
Generative AI could increase productivity at a value of 30-45% of current function costs. Use cases include enhancing self-service via automated channels and giving human customer care agents more targeted information to increase sales.
Enhancing marketing and sales performance
Marketing productivity could increase by a value of between 5-15% of total marketing spending, while sales productivity could deliver a value of 3-5%. Example use cases include faster content ideation and drafting, higher quality data insights, search personalization, and lead prioritization.
Product production savings from software engineering
The direct impact of generative AI on software engineering productivity could range from 20–45% of current annual spending. Productivity gains could come from reducing time to code, code correction, and market research for architecture solutions.
Productivity gains from R&D
A productivity valued between 10-15% of overall R&D costs could be achieved with use cases, including improving overall product quality, optimizing manufacturing designs, and reducing logistics and production costs.
Not losing sight of the downsides
The researchers acknowledge that while generative AI tools can create enormous value for the global economy, when it is pondering the huge costs of adapting and mitigating climate change, they can also be more destabilizing than previous generations of AI. Hence, they recommend that this should prompt companies and governments to move quickly to capture the potential value at stake while managing the risks that generative AI presents.
Need for an AI platform
In April, Nasscom’s report ‘Riding the AI Wave’ underscored how Generative AI, powered by large language models (LLMs) such as GPT-4, ChatGPT and Bard, will fundamentally change digital product development. In this context, the report pointed out that AI platforms will play a very important role because they combine the best components, data, advanced analytics and automation to drive efficiency and competitive differentiation.
AI platforms today, according to Nasscom, are also becoming more integrated with other technologies, such as the Internet of Things (IoT), edge computing, and blockchain, allowing businesses to leverage AI in new and innovative ways. AI-powered predictive maintenance solutions, for instance, can now be deployed at the edge, helping businesses to minimize downtime and reduce maintenance costs, while blockchain-powered AI platforms are enabling new types of applications, such as secure data sharing and decentralized AI training. The rise of cloud computing and big data has also helped launch new types of AI platforms, including AI-as-a-service or AIaaS.
The adoption of AI platforms is expected to continue increasing across various industries as more businesses recognize the potential benefits of using AI to streamline processes, reduce costs, and improve decision-making, according to Nasscom.
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