Unlocking Economic Potential: Generative AI as the Next Productivity Frontier

“Generative AI is likely to have the biggest impact on knowledge work, particularly for activities involving decision making and collaboration, which previously had the lowest potential for automation,” according to McKinsey. Generative AI can also help banks comply with complex and evolving regulations by creating automated systems that can monitor transactions, detect anomalies, flag risks, and generate reports. For example, generative AI can analyze large volumes of data from various sources to identify fraudulent activities, money laundering schemes, cyberattacks, and other threats.

Another concern is that AI could achieve wholesale automation of many sectors, triggering large-scale job losses. These concerns are real, but they overlook the barriers to full automation in many workplaces, as well as the compensatory job gains—some from growing demand for existing occupations, others from the rise of new occupations, as a result of AI, including generative AI. For example, research suggests that over the next couple of decades, some occupations—roughly 10 percent of all occupations according to some estimates—whose constituent tasks can almost all be automated, will likely decline. But the largest effect of AI on the economy overall, involving about two-thirds of occupations, will be to change the way that work is performed, as some constituent tasks—on average about a third—are augmented by AI.

If these various applications are implemented effectively across the economy, a large and extended surge in productivity and other measures of economic performance seems almost certain to follow. In the sectors where the technologies were widely implemented, productivity increased, much as it did after the first Industrial Revolution, when humans stopped digging trenches and turned instead to steam shovels. Above all, the technologies had little effect on knowledge industries and creative industries, such as medicine, law, advertising, and consulting, in which much of the value comes from specific expertise and the performance of nonroutine tasks.

The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it. FM is published by AICPA & CIMA, together as the Association of International Certified Professional Accountants, to power opportunity, trust and prosperity for people, businesses and economies worldwide. 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. Artificial intelligence can solve many problems that humans can’t, such as traffic congestion, parking shortages, and long commutes. Gen AI is expected to play a role in improving the quality, safety, efficiency, and sustainability of future transportation systems that do not exist today.

If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and provide immense benefits, not unlike the way the tractor, the cotton gin, and so many other technological advances have for our society. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. A related issue is how to accelerate the use of AI by the industries that stand to benefit from them most. In many cases, some stakeholders, including employees, will understandably focus on the risks and resist adopting AI systems. To counter this tendency, policymakers and companies will need to consult with all parties involved and ensure that their interests are taken into account.

Biggest Economic Applications of Generative AI: McKinsey

Adopting a proactive and responsible approach to its development and use ensures that it becomes a force for good in both the economy and society. In this section, we highlight the value potential of generative AI across business functions. In the entertainment industry, gen AI creates personalized recommendations for movies, TV shows, and music based on individual preferences. This technology can foster the same efficiency and accuracy that it does in other industries, making it a potential cost-saver for media companies.

• Unlike traditional AI, which relies on predefined rules, generative AI has the ability to generate new, original content.
• This paradigm shift in AI capabilities is opening doors to unprecedented opportunities across various sectors.
• In the case of the earlier digital revolution, a large body of research has documented highly uneven adoption across sectors and firms.

Once trained, the model can generate human-like outputs by simply predicting the next word or sequence of words in response to a prompt. Automate inventory management with image-based AI, Implement quality controlsUsers can provide images instead of text to search for products, report problems, or communicate with customer service, creating an unparalleled level of convenience and personalization. Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies. More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually.

Though generative AI will have a significant impact across all industry sectors, banking, high tech and life sciences are among the industries that could see the biggest impact on percentage of their revenues from generative AI, McKinsey said. There is a wide range of estimates available on generative AI’s economic potential as the industry continues to evolve. Generative artificial intelligence is a type of AI system that can generate text, images, or other media. These models use neural networks to identify patterns and structures within existing data to generate new and original content.

This month, US President Joe Biden meet industry leaders to discuss the “risks and enormous promises” of artificial intelligence. While much is unknown about how generative AI will influence the world economy and society, and it will take time to play out, there are clear signs that the effects could be profound. The report predicts that AI tools – particularly generative AI tools like ChatGPT – will automate nearly all types of work between 2030 and 2060. McKinsey had previously projected that AI would automate about half of all work between 2035 and 2075, but the recent explosion of powerful generative AI tools accelerated that prediction significantly. Using generative AI in just a few functions could drive most of the technology’s impact across potential corporate use cases.

The Economic Potential of Generative AI

For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover 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. To access the complete content, including sections on software engineering, research and development, risk and compliance, and the future of generative AI, please refer to the original McKinsey report.

Sales and marketing functions are crucial for driving business growth and revenue generation. The McKinsey report highlights the immense potential of generative AI in transforming sales and marketing strategies, from content creation to customer engagement. The economic impact of generative AI is substantial, with the potential to contribute trillions of dollars to global GDP annually. According to the McKinsey report, generative AI has the potential to add between $2.6 trillion and $4.6 trillion to the global economy each year. This staggering figure highlights the transformative power of generative AI and its capacity to drive economic growth.

This value would arise primarily from reducing time spent on certain activities, such as generating initial code drafts, code correction and refactoring, root-cause analysis, and generating new system designs. By accelerating the coding process, generative AI could push the skill sets and capabilities needed in software engineering toward code and architecture design. One study found that software developers using Microsoft’s GitHub Copilot completed tasks 56 percent faster than those not using the tool. In the foreseeable future, ambient intelligence and digital assistants could improve efficiency and transparency in supply-chain management as well as help with complex human tasks. According to the McKinsey Global Institute’s June 2023 report, generative AI has the potential to automate activities that currently take up 60 to 70 percent of workers’ time.

Will Generative AI Eliminate Jobs or Create Jobs?

The McKinsey report provides a comprehensive analysis of the economic impact and potential of generative AI. The authors delve into the various use cases and industries that can benefit from this technology, as well as the challenges and opportunities it presents. In this article, we will discuss the key insights from the report and shed light on the implications of generative AI on different sectors. I don’t have a well-developed sense of how this will ultimately affect the tasks we usually do at work, along with wages and inequality. But in particular, those in customer operations, marketing and sales, software engineering, and R&D should have eyes wide open to the evolving possibilities. “Generative AI can streamline business workflows, automate routine tasks and give rise to a new generation of business applications,” Kash Rangan, senior U.S. software analyst in Goldman Sachs Research, writes in the team’s report.

It handles service queries efficiently, integrates with the ERP and powers customer portals, ensuring a seamless service experience. For example, generative AI can help retailers with inventory management and customer service, both cost concerns for store owners. Gen AI can also help retailers innovate, reduce spending, and focus on developing new products and systems. 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. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity. Other areas are less impacted and this is explained by the nature of gen AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI.

And they will need to build an economy in which the use of AI systems is sensitive to the needs of workers themselves and in which shocks are minimized and the widespread fears of excessive automation are addressed—or they will likely encounter unnecessary resistance. AI, including its most recent addition, generative AI, has the potential to produce a large and decisive upswing in productivity and growth at a moment when the global economy desperately needs it. Among many current economic challenges are supply constraints, growing pressure on overindebted countries, demographic changes, and persistent inflation, all of which threaten to limit countries’ ability to sustain prosperity. But despite fears to the contrary, the prospect of large-scale AI-induced unemployment does not seem likely, especially given current labor shortages in a number of sectors. Those anxieties are based on the incorrect assumption that demand is fixed, or inelastic, and hence insensitive to price and cost changes.

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The rush to invest in gen AI reflects the rapid growth of its developed capabilities as explained in the timeline below. This continued technological innovation has been made possible by a significant and rapid growth in funds, reaching a total of $12 billion in the first five months of 2023. Allowing employees to use generative AI within their workload could increase productivity by 0.1% to 0.6% every year to 2043, the study finds, even beyond the 63 use cases.

Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task. The McKinsey report defines generative AI as applications typically built using foundation models. The McKinsey report serves as a comprehensive guide to understanding the economic potential of generative AI. It highlights the advancements in natural language understanding, which has been instrumental in enabling generative AI models to create human-like content.

From art and design to music composition, the technology empowers creatives to explore new frontiers. Imagine AI systems collaborating with artists to produce unique masterpieces or composing symphonies that resonate with human emotions. The economic impact of such collaborations is not only cultural but extends to new revenue streams and market opportunities. We hope this research has contributed to a better understanding of generative AI’s capacity to add value to company operations and fuel economic growth and prosperity as well as its potential to dramatically transform how we work and our purpose in society.

A short list of these includes handwriting recognition, speech recognition, and image and object recognition. Many of these tools have been used in smartphones and numerous business and consumer applications. Consider Google Translate, which employs deep learning and is used by more than one billion people; it can already handle more than 100 languages, a number that AI researchers aim to soon expand to more than 1,000. For example, AlphaFold, an AI system developed by Google’s AI lab, DeepMind, has been able to predict the protein structures of all 200 million proteins known to science.

Sales and Marketing: Unleashing the Power of Generative AI 💼

Furthermore, according to the same source, this is more than the United Kingdom’s GDP of $3.07 trillion over the same period. GDP is a standardized monetary tool that measures the market’s value based on the final goods and services produced in a country over a determined time period. PandoraBot.io provides custom, powerful AI bots that level the playing field by offering your business the unfair AI advantage.

That bias has been referred to as “the Turing trap,” a term coined by Brynjolfsson, after the mathematician Alan Turing’s argument that the most important test of machine intelligence is whether it can equal or surpass human performance. To get around this trap, public and private research funding for AI research should avoid an overly narrow focus on creating human-like AI. For example, in a growing number of specific tasks, AI systems can outperform humans by substantial margins, but they also require human collaborators, whose own capabilities can be further extended by the machines. More research on augmenting technologies and their uses, as well as the reorganization of workflow in many jobs, would help support innovations that use AI to enhance human productivity. The analyses in this paper incorporate the potential impact of generative AI on today’s work activities.

If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue.

This enables organizations to deliver targeted messaging and engage customers on a more individual level. Customer operations play a crucial role in any business, as they involve directly interacting with customers and addressing their inquiries and concerns. The McKinsey report emphasizes the potential of generative AI in revolutionizing customer operations and enhancing overall the economic potential of generative ai efficiency. On top of that, generative AI’s current capabilities, alongside other technologies, has the potential to automate work activities enough to absorb 60 to 70% of employee’s time today. The rapid development of generative AI also has the potential to “change the anatomy of work” and can automate work activities that absorb 60 to 70 per cent of employees’ time today.

Optimizing inventory management and recommending products to customers based on their purchase history and browsing behavior is only part of the value of gen AI in the retail industry. The use of gen AI in finance is expected to increase global gross domestic product (GDP) by 7%—nearly $7 trillion—and boost productivity growth by 1.5%, according to Goldman Sachs Research. Gen AI is a good fit with finance because its strength—dealing with vast amounts of data—is precisely what finance relies on to function. 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. Software engineers will spend less time coding, correcting code, and performing market research, and R&D will be able to use generative AI to improve overall product quality and optimising designs for manufacturing. The report from McKinsey comes as a debate rages over the potential economic effects of A.I.-powered chatbots on labor and the economy.

It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. You can foun additiona information about ai customer service and artificial intelligence and NLP. According to our analysis, the direct impact of AI on the productivity of software engineering could range from 20 to 45 percent of current annual spending on the function.

Shifts in workflows triggered by these advances could expose the equivalent of 300 million full-time jobs to automation, Briggs and Kodnani write. Beyond the issue of job displacement, other concerns include things like detrimental algorithmic biases and security vulnerabilities related to personal data. For example, the EU has been working on ironing out AI regulations, including the ‘AI Act’, initially introduced in 2021 by the European Commission. While there is much to be optimistic about, there is also reason to be cautious of the rapid adoption of AI technologies in so many industries. Amid the rapid advancement of AI technology, some experts have begun to express concern over potential risks. “Generative AI will change the future of work – work tasks will be re-imagined and industries transformed in a matter of months rather than years.

However, generative AI’s greatest impact is projected to be on knowledge work — especially tasks involving decision-making and collaboration. For example, according to McKinsey, the potential to automate management and develop talent (ie, the share of these tasks’ worktime that could be automated) increased from 16% in 2017 to 49% in 2023. Previous automation technology was particularly good at collecting and processing data — and these tasks can be further automated by generative AI’s natural language ability. Generative AI and other technologies have the potential to automate tasks that currently take up 60% to 70% of employees’ time, according to a McKinsey report, The Economic Potential of Generative AI. The impact of generative AI — such as ChatGPT and its competitors — is likely to be a business automation and productivity game-changer.

McKinsey & Co. estimates it would raise the financial value created by other types of AI by 15% to 40%. Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. The adoption of generative AI is expected to significantly impact various industries and job markets, including manufacturing, healthcare, retail, transportation, and finance.

In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries (Exhibit 9). Despite its immense potential, generative AI brings forth significant challenges and risks, spanning ethical, legal, social, and technical dimensions. Misuse of generative AI, resulting in fake content like deepfakes, spam, plagiarism, or propaganda, poses threats to individuals and society.

This paradigm shift in AI capabilities is opening doors to unprecedented opportunities across various sectors. It’s like a digital artist, drawing inspiration from massive datasets to produce never-before-seen outputs. Compound annual growth in the total number of workers worldwide slowed from 2.5 percent in 1972–82 to just 0.8 percent in 2012–22, largely because of aging.

Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution. 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. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles. As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10).

These AI-powered agents have the ability to engage in personalized conversations with customers, providing tailored solutions and recommendations. By leveraging natural language generation, generative AI enables chatbots to simulate human-like interactions, enhancing the overall customer experience. But in broader terms, the discussion makes me think about how people have interacted with computer technology over time. For example, it used to be that only a limited number of high-caste workers could access the computers. When I was in high school in the 1970s, there were only a couple of terminals for the entire class to use–and we felt pretty up-to-date to have those. The personal computer revolution of the 1980s and 1990s, and then the smartphone revolution of the last two decades, have democratized access to the technology.

• Generative AI’s impact on productivity could add trillions of dollars in value to the global economy and according to McKinsey and it is already having a significant impact across all industry sectors.
• Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions.
• The technology is making inroads in business applications, improving the day-to-day efficiency of knowledge workers, helping scientists develop drugs faster and accelerating the development of software code, among other things.
• Even if the potential for technology to automate a particular work activity is high, the costs required to do so have to be compared with the cost of human wages.

For the marketing industry, our platform can help you create content, automate product description creation, craft advertising messages, and generate customer communication to improve engagement, retention, and revenue. For the entertainment industry, our generative AI technology could help your company by creating customized content with a click, producing multiple headlines, calls-to-action, real-time commentaries, summaries, and valuable statistics. According to the report, the banking, technology, and life sciences industries will see the highest impact in terms of revenue generated by adopting and using this technology. The report mentions that, for example, the banking industry could see an additional $200 to $340 billion per year when generative AI is fully implemented. Another example highlighted by the report is the retail sector, where the financial return could be as high as $660 billion per year.

They further estimate that, of those occupations that are exposed, roughly a quarter to as much as half of their workload could be replaced. “Although the impact of AI on the labor market is likely to be significant, most jobs and industries are only partially exposed to automation and are thus more likely to be complemented rather than substituted by AI,” the authors write. Malaysia’s digital economy has experienced rapid growth, with the ICT sector contributing 22.6% to Malaysia’s GDP in 2021, driven by government initiatives, private sector investment, and increasing adoption of digital technologies.

For example, generative AI can generate signatures or fingerprints that can identify malware or phishing attempts. It can also generate countermeasures or responses that can neutralize or mitigate the impact of an attack. The insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over. We develop outstanding leaders who team to deliver on our promises to all of our stakeholders. In so doing, we play a critical role in building a better working world for our people, for our clients and for our communities.

Moreover, in some areas of the economy, facts and accuracy are not as important as new ideas or creativity. It is too soon to know whether AI-generated content will find a serious following in the creative and performing arts. Our best guess is that it will be used more for assisting and providing inspiration than for producing finished works of art. Numerous technological, process-related, and organizational hurdles, as well as industry dynamics, stand in the way of an AI-driven global economy. But just because the transformation may not be immediate does not mean the eventual effect will be small.

It will demand a positive vision of what AI can do and effective measures to turn that vision into reality. For the most likely risk that AI poses to the world today is not that it will produce some kind of civilizational catastrophe or a huge negative shock to employment. Rather, it is that without effective guidance, AI innovations could be developed and implemented in ways that simply magnify current economic disparities rather than bring about a strengthened global economy for generations to come. Given their demonstrable value, AI digital assistants will soon be performing a great assortment of tasks.

In the past, drivers took months and even years to learn the streets well enough to pass the city’s notoriously difficult taxi driver exam, known as “the Knowledge.” Then came Google Maps and Waze. These apps did not eliminate the differential between the veterans and the newcomers, but they certainly reduced it. This leveling-up effect on employee performance seems likely to become a general consequence of the advent of powerful AI digital assistants in many parts of the economy.