Generative AI Could Have Biggest Impacts on High Earners: McKinsey

This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. These include marketing and sales, product and service development and service operations such as customer care and back-office support.

• But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent).
• Countries may take varying approaches to regulation, as they often already do with AI and data.
• It will also impact production, parts reliability, servicing intervals, all those things.
• The report, which looks at the economic potential of generative AI, says it could add between $2.6 to $4.4 trillion to the global economy through “63 generative AI use cases spanning 16 business functions,” which is roughly the same amount as the UK’s GDP in 2021.
• Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation.

Image Generation is a process of using deep learning algorithms such as VAEs, GANs, and more recently Stable Diffusion, to create new images that are visually similar to real-world images. Image Generation can be used for data augmentation to improve the performance of machine learning models, as well as in creating art, generating product images, and more. For example, the technology can draft marketing materials, optimize SEO and improve customer service by analyzing existing data, preferences and trends to produce engaging assets that resonate with the target audience. For example, within Keysight, we’re already utilizing AI tools to help optimize SEO across all our content and generate promotional copy that reflects our voice. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Work and productivity implications

The models used for text generation can be Markov Chains, Recurrent Neural Networks (RNNs), and more recently, Transformers, which have revolutionized the field due to their extended attention span. Text generation has numerous applications in the realm of natural language processing, chatbots, and content creation. Generative AI (GenAI) is a type of Artificial Intelligence that can create a wide variety of data, such as images, videos, audio, text, and 3D models.

In this example, a company uses a foundation model optimized for conversations and fine-tunes it on its own high-quality customer chats and sector-specific questions and answers. The company operates in a sector with specialized terminology (for example, law, medicine, real estate, and finance). Deep learning is a subset of machine learning that uses deep neural networks, which are layers of connected “neurons” whose connections have parameters or weights that can be trained. It is especially effective at learning from unstructured data such as images, text, and audio. The public-facing version of ChatGPT reached 100 million users in just two months. It democratized AI in a manner not previously seen while becoming by far the fastest-growing app ever.

The data dividend: Fueling generative AI

Finally, I think it’s critical to redefine certain tasks that can now be done by machine. Given the rapid pace of tech innovation, consumer and retail companies often find it beneficial to tap into the ecosystem of tech-forward start-ups and supplier partnerships. The attraction is mutual because the retail ecosystem, unlike software, offers tangible products and various services along the value chain.

In this section, we will discuss the breadth of generative AI applications and provide a brief explanation of the technology, including how it differs from traditional AI. One reason pinpointing data quality issues is much more difficult in generative AI models than in classical ML models is because there’s so much more data and much of it is unstructured, making it difficult to use existing tracking tools. Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt. OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from boldface-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and Meta has released its Make-A-Video product based on generative AI. These companies employ some of the world’s best computer scientists and engineers.

The future is now

First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents. Our analysis finds that generative AI could have a significant impact on the pharmaceutical and medical-product industries—from 2.6 to 4.5 percent of annual revenues across the pharmaceutical and medical-product industries, or $60 billion to $110 billion annually. This big potential reflects the resource-intensive process of discovering new drug compounds. Pharma companies typically spend approximately 20 percent of revenues on R&D,1Research and development in the pharmaceutical industry, Congressional Budget Office, April 2021. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value.

Yakov Livshits
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.

They can therefore accelerate time to market and broaden the types of products to which generative design can be applied. For now, however, foundation models lack the capabilities to help design products across all industries. Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills.

Applying an ecosystem approach to partnerships

The race has already begun, as evidenced by the steady stream of announcements from software providers—both existing and new market entrants—bringing new solutions to market. In the weeks and months ahead, we will further illuminate value-creation prospects in particular industries and functions as well as the impact generative AI could have on the global economy and the future of work. In the near term, some industries can leverage these applications to greater effect than others. Banking, consumer, telecommunications, life sciences, and technology companies are expected to experience outsize operational efficiencies given their considerable investments in IT, customer service, marketing and sales, and product development. Today, training foundation models in particular comes at a steep price, given the repetitive nature of the process and the substantial computational resources required to support it. In the beginning of the training process, the model typically produces random results.

The challenge, then, is not so much mitigating these declines as it is ensuring that workers are properly trained for new roles. It also advises companies to head off eventual hiring challenges Yakov Livshits by expanding their applicant pools to include unemployed people and those without higher education. Michael Chui is a partner at the McKinsey Global Institute and is based in San Francisco.

As a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities. Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6).

Generative AI could propel higher productivity growth

One example of allostasis can be seen in our collective recovery in the aftermath of COVID—19. While work continues, the long-standing paradigm of going to the office for many has been replaced with hybrid work. As a society, we have learned to cope with the Information Age for better or worse. Some decades on, the benefits and losses from this technological advance have become clearer, although the topic remains richly debated. Now we are faced with even bigger changes from the impacts of AI and the commoditization of intelligence.

A specially trained AI model could suggest upselling opportunities to a salesperson, but until now those were usually based only on static customer data obtained before the start of the call, such as demographics and purchasing patterns. A generative AI tool might suggest upselling opportunities Yakov Livshits to the salesperson in real time based on the actual content of the conversation, drawing from internal customer data, external market trends, and social media influencer data. At the same time, generative AI could offer a first draft of a sales pitch for the salesperson to adapt and personalize.

On a recent episode of the Plain English podcast, health and science writer Brad Stulberg spoke about the various ways people deal with change. Stulberg is the author of Master of Change and he discussed “allostasis,” a concept from complex systems theory that could provide useful insight. The term applies to the ability of a system to dynamically stabilize in the face of disruption. This concept differs from homeostasis, where a system returns to its previous point as soon as possible following a disruption. Accenture found that 40% of all working hours can be impacted by [generative AI] LLMs like GPT-4.