Gen AI in high gear: Mercedes-Benz leverages the power of ChatGPT

For example, our analysis estimates generative AI could contribute roughly $310 billion in additional value for the retail industry (including auto dealerships) by boosting performance in functions such as marketing and customer interactions. By comparison, the bulk of potential value in high tech comes from generative AI’s ability to increase the speed and efficiency of software development (Exhibit 5). For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data.

• The challenge for today’s CDOs and data leaders is to focus on the changes that can enable generative AI to generate the greatest value for the business.
• For example, consider Harvey, the generative AI application created to answer legal questions.
• All of this is made possible by training neural networks (a type of deep learning algorithm) on enormous volumes of data and applying “attention mechanisms,” a technique that helps AI models understand what to focus on.
• Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply.
• Brings enormous potential for U.S. manufacturing in terms of higher productivity and better-paid jobs.

Generative AI leverages AI and machine learning algorithms to enable machines to generate artificial content such as text, images, audio and video content based on its training data. As you can see above most Big Tech firms are either building their own generative AI solutions or investing in companies building large language models. At some point between 2030 and 2060, half of today’s work activities could be automated, they wrote.

And automation of knowledge work is now in sight

For example, natural-language capabilities would be the key driver of value in a customer service use case but not in a use case optimizing a logistics network, where value primarily arises from quantitative analysis. 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. Banking, high tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenues from generative 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.

Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content. ChatGPT can produce what one commentator called a “solid A-” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. AI-generated art models like DALL-E (its name a mash-up of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza. Other generative AI models can produce code, video, audio, or business simulations. ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash.

Factors for retail and CPG organizations to consider

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Ben Ellencweig is a senior partner and global leader of QuantumBlack, AI by McKinsey in McKinsey’s Stamford office, and Matias Garibaldi is a consultant in the New York office. Episode was managed and produced by DJ Presser, Suhas Somnath, John-Michael Maas, and Sarthak Vaish.

As organizations begin experimenting—and creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk. The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do.

Streamlining AI adoption

For the other categories that account for the remaining one million occupational shifts still to come, the pandemic was a temporary headwind. Employment in fields like education and training should rise in the years ahead amid a continuous need for early education and lifelong learning. Demand for construction workers also stalled during the height of the pandemic but is expected to rebound strongly.

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.

The impact of generative AI is expected to be instrumental across all industries, especially in banking, high-tech, pharmaceuticals and medical products, and retail, McKinsey’s report says. The technology could add $200 billion to $340 billion in value to the banking industry, and $240 to $390 billion in value in retail. Broadly, we find that generative AI applications fall into one of two categories. The first represents instances in which companies use foundation models largely as is within the applications they build—with some customizations. These could include creating a tailored user interface or adding guidance and a search index for documents that help the models better understand common customer prompts so they can return a high-quality output.

Other forces affecting future labor demand

Organizations could also leverage proprietary data from daily business operations. A software developer that has tuned a generative AI chatbot specifically for banks, for instance, might partner with its customers to incorporate data from call-center chats, enabling them to continually elevate the customer experience as their user base grows. The release cycle, number of start-ups, and rapid integration into existing software applications are remarkable.

Previous waves of automation technology mostly affected physical work activities, but gen AI is likely to have the biggest impact on knowledge work—especially activities involving decision making and collaboration. Professionals in fields such as education, law, technology, and the arts are likely to see parts of their jobs automated sooner than previously expected. This is because of generative AI’s ability to predict patterns in natural language and use it dynamically. The advanced machine learning that powers gen AI–enabled products has been decades in the making. But since ChatGPT came off the starting block in late 2022, new iterations of gen AI technology have been released several times a month.

The goal is to increase the diversity of training data and avoid overfitting, which can lead to better performance of machine learning models. Much of generative AI’s initial appeal is the ease and speed of creating content for various mediums, be it text, video, audio or graphics. This has sparked the emergence of new solutions harnessing the technology where the algorithm ingests existing content to generate new assets. The more data consumed by the algorithm, the more it learns with the goal of improving the quality of the output. This has led to much debate about how generative AI will automate and replace humans, from writing code to creating content to detecting fraud.

It is therefore crucial for CDOs to set up systems to actively track and manage progress on their generative AI initiatives and to understand how well data is performing in supporting the business’s goals. Many vendors are already rolling out products, requiring CDOs to identify the capabilities for which they can rely on vendors and which they should build themselves. One rule of thumb is that for data governance processes that are unique to the business, it’s better to build your own tool. Note that many tools and capabilities are new and may work well in experimental environments but not at scale. When you’re asking a model to train using nearly the entire internet, it’s going to cost you.

Generative AI’s potential impact on knowledge work

The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. While AI high performers are not immune to the challenges of capturing value from AI, the results suggest Yakov Livshits that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge.

I think then we’ll actually be able to automate a lot of the tedious tasks currently preventing us from doing more value-added activities. While technology has already revolutionized this sector, not least with the advent and mass adoption of e-commerce, the next wave of transformation is imminent. Customers increasingly expect experiences powered by software and on par with those offered or enabled by the most successful software and tech players.

If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world. The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management.