What is generative AI, what are foundation models, and why do they matter?

The most advanced among them are shifting their thinking from AI being a bolt-on afterthought, to reimagining critical workflows with AI at the core. Clients receive 24/7 access to proven management and technology research, expert advice, benchmarks, diagnostics and more. The likely path is the evolution of machine intelligence that mimics human intelligence but is ultimately aimed at helping humans solve complex problems. This will require governance, new regulation and the participation of a wide swath of society. The net change in the workforce will vary dramatically depending on such factors as industry, location, size and offerings of the enterprise.

HR is stepping into a future of more powerful core capabilities and stronger strategic leadership—and GenAI is central to this change. BCG is collaborating with OpenAI to help our clients realize the power of OpenAI technologies and solve the most complex challenges using generative AI—responsibly. On a regional level, the market has been classified into North America, Asia-Pacific, Europe, Latin America, and Middle East and Africa, where North America currently dominates the global market.

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This technology can potentially disrupt industries and how businesses are run because it enables the generation of fresh content by learning from existing data. Generative AI can boost efficiency and productivity, cut costs, and provide new growth prospects by making it possible to automate numerous jobs that people previously performed. As a result, companies that can use technology well will probably have a considerable competitive edge.

Further, the increasing number of innovative software launches will lead to deeper penetration of the generative AI market. Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. 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.

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OpenAI, for instance, uses the latter approach to continuously train ChatGPT, and OpenAI reports that this helps to improve the underlying model. As customers rank the quality of the output they receive, that information is fed back into the model, giving it more “data” to genrative ai draw from when creating a new output—which improves its subsequent response. As the outputs improve, more customers are drawn to use the application and provide more feedback, creating a virtuous cycle of improvement that can result in a significant competitive advantage.

We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time. Specifically, this year, we updated our assessments of technology’s performance in cognitive, language, and social and emotional capabilities based on a survey of generative AI experts. These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves. Treating computer languages as just another language opens new possibilities for software engineering.

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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.

Executives should work with their data engineers to identify creative ways to discover new generative AI solutions and assess which solutions are likely to bring the most value to the company. Generative AI is still in its infancy and companies must think outside the box to identify unique or hidden applications that will provide unique competitive advantage. Generative AI has massive implications for business leaders—and many companies have already gone live with generative AI initiatives. In some cases, companies are developing custom generative AI model applications by fine-tuning them with proprietary data. As companies, employees, and customers become more familiar with applications based on AI technology, and as generative AI models become more capable and versatile, we will see a whole new level of applications emerge.

Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. 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. Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs.

Acumen Research and Consulting recently published report titled “Generative AI Market and Region Forecast, 2022 – 2030”

Moreover, animation studios and visual effects companies are increasingly leveraging generative AI techniques to streamline and enhance their production processes. The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific genrative ai work activity—developing such solutions takes time. Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy.

• It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”).
• In this section, we highlight the value potential of generative AI across business functions.
• There is a significant consumer demand for high-quality and visually appealing images, which has driven the adoption of generative AI techniques for image generation.
• Within the next ten years, it is predicted that at least 50% of online content will be generated by or augmented by AI.Generative AI and LLMs are the foundation of an important paradigm shift in content creation, communication, and knowledge generation.

The three key technologies gaining foothold in the generative AI market are generative pre-trained transformers (GPTs), reinforcement learning, and natural language processing. It is also anticipated that meta-learning – which involves training a model to learn how to learn – will substantially improve the efficiency of generative AI systems by allowing them to learn from fewer data points. Some factors driving the growth of the generative AI market include the innovation of cloud storage enabling easy access to data, evolution of AI and deep learning and rise in the era of content creation and creative applications.

To be an industry leader in five years, you need a clear and compelling generative AI strategy today. I agree the report was timely delivered, meeting the key objectives of the engagement. As the space matures, big tech companies and waves of new tech vendors are aggressively building out generative AI capabilities to meet the demand from businesses looking to adopt the technology. OpenAI was at one point reportedly paying up to $700,000 a day to keep the infrastructure hosting ChatGPT up and running. Back-of-the-napkin math pegs the cost of running a model the size of GPT-3, and older OpenAI-developed model, at $87,000 per year on a service like AWS. And AI21 Labs’ own research pegs the expenses for training a text-generating model with 1.5 billion parameters (i.e. variables that the model uses to generate and analyze text) at as much as $1.6 million.

OpenAI, for instance, reports that its recently introduced GPT-4 offers “broader general knowledge and problem-solving abilities” for greater accuracy. Developers must be prepared to assess the costs and benefits of leveraging these advances within their application. 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.

Popular generative AI tools include ChatGPT, GPT-3.5, DALL-E, MidJourney, and Stable Diffusion. Generative AI is at a developing stage, which will require a skilled workforce and high investment in implementation for development. According to IBM’s global AI adoption index 2022 report, 34% of respondents believed that a lack of Artificial Intelligence (AI) skills, expertise, or knowledge was restricting the adoption of Artificial Intelligence (AI) for industries. Hence, the unavailability of a skilled workforce and the high implementation costs are expected to slow down the pace of development of the market. Whereas the marketing & advertising sector growing at the highest CAGR during the forecast period owing to increased awareness about digital marketing, automated content creation, data analysis, customer interaction, and personalized marketing. This automation saves a lot of time, and the AI ​​can work 24/7, allowing users to produce content at scale.