Be flexible, imaginative and brave: experts give career advice for an AI world Artificial intelligence AI

Additionally, generative AI can be used to create more lifelike avatars that help to bring these environments to life, capable of more dynamic actions and interactions with other users. Our research found that equipping developers with the tools they need to be their most productive also significantly improved their experience, which in turn could help companies retain their best talent. Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow. They attributed this to the tools’ ability to automate grunt work that kept them from more satisfying tasks and to put information at their fingertips faster than a search for solutions across different online platforms. For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade.

• Generative AI systems are democratizing AI capabilities that were previously inaccessible due to the lack of training data and computing power required to make them work in each organization’s context.
• These innovative technologies are changing the way we interact with digital environments, solve complex problems, and even engage in creative pursuits.
• Transformer architectures learn context and, thus, meaning, by tracking relationships in sequential data.
• 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.
• 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.

Right now, if you’re an organization with tens of thousands of applicants, you may or may not have super customized ways of reaching out to the people who have applied. With generative AI, you can include much more personalization about the candidate, the job, and what other jobs may be available if there’s a reason the applicant isn’t a fit. BCG and Google Cloud are excited about generative AI’s transformative capabilities, devoting significant resources to jointly help customers apply this breakthrough technology.

Video related applications

GPT-3 in particular has also proven to be an effective, if not perfect, generator of computer program code. Given a description of a “snippet” or small program function, GPT-3’s Codex program — specifically trained for code generation — can produce code in a variety of different languages. The newest versions of Codex can now identify bugs and fix mistakes in its own code — and even explain what the code does — at least some of the time. The expressed goal of Microsoft is not to eliminate human programmers, but to make tools like Codex or CoPilot “pair programmers” with humans to improve their speed and effectiveness.

For one thing, gen AI has been known to produce content that’s biased, factually wrong, or illegally scraped from a copyrighted source. Before adopting gen AI tools wholesale, organizations should reckon with the reputational and legal risks to which they may become exposed. Keep a human in the loop; that is, make sure a real human checks any gen AI output before it’s published or used. For instance, a model-based tool GENIO can enhance a developer’s productivity multifold compared to a manual coder.

Responses show many organizations not yet addressing potential risks from gen AI

While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application. Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, genrative ai have much broader areas of application than models optimized for a single task. 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.

Yakov Livshits

Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost (Exhibit 3). Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases. While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation.

While this technology is advancing rapidly, other forces are also affecting labor demand. Overall, we expect significant shifts in the occupational mix in the United States through the end of the decade. Amid this disruption, workers changed jobs at a remarkable pace—and a subset made bigger leaps and moved into entirely different occupations (Exhibit 1). The total number of transitions through 2030 could be 25 percent higher than we projected a little over two years ago.2The future of work after COVID-19, McKinsey Global Institute, February 2021. Some companies are exploring the idea of LLM-based knowledge management in conjunction with the leading providers of commercial LLMs.

About Gartner for Information Technology Executives
Gartner for Information Technology Executives provides actionable, objective insight to CIOs and IT leaders to help them drive their organizations through digital transformation and lead business growth. In this brave new world, the name of the game is data, specifically, a company’s unique data. This proprietary information becomes the fuel that powers the generative AI engine. Whether it’s customer interactions, internal process data or market research, these unique datasets provide the raw material for AI systems to generate tailored solutions to specific business needs.

Types of Multimodal Models

In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. 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. genrative ai In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year. Key technologies that are enhancing DevX include AI-augmented software engineering, API-centric SaaS, GitOps, internal developer portals, open-source program office and value stream management platforms. At the same time, an array of new and modern tools is sprouting up, designed to harness the power of this data.

Generative AI could have a significant impact on the banking industry, generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk. Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly.