Data 2024 outlook: Data meets generative AI
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates. Certain services may not be available to attest clients under the rules and regulations of public accounting. Naveen has over 25 years of experience in services and technology, working with premier brands such as BMS, Sapient and IQVIA and has experience in consulting with Fortune 500 firms across their data and analytics journey. There will be two to five large, “general” public models, trained on public, limited private and sometimes indiscriminate data. The burgeoning and patchwork regulatory landscape around generative AI, with measures like the proposed EU AI Act, presents a significant factor that businesses need to understand and navigate.
For its part, north-central Florida and the University of Florida (UF) have been able to establish new relevance in AI by securing powerful new computational tools through a $70 million partnership with NVIDIA and UF alumnus Chris Malachowsky, NVIDIA’s co-founder. To be sure, such enterprises depend on fortuitous relationships between particular people and firms. Even so, the Florida accomplishment is an impressive example of how states and regions can and must bootstrap gains that will bolster their AI strengths.
The Future of Ingenuity with Generative AI and Human Creativity: Limits, Nuances, and Potential Impacts
These advantages might reinforce the incentive for entrepreneurs to take risks in the R &D phase, and investigating this in detail seems to be a promising avenue for future research. Data protection and privacy issues became a flashpoint in the media due in part to high-profile data breaches such as that at Equifax in 2017 and in part to the high-profile exposure of Facebook users’ data to Cambridge Analytica in 2016 and 2017. The results derived in the paper suggest that a complementary policy might be to support entrepreneurs’ access to and knowledge of ML technology since it stimulates creative entrepreneurship. The subsidization of R &D by small entrepreneurial firms will increase effort but not reduce risk taking. This paper investigates how ML applications and increased incumbent operational data affect entrepreneurship incentives.
This range implicitly accounts for the many factors that could affect the pace at which adoption occurs, including regulation, levels of investment, and management decision making within firms. Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing.
The Work of the Future, Building Better Jobs in an Age of Intelligent Machines
In this way, the partnership provides an interesting example of the importance of high-end computing resources and how they can be leveraged to support broader regional development. While generative AI may eventually be used to automate some tasks, much of its value could derive from how software vendors embed the technology into the everyday tools (for example, email or word processing software) used by knowledge workers. Generative AI can enable capabilities across a broad range of content, including images, video, audio, and computer code, and it can perform several functions in organizations, including classifying, editing, summarizing, answering questions, and drafting new content. Each of these actions has the potential to create value by changing how work is carried out at the activity level across business functions and workflows.
The latest report from McKinsey on the economic potential impact of generative AI points to what may be the next productivity frontier. The report studied 16 business functions, examining 63 use cases in which the technology can address specific business challenges in ways that produce one or more measurable outcomes. According to a recent study by McKinsey, Generative AI is poised to unleash the next great wave of productivity. They estimate that this technology could add the equivalent of between $2.6 trillion and $4.4 trillion in value to the global economy on an annual basis.
CEPS publishes EU AI Act report
Through these new technology subsets, workforce upskilling will offer both white-collar and blue-collar workers new skills to keep up with the requirements of a new world order where technology is involved in nearly every process of every industry. As a result, hourly-wage warehouse workers may soon become book-keeping supervisors, with machines largely replacing the manual brunt work. All of this will require individuals to continue to upskill themselves, and 2024 could be the year when more people take this seriously. To support the trend above, however, there will be a new segment of niche software engineers—who are already rising the trend ladder in 2023 itself. Called ‘prompt engineers’, these are software experts who understand how an LLM works, and subsequently use strategic text prompts to get diverse and accurate results. Prompt engineers often have by far the best industry expertise in generative AI, as well as a deep understanding of how specific algorithms work—and can therefore make the most of AI tools and applications to get the right generative AI results.
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. Not only would this provide a spur to productivity; it would also free up more human labor for the most advanced tasks and allow for more rapid innovation. But given their unusual attributes, combined with continuing rapid technical innovations by researchers and the huge amounts of venture capital pouring into AI research, their capabilities will almost certainly grow. Within the next five years, AI developers will introduce thousands of applications built on LLMs and other generative AI models aimed at highly disparate sectors, activities, and jobs. At the same time, generative AI models will soon be used alongside other AI systems, in part to address the current limitations of those systems, but also to expand their capabilities.
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. Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce.
Cognitive computing creates potential investment opportunities, as companies develop the technology and use it to transform their business. As companies try to shore up profit margins amid high inflation, Morgan Stanley sees an investment opportunity among sectors producing technologies to reduce costs and increase productivity. While educators debate the risks and opportunities of generative AI as a learning tool, some education technology companies are using it to increase revenue and lower costs.
Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.
