Big Data Industry Predictions for 2024

More important, their capabilities are not confined to any one sector or area of knowledge. Unlike many previous AI innovations, which were tailored to specific functions, the LLMs that underlie generative AI have a strong claim to be a truly general-purpose technology. McKinsey’s 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% of the total annual value from generative AI use cases. Generative AI stands as a powerful and versatile technology, unlocking new dimensions of human creativity and productivity. Adopting a proactive and responsible approach to its development and use ensures that it becomes a force for good in both the economy and society.

According to this thesis, the top five markets that stand to benefit from productivity gains may not be the US or mainland China, but Hong Kong, Israel, Switzerland, Kuwait, and Japan. Emerging markets like India, Kenya, and Vietnam may see more modest productivity gains on a relative basis, as might be the case with mainland China. While the Chinese government has since issued somewhat less restrictive regulations, pushes for political control over technological developments highlight self-imposed challenges to AI development.

Big Data Industry Predictions for 2024

These tools have the potential to create enormous value for the global economy at a time when it is pondering the huge costs of adapting and mitigating climate change. At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence. The deployment of generative AI and other technologies could help accelerate productivity growth, partially compensating for declining employment growth and enabling overall economic growth. In some cases, workers will stay in the same occupations, but their mix of activities will shift; in others, workers will need to shift occupations. 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, have much broader areas of application than models optimized for a single task.

In this framework, an incumbent firm obtains an advantage from being able to employ ML on previously collected proprietary data and incoming sales data to increase consumers’ willingness to pay. The incumbent firm also faces potential competition from an entrepreneurial firm that can invest in research to develop new products and use ML to obtain access to operational data to increase consumers’ willingness to pay. Note that we could have cast the model so that ML reduced the production cost for firms, reaching similar results.

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As a significant leap forward in AI technology, generative AI (GenAI) is powered by data. Since the FinTech industry draws upon enormous amounts of data, it’s an opportunity to leverage the advantage of generative AI. With the inclusion of Generative AI in FinTech, you can offer a personalized experience tailored to the end user’s unique needs. All of this is backed by a seamless flow of complex tasks, streamlined processes, and informed decisions where risks are not merely mitigated but proactively managed. This report therefore makes the case that a combination of local, state, and federal initiatives will be necessary to spread AI innovation and productivity gains more broadly across the U.S.

• Many companies are entering the market to offer applications built on top of foundation models that enable them to perform a specific task, such as helping a company’s customers with service issues.
• The journey toward AGI is filled with scientific intrigue and technological challenges.
• Early iterations of this concept are already in development, with AI-powered task management projects like AutoGPT and Baby AGI leading the way.
• We also uncover firms’ economic, competitive and organizational, and innovation factors as key outcomes of AI deployment.
• Despite the positive impacts, there are also challenges with the widespread use of Generative AI.

The Georgia AI Manufacturing (GA-AIM) coalition, led by the Georgia Tech Research Corporation, represents a strong example of a regional cluster development strategy in the AI sector. Called forth by the EDA’s BBBRC competition, the $65 million initiative will establish the AI Manufacturing Pilot Facility at Georgia Tech as a hub for research, testing, and training in AI systems across the region. A key component of all this is preparing Georgia’s workforce for the rise of AI-enabled automation in established state industries such as semiconductors, battery manufacturing, food production, and defense. Along those lines, GA-AIM will add a new hub to the nation’s AI map, even as it advances a national model for how to accelerate the transition to automation in manufacturing while diversifying the next generation of AI leadership.

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And while the rise of LLMs meaningfully advanced the state of the art in AI, new and even more powerful paradigms will likely emerge. To understand AI’s commercial and geopolitical significance, it is important to consider how the technology might progress. As the focus of AI shifts from pattern recognition to generation and extrapolation, so too will the arenas of technological competition.

This all said, existing markets are only a proof point of value, and perhaps merely a launch point for generative AI. Historically, when economics and capabilities shift this dramatically, as was the case with the Internet, we see the emergence of entirely new behaviors and markets that are both impossible to predict and much larger than what preceded them. It’s also worth noting that AI is often held to a higher goalpost than simply what humans can achieve (why change the system if the new one isn’t significantly better?). Haziqa is a Data Scientist with extensive experience in writing technical content for AI and SaaS companies.

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A year ago, we forecast that data, analytics and AI providers would finally get around to simplifying and rethinking the modern data stack, a topic that has been near and dear to us for a while. There was also much discussion and angst over data mesh as the answer to data governance in a distributed enterprise. The future of Generative AI in FinTech is not just a tech evolution; it’s a paradigm shift towards a more agile, responsive, and user-centric financial ecosystem. Sign up for news and resources to navigate the world of B2B technology, from AI and data, to security and SaaS, and more. So even though there doesn’t seem to be obvious defensibility endemic to the tech stack (if anything, it looks like there remain perverse economics of scale), we don’t believe this will hamper the impending market shift. These unprecedented levels of adoption are a big reason why we believe there’s a very strong argument that generative AI is not only economically viable, but that it can fuel levels of market transformation on par with the microchip and the Internet.

Read more about The Economic Potential of Generative Next Frontier For Business Innovation here.