Generative Artificial Intelligence (AI) has been one of the most popular technology topics of 2023. The hype for generative AI is justified, promising enterprises huge leaps in productivity if implemented correctly. While most enterprises are still years away from company-wide adoption, some early movers are showing huge progress, with most having at least placed generative AI on their radar with plans to adopt it. ABI Research’s Generative AI Business Outcomes: Identifying Enterprise Commercial Opportunities report provides a full-fledged evaluation of generative AI, including business use cases, challenges to implementation, market trends, and strategic recommendations. Here are five of the biggest trends from our research report that you should know about.
In our blog post, How the Edge Enables Groundbreaking AI Applications, we provided a definition for edge Artificial Intelligence (AI). To refresh, ABI Research defines edge AI as “the implementation of AI platforms and solutions on the edge of a network, close to the end user's environment.” In other words, edge AI use cases and applications are processed locally instead of at a remote data center. Edge AI platforms include Internet of Things (IoT) hardware, edge computers, and small, localized data centers.
Generative Artificial Intelligence (AI) and AI more broadly, has launched a new technology hype cycle that, on the surface, appears to have rather unceremoniously ended during the previous metaverse hype cycle. AI, however, is a critical enabling technology of a future metaverse, from networking to data/workflow management and content creation. AI should not be viewed as a “metaverse replacement,” but rather as a critical pillar of that future, regardless of what that future is called; metaverse is simply a convenient term to codify pre-existing trends with a vision of the future.
The following forecast looks at how generative AI will create incremental value for companies in each different enterprise vertical. ABI Research expects each generative AI vertical market to adopt the technology at different rates depending on the business use cases that will create the most value.
Whether it’s new recreational entertainment or a novel healthcare monitoring application, consumer technology is always evolving. Older tech is phased out and replaced with the latest and greatest offerings brought about by various companies’ exhaustive product developments. To help manufacturers, content providers, and other vendors pinpoint the current state of consumer markets, here’s a thorough rundown of 8 consumer technology trends in 2023.
The most efficient manufacturers worldwide generate profits and provide value for customers while using minimal resources, including energy, scarce minerals and materials, and freshwater. With electricity and commodity costs at record highs, there are many reasons why companies are pursuing long-term business strategies focused on resource efficiencies and sustainability. Global regulations and stakeholders, including investors, employees, and customers, are also demanding that businesses respond to climate change. According to the World Economic Forum (WEF), roughly 40% of global energy consumption and more than 30% of global Greenhouse Gas (GHG) emissions come from the industrial sector processes. Unsurprisingly, manufacturers are looking for cost-effective ways to reduce their carbon footprint.
In the technology space, it’s vital that your organization stay on top of prevailing trends in your respective industry. This extends to keeping tabs on the competition or, if you’re seeking a solution provider, weighing your options. That’s why ABI Research released its 40 Technology Companies To Watch In 2023 whitepaper to better understand which tech firms are winning in terms of innovation and implementation of their technology products/software.
By 2027, ABI Research forecasts the installed base of Artificial Intelligence (AI)-enabled cameras to be 976.4 million, signaling a growing need for machine vision technologies. Today’s companies offering machine vision solutions are vital to the betterment of a number of applications, encompassing manufacturing, smart cities, building development, public safety, logistics, and transportation. An abundance of modern Machine Learning (ML)-based software products are embedded with edge ML capabilities that vastly improve a wide range of use cases, notably image-based inspection. This means a robotic system with an installed camera stacks pallets correctly or a flaw on a product's surface can be detected. To give you a better idea of how these systems work and who is providing them, this post highlights eight machine vision software companies that are helping enterprise customers work more efficiently.
Machine Learning (ML) is increasingly being integrated into edge computing solutions. ML at the edge improves the user experience via latency reduction, near-real-time data analysis, privacy, and security. Vendors are stepping up to the plate to provide services and platforms that streamline processes and reduce the time to market. In this post, you'll read about five examples of edge ML to better conceptualize this helpful combination.