Did you miss a session from the Future of Work Summit? The enterprise is quickly discovering the many ways AI can streamline and improve processes, but so far, most of these successes are happening at limited scale. Like any technology, AI functions well in controlled situations, but pushing it far and wide throughout an increasingly diversified data ecosystem is not without its perils. At scale, the enterprise is no longer a cohesive, fully integrated digital environment, but a loose collection of processes, platforms, and cultures. Of course, AI promises to change all that (or at least paper it over), but in a Catch-22, it really can't function at scale until it achieves scale -- meaning there is still a lot of work to do before organizations can push the value proposition of AI to its limits.

Did you miss a session from the Future of Work Summit? This article was contributed by Taesu Kim, CEO of Neosapience. An AI revolution is going on in the area of content creation. Voice technologies in particular have made tremendous leaps forward in the past few years. While this could lead to a myriad of new content experiences, not to mention dramatically reduced costs associated with content development and localization, there are ample concerns about what the future holds.

Did you miss a session from the Future of Work Summit? This article was contributed by Loren Goodman, cofounder and CTO at InRule Technology. Traditional machine learning (ML) does only one thing: it makes a prediction based on historical data. Machine learning starts with analyzing a table of historical data and producing what is called a model; this is known as training. After the model is created, a new row of data can be fed into the model and a prediction is returned.

Did you miss a session from the Future of Work Summit? This article was contributed by Wilson Pang, CTO at Appen. The new foundation of the artificial intelligence (AI) economy is flexible, remote work. Thanks to advances in technology that enable remote work at an unimaginable scale, organizations developing AI can now collaborate with people from almost anywhere, including previously inaccessible areas. People across the globe can now contribute to building AI in meaningful ways, particularly through data preparation and annotation work.

Did you miss a session from the Future of Work Summit? This article was contributed by Berk Birand, CEO of Fero Labs. Is the hype around AI finally cooling? That's what some recent surveys would suggest. Most executives now say the technology is more hype than reality-- and 65% report zero value from their AI and machine learning investments.

Did you miss a session from the Future of Work Summit? Fifty-three percent of enterprise technology leaders are worried they will run out of computing power in the next decade -- one of several challenges hindering organizations as they look to scale up artificial intelligence initiatives, according to a new report by SambaNova Systems. With AI and ML becoming ubiquitous across industries, it has the same potential to refactor the Fortune 500 as the internet has had over the past several decades. But as the AI revolution accelerates, there's a burgeoning gulf between the haves and the have-nots. That is, a growing number of top companies have figured out how to deploy AI initiatives at scale, gaining a competitive edge against businesses that have yet to do so.

Did you miss a session from the Future of Work Summit? If you ask most people to name the key technologies of the metaverse, they'll usually focus on the eyewear and graphics engines. If they're sophisticated, they'll also bring up 5G and blockchain. But those are the nuts and bolts of our immersive future. The technology that will pull the strings, creating and manipulating our experience, is AI.

Did you miss a session from the Future of Work Summit? PSA, for the uninitiated, is software that is typically used by professional services companies to help plan, manage, and measure their projects' performance through centralizing processes such as project management, time-tracking, invoicing, resource planning, business intelligence, and more -- while automating many of the manual work involved. RMM, meanwhile, is software that is typically installed locally by managed service providers (MSPs) and IT professionals so they can oversee systems and devices remotely. The problem that SuperOps.ai is ultimately trying to solve is that MSPs are facing growing complexities in demands from their customers, which is exacerbated by the multitude of different tools and platforms that they use and -- a problem that is impacting most industries -- a shortage of technical talent. Replacing a patchwork of PSA and RMM tools and plugins that were not designed with integration in mind, SuperOps.ai

Did you miss a session from the Future of Work Summit? During the pandemic, a growing number of manufacturers have begun to pilot -- or fully embraced -- AI in their organizations. While technical and human roadblocks threaten to slow adoption, manufacturers are deploying AI across a range of maintenance, quality assurance, and production processes. Ninety-three percent of enterprises believe that AI will be a pivotal technology to drive growth and innovation in the manufacturing sector, according to Deloitte. And manufacturing companies are expected to spend $13.2 billion on AI software, hardware, and services in 2025, up from $2.9 billion in 2018.

Did you miss a session from the Future of Work Summit? In health care, the process of underwriting and claims analysis can be both labor-intensive and error-prone. Claim adjusters and underwriters are often required to read and carefully parse hundreds of documents per case. Each year, the insurance market invests an estimated more than $3 billion in work hours devoted solely to collating and summarizing medical records. A 2006 U.S. National Institutes of Health study identified several major challenges in researching medical records, including assessing the quality of data and combining data from companies with dissimilar coding systems.