The transformation of workplaces over the past two years has led U.S. enterprises to create more flexible, collaborative work environments to better attract and retain employees, according to a new research report published today by Information Services Group (ISG) (Nasdaq: III), a leading global technology research and advisory firm. "Now they are defining business and operating models at companies facing rapid changes in work styles and environments." The 2022 ISG Provider Lens Future of Work -- Services and Solutions report for the U.S. finds many organizations in the U.S. are already successfully moving toward a modern workplace, while others have plans for digital workplace transformations to ensure business continuity and growth. Companies are focused on enhancing communication and employee well-being and managing cultural shifts among different generations of workers, ISG says. "U.S. enterprises want to design experiences that meet the expectations of employees for consistency of service, regardless of location or device, and ease of use," said Jim Kane, director, Collaboration and Experience at ISG. "Many enterprises are seeking service providers to help them develop workplace transformation strategies that cover a range of channels and ensure the service experience is secure."

Quickly shifting to remote work has enterprises looking to meet the ops needs of a suddenly distributed team, and there are open source options to get them there. The recent mad rush to scale to remote work may prove to be a key chapter in DevOps and AIOps evolution. This need for rapid, widescale change is creating a real conundrum concerning AIOps, DevOps, and ITSM, as organizations seek the best monitoring and incident response solution for their now distributed enterprises. The key question both the DevOps and IT service management (ITSM) communities need to answer is how quickly they can pivot and adapt to increasing demands for operational intelligence. Artificial intelligence for IT Operations (AIOps) brings together artificial intelligence (AI), analytics, and machine learning (ML) to automate the identification and remediation of IT operations issues.

Machine learning is taking over more previously manual human tasks across all industries and the financial services sector is no exception. In fact, fintech is driving rapid change across the whole sector including invoice finance. The use of incredibly clever computer algorithms which can be programmed to learn as new data comes in is and will undoubtedly result in huge improvements in certain areas of invoice finance provision. Fintech is driving rapid change across the whole sector. However, human experience and judgement will remain a vital part of the process as even the most complex machines have their limitations.

You might have years and years of equilibrium, with little improvements here and there, and then all of a sudden a massive technology and/or business model change takes us to a new level. For example, the taxis of 1950 looked a lot like the taxis of 2010. Then along came ride-sharing apps. Now, as we look forward to self-driving cars, I would say that the "rent-a-ride" market is in the middle of an explosion of rapid change – how it will end, we don't know. Looking at how technology has changed the healthcare industry, I would argue that we have already had one explosion of rapid change, and we are in the middle of a second one now.

Machine learning has already captured the industry's attention and driven rapid changes in ad technology, which is the least it could do given the amount of hype it has received. What's even more fascinating, though, is that the pace of the ML revolution is only increasing, and the real change has barely begun. Smart use of ML is now a differentiator and competitive advantage, but it is about to become an absolute requirement to remaining relevant in ad tech. While there continues to be breakthroughs in core ML research, it is not the academic vanguard that is driving rapid change in our industry, but rather the broadening base of knowledge among nonspecialist engineers. Just a few years ago machine learning was largely restricted to a small group of experts -- a handful of Ph.D.s from a handful of top universities. The ML bottleneck for most ad tech companies was not technology but the recruiting and retention of this rare talent.

The autonomous driving world is about as incestous a place as Caligula's palace, and it got a little more so today, when Ford and Volkswagen announced a formal and long-anticipated alliance. "The alliance we are now building, starting from first formal agreement, will boost both partners' competitiveness in an era of rapid change," Herbert Diess, the CEO of Volkswagen, said on a call with reporters. He and Ford CEO Jim Hackett said the partnership--which is not a merger--will begin with the companies jointly developing and building medium-sized pickups and commercial vans, to debut as early as 2022. The automakers said the arrangement should "yield improved annual pre-tax operating results" by 2023. So hopefully, this makes everyone richer.

In my five years of serving as a director and chairman in the boardroom, it's clear to me that embedding an innovative mindset in an organization has never been more important than it is right now. Cutting-edge technology such as artificial intelligence, data analytics, cloud applications and robot process automation are helping drive exponential change in the business world. Organizations that embrace this innovative technology may have a better chance at capitalizing on opportunities. You see innovation in the newcomers to the C-suite. Chief digital officer, chief data officer and chief automation officer are just a few of the emerging titles that are more common in today's marketplace.

Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but he didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.

Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but he didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.

Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.