Hyperautomation is the combination of multiple machine-learning (ML), packaged-software and automation tools to deliver work. Hyperautomation refers not only to the breadth of tools available, but also to all the steps of automation itself (discover, analyze, design, automate, measure, monitor and reassess), Cearly said. Understanding the range of automation mechanisms, how they relate to one another and how they can be combined and coordinated is a major focus for hyperautomation. Hyperautomation requires a combination of tools to help support replicating pieces of where the human is involved in a task. Through 2028, the user experience will undergo a significant shift in how users perceive the digital world and how they interact with it.
Just a few years ago, many expected all the Internet of Things (IoT) to move to the cloud--and much of the consumer-connected IoT indeed lives there--but one of the key basics of designing and building enterprise-scale IoT solutions is to make a balanced use of edge and cloud computing.1 Most IoT solutions now require a mix of cloud and edge computing. Compared to cloud-only solutions, blended solutions that incorporate edge can alleviate latency, increase scalability, and enhance access to information so that better, faster decisions can be made, and enterprises can become more agile as a result. That being said, complexity introduced by edge computing should justify the objectives at hand, which include scale, speed, and resiliency. A choice that goes too far in one direction typically introduces substantial operational complexities and expenses.
Artificial intelligence uses data science and algorithms to automate, optimize and find value hidden from the human eye. By one estimate, artificial intelligence will drive nearly $2 trillion worth of business value worldwide in 2019 alone. Hence, that's an excellent incentive to grab a slice of the AI bounty. Also, fortune favors those who get an early start. Therefore, the laggards might not be so fortunate.
Cloud computing allows companies to store and manage data over cloud platforms, providing scalability in the delivery of applications and software as a service. Cloud computing also allows data transfer and storage through the internet or with a direct link that enables uninterrupted data transfer between devices, applications, and cloud. We know that the Internet of Things (sensors, machines, and devices) generate a huge amount of data per second. Cloud computing helps in the storage and analysis of this data so that enterprise can get the maximum benefit of an IoT infrastructure. IoT solution should connect and allow communication between things, people, and process, and cloud computing plays a very important role in this collaboration to create a high visibility.
At the end of 2017, we took a look at what enterprises would be spending money on over the course of 2018 in the artificial intelligence (AI) space and were able to identify 10 different kinds of products. In fact, looking at the wider technology market we found that AI was the common denominator in all predictions of enterprise buying behavior over the year. At the beginning of 2018, folks working in the AI space predicted its slow, but steady, move into the digital workplace and take over some of the mundane tasks to let employees focus on higher-level tasks. Now at the end of 2018, it appears we got it right. One of the most significant predictions was that AI would move into the digital workplace via customer experience technologies.