Research by Carl Benedikt Frey and Michael Osborne of the Oxford Martin School argued that employees engaged in "tasks following well-defined procedures that can easily be performed by sophisticated algorithms" are at risk of displacement, and 47 per cent of jobs in the US were at risk. The study, released as a working paper in 2013 and published in 2017, predicted that automation would put low-paid physical occupations most at risk, by surveying a group of colleagues on their opinions.[91] However, according to a study published in McKinsey Quarterly[92] in 2015 the impact of computerization in most cases is not replacement of employees but automation of portions of the tasks they perform.[93] The methodology of the McKinsey study has been heavily criticized for being intransparent and relying on subjective assessments.[94] The methodology of Frey and Osborne has been subjected to criticism, as lacking evidence, historical awareness, or credible methodology.[95][96] In addition the OCED, found that across the 21 OECD countries, 9% of jobs are automatable.[97]
In open loop control, the control action from the controller is independent of the "process output" (or "controlled process variable"). A good example of this is a central heating boiler controlled only by a timer, so that heat is applied for a constant time, regardless of the temperature of the building. (The control action is the switching on/off of the boiler. The process output is the building temperature).

In August 2015, Trump told a press conference that American-born children should not be citizens if their parents are undocumented. “A woman is getting ready to have a baby, she crosses the border for one day, has the baby, all of a sudden for the next 80 years, hopefully longer, but for the next 80 years we have to take care of the people. No, no, no, I don’t think so … There are great legal scholars, the top, that say that’s absolutely wrong.”

Augmentation, in contrast, means starting with what humans do today and figuring out how that work could be deepened rather than diminished by a greater use of machines. Some thoughtful knowledge workers see this clearly. Camille Nicita, for example, is the CEO of Gongos, a company in metropolitan Detroit that helps clients gain consumer insights—a line of work that some would say is under threat as big data reveals all about buying behavior. Nicita concedes that sophisticated decision analytics based on large data sets will uncover new and important insights. But, she says, that will give her people the opportunity to go deeper and offer clients “context, humanization, and the ‘why’ behind big data.” Her shop will increasingly “go beyond analysis and translate that data in a way that informs business decisions through synthesis and the power of great narrative.” Fortunately, computers aren’t very good at that sort of thing.
The special-counsel office’s attention to this scheme and its decision to release a rare statement about it indicates the seriousness with which the team is taking the purported plot to discredit Mueller in the middle of an ongoing investigation. Carr confirmed that the allegations were brought to the office’s attention by several journalists, who were contacted by a woman who identified herself as Lorraine Parsons. Another woman, Jennifer Taub, contacted Mueller's office earlier this month with similar information.
Human-machine interfaces (HMI) or computer human interfaces (CHI), formerly known as man-machine interfaces, are usually employed to communicate with PLCs and other computers. Service personnel who monitor and control through HMIs can be called by different names. In industrial process and manufacturing environments, they are called operators or something similar. In boiler houses and central utilities departments they are called stationary engineers.[57]
This approach works fine for the first weeks, when running checks only takes five minutes. Over time, though, five minutes turn into an hour, then two, then three. Before you know it, testing locks up the tester's computer or test environment all afternoon. So you start kicking off automated test runs at 5 am or 5 pm and get the results the next day. Unfortunately, if something goes wrong early on, all the results will be corrupted. That slows to a crawl the feedback loop from development to test, creating wait states in the work.
Automation is a real issue and challenge for labor conditions from industrial to white collar jobs. There are many benefits to find in it but it also might lead to a standardization of processes. I wrote on this subject if you want more information about how automation is changing the way we get productive.
Once the software passes automated tests, it may be released into production (depending on the preferred rate of deployment). This process is called Continuous Delivery. The preferred frequency is the difference between Continuous Delivery and Continuous Deployment. You achieve Continuous Delivery with the steps required for CI. The emphasis on automated testing (and automated builds) for quality assurance capitalizes on the efficiency of successful test automation and is essential to this practice.

The market is, however, evolving in this area. In order to automate these processes, connectors are needed to fit these systems/solutions together with a data exchange layer to transfer the information. A process driven messaging service is an option for optimizing your data exchange layer. By mapping your end-to-end process workflow, you can build an integration between individual platforms using a process driven messaging platform. Process driven messaging service gives you the logic to build your process by using triggers, jobs and workflows. Some companies uses an API where you build workflow/s and then connect various systems or mobile devices. You build the process, creating workflows in the API where the workflow in the API acts as a data exchange layer.