Robotic process automation (RPA)—typically used to automate structured, back office digital process tasks—turns out to be the opening gambit in many organizations’ digital transformation strategies. It also appears to be a precursor to artificial intelligence (AI).  In a recent research project on priorities in process and performance management,  APQC, a business research institute, found that RPA was a nucleus of 69 percent of digital strategies. In another survey on investments in process automation, anticipated RPA projects were right behind analytics and data management, and almost twice as likely as near-term investments in AI or intelligent automation.  (See Figure 1) Only 12 percent of those APQC surveyed had no plans to invest in any of these technologies in 2018.

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In software testing, test automation is the use of special software (separate from the software being tested) to control the execution of tests and the comparison of actual outcomes to predicted outcomes. Test automation can automate some repetitive but necessary tasks in a formalized testing process already in place, or add additional testing that would be difficult to perform manually.
Clearly this is a realm in which knowledge workers need strong skills in computer science, artificial intelligence, and analytics. In his book Data-ism, Steve Lohr offers stories of some of the people doing this work. For example, at the E. & J. Gallo Winery, an executive named Nick Dokoozlian teams up with Hendrik Hamann, a member of IBM’s research staff, to find a way to harness the data required for “precision agriculture” at scale. In other words, they want to automate the painstaking craft of giving each grapevine exactly the care and feeding it needs to thrive. This isn’t amateur hour. Hamann is a physicist with a thorough knowledge of IBM’s prior application of networked sensors. Dokoozlian earned his doctorate in plant physiology at what Lohr informs us is the MIT of wine science—the University of California at Davis—and then taught there for 15 years. We’re tempted to say that this team knows wine the way some French people know paper.
It is hard to read the White House report without thinking about the presidential election that happened six weeks before it was published. The election was decided by a few Midwest states in the heart of what has long been called the Rust Belt. And the key issue for many voters there was the economy—or, more precisely, the shortage of relatively well-­paying jobs. In the rhetoric of the campaign, much of the blame for lost jobs went to globalization and the movement of manufacturing facilities overseas. “Make America great again” was, in some ways, a lament for the days when steel and other products were made domestically by a thriving middle class.

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This article covers the fundamentals of automation, including its historical development, principles and theory of operation, applications in manufacturing and in some of the services and industries important in daily life, and impact on the individual as well as society in general. The article also reviews the development and technology of robotics as a significant topic within automation. For related topics, see computer science and information processing.
When it comes to business process automation efforts, our experts from around the web give several recommendations for how to get started. Some say that the projects are only successful if you initially approach them from the position of the desired outcome. Others recommend automating first, then figuring out the processes. Some claim that a full map and model of your processes are required prior to any automation. Experts also extol the virtue of having your business leaders on board, even as part of the team. Finally, they emphasize that you should improve all your processes (not just the automated ones) to maximize usefulness. In other words, garbage in, garbage out.

At some point, someone may want to change the way the code works. Some operation you call a hundred times suddenly requires that the users fill out a captcha or click a button before they can proceed, and all of the automation breaks. Fixing it requires a great deal of searching and replacing, and that could take days, while the programmers continue to move further and further ahead of you. Once this happens a few times, the test process becomes messy and expensive, and fails to deliver much value.
The two main methods will be through blogging and training. All our blogs about AiT will be posted over on the new AiT site, https://automationintesting.com. I’ve migrated my three free programming courses to that domain and redirected the existing links. Mark has also added a new one, Javascript/Node.js Basics. We’ll also be presenting lots of AiT material at conferences around the world as well as through various online channels. We are also discussing setting up an annual peer conference solely focused on automation.
Business process automation (BPA) is defined as the automation of complex business processes and functions beyond conventional data manipulation and record-keeping activities, usually through the use of advanced technologies. It focuses on “run the business” as opposed to “count the business” types of automation efforts and often deals with event-driven, mission-critical, core processes. BPA usually supports an enterprise’s knowledge workers in satisfying the needs of its many constituencies.
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