A final example of automation is for customer support. SiriusDecisions reports that about 64 percent of a salesperson’s time goes to administrative tasks instead of selling, and 73 percent of customer support professionals say that the most challenging part of their job is managing time and workload. Automation can minimize the burnout for these professionals by enabling them to concentrate on the higher-level functions that touch your customers.
Ultimately, there is no magic bullet for implementing RPA, but Srivastava says that it requires an intelligent automation ethos that must be part of the long-term journey for enterprises. "Automation needs to get to an answer — all of the ifs, thens and whats — to complete business processes faster, with better quality and at scale," Srivastava says.
Vendors and user firms are also combining RPA with AI tools like machine learning, natural language processing (NLP) and image recognition. Organizations that take a phased approach to their RPA efforts set themselves up for success as RPA continues to get smarter. One financial services organization accomplished this by categorizing its RPA projects into three categories:
A report cited in the book found that software developers in the 1990s routinely missed ship dates and deadlines. The pressure to reduce costs and keep up with the demands of a rapidly changing market is now dependent on faster software development. With growth and competition in commercial software development came new technology that changed software forever. The new graphical user interface (GUI), networked personal computers, and the client-server architecture demanded new development and testing tools.
Solenoid valves are widely used on compressed air or hydraulic fluid for powering actuators on mechanical components. While motors are used to supply continuous rotary motion, actuators are typically a better choice for intermittently creating a limited range of movement for a mechanical component, such as moving various mechanical arms, opening or closing valves, raising heavy press rolls, applying pressure to presses.
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RPA alone covers mostly low-value tasks, but when combined with ML and AI, it can automate higher cognitive tasks. This includes work that requires perception and judgment, sometimes intelligently automating 15-20 steps of a process. Gartner says that by 2020 the RPA market will top $1 billion, going from use in less than 10 percent of businesses to about 40 percent, and reducing the human need in service-share centers by 65 percent.
Jones defines BDD as the process where teams use domain-specific language to express the expected behavior of an application through scenarios. She points out that this is not magic - there is automation code involved in the process - but that BDD is ideal for developers and testers sharing automation work. Specialized tools like Cucumber, the most popular open source tool for automation code integration, executes this work and is the tool of choice for Jones.
Mokyr describes himself as “less pessimistic” than others about whether AI will create plenty of jobs and opportunities to make up for the ones that are lost. And even if it does not, the alternative—technological stagnation—is far worse. But that still leaves a troubling quandary: how to help the workers left behind. “There is no question that in the modern capitalist system your occupation is your identity,” he says. And the pain and humiliation felt by those whose jobs have been replaced by automation is “clearly a major issue,” he adds. “I don’t see an easy way of solving it. It’s an inevitable consequence of technological progress.”
One could also argue that RPA lays the groundwork for machine learning and more intelligent applications. It both gathers useful data and is being combined with AI capabilities. One of us (O’Dell) recently interviewed Eric Siegel, a predictive analytics expert and author of the book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Siegel pointed out an often overlooked benefit of starting by digitizing processes with simple RPA: the digital bread crumbs it now leaves behind. “This data wasn’t amassed in order to do machine learning. It’s just a side effect of doing business as usual. The transactional residue accumulates and, lo and behold, it turns out this stuff is really valuable because you can learn from it. You can derive these patterns to help improve the very transactional processes that have been accumulating the data in the first place.”
Some software testing tasks, such as extensive low-level interface regression testing, can be laborious and time-consuming to do manually. In addition, a manual approach might not always be effective in finding certain classes of defects. Test automation offers a possibility to perform these types of testing effectively. Once automated tests have been developed, they can be run quickly and repeatedly. Many times, this can be a cost-effective method for regression testing of software products that have a long maintenance life. Even minor patches over the lifetime of the application can cause existing features to break which were working at an earlier point in time.
The White House report points in particular to the current wave of AI, which it describes as having begun around 2010. That’s when advances in machine learning and the increasing availability of big data and enhanced computation power began providing computers with unprecedented capabilities such as the ability to accurately recognize images. The report says greater deployment of AI and automation could boost economic growth by creating new types of jobs and improving efficiency in many businesses. But it also points to the negative effects: job destruction and related increases in income inequality. For now at least, “less educated workers are more likely to be replaced by automation than highly educated ones.” The report notes that so far automation has displaced few higher-skill workers, but it adds: “The skills in which humans have maintained a comparative advantage are likely to erode over time as AI and new technologies become more sophisticated.”
This helps to make output more predictable, reduce mistakes, and make your team happier (whoever used to have to trawl through the most spreadsheets will suddenly feel a lot better about their job!). Since a machine can run constantly without rest, you could have it process large sets of data on autopilot, 24/7. That’s something you’re not going to get out of even the most dedicated employee.