Robotic Process Automation Takes on the Data Challenge

A relatively new field, Robotic Process Automation (RPA), is best thought of as robotics. The key difference is that the thing being manipulated at lightning fast speeds is data, not automobiles or electronics.

RPA is the performance by bots of repetitive rules-based processes that involve databases and other forms of structured data (such as data captured in databases). It is used in data-intensive industries such as banking, financial services, insurance and health care, according to Adam Devine, vice president and head of marketing for WorkFusion. “In fewer words, RPA helps companies do with technology what they did before with people.”

It is a technology whose time has come. The advent of the Internet of Things (IoT) and Big Data – interrelated disciplines that collect and analyze huge amounts of data, respectively – makes RPA a necessary tool, not something that is nice to have, but not mission-critical.

A Booming RPA Market Potential

 The demand for RPA is clearly showing up in the numbers. “We have seen projections of 60 percent compounded annual growth rate,” wrote David Schatsky, the managing director of Deloitte LLP, in response to emailed questions. “There is no question that here in the U.S., interest is very strong.”

Ian Barkin, the co-founder and chief strategy officer of Symphony Ventures, holds an even more upbeat view of the category’s potential. “[W]hite-collar office jobs are everywhere; this is a service economy after all,” Barkin wrote. “So, yes, RPA robots have a much bigger impact. Our estimates are that there is a $2.5 to $3 trillion market for middle and back-office work addressable in Shared Services centers alone.”

RPA and Jobs

The impact of RPA mirrors concerns about automation and physical robotics. It is impossible to say exactly how it will play out, but the early indications are that the outcome will be positive.

The negative interpretation is simple: RPA will replace people. The more positive (and perhaps more nuanced) outlook layer, though, starts with the idea that the emergence of Big Data and the IoT will make it impossible for people to accurately process enough data to keep up. Humans would become the limiting factor. Thus, RPA doesn’t eliminate jobs – it makes new areas of work possible.

These people will be freed up for advanced training and, ultimately, more challenging jobs. “For every task that a machine automates, a human is elevated to a new, more complex task,” Devine wrote in response to emailed questions from IT Business Edge. “By redistributing repetitive tasks to robots – even those that can’t be scripted with rules – companies can use their human capital for tasks that really need human intelligence. This promotes capacity and competitive advantage within a company thanks to cost reduction and speed gains (robotics complete these tasks faster, and with fewer errors). This means more jobs, deeper skill sets, improved productivity, and the chance for humans to put their creative power toward future innovation.”

While a technology that starts by reducing the work force seems threatening, the end result could be to free people from mental drudgery. “We should embrace the technology of automation as not only a digital revolution, but also an intellectual revolution where we allow robots to take over repetitive, boring tasks and move forward to more challenging roles that involve problem-solving, critical thinking and creativity that will deliver more value to our work and even enrich our lives,” he wrote.

Francine Haliva, the head of marketing for Kryon Systems, listed dozens of new work titles that RPA will make possible. She added that there are different levels of RPA technologies. Some run on virtual machines and are more or less self-contained. Others are deployed on the employees’ desktops to support work that he or she is doing. “With ‘attended’ automation, human workers can trigger automation processes right from their desktop, at the time of need,” she wrote. “This dramatically cuts time to proficiency for anyone operating new applications or software release, reduces errors, increases productivity and improves job satisfaction.”

Schatsky agrees that the idea of RPA may be freeing. “Over time, we may see people’s jobs evolve, from interacting with systems in routine ways to performing tasks that require more judgement and flexibility,” he wrote. “But we haven’t seen large-scale job losses associated with RPA so far. Automation often, but not always, evolves, changing people’s jobs, rather than entirely eliminating jobs.”

Whatever the details are, RPA as a category is expanding rapidly.

“From our vantage point, it’s exploding,” wrote Symphony Ventures’ Barkin. “All major enterprises are now asking the question, ‘Can we automate this?’ first, rather than ‘Can we centralize, nearshore, offshore, outsource, etc.?’ That has happened only in the last two years. 2017 is going to be a frantic race for all enterprises to prove that transformation is going digital in meaningful and impactful ways.”

Source: itbusinessedge.com-Robotic Process Automation Takes on the Data Challenge

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The hidden figures behind automation

The current job description of an accounts payable clerk will disappear in possibly as little as 20 years. This may seem bleak, but the reality is that software advances, developments in robotics, AI and machine learning are bringing a new age of automation — one in which machines will be able to outperform humans in various work tasks.

According to McKinsey Global institute’s January 2017 report on the future of automation, nearly half of the activities that people are paid to do in the global economy can be automated by adapting currently demonstrated technology. Activities most susceptible to this automation are repetitive, non-creative tasks such as data collection and processing. This puts at risk many jobs in customer service, sales, invoicing, account management and other data entry positions, not the least of which includes AP clerks.

However, these projections don’t necessarily mean that the future is hopeless for those holding AP positions. In McKinsey’s words, “People will need to continue working alongside machines to produce the growth in per capita GDP to which countries around the world aspire.”

Skilled employees will work alongside software automation and RPA (robotic process automation) to approve data analyzation, guide software in the right direction and even perform tasks that we may not know exist yet. This will require some new skills-based learning, but it is also an opportunity for AP department employees to step out from behind the curtain, develop their job descriptions and have more interesting and meaningful jobs. Employees will be able to focus on raising their profile, supporting the business with more meaningful work, providing good internal service, and in turn, be more motivated.

Reckon this is wishful thinking? Think again. It’s been done before.

After all, the first “computers” wore skirts.
In the early decades of the 1900s, mathematical and technical calculations were made manually rather than by machine. This work required a large workforce to compute all the information. With the industrial boom brought on by WWII, organizations like NASA began recruiting women for this work, who they called “computers.” It has even been said that “the first computers wore skirts.”

Eventually, as the machines we know today as computers began to develop, many of these manual tasks were automated. Rather than discarding the women that had previously done this job, NASA and other organizations simply retrained employees to work alongside these machines and perform less menial tasks. This conscious step allowed the women who had been the quiet backbone of the organization to make themselves and their work known.

One example recently made popular by the book and award-winning film Hidden Figures is that of African-American physicist and mathematician Katherine Johnson and her team. Johnson worked as a “computer” on NASA’s early team from 1953-1958, where she analyzed topics such as gust alleviation for aircrafts. When NASA used electronic computers for the first time to calculate John Glenn’s first orbit around the earth, officials asked Johnson to verify the computer’s numbers and her reputation for accuracy helped establish confidence in the new technology. Johnson herself went on to use these new computers to aid in calculations until her retirement in 1986. Similarly, the value of AP clerks and other accounting professionals will shift as they become valuable as human analysts and strategists, vital in the role of validating a machine’s processes.

These kinds of shifts can be seen throughout history, like in the move away from agriculture and decreases in manufacturing share of employment in the United States, both of which were accompanied by the creation of new types of work not foreseen at the time.

We can expect a similar response to automation in the accounts payable department. As AP software becomes more advanced, clerks and controllers will evolve to work with it, not be replaced by it. The important work of AP clerks will no longer be in the shadows. The job will be transformed from “paper pusher” to vital business asset.

Source: accountingtoday.com-The hidden figures behind automation

How to supercharge robotic process automation

Enterprises across industries have deployed RPA with cognitive technologies to automate routine business processes such as fulfilling purchase orders. (Image: Deloitte)

Robotic process automation (RPA), technology that lets software robots replicate the actions of human workers for routine tasks such as data entry, is altering the way organizations handle many of their key business and IT processes.

Advances in automation and robotics are putting a lot of jobs at risk. Here are ten jobs first in line for the robot takeover.

 

When RPA is used in conjunction with cognitive technologies, its capabilities can be significantly expanded.

“The integration of cognitive technologies with RPA makes it possible to extend automation to processes that require perception or judgment,” said David Schatsky, managing director at consulting firm Deloitte.

“With the addition of natural language processing, chatbot technology, speech recognition, and computer vision technology, for instance, bots can extract and structure information from speech audio, text, or images and pass that structured information to the next step of the process,” Schatsky said.

In another example Schatsky cited, machine learning can identify patterns and make predictions about process outcomes, helping RPA prioritize actions. “Cognitive RPA has the potential to go beyond basic automation to deliver business outcomes such as greater customer satisfaction, lower churn, and increased revenues,” he said.

In a report Schatsky authored in 2016, called “Robotic Process Automation: A Path to the Cognitive Enterprise,” he noted that enterprises are beginning to employ RPA together with cognitive technologies such as speech recognition, natural language processing, and machine learning to automate perceptual and judgment-based tasks once reserved for humans.

The integration of cognitive technologies and RPA is extending automation to new areas and can help companies become more efficient and agile as they move down the path of becoming fully digital businesses, the report said.

Processes that require human judgment within complex scenarios, such as complex claims processing, can’t be automated through RPA alone, the report noted. It cited one RPA vendor as saying even its most mature clients automate at most 50 percent of back-office processes, and the majority of clients automate far fewer.

Cognitive RPA has the potential to go beyond basic automation to provide business outcomes such as enhanced customer satisfaction, lower churn, and increased revenues, the report said.

The Deloitte report provided an example of a leading global bank that used cognitive RPA to automate 57 percent of its payments work in the highly regulated area of foreign trade finance.

The challenges of automating this process end-to-end included: the need to work with highly unstructured data such as invoices, bills, declarations, certificates, and letters; a high daily volume of transactions that needed same-day processing; and the need to interface with multiple core systems. The solution combined traditional RPA techniques with several cognitive technologies to automate most steps in the process.

Leading RPA vendors are incorporating cognitive technologies into their offerings, and large RPA providers are partnering with vendors of cognitive technologies. For example, Blue Prism and IBM Watson have partnered to bring cognitive capabilities to customers.

Enterprises across industries such as banking, insurance, and transportation have deployed RPA with cognitive technologies to automate routine business processes such as fulfilling purchase orders and new hire on-boarding, the report said.

Source: ZDNet-How to supercharge robotic process automation

The optimist’s guide to the robot apocalypse

Machines, you may have heard, are coming for all the jobs.

Robots flip burgers and work warehouses. Artificial intelligence handles insurance claims and basic bookkeeping, manages investment portfolios, does legal research, and performs basic HR tasks. Human labor doesn’t stand a chance against them—after the “automation apocalypse,” only those with spectacular abilities and the owners of the robots will thrive.

Or at least, that’s one plausible and completely valid theory. But before you start campaigning for a universal basic income and set up a bunker, you might want to also familiarize yourself with the competing theory: In the long run, we’re going to be just fine.

We’ve been here before

Our modern fear that robots will steal all the jobs fits a classic script. Nearly 500 years ago, Queen Elizabeth I cited the same fear when she denied an English inventor named William Lee a patent for an automated knitting contraption. “I have too much regard for the poor women and unprotected young maidens who obtain their daily bread by knitting to forward an invention which, by depriving them of employment, would reduce them to starvation,” she told Lee, according to one account of the incident. The lack of patent didn’t ultimately stop factories from adopting the machine.

Two hundred years later, Lee’s invention, still being vilified as a jobs killer, was among the machines destroyed by protestors during the Luddite movement in Britain. More than 100 hundred years after that, though computers had replaced knitting machines as the latest threat to jobs, the fear of technology’s impact on employment was the same. A group of high-profile economists warned President Lyndon Johnson of a “cybernation revolution” that would result in massive unemployment. Johnson’s labor secretary had recently commented that new machines had “skills equivalent to a high school diploma” (though then, and now, machines have trouble doing simple things like recognizing objects in photos or packing a box), and the economists were worried that machines would soon take over service industry jobs. Their recommendation: a universal basic income, in which the government pays everyone a low salary to put a floor on poverty.

Today’s version of this scenario isn’t much different. This time, we’re warned of the “Rise of Robots” and the “End of Work.” Thought leaders such as Elon Musk have once again turned to a universal basic income as a possible response.

But widespread unemployment due to technology has never materialized before. Why, argue the optimists, should this time be any different?

Automating a job can result in more of those jobs

Though Queen Elizabeth I had feared for jobs when she denied Lee’s patent, weaving technology ended up creating more jobs for weavers. By the end of the 19th century, there were four times as many factory weavers as there had been in 1830, according James Bessen, the author of Learning by Doing: The Real Connection between Innovation, Wages, and Wealth.

Each human could make more than 20 times the amount of cloth that she could have 100 years earlier. So how could more textile workers be needed?

According to the optimist’s viewpoint, a factory that saves money on labor through automation will either:

  1. Lower prices, which makes its products more appealing and creates an increased demand that may lead to the need for more workers.
  2. Generate more profit or pay higher wages. That may lead to increased investment or increased consumption, which can also lead to more production, and thus, more employment.

Amazon offers a more modern example of this phenomena. The company has over the last three years increased the number of robots working in its warehouses from 1,400 to 45,000. Over the same period, the rate at which it hires workers hasn’t changed.

The optimist’s take on this trend is that robots help Amazon keep prices low, which means people buy more stuff, which means the company needs more people to man its warehouses even though it needs fewer human hours of labor per package. Bruce Welty, the founder of a fulfillment company that ships more than $1 billion of ecommerce orders each year and another company called Locus Robotics that sells warehouse robots, says he thinks the threat to jobs from the latter is overblown—especially as the rise of ecommerce creates more demand for warehouse workers. His fulfillment company has 200 job openings at its warehouse.

A handful of modern studies have noted that there’s often a positive relationship between new technology and increasing employment—in manufacturing firms, across all sectors, and specifically in firms that adopted computers.

How automation impacts wages is a separate question. Warehouse jobs, for instance, have a reputation as grueling and low-paying. Will automation make them better or worse? In the case of the loom workers, wages went up when parts of their jobs became automated. According to Bessen, by the end of the 19th century, weavers at the famous Lowell factory earned more than twice what they earned per hour in 1830. That’s because a labor market had built up around the new skill (working the machines) and employers competed for skilled labor.

That, of course, is not the only option, but it is an outcome embraced by the optimist crowd. Similarly positive results of automation: If companies can make more money with the same number of workers, they can theoretically pay those workers better. If the price of goods drops, those workers can buy more without a raise.

As automation kills some jobs, it creates others

As the Industrial Revolution ended, about half of American workers were still employed in agriculture jobs, and almost all of those jobs were about to be lost to machines.

If nothing else had changed, the decrease in agriculture jobs could have led to a largely unemployed society. But that’s not what happened. Instead, as agricultural employment dwindled to less than 2% of American workers, jobs in other sectors grew during the same period. They involved working in factories, yes, but also working with computers, flying airplanes, and driving cargo across the country—occupations that weren’t feasible in 1900.

Today’s optimists believe that the latest automation technologies will create new jobs as well.

What kind of jobs, they really can’t say (this is where the optimism comes in handy). About a third of new jobs created in the United States over the past 25 years didn’t exist (or just barely existed) at the beginning of that period, and predicting what jobs might be created in the next 25 years is just guessing. In a report on artificial intelligence and the economy, the Obama White House suggested that automation might create jobs in supervising AI, repairing and maintaining new systems, and in reshaping infrastructure for developments like self-driving cars. But, the report’s authors note, “Predicting future job growth is extremely difficult, as it depends on technologies that do not exist today.”

Automation doesn’t necessarily make humans obsolete

In 2013, researchers at Oxford sparked fear of the robot revolution when they estimated that almost half of US occupations were likely to be automated. But three years later, McKinsey arrived at a very different number. After analyzing 830 occupations, it concluded that just 5% of them could be completely automated.

The two studies obviously counted differently. The Oxford researchers assessed the probability that occupations would be fully automated within a decade or two. But automation is more likely to replace part of a job than an entire job. When Amazon installs warehouse robots, they currently don’t replace full workers, but rather, the part of the job that involves fetching products from different shelves. Similarly, when my colleague used artificial intelligence to transcribe an interview, we didn’t fire him; he just worked on the other parts of his job. McKinsey’s researchers’ model didn’t attempt to sort jobs into “replaceable” and “not replaceable,” but rather to place them on a spectrum of automation potential.

Almost every occupation that McKinsey looked at had some aspect that could be automated. Even 25% of tasks inside of a CEO job, the analysis found, could be automated. But very few jobs could be entirely automated.

McKinsey’s conclusion was not that machines will take all of these jobs, but rather, “more occupations will change than will be automated away.” Our CEO, for example, won’t spend time analyzing reports if artificial intelligence can draw conclusions more efficiently, so he can spend more time coaching his team.

This part of the optimist’s theory argues that if humans aren’t bogged down by routine tasks, they will find something better to do. The weavers will learn the new job of operating the machines. My coworker will write more articles because he’s not transcribing interviews. The warehouse workers will each pack more boxes because they’re not running between shelves collecting each item to be packed.

“Any time in history we’ve seen automation occur, people don’t all of the sudden stop being creative and wanting to do interesting new things,” says Aaron Levie, the CEO of enterprise software company Box and an automation optimist. “We just don’t do a lot of the redundant, obsolete work.” He points to potential examples like automatically scheduled calendar appointments or automated research services. “Why won’t we make up that time with doing the next set of activities that we would have been doing?” he says. “What I think it does is make the world move faster.”

What might that look like? Sodexo’s CEO of corporate services, Sylvia Metayer, offers one example. She says the outsourcing company’s building maintenance crew has started using drones to survey roofs for maintenance needs in three locations. Before the drones arrived, a human climbed onto the roof to check things out. Now, that human stays on the ground, which is safer. “The service hasn’t changed, the clients still need someone to help maintain the roof,” she says. “If we do it with drones, the people who would have been going up on the roof have more value, talking with clients about what needs to be done.”

Examples also exist in back office automation. “From what we’ve actually seen on the ground, in real business operations, we’ve seen almost zero job loss,” says Alastair Bathgate, CEO of Blue Prism, a software company that helps automate tasks within customer service, accounting, and other jobs. One of his clients, a bank, trained the automation software to react when a customer overdrew an account by checking to see if there were a balance in another account that could be transferred to cover it. This was a process that had never been done by humans, because it would be too tedious and expensive. Another bank used the software to allow customer service representatives to direct customers who had a credit card stolen to an automated system that would input their information and close the account. What do they do now? “It allows them to take another call,” Bathgate says. On-hold time, not head count, went down.

We may need automation

As the birthrate in many countries declines, the share of the working age population will shrink. To maintain today’s GDP, those workers will each need to be more productive than workers today, and they’ll need to improve at a faster rate than they have in the past. Even if productivity continued to improve at the same rate that it has throughout the last 50 years—within which the computer and the internet both became mainstream tools—it wouldn’t be enough of an improvement to sustain GDP. Automation technology could be the answer. According to a McKinsey analysis, it could raise global productivity by as much as 0.8% to 1.4% annually—but only if humans keep working, as well.

Being an automation optimist doesn’t mean ignoring jobs lost to automation

The Industrial Revolution eventually led to an unprecedented high standard of living for ordinary workers.

But this prosperity didn’t immediately materialize. There was a period in which life inside of factories was miserable for the laboring class. It included paltry wages, terrible working conditions, and child labor.

Today, during what the World Economic Forum has dubbed the “fourth industrial revolution,” even optimists expect short-term labor displacement, wage depression, and, for some workers, pain. To take just one sector, the Obama White House estimated that nearly 3.1 million people could lose their job to the autonomous car. New jobs in other sectors could be created as these jobs disappear, but the people who are losing driving jobs won’t necessarily have the skills to fill the new ones. This is a big deal.

What separates the optimists from the pessimists is that they tend to believe that the economy as a whole will recover from this short-term adjustment period.

Pessimists argue that not everyone will benefit from this industrial revolution in the same way that the standard of living for ordinary workers rose after the last industrial revolution. Over the last two decades, most gains in productivity have gone to the owners of businesses rather than people who work for them. Global inequality has for the last several decades soared.

But there’s a lot of stuff going on outside of technological developments, argue the automation optimists, like the decline of unions, weakening of labor laws, tax laws that benefit rich people, and education policies that haven’t adapted to a changing world—these are policy problems, and we should fix them rather than blaming technology.

There is, however, one point that cannot be easily brushed aside. Pessimists point to the pace of innovation as a reason that, this time, advances in technology will impact jobs more brutally than they have in the past. “In the past, when you had disruption, the economy adjusted and jobs were created elsewhere,” says Ethan Pollack, an economist at the Aspen Institute’s Future of Work Initiative who says he wavers between optimism and pessimism on automation. “What happens if [in the near future], each period of disruption comes so quickly, that it never recovers?”

So who is right?

“There will be fewer and fewer jobs that a robot cannot do better,” Tesla and SpaceX CEO Elon Musk recently mused at the World Government Summit in Dubai, before suggesting that a universal basic income would be necessary. But even as he talked of the threat to jobs, he also spoke of positive impacts of automation technology. “With automation, there will come abundance,” he said. “Almost everything will get very cheap.”

The optimism camp tends to have similarly mixed feelings about automation’s impact. “AI can seem dystopian,” tweeted Box CEO Levie, “because it’s easier to describe existing jobs disappearing than to imagine industries that never existed appearing.” He doesn’t deny that automated technology will make some labor obsolete—he just focuses on the long-term, big-picture opportunity for potential benefits.

Both sides generally agree that there should be measures in place to reduce the impact of labor displacement from automation, like education programs for re-skilling workers who will lose their jobs. One side just tends to have a more darker view of what happens after that.

So which side is right? If history is any guide, both.

In the 1930s, economist John Maynard Keynes famously coined the term “technological unemployment.” Less famous is the argument he was making at the time. His case wasn’t that impending technology doomed society to prolonged massive unemployment, but rather that a reaction to new technology should neither assume the end of the world or refuse to recognize that world had changed. From his essay, Economic Possibilities For Our Grandchildren:

The prevailing world depression, the enormous anomaly of unemployment in a world full of wants, the disastrous mistakes we have made, blind us to what is going on under the surface to the true interpretation, of the trend of things. For I predict that both of the two opposed errors of pessimism which now make so much noise in the world will be proved wrong in our own time-the pessimism of the revolutionaries who think that things are so bad that nothing can save us but violent change, and the pessimism of the reactionaries who consider the balance of our economic and social life so precarious that we must risk no experiments.

The Obama White House, in a report about how automation may impact jobs, recommended responding to automation by investing in education; creating training programs for workers, like drivers, who will be displaced by automation technology; and strengthening the social safety net. Bill Gates has suggested that we tax robots’ productivity similar to how we tax humans’ income in order to finance retraining programs and jobs for which humans are well-suited, like care-taking. Others have suggested wage subsidies and direct government employment programs. These proposed solutions are not so dissimilar to those provided to President Johnson in 1964, which included “a massive program to build up our educational system” and “a major revision of our tax structure.”

Even so, little progress has been made since then in making the US more resilient to job displacement caused by automation. The cost of college education has never been higher. As a society, the US has not shown a commitment in building effective, equal-opportunity re-skilling programs. Inequality continues to increase. And the Trump Administration has so far focused on preventing companies from hiring people into manufacturing jobs overseas rather than preparing the economy for the impact of automation. This is an insufficient approach.

As MIT’s Erik Brynjolfsson and Andrew McAfee put it more recently than Keynes in their 2014 book about automation’s economic impact, The Second Machine Age: “Our generation has inherited more opportunities to transform the world than any other. That’s a cause for optimism, but only if we’re mindful of our choices.”

Source: Quartz-The optimist’s guide to the robot apocalypse

Five questions to ask before considering robotic process automation

Speed. Efficiency. Higher quality. Lower costs. These are the key benchmarks by which enterprise IT has been measured since the earliest days of its existence. These outcomes have always been achieved by some combination of man and machine, but with advances in cloud and digital technology, the balance is shifting away from human labor and moving toward technology-driven solutions.

Nowhere is this more evident than in the rise of Robotic Process Automation (RPA). Software robots are increasingly performing important and onerous tasks that have traditionally been done by humans: taking over work processes, manipulating data, communicating between systems, and processing and recording transactions.

Not surprisingly, RPA can do these tasks at a fraction of the cost of human equivalents, with dramatic improvement in quality and reliability. In fact, in the healthcare industry, one RPA bot – which can be procured at a cost of $10,000 -$15,000 – can process claims at the rate of 5-10 humans working full-time. Not to mention RPA allows businesses to dramatically reengineer and improve key processes.

Even the jobs question is not as sticky as one might expect. It’s true that full-time equivalent savings are essential to the business case for investing in RPA, and this often sets the stage for conflict between those advocating for RPA and those whose positions will be eliminated. However, there is a silver lining found in two essential elements of all RPA implementations:

1. The ability to move humans from routine, rote jobs into higher-value roles with work that is far more engaging increases employee satisfaction and often fills open positions with staff that have “tribal knowledge” that would take new employees years to build

2. Organizations seem to gain a greater appreciation for the importance of organizational change management – the communications, expectation management and planning – that lays the groundwork for successfully transferring work from humans to robots.

So, with relatively little investment and simple implementation, RPA may seem like an opportunity no-brainer – but realizing the real payoff of automation requires some careful deliberation. Here are five questions to ask when considering RPA for the first time:

1. Where are your pain points?

Where in your organization are humans spinning their wheels with repetitive and mundane tasks that take them away from more important, higher value work? Where are kinks hindering the workflow? Where are employees transferring data from one application to another or keying in large volumes of information by hand. Where are errors most likely to occur? Where is your staff not able to meet your SLA’s? Pinpointing these scenarios in your organization will help you create an “automate-ability map,” a picture of the processes that may be easily “automatable” with RPA tools. Start with a small number of these use cases so you can talk about the application of RPA in concrete terms and deliver quick results.

2. What are your priorities for an investment in automation?

After mapping out which processes in your environment are automatable, develop and evaluate the business case for doing so. Estimating the investment and the potential advantages for a particular use case is a smart way to initiate the RPA conversation with leadership. Enterprises across industries – including insurance, banking, telecommunications and manufacturing – are creating compelling business cases for deploying RPA to process invoices for payment or generate weekly reports that once required people to extract data from one application into a spreadsheet and manually save it into another. Estimated time and cost savings, and accurate estimates of what it will take to configure and deploy the automation, should be an important part of the decision matrix.

3. How ready is your organization for change?

Automating business processes with RPA requires more than simply giving people a head’s up that change is coming. The scope, nature and complexity of the processes being automated will determine the requirements for managing organizational change, which impacts the broader culture and operating characteristics of the organization. Managing major change is part art and part science. Before you dive into the RPA pool, determine your organization’s aptitude for change, organizational redesign and transition planning. Also take the time to determine the complexity of the automation scripts you are considering implementing so you know what change management practices will be needed, in which functional areas and across which parts of the business.

4. What is IT’s role?

Even though RPA tools can be implemented by business users and, once up and running, require relatively few IT resources, it’s important to involve IT early on – even as early as the consideration phase. RPA is a corporate asset; if it is going to deliver its full potential in terms of process automation and data integration, it needs to be configured correctly on a secure server in the IT environment and included in IT’s critical processes that support security management and back up/contingency planning. RPA also can be considered a boon to IT, since many times it can be leveraged by business users to automate requests that are otherwise pending with IT, thus reducing the backlog list of modifications and enhancements IT needs to deliver.
5. What are the security requirements for this particular initiative?

IT will need to know how your RPA tools are going to interact with IT infrastructure and applications. Credentials will be required to enable your bots to access network resources and interface with applications and file structures. Remember that bots often have an advantage here – while human workers may browse the Internet, shop and share information across contacts, RPA tools will execute only the scripts or commands they are programmed to execute, making them more secure than many human users. Still, be prepared to have the conversation with IT about how to credential RPA tools and how to govern their access to the network.

Thinking through these considerations upfront can make both the business and the CIO winners. By freeing workers from dull and monotonous back-office processes – and by offsetting the IT work required to support countless applications throughout the enterprise – RPA can set up the business to make higher-order, more creative work happen, all while driving savings to bottom line.

Source: information-management.com-Five questions to ask before considering robotic process automation

Will Automation will take away all out jobs?

TEDx Cambridge Sept 2016 by David Autor

Here’s a paradox you don’t hear much about: despite a century of creating machines to do our  work for us, the proportion of adults in the US with a job has consistently gone up for the past 125 years. Why hasn’t human labor become redundant and our skills obsolete? In this talk about the future of work, economist David Autor addresses the question of why there are still so many jobs and comes up with a surprising, hopeful answer.

Source: TEDx Cambridge- Will Automation will take away all out jobs?

Robotics Process Automation Deja Vu

Those of us who have been around the Robotic Process Automation (RPA) world are starting to get a sense of déjà vu. More and more companies are expecting non-technology resources within the organization to know how to use RPA. Although RPA technologies were originally intended for business resources to more easily develop and manage automation, it really wasn’t realistic as software providers promised: “If you can use Excel Macros, you can use X RPA solution.”

So what should be the make-up of a successful RPA implementation team?

Reality bites

When the rubber meets the road at the outset of an implementation, it becomes very clear that “macro monkeyswere building robots that had one of two problems:

  1. They broke when they got to real-world situations.
  2. They were not maintainable in the long run.

In fact, not only was it not possible for business resources to create reliable, real-world automation, but even hard-core technical resources needed a certain aptitude and specialized training. To develop good automation, specific engineering discipline is required, and most developers need training to cultivate it.

The rise of the machines

As RPA has risen in popularity over the last year or two, we have seen a resurgence in expectations that business resources will be able to develop automation. Is there hope that we will be successful this time around? Spoiler alert: It is possible, but definitely not easy.

At Cognizant, we call our vision “Robot Utopia” (more on that in a future post). Robot Utopia makes heavy use of the ability of business resources to identify, build, execute, monitor, and maintain automation.

Key automation ingredients

Our research on this is not yet complete, but there are a couple of principles I’d like to share with you.

The first is that the business resources cannot be trusted to engineer a good solution. But when properly trained, they can identify opportunities that are the foundation for creating reliable components for automation. It is very important to have professional automation engineers involved to provide reliable reusable components (think Lego blocks).

The second major lesson relates to engagement. The business resources have a real job to do and they will only invest in automation to the extent that automation will make their job easier. Convincing them to spend their time learning relevant automation techniques, and then building, operating, and maintaining automation is a critical component.

The edge of tomorrow

If business resources are going to be part of your RPA development strategy, take special care not to repeat the mistakes of the past. The best way that I can recommend to do that is to work closely with a partner who has experience in exactly these matters. Ask the tough questions. Get comfortable with the plan and make sure that your partner has attended this rodeo before.

Source: digitally.cognizant.com-Robotics Process Automation Deja Vu

RPA Technical Insights, Part 21: Transformation Begins With Education, A Guide to RPA Training Documentation – Symphony

This is part 21 of a 22 part blog series by the leading experts at Symphony Ventures. It addresses how to choose the right RPA tools for your business needs. Drawing from our global team’s extensive knowledge in automation consulting, implementation, and managed services across a range of diverse industries, we’ve drilled into the technical criteria to consider when selecting which RPA software best enables your company’s digital operation strategy.

Read part 20, Why You Shouldn’t Blindly Pile Work Onto Your Automation.

When choosing which RPA tool to employ, the maturity of the vendor’s support and training services should contribute to your decision-making process. Vendors strive to make their products powerful yet accessible, often supplementing training material and additional assistance to ease the transition to RPA. If you are new to this technology, this material can prove to be invaluable in getting you started.

The Importance of Basic Training Material

The focus of this blog is training documentation. Offerings like structured training materials are extremely useful for getting developers and users off the ground in terms of understanding how to use the software. These often come in the form of user manuals, with pages of structured content that walk a user through a development path. Ideally, they can be downloaded and viewed offline, allowing for easy access and readability. Having a user manual or training guide present and accessible means that users will have a baseline understanding of the tools abilities, which are invaluable when it comes to design and deployment.

The Growing Popularity of Video Tutorials

Aside from training manuals, other forms of visually informative content like video tutorials are being produced by a few top RPA vendors. A training video, for example, can provide a demonstration of the step-by-step process of designing a practical workflow or how to use one of the tools abilities. This sort of content is best presented in a format that viewers can follow along to. In general, you will find that visual training materials are especially helpful for RPA, since most RPA software utilize a visual interface.

Other Training Outlets

As a seeker of RPA information, you might have noticed that vendor training content is relatively scarce. Fortunately, this may not be true for long. The RPA world is currently undergoing a massive expansion of training materials and opportunities to improve accessibility and meet growing demand. Some of the top vendors are currently developing online training courses, to provide comprehensive e-learning environments. E-learning is beneficial because it offers the whole suite of training, practicing, and testing.

There are other outlets to receive training outside of vendor supported material. For instance, Symphony host RPA and AI training workshops in collaboration with The Global Sourcing Association (GSA UK). These are not designed as development courses, but rather focus on technical and business subjects, aiming to teach participants how to strategize their business around RPA. Outside of these, Symphony offers specialized training for clients as one of our main services. So, be on the lookout for these if you want to gain valuable knowledge from the experts and grow your internal capabilities.

Summary

In today’s fast-paced, digital environment, it is critical to avoid being slowed down by a lack of proper training. It is prudent to expect your RPA vendor or service provider to give the necessary tools to help your team succeed. Whether it is a structured manual, training guide or a series of video tutorials, make sure that quality training material is available. Most of all, don’t hesitate to reach out to the experts, like Symphony, for advice. We try our best to provide the proper tools and education to help your business succeed with RPA.

Be sure not to miss the last part of our RPA Technical Insights, where investigate the benefits of comprehensive implementation support from RPA vendors.

Source: symphonyhq.com RPA Technical Insights, Part 21: Transformation Begins With Education, A Guide to RPA Training Documentation – Symphony

Why branch bankers shouldn’t fear bots

Many bank employees likely fear robots will replace them, but to U.S. Bank’s Dominic Venturo, some bots are just there to be their wingman.

Much of the talk about artificial intelligence in banking has been about how technology can replace some functions currently performed by humans. But AI could help human bankers do their jobs more effectively by giving them quicker access to relevant information than ever before.

“AI won’t totally replace, but rather augment, a human’s ability to do their job in a lot of ways,” said Venturo, the Minneapolis-based bank’s chief innovation officer.

U.S. Bank is one of a number of financial institutions experimenting with how AI and machine learning can transform the financial industry. This year the bank announced the formation of an artificial intelligence enterprise solutions unit, which sits inside its payments, virtual solutions and innovation group.

The bank has begun experimenting with how artificial intelligence can help bankers serve customers that have a question on a product or service that is “infrequently asked about,” Venturo said. If a customer walks into a branch to ask a banker about such a product, the banker may not immediately know the answer. The banker may need to call a different expert in another location or spend time looking up additional information. That makes for a not-so-great experience for the customer. But if AI can contain an encyclopedic knowledge of U.S. Bank’s entire suite of products and services, the banker can quickly pull up information in seconds, he said.

“Let’s say there’s potentially hundreds of products a banker can get asked about; AI can give them the ability to get at the right answer for any of them in a rapid way,” Venturo added. “It enables your banker to become almost superhumanly smart about every product or service you might offer.”

This is important, he said, because although artificial intelligence can automate many repetitive tasks, and customers are more comfortable using digital financial services, they still want to talk to a human for many financial needs, Venturo said.

“The reality is there are many individuals who still prefer to talk to humans, and AI can augment that experience,” Venturo said.

Further, if the branch-based banker now doesn’t have to call a centralized contact center expert for information, that expert has more time to work on something more complicated.

“Now those folks can spend their time on other issues that require a higher level of service,” Venturo said.

AI can help improve not only in-person interactions between banks and customers, but human-initiated digital experiences, said Mitch Siegel, head of strategy for the financial services practice at KPMG. A wealth manager could use artificial intelligence to analyze customer’s financial data and send a personalized, automated report daily or weekly, for instance. If that client then had a further question or wanted to talk about something more complicated, such as changing investment strategies, they could then pick up the phone or talk to the wealth manager in person, Siegel said.

“There’s an opportunity for applying AI to better service customers, and give them a more personalized experience without getting rid of the human touch,” he said.

AI can also make suggestions to help banks build loyalty with customers by suggesting products based on their behavior. AI might be able to detect retail customers who typically have money left in their checking account when their next paycheck arrives. In that case, the bot can notify the bank, which can then push a digital message to the customers asking if they would like the bank to automatically sweep the remaining money into a savings account when the next check arrives.

“That’s something that could really deepen a customer relationship and a suggestion the customer might really appreciate,” Siegel said. “Insights into the ways you spend money, how you pay bills or savings habits are usually appreciated. Lower-value needs like these, customers are generally comfortable doing digitally, and when the customer has a financial need they want to speak to a human, with, like, a mortgage,” they’re then more likely to come to that bank first, he said.

AI can also free up bank employees to act in a more consultative, advisory role, said Adam Devine, head of marketing for WorkFusion, which provides robotic process automation and artificial intelligence solutions to several industries, including banks.

One example is contact centers where about a quarter of the calls are about the same topics — basic questions about transactions.

“So much of it is rules-based and repetitive,” Devine said.

If chatbots then could be trained to automatically handle these common calls, that could free up contact center employees to focus on providing more valuable services, Devine said.

“It allows the customer service agents to provide more of an advisory and recommendation service based on nuance and conversation,” he added. “It becomes less about transactions and provides more revenue opportunity for banks.”

In general, Devine said bankers he has met with as clients or potential clients have pointed to AI projects as top priorities.

“Two to three years ago we’d walk in [to a bank] and talk about machine learning and AI and they’d look at us like we had five heads,” he explained. “Now they’re showing us PowerPoint presentations about how it’s part of their core strategy.” For many banks, “if it’s not on their 2017 road map, it’s on their 2018 road map,” he added.

Source: americanbanker.com-Why branch bankers shouldn’t fear bots