We are at an interesting tipping point regarding how and where work gets done. As business leaders and managers, we have become increasingly capable of engaging a workforce that is some combination of virtual and on site, part time and full time, permanent and contingent. But just when we’ve sorted out preferred management routines, there is an entirely new landscape emerging with technology options central to the work and possibly your business model: work automation. How, when, and where should leaders be thinking about applying the various automation technologies to their businesses?
There are currently three technological enablers of work automation: robotic process automation, cognitive automation, and social robotics. Each technology fits a different kind of work and has different implications depending on the work to be done, as described in the chart below.
The simplest and most mature so far is robotic process automation. It can be used to automate high-volume, low-complexity, and routine tasks. It is particularly effective in automating the so-called “swivel chair” tasks, where data needs to be transferred from one software system to another. These tasks are traditionally done by humans. For example, they may involve taking inputs from emails or spreadsheets, processing the information by applying certain rules, and then entering the output into some other business systems, such as an ERP or a CRM. Creating a virtual workforce of software robots can help companies streamline operational processes as well as increase the quality and cost-effectiveness of shared services.
Nevertheless, most of the current excitement around work automation stems from systems that can replace humans in nonroutine, complex, creative, and often exploratory tasks — in other words, systems that can automate human cognition, or cognitive automation. Developments in machine learning, powered by scalable computing resources in the cloud and heavy investment in exceptional human talent by the large players in the IT industry, are making computers capable of recognizing patterns and understanding meaning in big data in a cunningly human-like way. This “recognition intelligence” is showcased in systems for voice recognition, voice-to-text, natural language understanding, image understanding, and a host of other applications that are increasingly becoming available to consumers and companies.
Companies can use these cognitive automation technologies in three ways. First, they can further automate, or completely reengineer, their business processes. Take, for example, the car insurance industry. Instead of having human agents visit cars to assess the damage, an app used by the car policy owner and powered with image recognition intelligence could process photos of the car damage, assess the degree of the damage, estimate and classify the size of the claim, and pass the information for final approval to a human, thereby significantly simplifying the claims process in terms of both time and cost. Cognitive automation like Google Glass can transform the work of a flight attendant, for example. The ability of such technology to enable traditional jobs to be disaggregated and to supplement or replace routine activities presents opportunities in efficiency, effectiveness, and impact.
The second area of opportunity with cognitive automation is for companies to develop new products and services. In the previous example, the intelligent app could be part of a new offering to car insurance clients, perhaps with added features such as a chatbot that could provide additional, on-demand advice about insurance to the policy owner.
Finally, cognitive automation can be used to gain new insights into big data. When it comes to transforming a company’s strategy around the future of work, talent analytics combined with machine learning can be a very powerful tool for analysis and prediction.
Another area that is rapidly evolving is social robotics. Unlike their predecessors, this new generation of robots is not bolted on an assembly line; they are mobile and move around in our everyday world. They can be drones that fly or swim, anthropoid robots that walk, or swarm robots that roll on wheels. They are programmable and can adapt to new tasks. This new generation of social robotics can automate routine as well as nonroutine tasks. Freed from the assembly line, the social robots can collaborate with humans in a variety of applications that were unthinkable a few years ago.
A good example is the Kiva robots that Amazon has been using to increase the efficiency of its order fulfillment process. Instead of walking the aisles to find the right packages, humans now stand on platforms while an army of social robots brings the right package to them at the right time. By reengineering the process using robots, Amazon did not replace the human workers but rather made them more productive in the same way the aforementioned app allows human adjustors to take on more cases by focusing on the “higher value added” activities while the app takes on the more routine aspects of the job.
Amazon’s employees now take 15 minutes to fulfill some orders instead of 90 minutes, an increase of 20% in efficiency; the small size of the robots also allowed Amazon to increase the size of ist inventory by 50%. Management oversees the entire fulfillment process, including the work interactions between robots and humans.
As the half-life of skills continues to shrink, the growing premium on reskilling is causing many organizations to rethink the risks associated with full-time employment in order to reduce the risk of obsolescence. The different variations of work-task automation, like the ones here, can deliver viable solutions to all of the above concerns. Selecting the right technology for automating work tasks and improving performance is therefore critical for business, as is the alignment of the selected technology with a comprehensive strategy for the future of work.
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.
There’s lots of prognosticating about what impact robotics and automation will have on the future of work — with mostly dystopian depictions predicting displaced workers and increasing unemployment. But new social data from Adobe Digital Insights suggests that the average worker is far less cynical when it comes to welcoming robots into the workplace.
According to Adobe, most people are talking (on social media) about how robots are helping their work, not taking it away. Workers are also upbeat about being able to hand over mundane tasks to robots so human workers can do more meaningful jobs. Machine learning, artificial intelligence, and robots were the most discussed FOW topics.
Adobe’s report is based on roughly 3 million social mentions captured from Twitter, news, blogs, and forums between January 2016 and January 2017. Interestingly, the FOW hashtag was mentioned twice as many times on Twitter than on workplace-focused LinkedIn.
Across social media, Future of Work (FOW) mentions are up 40 percent year over year, automation mentions have doubled year over year and average daily mentions of robots and jobs have increased 70 percent year over year.
“Overall, people seem to believe the FOW is promising, particularly when it comes to the automation of traditionally mundane tasks,” wrote Joe Martin, head of social insights for Adobe, in a blog post. “Automating document and signature processes, for example, could open up new possibilities for people as the tech revolution advances. Work environments should continue to improve as employees demand more from their space.”
Image: Getty Images/iStockphoto
More companies are investing in automation to streamline processes. But for an IT automation strategy to be successful, it’s important to start with clear, well defined goals.
“Automation” is a scary word for many IT workers — who contemplate images of robots, software and the like stealing their jobs as it becomes the norm.
But according to speakers at the annual Automation Innovation conference in New York last month, the short-term goal for an IT automation strategy should be to empower knowledge workers — not simply replace them.
“If you do the integration and infusion [of automation technology] correctly, you will expand [knowledge workers’] capabilities; you will not detract from them,” said conference speaker R.G. Conlee, CIO of Conduent. “We are not out to just replace people.”
More than 300 attendees packed the Bohemian National Hall in New York City for the conference to discuss IT automation strategy and potential roadmaps to make this strategy successful. Conference organizers noted that the goal was to help attendees “transition from traditional labor to RPA, intelligent automation and cognitive computing.”
But to realize the great business potential of IT process automation and attain a positive return on investment, it’s important to start with concrete goals such as improving price points or a particular process.
“It pays to focus on a specific purpose for automation rather than thinking of it as a broad platform with extended capabilities,” said Bill Galusha, senior product marketing manager of software provider Kofax.
Automation innovation challenges
Conlee noted that many companies have experienced “digital disillusionment” when it comes to the latest technological fads. CIOs and other members of the C-suite, have gotten tired of chasing the latest shiny new technological toy, Conlee said, and are seeking definitive results from these investments.
“They are saying we want it to be practical, something we can use, and we want it to make a difference,” Conlee said. “In other words, we want to improve the way work is done; we don’t just want new ways of doing the work.”
It’s important to remember that moving to automation takes patience, Conlee added, because many IT and digital systems actually negatively impact productivity while they are being integrated with company processes. Implementation of automation is a big challenge, and a plan is required that takes speed to market and complexity into consideration, he added.
We want to improve the way work is done; we don’t just want new ways of doing the work.
“There is a training curve for digital systems that does not elicit better work in the short term; it takes time to get there,” Conlee said.
An IT automation strategy can be a huge help when it comes to one common issue facing modern digitized companies, Galusha said: process and data complexity. Because these companies are responsible for many systems with numerous internal/external data sources, it is difficult to connect all that information to the company’s processes.
A robotic process automation strategy can help with consolidating data for analytics purposes, Galusha said, and apply unique business rules to information contained in these numerous data sources.
“Your processes, and how you are making decisions, [is] only as good as the information,” Galusha said. “If you are doing it manually, it’s slow, it’s inefficient, and you’re probably making errors along the way.”
Galusha was quick to point out that obstacles to the automation revolution remain. Robots still have difficulty with distinguishing visual content such as invoices, purchase orders and email correspondence, for example.
“We also have to understand the complexity, really understand the use cases and how more sophisticated learning technology can be applied,” Galusha said.
IKEA’s automated customer service
Speakers at the conference also noted that automation will bring cost savings in the long run, but it could also expand business opportunities and help provide better premium service to customers. One company that is seeing these types of benefits is IKEA, which uses process automation to improve customer experience and engagement.
Martijn Zuiderbaan, Solution Owner at IKEA Retail AB, noted that IKEA is responsible for its entire supply chain, so prior to its implementation of automation, there were several potential areas that could have benefitted from it. The company decided to start small, by implementing automation processes into its customer call centers, with the goal of making its online customer service more efficient, engaging and effective.
“I think most enterprise companies are working towards a perfect future where all their solutions can talk to each other and everything works together, communicates and shares data,” Zuiderbaan said. “The reality is not really there.”
IKEA receives 20 million customer inquiries per year via voicemail, chat, mail and social media, Zuiderbaan said. The company is using automation to keep up with this demand and help it engage with customers in a smarter way.
In short, Zuiderbaan said the automation solutions were used to meet IKEA’s need to bridge gaps in the existing IT solutions to improve both customer and worker experience at the furniture giant.
“We tried to be more efficient, we tried to make our co-workers more engaged, and we tried to make work more effective,” Zuiderbaan said of IKEA’s automation efforts. “By combining these three, we tried to get a better customer experience.”
There’s been no shortage of hand wringing over the job costs of automation—and it’s not without cause. Automated devices are increasingly replacing people in all sorts of occupations. Just as a long list of other technologies have done before. Because dramatic workplace transformations do not occur often, it’s easy to see such shifts as zero-sum occurrences and overlook the new options presented as the familiar ones fade away.
The last time such a major shift took place was during the transition from an agricultural-based economy to an industrial-based economy. And though the tractor and other automated farm equipment did put many farmers and other agricultural workers out of work, people did successfully make the shift. This example has been used so frequently and is so far removed from our current reality that many dismiss this comparison and say that the disruption being brought by automation today is different from the change brought by automation to farming a century ago.
David Autor, an economist who assesses the labor market consequences of technological change and globalization, disagrees. In a recent TedTalk to explain why automation does not just eliminate jobs, Autor highlighted an interesting employment development that has taken place in the banking industry since the advent of the ATM. In his presentation, Autor said: “In the 45 years since the introduction of the automated teller machine…the number of human bank tellers employed in the United States has roughly doubled, from about a quarter of a million to a half a million. A quarter of a million in 1970 to about a half a million today, with 100,000 added since the year 2000.”
Though the number of tellers per bank dropped by roughly a third due to the ATM, banks also discovered that, as a result of the ATM, it was less costly to open new branches, said Autor. He added that the number of bank branches has increased about 40 percent since the appearance of the ATM. That’s why there are more tellers now than there were before ATMs replaced so many of them.
Of course, tellers today do different work than they did historically. “As their routine, cash-handling tasks receded, they became less like checkout clerks and more like salespeople, forging relationships with customers, solving problems and introducing them to new products like credit cards, loans and investments,” said Autor. The tellers are now performing “a more cognitively demanding job.”
The same thing has been happening in the manufacturing and processing industries for decades now. While automation has eliminated many industrial jobs, it is also largely responsible for the plethora of industrial jobs that have been coming back to the U.S. It’s also the reason so many new manufacturing jobs are starting here rather than elsewhere. But today’s manufacturing jobs—just like today’s bank teller jobs—are clearly different from what they used to be. Autor pointed out, “As our tools improve, technology magnifies our leverage and increases the importance of our expertise and our judgment and our creativity.”
In the interim phase we find ourselves in—between manufacturing’s past and its future—we face a challenge. A challenge we have faced before. The last time was a century ago, during the transition from the agricultural economy to an industrial one. As automation was eliminating agricultural jobs, the farm states “took the radical step of requiring that their entire youth population remain in school and continue their education to the ripe old age of 16,” said Autor. “This was called the high school movement, and it was a radically expensive thing to do. It also turned out to be one of the best investments the U.S. made in the 20th century. It gave us the most skilled, the most flexible and the most productive workforce in the world.”
Autor pointed out that, when surveying our current industrial employment situation with an eye toward correcting its current course, “It’s foolish to say there’s nothing to worry about. Clearly we can get this wrong. If the U.S. had not invested in its schools and in its skills a century ago with the high school movement, we would be a less prosperous, a less mobile and probably a lot less happy society. But it’s equally foolish to say that our fates are sealed. That’s not decided by the machines. It’s not even decided by the market. It’s decided by us and by our institutions.”
Given the current state of political matters in the U.S., it’s clear than any large scale, government-driven support for an educational solution—like the one Autor referenced—is highly unlikely. However, action is being taken on a smaller scale in localized areas, such as in Ohio and Indiana, and even in the manufacturing corridor in upstate South Carolina. Despite such positive actions by many of our institutions, the scale of the problem will require additional help from the automation industry and its manufacturing customers.
An example of how automation suppliers are taking action can be seen in Festo’s efforts to bring the German apprenticeship model to the U.S. Yaskawa is another automation technology supplier stepping up to the plate to address the automation-versus-jobs issue through its robotic training programs.
Having seen his former aerospace manufacturing employer go through massive layoffs in the mid-1990s—reducing its workforce by more than 60 percent in three years—Buddy Smith, who is now manager of the Yaskawa Academy for Yaskawa America, knows firsthand how technology suppliers and their manufacturing customers can play a more active role in improving today’s manufacturing workforce situation.
“If a manufacturer of any size decides to add robots to its workforce,” said Smith, “the company’s owners and executives can and should prepare the workforce in advance. There are a lot of communication opportunities to start a dialogue with workers. For example, explain why robotics will help workers, boost production and increase profit sharing. When a company capitalizes on these opportunities, it creates excitement by outlining how robotics can open doors to new training, provide a chance to tackle new responsibilities and reduce injuries.”
To help manufacturing customers who have purchased a robot communicate more effectively with their workers, Smith explained Yaskawa Motoman offers customers a range of robotics classes covering everything from programming to maintenance. “For our maintenance training, a student can spend one week with us and learn about 85 percent of what they’ll face on the job. That’s a terrific example of how retraining an already-employed worker can give them new skills and reap rewards for his or her employers,” he said.
Smith noted that different companies will, of course, have different workforce needs. For example, a large manufacturer may already have some technically inclined workers on staff who “could transition easily into robotics training,” he said. A small job shop, however, may need to look outside its walls to hire a robotics-qualified employee.
“Each company has to think about how to get a return on its robotics investment,” Smith said. “Improving production is not just about purchasing a robot. It’s also about weighing the decision to bring in someone skilled at programming a robot or retraining an employee to program and work with a newly purchased robot. Ultimately, though, the answer is not really a choice between retaining, retraining or recruiting. The solution is a mix of these things depending on the company’s position.”
If CIOs are not already looking at RPA as a digital enablement to their digital strategy, they are already behind the eight ball a little bit
A bank slashed the time it would approve an application for credit from 21 days to 11 minutes, by deploying robotic process automation (RPA).
With RPA, the bank was able to process all paperwork and data for credit, criminal and employment checks, to determine whether the bank should be lending more money to a customer.
For David Snell, an independent sourcing advisor, this case study is just one of the ways RPA brings “positive disruption” to industries.
“Its potential to positively disrupt the workplace is incredible, while its potential to disrupt the outsourcing market is far reaching,” says Snell, who was New Zealand country manager for Alsbridge (now part of Information Services Group).
He argues RPA and related automation technologies should already be on the agenda for CIOs.
“If CIOs are not already looking at RPA as a digital enablement to their digital strategy, they are already behind the eight ball a little bit,” Snell tells CIO New Zealand.
“Early adopters are already starting to see significant benefits and not only in their own operations,” he states.
He predicts there will be a rise in the uptake of RPA, because of the net benefits of potential productivity, cost savings and better CX (customer experience).
Apart from banking, RPA has practical applications in insurance, healthcare and manufacturing according to a white paper written by Craig Nelson, Alsbridge managing director and now partner at Information Services Group.
“RPA is a disruptive and game-changing technology that presents an opportunity to adapt, change and achieve significant results,” writes Nelson.
Though before embarking implementing RPA, ICT managers need to assess the interests of key stakeholders, their current and desired states of operation, their existing talent pool and senior executive willingness to sponsor the integration of human and virtual labour, according to the paper.
RPA sponsors, Nelson believes, must be prepared to address three questions:
- How will work processes change?
- How will skills requirements change?
- How will the organisation change?
Big data challenge
Snell, on the other hand, links the take-up of RPA to the challenges of data management.
“Getting data into the right place in a usable form is massive and it continues to be a big challenge for a lot of the organisations,” he states.
“Unless you are able to feed that data in those powerful algorithms, you really are not going to get the results that will give you some predictive capabilities.”
For Snell, the most important part of an RPA deployment is change management.
“The technology is not difficult,” he states, but there will always be “people, politics and an appetite for change” that will be involved in the deployment.
KPMG’s Todd Semanco discusses how the leveraging of regulatory technology, digital labour and data analytics is transforming compliance programmes, and why cognitive automation is seen as the future of regulatory infrastructure.
In today’s environment, risk and compliance leaders are increasingly challenged to take on more responsibility though funding levels often do not keep pace. This is prompting many to pursue sustainable means of becoming more efficient while improving quality and broadening capabilities. Two areas rife with opportunity for redesign are monitoring and testing, and leaders are considering how digital labour and automation can transform the function in a variety of ways: broader, deeper and more frequent coverage; enhanced synergy across multiple assurance functions; a more dynamic and robust risk assessment process; and more meaningful and streamlined reporting, including data visualisation.
What should organisations consider when transforming a monitoring and testing programme?
Todd Semanco: In order to maximise the value in such a transformation, it is essential at the outset to confirm an enterprise view of all the various testing activities across the organisation. Not only will this be critical in identifying gaps and redundancies in coverage, but is also required to evaluate and determine an optimal target-state model – whether it be a centralised monitoring and testing function or a decentralised function operating under a common mandate and set of standards. Test scripts and procedures should be analysed to confirm appropriate alignment to regulatory obligations and policies, and such analysis should include the availability and integrity of testing data, as well as documentation of the products, activities and systems covered by the respective testing and monitoring plans. Considering these elements, leaders can develop tangible, prioritised automation road maps focused on addressing gaps and/or shifting execution from human capital to digital labour.
The foundation of monitoring and testing programmes is a robust compliance risk assessment. What are the recent trends in this area?
Todd Semanco: Risk assessment continues to represent an area of heightened attention for many. Among the top focus areas are the breadth and frequency of risk assessments, key variables and drivers considered during the risk assessment process, the qualitative and quantitative mechanics, and, of course, the alignment of risk assessment results to testing and monitoring efforts. Risk assessment is a dynamic exercise in which numerous inputs such as testing results, complaints, hotline calls, investigations, etc, are considered ‘real time’, and testing and monitoring plans are either confirmed or adjusted accordingly. The reality is, given the volume and pace of change of this data, many organisations struggle to surface and apply insights at an appropriate rate. For many, it is becoming clear that enhanced automation, leveraging integrated regulatory technology and digital labour, is a ‘must-have’ to sustainably assess risk in a timely manner.
What role is technology playing in monitoring and testing programmes?
Todd Semanco: Regtech – broader than fintech and not limited to a specific technology – and digital labour are being leveraged to transform programmes end-to-end. From the way organisations monitor and manage global changes, to regulatory obligations and how testing is performed, analysed and reported, all components of managing the function are under review. The demands, from internal and external stakeholders alike, to demonstrate adequate coverage and provide precise impact and root-cause analysis are very high. To meet these demands, leaders are increasingly turning to technology to collect, consolidate and map key data elements together – for example, obligations, policies, risks, controls, process detail – at a granular level. This capability supports not only dynamic regulatory change management activities, but also the oversight of business process and technology changes. Further, by consolidating and integrating monitoring and testing scripts within this technology, forming rules engines, outcomes and impacts may be more immediately assessed and remediated while data is accumulated to support predictive analytics.
With the speed of technology advancement, how are organisations leveraging digital labour?
Todd Semanco: We’re seeing all levels of organisations – from the business to risk, compliance and internal audit partners – intensifying their efforts to further cognitive automation, integrate digital labour and establish an enterprise-wide automation infrastructure. Efforts are typically phased along a continuum – from basic process automation of repeated high-volume transactions to enhanced robotic process automation reliant upon both structured and unstructured data sources, through to cognitive automation. In the monitoring and testing space, digital labour is driving a shift from sample-based testing to the testing of full populations, and resultant outcomes are increasingly available for use in analytics, predictive forecasting and enhanced monitoring and surveillance. Leading organisations are viewing this as an investment opportunity to revamp monitoring and testing capabilities in a sustainable way while operationalising compliance – often while yielding a compelling return on investment and competitive advantage.
he development of artificial intelligence (AI) is more significant than any advancement yet: not only can machines do tasks for us, they can make better decisions than we can. What does this mean for the workforce of the future? Will humans be completely replaced by computers and robots? The answer may not be as pessimistic as some suggest. Read on to explore the impact AI can have on the future of work.
Will automation replace today’s workforce?
Researchers at the University of Oxford conducted a study in 2013, proposing that as many as 47% of jobs in the United States could be computerized. Artificial intelligence is what separates today’s automation advancements from those that came before. Self-driving cars are no longer a dream; they’ve already taken shape. We rely on Siri and Google Now to help us with the simplest of tasks. The impending reality of artificial intelligence leaves many of us scared about the future, for ourselves and our children. What will we do with our lives if computers can do everything for us?
It turns out more of us are concerned about this than not: a Pew Research Center survey showed that 65% of Americans believe that within 50 years most of today’s work will be done by computers or robots. Businesses have only added to the impression that automation means replacing humans with computers. Sometimes it’s a matter of cutting costs, other times it has to do with efficiency — businesses have laid off thousands of workers in favor of automation, and will likely continue to do so. Looking into the future, what happens in a society that doesn’t need work? How do people survive without a regular income?
These are serious questions, but they’re also one-sided. They’re based on the assumption that humans will be replaced with computers and robots, and left with nothing (an assumption 65% of us are concerned about). What if we consider a future where automation doesn’t replace, but instead complements human ability?
Automation vs. augmentation
Indeed, there are some who envision this very future. The Harvard Business Review published an article in 2015 explaining the concept, called augmentation. While smarter, advanced computers can analyze big data and reveal insights humans can’t, computer intelligence is only part of the puzzle. Any good business leader knows that all business decisions must be made within a context. The backstory and implications need to be understood — in other words, a narrative or story needs to be told and weighed into the decision-making. Computers aren’t great at this, but humans are.
From this perspective, automation gives humans more opportunities to pursue positions that require high-level — or “big picture” — thinking. Rather than pushing humans out of the workforce, augmentation allows humans to work alongside machines, contributing in ways that computers can’t. These high-level positions are more fulfilling for us than factory work or data-crunching.
A recent want ad for a job at a professional services companies had this interesting description:
“Robotic Process Automation Analyst: Key responsibilities include: work with business teams to simplify and improve operations by analyzing as-is processes and create end-to-end automation solution designs… Work with process owners/SMEs and technology teams to analyse and assess automation feasibility… Document business requirements, processes and work instructions…. Operate as an advisor or business consultant and recommend process automation improvements. Implement automation within RPA software to deliver against business requirements, software and industry best practices….”
So, we have robot process automation (RPA) and a bunch of new jobs arising around it — what is this oddly named thing?
Never mind the image of humanoid-looking robots sitting at terminals or shuffling paperwork. It’s more about software running behind the scenes. Leslie Willcocks, professor of technology, work, and globalization at the London School of Economics’ department of management, was recently interviewed by McKinsey on what robot process automation (RPA) is, and what it will mean for administrative and back-office tasks.
Not to worry about RPA taking away jobs — it is meant to elevate roles — and even may be a stress-reduction strategy, Willcocks explains. “The average knowledge worker employed on a back-office process has a lot of repetitive, routine tasks that are dreary and uninteresting,” says Willcocks. “RPA is a type of software that mimics the activity of a human being in carrying out a task within a process. It can do repetitive stuff more quickly, accurately, and tirelessly than humans, freeing them to do other tasks requiring human strengths such as emotional intelligence, reasoning, judgment, and interaction with the customer.”
Recent research from Transparency Market Research estimates that the IT robotic automation market will expand at an annual growth rate of 47% between 2016 and 2024. TMR identified key providers in this market: Blue Prism, Be Informed, Appian, IPSoft, Tata Consultancy Services, Infosys, Cognizant Technology Solutions, Atos, Capgemini, Genfour, Genpact, Automation Anywhere, Sutherland Global Services, and UiPath.
Willcocks identified four classes of RPA solutions:
- “Highly customized software that will work only with certain types of process in, say, accounting and finance.”
- “Screen scraping or web scraping. A user might be collecting data, synthesizing it, and putting it into some sort of document on a desktop. You automate as much of that as possible.”
- “Self-development kits where a template is provided and specialist programmers design the robot. That’s usually customized for a specific organization.”
- “Enterprise/enterprise-safe software that can be scaled and is reusable.”
RPA’s clearest and earliest benefits are within organizations with compliance obligations, such as financial services. Such companies are finding “that automation is a cheap and fast way of applying superior capability to the problem of compliance.” Robo-automating customer service is another piece of low-hanging fruit, as routine issues are addressed on an automated basis, while staff is freed up to deal with higher-level or more complex problems.
Employees respond positively to RPAs, too, Willcocks explains. “People welcomed the technology because they hated the tasks that the machines now do, and it relieved them of the rising pressure of work,” he says. “Every organization we have studied reports that it is dealing with bigger workloads. I think there will be an exponential amount of work to match the exponential increase in data–50 percent more each year. There is also a massive increase in audit regulation and bureaucracy. We need automation just to relieve the stress that creates in organizations.”
So how is RPA different from the other automation pushes that have come through organizations over the past three decades? For example, event processing — which arose in the mid-2000s — also sought to take the manual steps out of administrative processes. Perhaps RPA represents another step in digitizing organizations drowning in paperwork (even if it is virtual). As TMR explains it, the prices of RPA are far more economical than previous generations of process automation solutions. “IT robots (automation software) are programmed to be used with data-driven and rule-based processes, which work towards aspects such as improving productivity and compliance accuracy,”
Not every process can be automated, Willcocks cautions. “It can’t structure the data,” he says. Exception handling is something that is going to take time for RPA to be able to handle automatically.
Implementing RPA requires the same due diligence as any other major IT initiative, Willcocks explains. Get an executive champion to gain organizational support, and pay attention to change management requirements. It may not take away jobs, but it’s going to significantly change many jobs across the enterprise.