Top RPA Conversations So Far

Earlier this year, we made several interesting predictions as to what would dominate the conversation throughout 2017 in the automation landscape as part of a preview for the year in RPA. After consulting with industry thought-leaders, analysts, and RPA specialists, we zeroed-in on a handful of critical topics poised to drive important discussions about RPA trends and developments. As we pass the midpoint of the year, we thought it would be interesting to revisit our predictions and determine which conversations have not only been taking place, but have also proved to be critical talking points for those within the automation industry.

Why is this look back to the beginning of 2017 in terms of automation so important? A 2016 study from the industry research firm Gartner suggested the demand for RPA tools is growing quickly…”at about 20 percent to 30 percent each quarter.” This growth was projected to continue into 2017 and thus far industry analysts believe it has, at least through Q1 and Q2 of this year.

With these statistics and figures in mind, let’s take a moment to check the pulse of the automation industry thus far in 2017 by considering which predictions and projections have evolved to be critical conversations in RPA.

RPA deployment will increase across new industries

At the outset of 2017, RPA adoption patterns were poised to experience a significant shift as companies in new and emerging industries discovered the power of automation solutions to enhance their business operations. While the adoption of automation technologies has functioned in somewhat of an opportunistic manner by early adopter companies, wider adoption moments have come to the forefront in such industries as healthcare, insurance, banking, manufacturing, and retail. This also dovetails with industries that have already experienced the benefits of RPA increasing their deployments into the front-office and other customer-facing tasks, actions which have yet to be fully realized by many companies.

In a 2016 report that surveyed the adoption of eight new technologies — including robotics and automation — by the professional services firm Deloitte suggested that:

More companies are increasing investments in these technologies. New technology investments over $1 million have increased…[and some companies are planning to] spend at least $100 million on new technologies over the next two years.

Rather than just being a way for companies to streamline their business operations, RPA and other automation platforms are transitioning from a luxury to a necessity. This conversation has not only persisted as the year progressed, but the drumbeat for automation platforms as a key aspect of any given company’s operation strategy has only increased as global economies and industries become more and more connected.

Automation will replace more tasks, not actual jobs

The emergence and proliferation of automation has caused a certain degree of panic over the possibility these technologies could replace the need for human employees in the workplace. Especially as automation moves from the back-office to more customer-facing tasks, customer relations-related areas such as call centers and other customer service platforms have suddenly become the subject of how and when automation could render these functions less necessary from a manual intervention standpoint. However, as we’ve moved through 2017, this concern has continually been minimized be the actuality of automation deployment.

Automation certainly has the ability to replace certain tasks or remove human personnel from specific department or business moments, especially those that are tedious, repetitive, and time-consuming: in fact, that’s what it is meant to do. However, this doesn’t mean the entire workforce will be replaced by robots. In fact, a 2017 publication by the McKinsey Global Institute suggests that:

The right level of detail at which to analyze the potential impact of automation is that of individual activities rather than entire occupations. Given currently demonstrated technologies, very few occupations—less than 5 percent—are candidates for full automation. However, almost every occupation has partial automation potential.

This kind of automation paints a hopeful picture for the future, one where humans and automation work side by side, which is the critical point of this conversation: debunking the myth RPA will essentially replace all methods of human intervention. In point of actual fact, RPA and human personnel will work in conjunction with each other which will allow human employees to focus on higher-level tasks that are meaningful and interesting, thus creating a space for automation to work through more repetitive, high volume tasks.

AI and machine learning will advance RPA

It has long been discussed how intelligent technologies like artificial intelligence, cognitive computing, and machine learning are expected to develop in the coming years and decades. In addition, a critical element of discussion within the automation landscape is how these technological developments will integrate and bolster automation functionality.

In a recent discussion with Forbes, Ash Ashutosh, the founder and CEO of Actifio, predicted that:

Just as most companies evolved to include cloud capabilities and features, 2017 will bring machine learning to almost every aspect of IT…[these technologies] will usher in a new era of data understanding and analysis.

While this is certainly a salient point, what’s less often considered is how RPA solutions will combine with intelligent technologies to deliver even greater automation potential. No longer can these technologies be viewed as disparate from each other when companies consider automation and how RPA can help enhance their business operations.

But what’s perhaps most important in this discussion is how intelligent technologies will be able to learn and make decisions beyond their initial programming. This means they are able to learn from previous actions and deal with unforeseen exceptions in a business process. Because RPA is able to quickly generate and gather data, combining RPA with intelligent technologies means that the “learning” process can take place at accelerated rates. While these two technologies are only starting to be used together, the smart automation they can produce means that companies will be able to foster both increased productivity and creativity going forward.

Source: UIpath-Top RPA Conversations So Far for 2017

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Robotic Process Automation The Question of If Rather When

RPA is one of the most efficient emerging technologies that can deliver results in terms of business efficiency, customer acquisition, customer engagement, and experience.

Robotic Process Automation (RPA) is in its early days. Having said that at times, the hype could get ahead of the reality. It is no longer a question of “if?” rather “when?”. RPA promises a lot for the enterprises especially in the areas of:

  • Customer Experience by improving process quality, increasing accuracy, reducing response-time, and allowing higher predictability
  • Business Efficiency by increasing the consistency in processes and tasks to reduce output variation, improves productivity by freeing up the brains and hands to do more value added tasks, enhances flexibility to respond to flux in demands and allows geographical independence.

This, in turn, enables enterprises to undergo a significant Digital transformation and expedite business processes, reduce errors and cut operational costs.

The physical and digital worlds are converging at esoteric speed. In 2004, when we all watched the famous Sci-fi action movie, iRobot, we never anticipated that a significant piece of work being taken over by robots would be a reality, but yes, it is knocking our front door now, is real and here to stay.

RPA virtually connects multiple disparate systems while executing repetitive work more accurately and reliably than people can do. At the same time, it necessarily doesn’t mean the complete replacement of human workforce by the robots, making humans redundant in their work environments. If RPA has to be effective, along with right intervention and human touch points, it is important to identify the right work that can be automated and executed under RPA by careful evaluation.

  • Type of transactions
  • Availability of knowledge to power automation
  • End to end process improvement
  • Total cost of ownership
  • Ease and time to deploy
  • Scalability & flexibility for the future requirements
  • Return of Investment.

RPA should be considered as an enabler that will free up people from doing high volume, highly mundane tasks that are better suited for robots, who can work tirelessly and continuously without making errors and the human workforce can take on more creative, collaborative and interactive work that makes them productive and efficient. Leveraging technologies to make machines work for you than they replacing you, that is the message that needs to be driven to avoid the fear of jobocalypse.

Any RPA project, undertaken, will be changing the nature of the work and the way it is executed currently, however, with better quality and improved efficiency. At the same time, it warrants to be conscious of balancing automation with right decision-making ability. Technologies like big data, analytics, Artificial intelligence and Machine Learning, in tandem with RPA can drive improved decision making and there by providing a new layer of engagement and transform enterprises to become customer centric.

With RPA, applications attempts to understand natural human communication such as touch, gesture, speech or even to the extent of reading neural signals and convert that to actions and communicate back in similar natural language. Artificial Intelligence variants like natural language processing, knowledge representation and reasoning, in the form of the algorithms equipped with machine learning capabilities, self- trains the applications to take right decisions. In parallel, the transactions and processes are being enriched with insights; predictive & prescriptive, and intelligence generated from ever growing repository of data; real time or operational, video streams, photographs, handwritten comment cards, data from security kiosks and other varied sources.

RPA is one of the most efficient emerging technologies that can deliver results in terms of business efficiency, customer acquisition, customer engagement, and experience.

What we are witnessing is the tip of the iceberg, and with Artificial Intelligence and Machine Learning evolving in tandem, RPA will become the catalyst, orchestrating the next level of digital transformation.

Source: bwdisrupt.businessworld.in-Robotic Process Automation The Question of If Rather When

AI Demystified: Shaping the future for positive change

The debate between Mark Zuckerberg and Elon Musk on the misunderstandings of artificial intelligence (AI) has brought to the forefront concerns and dangers of a robot takeover.

Often misrepresented and misunderstood, AI continues to serve as a source of significant intrigue. It has long been lauded as the future of work, but according to notable Hollywood movies, is also a harbinger of a robot takeover.

Futuristic movies, like I, Robot, and Avengers: Age of Ultron, portray AI as the precursor to a robot revolution wherein a seemingly innocuous utilization of the tool devolves into dystopia. And in many cases, despite being an effective money making tool, it is a mischaracterization of AI. Still, it is believable because of the lack of public fluency on the issue.

As the idiom states: “We fear what we don’t understand.”

With anxieties abound, it is important to understand that every technology shift has its own set of winners and losers. The advent of the car was initially rejectedby the public, and even ridiculed by horse owners. The only difference, is that the pace of advancing technology is now much quicker than it was in the past. When we do not understand a technology, we automatically tend to demonize it.

Similarly with AI, being able to define it and have awareness towards how it is impacting various industries for positive change will offer a more profound understanding that may ease concerns and lead it to be more widely accepted.

What is AI?

The first step in busting AI myths is to arrive at a reasonable, inclusive and thoughtful definition of the term.

Oxford Dictionary defines AI as, “The theory and development of computer systems able to perform tasks that normally require human intelligence. Examples include tasks such as visual perception, speech recognition, decision making under uncertainty, learning, and translation between languages.”

This definition is effective because it makes clear that AI is, in many instances, simply streamlining a process to make it more efficient. And while it is executing tasks that “require human intelligence,” the tasks themselves – like mass data analysis or translation, complex calculations or immediate responsiveness – are rarely those which people are otherwise capable of or willing to perform.

The Robot Workforce

There are concerns about how AI will impact the workforce and the global economy. For example, some fear that the rise of AI will lead to the replacement of jobs. In fact, researchers at Oxford University projected that 47 percent of U.S. employment may end up “at risk” with the expansion of AI.

However, it is important to keep an open mind on the opportunities it presents.

AI alone, is not enough. It requires humans to help AI understand language and make subjective decisions for a business. With the availability of online education, workers are able to receive the training and schooling that will present new employment opportunities. There are tailored courses for data scientists or machine learning engineers specifically designed to assist with AI.

While the concern exists that a sizeable number of jobs across all levels will be displaced by AI, a new study from Forrester Research argued that the development of AI and automation will actually transform and advance current jobs as humans get familiar working alongside their machine counterparts. Furthermore, Forrester estimated that in the next decade, 15 million new jobs will be created in the US as a result of AI and automation technology.

As the workforce modernizes, the door will open for new, previously unexplored jobs.

Change for Good

Healthcare is seen as one of the industries which will see tremendous benefits from AI-powered tools.

At a recent Stanford University conference, Andy Slavitt, former acting director, Center for Medicare and Medicaid Services (CMS), said that the expansion of AI in healthcare is designed to address productivity concerns. Specifically, “We need to be taking care of more people with less resources, but if we chase too many problems and business models or try to invent new gadgets, that’s not going to change productivity. That’s where data and machine learning capabilities will come in.”

CB Insights reported that there are now over 100 AI-based healthcare startups. The companies have wide-ranging aims, from aiding oncology treatment to reducing administrative responsibilities for doctors and nurses to powering digital journaling tools. In each case, AI is enhancing productivity through machine learning and deep data analysis.

This is precisely why AI-anxiety is misguided. These are unexplored tools, each of which has the potential to revolutionize healthcare delivery and improve outcomes for the issue which they are intended to address.

As the healthcare industry undergoes several paradigm shifts – from fee-for-service to value-based care, impersonal to precision medicine, traditional to digital healthcare delivery – AI is becoming essential. There are, for example, an overwhelming number of cancer variations that depend upon one’s family history, upbringing, DNA, environment, work and medical history. Coordinating care delivery and analyzing treatments and outcomes is essential, but with finite manpower and resources, impossible without the use of AI.

AI is a catch-all and a flashpoint, a source of concern and of intrigue. But it does not need to be. Instead, it should be recognized for what it is – a state-of-the-art way to utilize limited resources to advance an industry. It is not without its share of concerns, like other innovation groundswells before it. But it is also not an issue to be feared.

Eliezer Yudkowsky, an American AI researcher and writer who champions friendly AI, wrote in Singular Hypotheses: A Scientific and Philosophical Assessment, “By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” Misinformed generalizations that lead to stigmatizing attitudes, fears and misconceptions, limit the public’s scope to further the conversation surrounding the benefits of AI development.

With a broader and deeper understanding of the technology and the opportunities it presents, I am hopeful that fear and hesitation will become excitement. Unlike it had initially been thought to be, AI is not all doom and gloom.

Source: itproportal-AI Demystified: Shaping the future for positive change

What if Humans are Really the Ones Taking Jobs Away from Robots?

Many people view the growth of AI as a threat to their current employment, but for some it is actually an opportunity.

There’s a lot of talk and fear these days about robots taking jobs away from humans. As AI and machine learning grows, we hear more stories of robots replacing humans when it comes to customer service, transportation, financial management, and more and even holding positions on boards of directors and as managers. A report from Forrester found that robots will eliminate 6% of all U.S. jobs by 2021, which can be a cause for concern for employees in at-risk industries like logistics, consumer services, and customer service. Between driverless cars, chatbots, and smart home appliances and concierges like Alexa and Siri, there’s lots of room for growth in the AI field that can make life easier, streamline processes, and take humans out of the picture. Some people view it as an opportunity, but for many people it is a serious threat to their current employment.

But here’s another way to look at it: instead of robots taking jobs away from humans, what if humans are actually taking jobs away from robots? Consider this: Over the last few decades, companies have been organized to focus on process and maintain the status quo. Between standardized workspaces, rigid rules, and traditional HR programs, the focus was on getting work done and producing results with little thought to the individual employee. We all worked the same schedule, did the same type of work, and took breaks at the same time. If you’ve ever set foot in a traditional customer service call center, you can tell this is true, as employees simply fill a chair and are easily replaceable by someone else who can work the same schedule. These process-centric, homogeneous jobs are perfect for robots and automation, though we didn’t know it at the time. When we created these generic systems we were in effect just creating holding patterns until robotic technology was ready to take over. Just like the old factories full of workers from the 19th and 20th centuries have been replaced in large part by assembly lines and machines once the technology was available, so too are conditions that use humans where robots could easily do the job.

When we think about it that way, it’s not AI and robots taking away jobs from humans because humans were only the placeholders in the business framework until AI technology came along that could reclaim the jobs that had been designed for it in the first place. As a result, many organizations today are re-thinking and re-designing what they do with employees. This change creates lots of opportunities for human employees to try something new, branch out into new areas, and take advantage of new changes. Instead of the traditional definition of work as an 8-5 job in a cubicle, we’re now expanding our minds to think about work in a variety of ways–it can be anything from the traditional model to a flexible schedule, freelance-based workload, tele-commuting, or working in positions that didn’t exist just a few years ago. Now that we are done being placeholders for robots and automation, we can find more creative ways to apply ourselves within our organizations. For all the talk about robots taking away jobs, it is really allowing the us the opportunity to create new jobs and to change and expand how we think about work.

Robots will undoubtedly continue to take over jobs that have long been filled by human placeholders, but them doing so shouldn’t be viewed as a threat to humans in the workplace–instead, let’s consider it an opportunity for humans to play a more integral role in our organizations.

Source: inc.com-What if Humans are Really the Ones Taking Jobs Away from Robots?

Top RPA Conversations So Far for 2017

Earlier this year, we made several interesting predictions as to what would dominate the conversation throughout 2017 in the automation landscape as part of a preview for the year in RPA. After consulting with industry thought-leaders, analysts, and RPA specialists, we zeroed-in on a handful of critical topics poised to drive important discussions about RPA trends and developments. As we pass the midpoint of the year, we thought it would be interesting to revisit our predictions and determine which conversations have not only been taking place, but have also proved to be critical talking points for those within the automation industry.

Why is this look back to the beginning of 2017 in terms of automation so important? A 2016 study from the industry research firm Gartner suggested the demand for RPA tools is growing quickly…”at about 20 percent to 30 percent each quarter.” This growth was projected to continue into 2017 and thus far industry analysts believe it has, at least through Q1 and Q2 of this year.

With these statistics and figures in mind, let’s take a moment to check the pulse of the automation industry thus far in 2017 by considering which predictions and projections have evolved to be critical conversations in RPA.

RPA deployment will increase across new industries

At the outset of 2017, RPA adoption patterns were poised to experience a significant shift as companies in new and emerging industries discovered the power of automation solutions to enhance their business operations. While the adoption of automation technologies has functioned in somewhat of an opportunistic manner by early adopter companies, wider adoption moments have come to the forefront in such industries as healthcare, insurance, banking, manufacturing, and retail. This also dovetails with industries that have already experienced the benefits of RPA increasing their deployments into the front-office and other customer-facing tasks, actions which have yet to be fully realized by many companies.

In a 2016 report that surveyed the adoption of eight new technologies — including robotics and automation — by the professional services firm Deloitte suggested that:

More companies are increasing investments in these technologies. New technology investments over $1 million have increased…[and some companies are planning to] spend at least $100 million on new technologies over the next two years.

Rather than just being a way for companies to streamline their business operations, RPA and other automation platforms are transitioning from a luxury to a necessity. This conversation has not only persisted as the year progressed, but the drumbeat for automation platforms as a key aspect of any given company’s operation strategy has only increased as global economies and industries become more and more connected.

Automation will replace more tasks, not actual jobs

The emergence and proliferation of automation has caused a certain degree of panic over the possibility these technologies could replace the need for human employees in the workplace. Especially as automation moves from the back-office to more customer-facing tasks, customer relations-related areas such as call centers and other customer service platforms have suddenly become the subject of how and when automation could render these functions less necessary from a manual intervention standpoint. However, as we’ve moved through 2017, this concern has continually been minimized be the actuality of automation deployment.

Automation certainly has the ability to replace certain tasks or remove human personnel from specific department or business moments, especially those that are tedious, repetitive, and time-consuming: in fact, that’s what it is meant to do. However, this doesn’t mean the entire workforce will be replaced by robots. In fact, a 2017 publication by the McKinsey Global Institute suggests that:

The right level of detail at which to analyze the potential impact of automation is that of individual activities rather than entire occupations. Given currently demonstrated technologies, very few occupations—less than 5 percent—are candidates for full automation. However, almost every occupation has partial automation potential.

This kind of automation paints a hopeful picture for the future, one where humans and automation work side by side, which is the critical point of this conversation: debunking the myth RPA will essentially replace all methods of human intervention. In point of actual fact, RPA and human personnel will work in conjunction with each other which will allow human employees to focus on higher-level tasks that are meaningful and interesting, thus creating a space for automation to work through more repetitive, high volume tasks.

AI and machine learning will advance RPA

It has long been discussed how intelligent technologies like artificial intelligence, cognitive computing, and machine learning are expected to develop in the coming years and decades. In addition, a critical element of discussion within the automation landscape is how these technological developments will integrate and bolster automation functionality.

In a recent discussion with Forbes, Ash Ashutosh, the founder and CEO of Actifio, predicted that:

Just as most companies evolved to include cloud capabilities and features, 2017 will bring machine learning to almost every aspect of IT…[these technologies] will usher in a new era of data understanding and analysis.

While this is certainly a salient point, what’s less often considered is how RPA solutions will combine with intelligent technologies to deliver even greater automation potential. No longer can these technologies be viewed as disparate from each other when companies consider automation and how RPA can help enhance their business operations.

But what’s perhaps most important in this discussion is how intelligent technologies will be able to learn and make decisions beyond their initial programming. This means they are able to learn from previous actions and deal with unforeseen exceptions in a business process. Because RPA is able to quickly generate and gather data, combining RPA with intelligent technologies means that the “learning” process can take place at accelerated rates. While these two technologies are only starting to be used together, the smart automation they can produce means that companies will be able to foster both increased productivity and creativity going forward.

Source: UIPath-Top RPA Conversations So Far for 2017

5 Pitfalls to Avoid with RPA Implementation

Do the benefits outweigh the costs? Do the liabilities cancel out the opportunities? Is there greater upside versus down? When it comes to deploying a robotic process automation (RPA) solution, these questions more often than not are answered on the positive side of the equation where the value proposition of RPA for companies in a variety of industries far exceeds any drawback. In today’s modern, global business climate, companies require some form of RPA solution to enhance operational functions, mitigate risks, and leverage a competitive advantage in crowded marketplaces.

Implementing an RPA software solution can be tricky and companies who do not fully understand the challenges can find themselves struggling to reap the benefits RPA provides; in fact, companies who do not address these challenges and create strategies to combat them can actually experience RPA as a negative value proposition. However, putting together an implementation team and creating a preliminary strategy to ensure smooth deployment is a company’s best bet to experience the greatest potential increases in productivity and revenue at the hands of RPA.

Let’s examine 5 pitfalls to avoid in RPA implementation and how companies can turn these pitfalls into opportunities to leverage growth and profitability.

Premature process automation plan

Most of the discussion around adoption of new technology tends to focus on slow acceptors and how business and IT resources cling to old ways of doing business – however, with RPA software the opposite is often true. Advantages are so drastic that the transition to automated processes takes place without clearly thinking things through the process. Process automation should be part – a focal part – of an overall process optimization strategy consisting of many elements or components. Before any process is automated, all processes should be filtered through a centralization, standardization and optimization filter. This doesn’t mean obvious automation opportunities should be delayed and it doesn’t mean centralization, standardization and optimization has to come first (in fact, automation savings may have to fund those efforts). But this does mean automation is directed only at relevant and well-qualified business processes.

Operational oversight

One of the great strengths of automation is accuracy – the fact that configured software never deviates from its algorithms. Of course, this can also be a critical weakness. Should any internal or external aspect of a business process change, the software will likely fail to perform. In order to avoid automation failures, changes or alterations will have to be planned, communicated, tested, and executed within a strong operational oversight framework. This will be an organizational and 3rd party challenge because many business processes are undocumented and revisions occur less formally than in a governed change control process. This level of operational oversight is rarely conducted successfully in a reactive mode, which means the framework should be proactively put in place before any deployment begins.

Optimism in effort

At the end of the day, robotic process automation software is robotic process automation software. The roadmap for RPA deployment should reflect a more conservative optimistic in effort than guidelines stipulate from product vendor or third-party advisory firms. Despite all the talk about the ease of implementation, the fact remains automation software is not quite that user-friendly and quick to configure. The fault lies in the nature of business processes and the fact software is literal – it can do only what it’s been told to do. Because humans are anything but literal, there are often missing rules in a process simply because the decisions-making only resonates with the human operator. It will take time for current process employees to identify and document the innate rules they follow without consciously thinking about them.

New or added roles

Via process automation, changes or turnover within a company or organization is a means to an outcome but not the outcome itself (though accuracy, efficiency and costs will improve). This means it’s important the deployment process incorporate new roles well beforehand. New team leads will be required to manage the output of robots rather than FTEs. This lead will have a variety of new and expanded responsibilities: maintaining and improving service levels for the human interventions needed for process exception-handling; a liaison with the IT Dept; and turnover management, proactively re-configuring robotic software in coordination with changes in process activities and rules. Depending upon the size of the deployment, a management slot above the lead position may be needed to implement the rollout across other business units.

Overconfidence in ability

While deploying robotic software to automate business processes entails risk and requires an investment of human and financial capital, the companies to undertake such deployment stand to experience two attractive benefits: the associated savings and cost-reduction, and complete operational control. An early part of the strategic plan should question whether automation should be done in-house or by a BPO provider. Experienced BPO providers have robust service offerings with well-integrated capabilities across technology, process optimization, change management, automation technology, and labor arbitrage. Outsourcing process automation does present its own set of issues, particularly the risk of becoming locked into a provider’s technology. With this technology changing so rapidly, entering into a long-term contract with a third party provider has to be balanced against an objective measurement of a company’s internal capability to do the job itself.

Source: UIpath-5 Pitfalls to Avoid with RPA Implementation

Robotic Process Automation’s Reach Expands in the Enterprise

According to a new report by Constellation Research, you don’t need to look hard to find examples of robotic process automation in the enterprise PHOTO: Max Kiesler

Wang, principal analyst and co-founder of Cupertino, Calif.-based Constellation Research, told CMSWire in an interview that robotic process automation (RPA) exists in practically any business function you can automate with a software robot.

“It’s in everything,” Wang said. “Anything from customer experience testing, to what’s happening with something like insurance claims testing algorithms. If you can program a software requirement into it, you can pretty much do it.”

RPA, Your ‘Virtual Worker’

Wang wrote a report on RPA released this month, “Robotic Process Automation: Automation Technologies That Will Transform the Enterprise.” It was part of the Constellation Shortlist, enterprise software vendor reports the analyst firm churns out every six months.

In the RPA shortlist report, its first in this space, Constellation identified seven RPA “solutions to know”: Blue PrismEdgeVerveAutomation AnywhereRedwood SoftwareThoughtonomyUiPath and WorkFusion.

Constellation researchers determined the vendor shortlists through conversations with early adopters, independent analysis and briefings with vendors and partners.

Constellation defines RPA as technology that “trains software robots to interpret the user interfaces of applications through demonstrative steps.”

“Instead of creating another coding platform for IT users,” Wang wrote in Constellation’s RPA report, “the software robot emerges as a virtual worker that can be easily configured or trained to perform work. RPA should be code-free approaches that allow configuration through visual process maps and process definitions.”

RPA allows humans to focus on “higher-level skills” as they are “redeployed to do more interesting and advanced work.”

Source: CMSWire-Robotic Process Automation’s Reach Expands in the Enterprise

A Survey of 3,000 Executives Reveals How Businesses Succeed with AI

 

he buzz over artificial intelligence (AI) has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Investment in AI is growing and is increasingly coming from organizations outside the tech space. And AI success stories are becoming more numerous and diverse, from Amazon reaping operational efficiencies using its AI-powered Kiva warehouse robots, to GE keeping its industrial equipment running by leveraging AI for predictive maintenance.

While it’s clear that CEOs need to consider AI’s business implications, the technology’s nascence in business settings makes it less clear how to profitably employ it. Through a study of AI that included a survey of 3,073 executives and 160 case studies across 14 sectors and 10 countries, and through a separate digital research program, we have identified 10 key insights CEOs need to know to embark on a successful AI journey.

Don’t believe the hype: Not every business is using AI… yet. While investment in AI is heating up, corporate adoption of AI technologies is still lagging. Total investment (internal and external) in AI reached somewhere in the range of $26 billion to $39 billion in 2016, with external investment tripling since 2013. Despite this level of investment, however, AI adoption is in its infancy, with just 20% of our survey respondents using one or more AI technologies at scale or in a core part of their business, and only half of those using three or more. (Our results are weighted to reflect the relative economic importance of firms of different sizes. We include five categories of AI technology systems: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.)

For the moment, this is good news for those companies still experimenting or piloting AI (41%). Our results suggest there’s still time to climb the learning curve and compete using AI.

However, we are likely at a key inflection point of AI adoption. AI technologies like neural-based machine learning and natural language processing are beginning to mature and prove their value, quickly becoming centerpieces of AI technology suites among adopters. And we expect at least a portion of current AI piloters to fully integrate AI in the near term. Finally, adoption appears poised to spread, albeit at different rates, across sectors and domains. Telecom and financial services are poised to lead the way, with respondents in these sectors planning to increase their AI tech spend by more than 15% a year — seven percentage points higher than the cross-industry average — in the next three years.

Believe the hype that AI can potentially boost your top and bottom line. Thirty percent of early AI adopters in our survey — those using AI at scale or in core processes — say they’ve achieved revenue increases, leveraging AI in efforts to gain market share or expand their products and services. Furthermore, early AI adopters are 3.5 times more likely than others to say they expect to grow their profit margin by up to five points more than industry peers. While the question of correlation versus causation can be legitimately raised, a separate analysis uncovered some evidence that AI is already directly improving profits, with ROI on AI investment in the same range as associated digital technologies such as big data and advanced analytics.

Without support from leadership, your AI transformation might not succeed. Successful AI adopters have strong executive leadership support for the new technology. Survey respondents from firms that have successfully deployed an AI technology at scale tend to rate C-suite support as being nearly twice as high as those companies that have not adopted any AI technology. They add that strong support comes not only from the CEO and IT executives but also from all other C-level officers and the board of directors.

You don’t have to go it alone on AI — partner for capability and capacity. With the AI field recently picking up its pace of innovation after the decades-long “AI winter,” technical expertise and capabilities are in short supply. Even large digital natives such as Amazon and Google have turned to companies and talent outside their confines to beef up their AI skills. Consider, for example, Google’s acquisition of DeepMind, which is using its machine learning chops to help the tech giant improve even core businesses like search optimization. Our survey, in fact, showed that early AI adopters have primarily bought the right fit-for-purpose technology solutions, with only a minority of respondents both developing and implementing all AI solutions in-house.

Resist the temptation to put technology teams solely in charge of AI initiatives. Compartmentalizing accountability for AI with functional leaders in IT, digital, or innovation can result in a hammer-in-search-of-a-nail outcome: technologies being launched without compelling use cases. To ensure a focus on the most valuable use cases, AI initiatives should be assessed and co-led by both business and technical leaders, an approach that has proved successful in the adoption of other digital technologies.

Take a portfolio approach to accelerate your AI journey. AI tools today vary along a spectrum ranging from tools that have been proven to solve business problems (for example, pattern detection for predictive maintenance) to those with low awareness and currently-limited-but-high-potential utility (for example, application of AI to developing competitive strategy). This distribution suggests that organizations could consider a portfolio-based approach to AI adoption across three time horizons:

Short-term: Focus on use cases where there are proven technology solutions today, and scale them across the organization to drive meaningful bottom-line value.

Medium-term: Experiment with technology that’s emerging but still relatively immature (deep learning video recognition) to prove their value in key business use cases before scaling.

Long-term: Work with academia or a third party to solve a high-impact use case (augmented human decision making in a key knowledge worker role, for example) with bleeding-edge AI technology to potentially capture a sizable first-mover advantage.

Machine learning is a powerful tool, but it’s not right for everything. Machine learning and its most prominent subfield, deep learning, have attracted a lot of media attention and received a significant share of the financing that has been pouring into the AI universe, garnering nearly 60% of all investments from outside the industry in 2016.

But while machine learning has many applications, it is just one of many AI-related technologies capable of solving business problems. There’s no one-size-fits-all AI solution. For example, the AI techniques implemented to improve customer call center performance could be very different from the technology used to identify credit card payments fraud. It’s critical to look for the right tool to solve each value-creating business problem at a particular stage in an organization’s digital and AI journey.

Digital capabilities come before AI. We found that industries leading in AI adoption — such as high-tech, telecom, and automotive — are also the ones that are the most digitized. Likewise, within any industry the companies that are early adopters of AI have already invested in digital capabilities, including cloud infrastructure and big data. In fact, it appears that companies can’t easily leapfrog to AI without digital transformation experience. Using a battery of statistics, we found that the odds of generating profit from using AI are 50% higher for companies that have strong experience in digitization.

Be bold. In a separate study on digital disruption, we found that adopting an offensive digital strategy was the most important factor in enabling incumbent companies to reverse the curse of digital disruption. An organization with an offensive strategy radically adapts its portfolio of businesses, developing new business models to build a growth path that is more robust than before digitization. So far, the same seems to hold true for AI: Early AI adopters with a very proactive, strictly offensive strategy report a much better profit outlook than those without one.

The biggest challenges are people and processes. In many cases, the change-management challenges of incorporating AI into employee processes and decision making far outweigh technical AI implementation challenges. As leaders determine the tasks machines should handle, versus those that humans perform, both new and traditional, it will be critical to implement programs that allow for constant reskilling of the workforce. And as AI continues to converge with advanced visualization, collaboration, and design thinking, businesses will need to shift from a primary focus on process efficiency to a focus on decision management effectiveness, which will further require leaders to create a culture of continuous improvement and learning.

Make no mistake: The next digital frontier is here, and it’s AI. While some firms are still reeling from previous digital disruptions, a new one is taking shape. But it’s early days. There’s still time to make AI a competitive advantage.

Source: HBR-A Survey of 3,000 Executives Reveals How Businesses Succeed with AI

AI projects are taking off: What does this mean for the future of work

he robots are coming and the world of work is set to change forever: recent research from consultants PWC estimates a third of existing jobs are susceptible to automation, due to the use of robots and artificial intelligence (AI) by 2030.

The survey adds more weight to a fast-growing body of work on the impact of AI. Take KPMG’s recent global CIO survey in conjunction with recruiter Harvey Nash, which found almost two-thirds of CIOs are investing or planning to invest in digital labour, which broadly covers robotics, automation, and artificial intelligence.

A quarter of these technology chiefs have already see very effective results. The survey suggests digital leaders are investing in digital labour at four times the rate of other executives. These CIOs are also implementing digital labour solutions across the enterprise, in some cases at twice the rate of their less-pioneering peers.

Understanding the scale of change

 

There are some things that machines are simply better at doing than humans, but humans still have plenty going for them. Here’s a look at how the two are going to work in concert to deliver a more powerful future for IT, and the human race.

However, it is important not to overemphasise the pace of change. Lisa Heneghan, global head of KPMG’s CIO advisory practice, says we are still in the early days of AI. Digital leaders are using robotic process automation to deal with repetitive manual processes, such as claims processing and data entry. Heneghan says spending decisions around more advanced digital capabilities, such as machine learning, are still to be made.

“We’re seeing pilots and small amounts of investment, but that’s not where the money is now,” she says, referring to the CIO’s role in assessing AI. “We’re seeing CIOs start to focus on building centres of excellence around digital labour. When they build this centre, it enables CIOs to look at the opportunities from digital across their business.”

First Utility CIO Bill Wilkins, for example, has created a customer-driven approach to data analytics and continues to look for new ways to help his business grow through technology, including via AI and automation. Wilkins says that, to remain competitive, his organisation must continue to innovate through information.

It is a sentiment that chimes with Brian Franz, chief productivity officer at Diageo, who has responsibility for the firm’s shared services around the world. His chief priority centres on driving efficiency and effectiveness in a sustainable manner — and that work aims to makes the most of advanced technology, including AI.

“We’ve started using robotics in some of the processes that we run in shared services where we have a level of confidence that we’ll be successful,” he says, referring to the firm’s initial forays into automation. “We’re looking at AI in some experimental ways in terms of how we interact with consumers and how they interact with our brands.”

Mark Ridley, group technology officer at venture builder Blenheim Chalcot Accelerate, is another CIO who is keen to establish how machine learning and automaton might be used within the IT department and out across the wider business. For now, Ridley says AI is a great way of making many decisions quickly.

He expects that focus to remain until businesses start seeing the benefits of developments in deep learning. “That will obviously take a few years to become pervasive. But when it does, AI will start making trivial decisions instead of humans and that will be very interesting,” says Ridley.

“That level of development will have a huge impact on work. How do we, as senior managers, deal with a society where human input isn’t necessary for simple tasks? The combination of AI and robotics represents a very interesting area to consider when it comes to the future of humanity.”

Preparing for the transformation

What is true, therefore, is that the various flavours of AI will have an impact on the way that businesses and their employees work. Interim CIO Christian McMahon, who is managing director at transformation specialist three25, also believes the biggest short-term impact of AI is likely to be confined to the rapid completion of routine tasks.

Yet there is no room for complacency. McMahon says AI will eventually create a seismic shift in business operations and CIOs must build awareness. “I cannot stress enough the importance of putting the effort in now to give your organisation tangible competitive advantage in how it can use or acquire these technologies,” he says.

Other lines of business executives have a role to play, too. Peter Markey, chief marketing officer at TSB, is at the vanguard of digital marketing and has spent his career melding business data with customer requirements to help create innovative services. He expects AI to offer similar opportunities for CMOs, but agrees there is much work to be done.

“I love the idea but I don’t think we’ve really explored its full potential or limitation. Marketeers need to strike the balance between business and personal. Do you lose something by automating a marketing programme within an inch of its life?” says Markey.

“Machines will get better at learning but I don’t see a day where all interactions are replaced by AI. We could get close, of course. The key to how far AI develops in terms of marketing is understanding your customers’ demands and how they want to interact with your business.”

Scope CDO Mark Foulsham is similarly customer-focused and believes AI could help his charity make smarter decisions. He believes developments in machine learning and automation run alongside attempts to exploit big data. The charity is not using AI currently but Foulsham expects the organisation to take advantage of the technology as it develops its data insight strategy.

“There’s huge potential,” he says. “All charities are speaking to customers and donors across several channels. What they need to provide is a seamless and transparent experience across those channels.”

AI, says Foulsham, can help charities to take a more proactive approach. He anticipates a situation where AI technologies work in the background and help senior managers at charities to make timely interventions and to boost the level of service provided.

“As executives, we need to know how customers are acting,” says Foulsham. “We need to know what their needs might be and we need to make sure that they have a seamless experience when they connect to us, be that through web, mobile or whatever platform they choose to use. AI provides another potential means to that end.”

Source: ZDNet-AI projects are taking off: What does this mean for the future of work

Center of Excellence and Robust Governance: Mantra for Successful RPA Adoption

To avoid unwanted obstacles and time consumption and ensure efficient process execution, it is important to have streamlined RPA adoption. To fully unleash the power of RPA technology, enterprises should spend effort in making the conducive environment, with robust governance program, and establishing best practices with CoE in place.

Robotic Process Automation (RPA), one of the most talked about disruptive technologies today, involves the use of software ‘bots’ that are easy to configure, require minimal to no code and can be quickly ‘trained’ and deployed, scaled to automate manual tasks. Their adoption is on a rise across all business functions providing them access to relevant processes and sensitive information.

More than 20% of the firms adopt RPA to enhance productivity while other 20% adopt the technology for increased efficiency. While RPA can transform the economics and service level of current manual operations, it is observed that many organizations struggle with initial RPA projects. This isn’t a reflection of the technology; but there are some common mistakes which could potentially prevent an organization from delivering on the promise of RPA, if not done right. Business disruption and inefficiencies are highly probable if RPA is not executed correctly. To reap full benefits of RPA it is important to have proper alignment to enterprise’s existing architecture, relevant training programs and adequate robotics security measure. Thus it is essential to have robust governance strategy in place.

Getting ready for successful adoption of RPA

Center of Excellence – Avoiding RPA silos

As the use of robotics scales from a pilot to a standard solution for the firm, it should be embedded in the organization rather than siloed. Enterprises should aim to establish Center of Excellence (CoE) in RPA space. A CoE enables employees at varied levels to be self-sufficient to control, manage and scale RPA efforts. Therefore, it should focus on six major aspects:

Lifecycle process – An aid in identifying right processes to prioritize and deploy RPA can lead to smooth adoption of the technology. A referral for on-going operations can further enhance overall lifecycle of bot adoption.

Alignment and change – Best practice to communicate organizational changes related to RPA adoption along with relevant skill development procedure can ensure readiness of enterprise to embrace the RPA adoption.

Technology – It is important not to assume that a single RPA tool will be suitable for all enterprise needs. While adopting RPA technology, it is imperative to have correct infrastructure in place.  A robust vendor management program and expert network can further guarantee increased technological know-how.

Enterprise integration – To ensure RPA is well integrated in the enterprise architecture, it is vital to establish pertinent IT processes and security measures.

Value measurement – Once RPA is adopted in an enterprise, it is imperative to continuously review its performance and relevance for the enterprise. Operational and Performance Metrics along with Benefit Measurement and Reporting assist in timely value measurement.

Strategy and Governance – Robotic adoption call for transparent and accurate decision making process as it involves enterprise-wide deployment. The CoE is vulnerable to encountering obstacles associated with cross-departmental interaction and collaboration. This calls for an effective governance process that the CoE will follow to facilitate collaboration and communications.

Robust governance policies underpin a successful robotics implementation. Adequate controls must be present to ensure regulatory compliance, user access management and authorization and protection from cyber threats.

Governance framework – Backbone of RPA adoption

Once a suitable process is identified for RPA pilot and awareness is spread through robust communication and training mechanism, the enterprise is almost ready for RPA adoption. However to make the adoption effective, it is important to establish a strong central governance process to ensure standardization and prioritization of right opportunities.

Regulatory governance – Bots are essentially new systems and should adhere to regulatory environment of the enterprise. Compliance requirement around data privacy and security, Risk & Control Matrices (RACM) and IT general controls (ITGC) should be diligently followed.

Cyber governance – Robust governance is required around cyber security as bots are exposed to cyber threats. An unauthorized access to bots can have serious implications on security of critical data. Bot identity, access and authorization management along with threat modeling and vulnerability management for important part of cyber governance.

Tech governance – Integration of bots in enterprise architecture, user access management and access authorization are imperative for secure adoption of RPA. Under this governance, logical access policy for RPA should be developed; process to perform periodic access review in RPA and target application should be determined; and secure configuration policies should be put in place.

Further, regular governance related to sizing of hardware for RPA and virtual servers, sizing the bandwidth of network for RPA, backup and recovery options for RPA are essential. Developing release management process for RPA, defining roles and responsibilities and overall workforce management is crucial for RPA program.

Operative governance – Should any internal or external aspect of a business process change, the bots require real-time updates. Business continuity can take a serious hit in case the bots are not updated. Appropriate policies related to day-to-day operations help in removing ambiguity and result in lower failure rates. In order to avoid automation failures, changes should be planned, communicated, tested with RPA system and made within a strong governance framework.

Program management – While adopting RPA, it is essential to ensure business alignment and have resilient organization change management in place. Value measurement, return on investment reporting and risk management in a continuous manner increases reliability of RPA program.

Getting RPA right!

To avoid unwanted obstacles and time consumption and ensure efficient process execution, it is important to have streamlined RPA adoption. To fully unleash the power of RPA technology, enterprises should spend effort in making the conducive environment, with robust governance program, and establishing best practices with CoE in place.

Source: bwdisrupt.businessworld.in-Center of Excellence and Robust Governance: Mantra for Successful RPA Adoption