New Independent Study Reveals Why Not All Software Robots Are Created Equally

By Leslie Willcocks

Robotic Process Automation (RPA) continues to be a growing success story. In 2016, RPA alone experienced a 68 percent growth rate in the global market, with 2017 maintaining this momentum. Some reports have even predicted a US$ 8.75 billion market by 2024. However, merely investing in RPA is not an instant recipe for growth.

In “Service Automation Robots and The Future of Work” (2016), my colleague Mary Lacity and I highlighted successful RPA deployments and how organizations were achieving triple wins for their shareholders, customers, and employees alike. We continued tracking these developments in 2017 and also noticed something different — many less successful journeys. In practice, it appears that automation success is far from guaranteed. Wider reports provide anecdotal evidence of between 30 to 50 percent of initial projects stalling, failing to scale, being abandoned or moving to other solutions. Our most recent research has examined in detail both successful and more challenged automation deployments. It turns out that service automation — like all organizational initiatives that try to scale — can be fraught with risk. We’re seeing 41 specific risks that need to be managed in eight areas: strategy, sourcing, tool selection, stakeholder buy-in, project execution, change management, business maturity and an automation center of excellence.

One of the key risk areas is tool/platform selectionBecause of the hype and confusion in the RPA marketplace, clients risk choosing the wrong tool(s), too many tools, or bad tool(s). By early 2018, over 45 tools or platforms were being sold as “RPA” and over 120 tools were being sold as some form of cognitive automation. Because the space is relatively new to many clients, it’s difficult to assess the actual capabilities and suitability of these tools. Clients must be wary of hype and “RPA washing”.

In our new report on Benchmarking the Client Experience, we extensively polled clients at Blue Prism on the results they’ve been getting by integrating RPA into existing business processes. In order to get the most valuable feedback, we set the bar high in requesting client assessments of the Blue Prism RPA platform on the following criteria: scalability, adaptability, security, service quality, employee satisfaction, ease of learning, deployment speed and overall satisfaction. From our qualitative research into process automation, these emerged as the most critical and essential characteristics and requirements for a successful enterprise-grade RPA implementation.

The overall level of satisfaction with the Blue Prism platform was extremely high in our survey. Respondents reported a 96 percent overall satisfaction rate, with 79 percent of respondents ranking Blue Prism’s platform a six or seven on a seven-point Likert Scale. Based on our 25-year research history into process improvement initiatives (BPM, shared services, outsourcing, six sigma, etc.), these are extremely high RPA satisfaction levels. Our research into IT and Business Services outsourcing finds only 20 percent of vendors getting “world class” performance, 25 percent getting good performance, 40 percent “doing OK”, while 15 percent experience poor outcomes. The record on IT projects also continues to frustrate. The most recent (2017) Standish Group CHAOS report found only a third of IT projects were successfully completed on time and on budget over the past year – the worst failure rate the Standish Group has recorded.

What, then, accounts for the impressive 96 percent overall satisfaction rate with Blue Prism?

Our observation is that not all RPA offerings are the same. The capability of RPA software depends greatly on the origins and orientations of the supplier. If designed as a desktop assistant, many RPA tools experience problems with scaling, security and integration with other information systems. Other RPA vendors offer RPA which is effectively a disguised form of what we have described as a “software-development kit,” needing a lot more IT development by the in-house team or the RPA vendor than first imagined, and incurring unanticipated expense, time and resources. True enterprise RPA, however, is designed from the start with a platform approach, to fit with wider enterprise systems. This might make it more expensive initially, and require more attention in the first few months of trial, but true enterprise RPA platforms have proven to be an investment in success later in the deployment cycle, when compared to other RPA software that tends to run into real problems.

Our qualitative research also suggests that some RPA tools are not easily scalable, especially those based on a recording capability, or requiring a lot of IT development. This occurs because some RPA tools are not designed as configurable service delivery platforms that can be integrated with other existing systems. These also need a lot more management involvement than clients and their vendors often expect. Many clients, moreover, do not put in place the necessary IT, project and program governance (rules and constitution, who does what, roles and responsibilities), and often do not use built-in tools that contain technical governance.

This, of course, is not the whole story. An RPA and cognitive skills shortage is already upon us. This means that retained capability and in-house teams are sometimes not strong enough – a situation not helped by sometimes skeptical senior management under-resourcing automation initiatives and not taking a strategic approach. Consultants are also hit by skills shortages and cannot always provide the support necessary — this is also true with business services outsourcing providers. We are also finding that clients often do not give enough attention to stakeholder buy-in and change management. Given these emerging challenges, the Blue Prism client satisfaction level are very notable indeed.

To download the report, click here.

Leslie Willcocks is Professor in the Department of Management at the London School of Economics, and co-author, with Mary Lacity, John Hindle and Shaji Khan, of the Robotic Process Automation: Benchmarking The Client Experience (Knowledge Capital Partners, London).

Source: Independent Study Reveals Why Not All Software Robots Are Created Equally


Robotics Process Automation: 5 Lessons Learned on How to Get Started

I suspect that a high percentage of people reading this article are already familiar with the basics of Robotics Process Automation (RPA), and the significant cost savings and efficiencies that RPA can bring to your organization by automating high volume, transactional processes in multiple functions including Finance, HR and IT.

Now that it’s clear that you need to start evaluating RPA and how to implement it in your organization, I’m sure there’re many open questions about how to get started. The purpose of this article is to briefly point out five key lessons learned on how to jump start a Robotics Journey, based on our experience as both an advisor and a managed services operator of RPA:

1. Don’t drink the vendor “Kool-Aid”

As Gartner cited in a recent RPA study, “The expectations of robotic process automation (RPA) are as significant as the confusion about the technical capabilities of the RPA tools themselves.”

Many RPA software vendors will try to convince you that almost anything can be automated and tend to oversimplify the actual deployment process. While the power of their tools can’t be denied, programming a dysfunctional process wastes time and effort, and is less stable upon deployment. In our experience, processes will often require some degree of standardization or redesign during the automation journey.

Plus, RPA vendors often talk about processes that require dozens of robots and hundreds of workers. While these processes and operations clearly exist (e.g. Financial Services), and the benefits can be significant, they are not the “norm” for most businesses. Most companies we have spoken with are looking to leverage RPA for back office processes that often utilize dozens of employees, not hundreds. In these scenarios, the investment in RPA may not yield the ROI that the vendors are touting, and a more practical implementation approach is required.

Deep business process expertise and knowledge of your back office environment is equally as important as selecting the right RPA tool. Process expertise and knowledge of your back office do not come from the RPA software provider. And their business case methodology needs to be adapted to a much smaller footprint for most businesses.

2. Partner with an RPA expert

This second lesson learned goes hand in hand with the first. Many organizations try to implement robotics by themselves, with no internal or external RPA expertise. Companies often do not realize this can cause their RPA initiatives to go off track, take longer and cost more money.

We’ve seen how some companies have chosen the wrong RPA tool because they didn’t get the right advice. They just selected a solution based on price or market presence, and then realized it had many limitations, was not scalable, or simply did not deliver the expected (and needed) ROI.

Another common mistake is for companies to task one of its employees to lead the RPA initiative, but the person is so embedded in the day-to-day operation, and the way processes currently operate, that they lack the necessary objectivity, and fresh eyes to identify how activities can be changed and adapted to RPA.

Most RPA advisors or managed services providers can quickly assess your organization, identify the key automation opportunities and provide a realistic implementation timeline based on having delivered these initiatives multiple times. This initial assessment will provide you with a good estimate of the savings and efficiency opportunity before you decide to embark on this journey.

3. Understand your deployment model options

RPA can be deployed in a model where it is managed internally or through an outsourced Managed Service model. An organization will need to determine which RPA deployment model fits its “DNA.” Many midsize firms do not internally possess the process excellence, technical expertise, and change management skill set to manage the deployment internally successfully.

If built internally, the firm will need to document processes, train the robot (through development and configuration) to perform the process, and build an ongoing support function to monitor the robots and continually reconfigure the robot as systems and process evolve. Their IT departments may be too overloaded managing their existing systems and other priorities and do not have time or resources needed to support the RPA software and ongoing environment adequately. It is often less costly for these firms to turn to an Outsourced BPO Provider to build these capabilities as part of their scope of services.

4. Embrace your workforce

It’s not a secret that the impact of RPA on your workforce is huge (according to ISG Insights the rate of RPA/AI adoption is set to double by 2019). But this doesn’t mean your employees should feel disengaged with the initiative.

Whether they want it or not, automation is going to come sooner or later, and the sooner they get involved with it, the bigger competitive advantage they will have if they want to make a career within the back office and process optimization. If not, they better look at shifting their career path.

RPA requires a new set of skills and capabilities that will need to be learned and performed by someone, ideally by those “operators” currently executing the tasks manually. In the end, the tasks to be automated are those that most employees would gladly relinquish. Educating employees on how RPA will allow them to focus their time on higher value-added activities will help get them on board. It is not uncommon for your highly engaged best performers to embrace the change and jump at the opportunity to perform higher value work, while less engaged bottom performers resist change and look for an exit.

5. Start small

It is important to realize that RPA should be viewed as an integral component of your back office operation going forward, and not as a “project.” The key to success is to start small, prioritize initiatives and build momentum towards full deployment over time. An initial “Fit Analysis” can quickly identify & prioritize processes that deliver the most benefit with the least amount of complexity.

A typical best practice is to perform an initial Proof of Concept by automating a small number or processes with limited investment. If successful, the Proof of Concept confirms the value of Automation while also delivering savings to fund additional deployment. Additionally, success will build organizational and executive buy in. Any major change initiative needs quick wins to build and sustain the momentum needed to push through an organization’s inherent discomfort with change. It also creates an opportunity to reflect on lessons learned and adjust the implementation strategy based on the initial experience.

After a successful pilot, you are now ready for a broader deployment across the organization. This phase of the RPA journey involves an iterative process of business analysis, BOT development, and configuration, training, and testing. Incorporating the lessons learned from the pilot. During this time you should also be building a support structure for ongoing operations and management. The BOTS are now part of your ongoing operation and like other aspects of your business require continuous monitoring, support, and tweaking. The benefits of RPA can be significant for an organization, but like anything else, it requires commitment, investment, and planning.

Our experience has shown that the mistakes that companies make in implementing RPA are from under-estimating the effort and knowledge needed to implement it. Having realistic expectations, and good guidance and support, coupled with a well-thought out implementation strategy and a rigorous deployment methodology, will help to ensure RPA success.

Watch this quick demo for a real-world example of RPA in action.

Ready to start your RPA journey?

Source: Process Automation: 5 Lessons Learned on How to Get Started

Building An RPA Project Specification Document

Most successful RPA projects emanate from a good design. Regardless of one’s preferred method for arriving at that design (e.g. Agile, Waterfall, etc.), the process should include the development of a formalized specification document (spec), that details the road ahead and builds project team consensus. While this might seem obvious to the experienced developer, RPA’s new-found celebrity has attracted a lot of new and eager practitioners whose enthusiastic desire to produce quick wins might also encourage some short-cutting. The purpose of this article is to articulate some the best practices our professional services team has identified over the years when it comes to building an RPA specification document.

Just the facts. The most important aspect of constructing the spec document is that it precisely captures just the process being automated. While “color commentary” can be helpful to the PD (Process Designer), when trying to understand the process and assessing potential improvements, such commentary should not be included in the spec. The goal of the document is to focus the AA (Automation Architect), only on the aspects of the process that are required to complete the transaction. All information beyond what is needed to complete the process is often considered noise. In other words, the AA cares about the: “who, what, which and where”, and not much about the “why”. While the spec must contain as many details as possible regarding the transaction, the details should focus on:

  1. Where to start?
  2. Which screens to navigate?
  3. Which user interface controls to manipulate?
  4. What data should be extracted and/or pasted?
  5. How should data be manipulated?
  6. What application and screen states to look for?
  7. How to handle error conditions and exceptions?
  8. What state to leave the application in when the transaction completes?
  9. How should logging be performed?

Minimize jargon. The AA is usually not intimately familiar with the user’s business nor conversant in the specific language of the business. Therefore, keeping jargon down to a minimum is important. The best method is to try to relate jargon to standardized business terms most AAs do understand such as: invoices, purchase orders, inventory item, price, etc. If it is required to include jargon to help the customer team understand the spec, make sure you include a glossary of terms early in the spec.

Make each step as atomic as reasonably possible. Each individual function the automation must perform is called a “step”. In the spec, each step is uniquely numbered so it can be cross-referenced and tested individually, and linked back to when viewing logs. For this reason, it is important to define each discrete function the automation performs, (e.g. the pasting of data to one field, or the clicking of a button), as its own step, and not lump multiple functions together. Break each process down to its most reasonable atomic function. When I say “reasonable”, I mean the step that represents a logical unit of work that you would want to link back to from a log. Let us review a couple of simple examples.

Example 1: If a step calls for pressing the key combination, “Alt+K”, this should be expressed as one step, not two.

Example 2: What about if you want select a sub navigation menu bar that requires two sets of key presses (e.g. “Alt+F” and “O” to pop a File Open dialog). Should this be represented as one step or two? In this case, it makes sense to combine the key presses into one step since the logical unit of work is the popping of the File Open dialog. However, it would not be wrong to break the process down into two steps. It ultimately comes down to style and it is probably more important to represent these steps consistently in one given spec than to definitively handle them one way or another.

Steps should be numbered and sub-numbered. Well-designed automations can cross reference their functions back to the steps defined in a spec when a user is viewing an action execution log. This allows the user to easily figure out, using the language of the user, what the action was supposed to be doing at a specific point during the execution. However, this cross-referencing can only happen if the steps defined in the spec are uniquely numbered.

A picture is worth a thousand words. While narrative descriptions are important, nothing informs the AA more about the task at hand than a picture (see Figure 1).

Figure 1

A spec should make heavy use of screen shots to communicate information such as:

  • What should the state of the screen look like at this step?
  • Via highlights, which user interface (UI), elements are the elements to be manipulated in this step(s).

Other points to consider when working with screen shots:

  • Screen highlights should use colors that are not contained within the screen upon which they are overlaid and those colors should be used consistently throughout the spec.
  • It is a common practice to include the step number in a screen shot highlight for each UI element highlighted.
  • Screen shots usually embody references to multiple steps (i.e. multiple screen shots of the same screen should be avoided).

Spec the negative condition. Documenting a process where everything goes according to plan is easy. However, accounting for error or unexpected conditions can be more of a challenge. For example, a step that states; “4. Select part number from list.”. What should the automation do if the part number is not in the list? This is the kind of “negative” condition that should be accounted for in your spec. The more negative conditions you can capture in the spec (again, within reason), the less back and forth the AA will have with the user team for clarifications.

Seek out keyboard short cuts and mnemonics where possible. While most RPA tools support drag/drop and icon clicking, it is always faster and less subject to error when a keyboard shortcut or menu mnemonic is used in an automation. Although the AA will ultimately decide the best method for automating the user interface, calling out such shortcuts are always helpful.

Demarcate commentary via notes. Even though sticking to the facts is paramount when building a spec, there are times when some commentary is required (e.g. how something is calculated or the conditions under which certain states arise). In these cases, it is a best practice to not include the commentary in a step, but rather, break it out as its own “section note”.

Include an automation start state & preparation section if applicable (usually applicable to attended bot automations). If access to the development environment is proctored, then in most cases, the proctor should be able to navigate the AA to the application screens from which the automation is initiated. However, if access is not proctored, it is important to include in the spec (prior to the step definition), an automation start state section that includes the following:

  • Application load methods.
  • Login credentials for the applications.
  • Navigation path to get to the automation start screen.
  • Any data required to support the navigation path.

Include incomplete transaction rollback instructions. Some transactions commit data at specific steps prior to the completion of the process. If this is the case, the spec should include the process for rolling back the transaction to its pre-processing state. If the rollback conditions are only applicable to the testing of the action, it should be demarcated as a note. If the rollback must happen in production as well, it should be documented as its own set of steps.

Defines skills required to handle a prompt. This point applies exclusively to unattended bot projects. When an unattended bot encounters a processing condition that requires assistance from a human, it can raise a “prompt” and send a notification to one or more authorized users. When the prompt notification is received, the user can either provide the information requested by the prompt or click through the notification and take control of the unattended bot’s desktop. This is a powerful RPA feature that helps reduce job rejections and speeds up transaction processing. However, not all users may be able to handle all raised prompts. Most RPA tools that support prompting usually allow you to associate a “skill” with a prompt so that only users possessing that skill will be sent specific prompts. This being the case, it is important the spec define where and what specific skills are required to address any defined prompts.

Building and getting the spec approved is an iterative process. Though things seem clear upon the first pass of a design, there are always clarifications and modifications that take place as people give the process more scrutiny. All changes should be incorporated into a new version of the spec. The initial version of the spec draft should be versioned “v1.0”, with subsequent versions incrementing the sub number (e.g. v1.1, v1.2, v1.3, etc.), assuming the modifications and clarifications do not change the scope of the project. Most specs do not require the major number to be incremented, but it does happen. This is usually the case when a project has major functional changes added to it during the design phase.

Once the specification is approved by the user, that version is considered the “build draft”. I call it a build draft because, undoubtedly, the development process will uncover issues that were not captured properly in the original spec, thus requiring final modifications. This is normal part of the process.

Finally, one of the most important aspects of the spec document is that it be kept current during the development and testing phases. It is critical that any modifications made to the process to accommodate variances uncovered downstream of the build draft get incorporated back into the spec. Otherwise, the spec will have little use when users try to use it to understand exceptions or use it as the basis for a phase II project.

Source: Joe Labbe-Building An RPA Project Specification Document

Leveraging Cognitive Computing for Business Gains

Cognitive computing systems have been one of the trendiest aspects of modern day technologies. Deploying computerized models to simulate the human cognition process to find solutions is what cognitive computing systems do. A cognitive computing system is used in complex situations for ambiguous and uncertain outcomes. The term cognitive computing is closely associated with IBM’s cognitive computer system, Watson and overlaps with Artificial Intelligence (AI) using the same technologies to power cognitive applications, like neural networks, expert systems, virtual reality (VR) and robotics.


The Technology Behind Cognitive Computing

Cognitive computing systems can synthesize data from multiple information sources, analyzing the context and conflicting evidence to offer the best-suited solutions. For the best solutions, Cognitive computing systems apply self-learning technologies which use data mining, natural language processing (NLP) and pattern recognition to mimic how the human brain works.

Cognitive systems aggregate vast amounts of structured and unstructured data which are fed into machine learning algorithms for further analysis. With technological upgrades, cognitive systems are poised to refine the way they identify patterns and process data to anticipate new problems and give the best solutions on a case to case basis

To achieve the best solutions, cognitive computing systems must employ five key attributes, as pointed by the Cognitive Computing Consortium.

•  Adaptive: Cognitive systems must be flexible to learn and relearn information changes as priorities change. These systems must be adaptive to real-time dynamic data adjustments as business environment change.

•  Interactive: Human-computer interaction (HCI) is a critical component that is indispensable to cognitive systems. User interaction with cognitive machines, processors, devices and cloud platforms for requirement gathering must be top notch.

•  Iterative: Cognitive computing technologies should be able to perform iteration to the maximum levels. They must identify problems by asking questions or pull additional data if a problem is vague or incomplete by historical analysis about similar situations that have previously occurred.

•  Contextual: Understanding context is critical to the business problem, thus cognitive systems must understand, mine and identify contextual data like syntax, domain, location, time requirements, user profile, tasks or end goals. This contextual data may be drawn from multiple sources of information, like visual, auditory structured and unstructured data or sensor data.


Harnessing the Power of Cognitive Performance Computing

Cognitive performance computing has taken the leaders in business, management consulting and government around the globe by storm.  As the policymakers analyze and debate how they can leverage cognitive computing for their work, cognitive operations are increasingly being adapted in organizations where there is a constant set of unknowns. Senior officials, trusted advisors are setting the best practice for internal users and clients alike. Regulators are working forward to create the laws requiring cognitive compliance from organizational leaders. The next evolution of cognitive computing answers to fulfilling the organizational goals including helping executives and management consultants work through their risk-reward trade-offs matrix also called as cognitive performance. Organisations focus to enhance their cognitive performance as it leads to improved critical thinking, stakeholder communications, advisory collaboration, decision-making, uncertainty monitoring and cognitive compliance.


Mixed Bag Performance

So far the initial results to leverage the gains from cognitive computing have been mixed. Even Watson sometimes gets into a trouble filtering solutions from often conflicting datasets. Cognitive system scores high as it has the ability to learn-relearn and adapt to changing environments. This leads them to improve their results without manual coding. The path to autonomy is leading to a very real possibility that very soon business systems will be largely managed by autonomous, self-learning platforms.

But the path to this development is a two-way street. As cognitive evolves and change to exponential learning and continuous, self-directed optimization, business enterprises must learn to adapt to a radical change in their working to gain an advantage in blockchain, the IoT and advanced 3D printing technologies.

To successfully navigate this transition businesses and organizations need to adopt changes with a clear mind. As industries go digital there will be an opportunity to create service driven lines or entirely new ones for an increasingly connected world.


Digital Footprint in Cognitive Performance

Many organizations have a complex structure involving multiple teams who are responsible for operating processes and digital transformation. These teams are organizations and business enterprises that spend time to embrace the cognitive performance of their teams and will leapfrog their competitors as they leverage the competition.  There is still a long, bright road ahead for reaping the maximum gains from cognitive performance.

Cognitive technology may be referred to sometimes as “thinking” computer, but this is not entirely true and correct. The mysteries of the human thought and consciousness are still unfathomed, as cognitive systems make a sincere attempt to mimic the human intellect through highly advanced algorithms. A definite change has been made as cognitive solutions can outperform the human brain, particularly in processing large, complex datasets. But ultimately, the human brain is the winner for its unique and mysterious thinking and capability to achieve the unconquered.

Source: Cognitive Computing for Business Gains


Digital Operations through AI-Driven Software Robots

A common problem affecting enterprise companies is how to best expand revenue growth, while at the same time, minimize cost growth. Businesses like Amazon and Google have a significant operational advantage through the use of digital technologies like artificial intelligence (AI), which drive exponential growth while at the same time, contain costs. This video introduces how digital operations can be performed through AI-driven software robots.

Source: etftrends-Digital Operations through AI-Driven Software Robots

Global Cognitive Robotics Process Automation Market Is Set For A Rapid Growth And Is Anticipated To Reach USD 1,705.7 Million By 2024

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Source: Cognitive Robotics Process Automation Market Is Set For A Rapid Growth And Is Anticipated To Reach USD 1,705.7 Million By 2024

Modern Infrastructure for Dummies

Modern businesses are increasingly embracing digital transformation — leveraging new technologies to reinvent core processes, business models, product offerings, and the customer experience. As businesses embark on the digital transformation journey, they need to modernize their underlying technology infrastructure to enable a more agile, customer focused, flexible, and innovative digital workplace. For more information, visit

Intel and the Intel logo are trademarks of Intel Corporation or its subsidiaries in the U.S. and/or other countries


You can download it here

Source: Infrastructure for Dummies

The Future of Human Work Is Imagination, Creativity, and Strategy

It seems beyond debate: Technology is going to replace jobs, or, more precisely, the people holding those jobs. Few industries, if any, will be untouched.

Knowledge workers will not escape. Recently, the CEO of Deutsche Bank predicted that half of its 97,000 employees could be replaced by robots. One survey revealed that “39% of jobs in the legal sector could be automated in the next 10 years. Separate research has concluded that accountants have a 95% chance of losing their jobs to automation in the future.”

And for those in manufacturing or production companies, the future may arrive even sooner. That same report mentioned the advent of “robotic bricklayers.” Machine learning algorithms are also predicted to replace people responsible for “optical part sorting, automated quality control, failure detection, and improved productivity and efficiency.” Quite simply, machines are better at the job: The National Institute of Standards predicts that “machine learning can improve production capacity by up to 20%” and reduce raw materials waste by 4%.

It is easy to find reports that predict the loss of between 5 and 10 million jobs by 2020. Recently, space and automotive titan Elon Musk said the machine-over-mankind threat was humanity’s “biggest existential threat.” Perhaps that is too dire a reading of the future, but what is important for corporate leaders right now is to avoid the catastrophic mistake of ignoring how people will be affected. Here are four ways to think about the people left behind after the trucks bring in all the new technology.

The Wizard of Oz Is the Wrong Model

In Oz, the wizard is shown to run the kingdom through some complex machine hidden behind a curtain. Many executives may think themselves the wizard; enthralled by the idea that AI technology will allow them to shed millions of dollars in labor costs, they could come to believe that the best company is the one with the fewest people aside from the CEO.

Yet the CEO and founder of Fetch Robotics, Melonee Wise, cautions against that way of thinking: “For every robot we put in the world, you have to have someone maintaining it or servicing it or taking care of it.” The point of technology, she argues, is to boost productivity, not cut the workforce.

Humans Are Strategic; Machines Are Tactical

McKinsey has been studying what kind of work is most adaptable to automation. Their findings so far seem to conclude that the more technical the work, the more technology can accomplish it. In other words, machines skew toward tacticalapplications.

On the other hand, work that requires a high degree of imagination, creative analysis, and strategic thinking is harder to automate. As McKinsey put it in a recent report: “The hardest activities to automate with currently available technologies are those that involve managing and developing people (9 percent automation potential) or that apply expertise to decision making, planning, or creative work (18 percent).” Computers are great at optimizing, but not so great at goal-setting. Or even using common sense.

Integrating New Technology Is About Emotions

When technology comes in, and some workers go away, there is a residual fear among those still in place at the company. It’s only natural for them to ask, “Am I next? How many more days will I be employed here?” Venture capitalist Bruce Gibney explains it this way: “Jobs may not seem like ‘existential’ problems, but they are: When people cannot support themselves with work at all — let alone with work they find meaningful — they clamor for sharp changes. Not every revolution is a good revolution, as Europe has discovered several times. Jobs provide both material comfort and psychological gratification, and when these goods disappear, people understandably become very upset.”

The wise corporate leader will realize that post-technology trauma falls along two lines: (1) how to integrate the new technology into the work flow, and (2) how to cope with feelings that the new technology is somehow “the enemy.” Without dealing with both, even the most automated workplace could easily have undercurrents of anxiety, if not anger.

Rethink What Your Workforce Can Do

Technology will replace some work, but it doesn’t have to replace the people who have done that work. Economist James Bessen notes, “The problem is people are losing jobs and we’re not doing a good job of getting them the skills and knowledge they need to work for the new jobs.”

For example, a study in Australia found a silver lining in the automation of bank tellers’ work: “While ATMs took over a lot of the tasks these tellers were doing, it gave existing workers the opportunity to upskill and sell a wider ranges of financial services.”

Moreover, the report found that there is a growing range of new job opportunitiesin the fields of big data analysis, decision support analysts, remote-control vehicle operators, customer experience experts, personalized preventative health helpers, and online chaperones (“managing online risks such as identify theft, reputational damage, social media bullying and harassment, and internet fraud”). Such jobs may not be in your current industrial domain. But there may be other ways for you to view this moment as the perfect time to rethink the shape and character of your workforce. Such new thinking will generate a whole new human resource development agenda, one quite probably emphasizing those innate human capacities that can provide a renewed strategy for success that is both technological and human.

As Wise, the roboticist, emphasized, the technology itself is just a tool, one that leaders can use how they see fit. We can choose to use AI and other emerging technologies to replace human work, or we can choose to use them to augment it. “Your computer doesn’t unemploy you, your robot doesn’t unemploy you,” she said. “The companies that have those technologies make the social policies and set those social policies that change the workforce.”

Source: HBR-The Future of Human Work Is Imagination, Creativity, and Strategy

UK SMEs shouldn’t fear falling behind on RPA

A recent industry survey has found more than half (57 per cent) of the UK’s SMEs fear big businesses use of robotic process automation (RPA) will help to drive them out of business in the next five years.

Robotic process automation, put simply, is the use of software robots to automate business processes, for example, in back-office functions or your other core business processes. By automating time-consuming, repetitive tasks SMEs stand to improve their productivity and gain competitive advantage.

With a lack of time and resources, it might seem that such a digital transformation is a daunting prospect for many small and medium-sized businesses. But SMEs need to embrace the new technologies available and shouldn’t fear them. Digital transformation – and by that I mean the transformation of your business through the use of digital technology to fundamentally improve business efficiency and productivity – will be key to staying competitive.

How does it work? RPA increases productivity by speeding up the time taken to do mundane tasks; it ensures greater accuracy and compliance by removing human error, and it also ensures greater security of data and information – a bonus as we approach GDPR deadlines too. RPA can be used to improve business processes in many areas including, HR, legal, finance and IT.

It’s not surprising 57% of UK SMEs fear big businesses use of it will put them out of business, as until now the technology has been out of the reach of SMEs and was only available to the large enterprises that could afford it. But this is changing and SMEs need to know that.

On a positive note, the One Poll survey we conducted of SME c-suite executives found that two-thirds of businesses want to use robotic process automation. Sixty-five per cent of companies reported that they either plan to or already automate repetitive, time-consuming tasks. The financial services sector leads the charge, where more than 80 per cent of companies either plan to or already automate at least some business processes.

As a firm, we have started to automate our own processes, including some IT, HR and finance processes. For example, we now use software robots to handle the processing of tickets that come into our IT managed services desk. Our software robots are available 24/7, 365 days of the year so we are able to respond to customer needs faster and more accurately as the robots leave no room for human error.

One of the most exciting ways we are using RPA is to automate some of the forecasting and planning tasks within the business. Our software robots collate real-time sales and marketing information and now process all the information they collect during the day overnight to produce detailed forecasts and business intelligence. To collate this information and analyse it would have taken approximately 8 to 10 hours per day of staff time. Now we have improved business intelligence to plan with, and staff have more time to spend on customer service and strategic thinking than before.

We are also using RPA to conduct some of the more mundane HR aspects of processing the needs of joiners to the firm. For example, ordering their equipment and setting them up on financial and IT systems. In addition, we have automated some of our invoice posting activities. Overall I would estimate we have increased our productivity as a firm by a factor of about two, which means we are better able to focus on growing the business and building an improved customer experience for our clients.

For SMEs now is the time to sit down and think about which of their internal processes could be automated to create efficiencies in their business. We have only automated five key processes at the moment, but the returns have been dramatic. Most businesses will have several processes they can automate; some businesses will have a myriad of processes that can be automated. Working out which ones to automate should be done on a clear ROI basis and by looking at where mundane tasks are hampering staff’s ability to work on more important tasks.

Importantly, RPA doesn’t necessarily mean job losses. McKinsey’s research has shown clearly that employees welcomed the technology because they hated the boring tasks that the machines now do, and it relieved them of the rising pressure of work. We call our software robots ‘Virtual Workers’ as they are there to work alongside humans to do the work they don’t need or want to do. They allow SMEs to free up their staff to spend more time on strategic and creative projects that will give them a competitive advantage, while also improving productivity. In the longer-term, as Professor Leslie Willcocks at the LSE says, ‘it will mean people will have more interesting work.’

Interestingly, the survey backed-up our belief that RPA will help employees become free of the uninteresting tasks and able to focus on more strategic work. 77% of respondents want to use RPA to automate mundane, transactional tasks, and 56% saying freeing up staff time to focus on more strategic work was a key driver for using RPA.

For SMEs, the cost of purchasing RPA has appeared prohibitive. Many have understandably felt they would be left behind as only larger enterprises can afford such technology. But RPA is now an affordable option thanks to the ability to provide RPA as a SaaS (software-as-a-service) offering. With a simple cloud deployment and as-a-service delivery SMEs can now access RPA without having to build costly infrastructures and re-architect applications.

Now is the time for SMEs to embrace the opportunity of implementing RPA in their businesses to increase productivity and help them remain competitive. RPA and the digital transformation that it brings by automating tasks and procedures that allow specialist teams to focus on higher-value tasks is exciting. It means that smaller businesses will be able to deliver tasks at a scale and speed that would only have previously been imaginable for a traditionally ‘big’ organisation. SMEs really need have no fear of falling behind on RPA; indeed they should see it as a huge opportunity to help them compete alongside the ‘big boys’.

Source: SMEs shouldn’t fear falling behind on RPA

Robotic process automation software unites vendors, partners

RPA solution vendors are gearing up for the next wave of software robot deployment, cementing alliances with channel partners to extend market reach.

Channel partners and robotic process automation software vendors are joining forces to take software robot deployments to the next level.

Indeed, a flurry of partnering activity has unfolded in recent weeks among RPA vendors and an array of consulting firms, systems integrators and business-process specialists. Consider the following moves:

  • Earlier this month, Thoughtonomy, an RPA solution provider based in London, reported it will boost its channel business from about 45% of its current transactions to more than 90% of its business within the next 12 months.
  • Consulting firm Deloitte and UiPath, an RPA vendor, recently captured several automation projects in the federal government, initiatives that could result in thousands of bots being deployed in the next year to 18 months.
  • In January, Blue Prism announced $100 million in funding, raised from an issuance of new shares, would be used, in part, to expand its channel partner base. The RPA vendor has more than 100 partners worldwide, which include KPMG, EY, PwC, Accenture, Capgemini and Deloitte.
  • Kofax said one of its resellers placed an order for 1.8 million of Kofax Kapow, the company’s RPA product. Kofax has declared 2018 the “Year of the Robot.”

Channel companies have been partnering with robotic process automation software vendors for a while, but the recent moves come as the industry takes on a shift from software robot pilot projects to enterprise rollouts. As the deployments scale up, vendors are cementing relationships with partners they think can help their customers make the transition.

Global 2000 enterprises that have been engaged in RPA pilots over the past two to three years are now looking to expand the technology’s use, suggested Terry Walby, CEO and founder of Thoughtonomy

“They have evaluated the technology … and are now coming out of [pilots] into scale-up deployments,” he said.

Marc Mancher, a principal at Deloitte and leader of its Federal Analytics Service Business, described the RPA market transition with respect to waves. In the first wave, organizations were introduced to robotic process automation software technology. The second wave involved small pilots and proof-of-concept projects. The third wave, which Mancher said is just getting underway, is the scaling phase.

Mancher said customers are now asking Deloitte for advice on how to grow RPA beyond pilots: “Now we understand what it is. How do I build that at an enterprise level?”

Chart showing the phases of RPA adoption
Industry executives believe RPA is entering a period of scale-up deployments and mass adoption

Services opportunity

“The adopting of this technology is really taking off,” added Bobby Patrick, chief marketing officer at UiPath. “It has really been an amazing 12 months for this industry.”

Patrick noted over the past year, organizations have gone from conducting proof-of-concept experiments to beginning to build a digital workforce. Overall, the market has shifted from early adopters to “the next wave of early majority.”

Consultants and integrators are riding the wave of wider RPA solution adoption and more sophisticated projects involving software bots.

“There’s a massive services opportunity around these RPA implementations,” Patrick said, noting that companies such as Deloitte and Accenture are building huge practices around software robot technology.

There’s a massive services opportunity around these RPA implementations.

Bobby Patrickchief marketing officer, UiPath

Deloitte, for example, has created an RPA Center of Excellence at its Delivery Center in Orlando, Fla., and has trained and certified more than 500 practitioners on RPA software, according to the company.

Deloitte, Mancher said, is seeing demand for automation from across the federal government — defense, civilian and health-related agencies — and at both the operational and back-office levels. Deloitte’s research points to a nearly $40 billion opportunity for AI and robotics in the federal market.

“There is a large amount of opportunities to improve services to citizens through the use of robotic process automation software,” he said.

A force multiplier

Vendors, meanwhile, view channel partners as a force multiplier able to expand the reach of their software robot products.

Thoughtonomy, which is launching a new partner program, plans to cultivate a “significant number” of channel companies to support its international expansion. The company works with a dozen partners in North America. Walby said partners have the industry expertise and customer relationships to help Thoughtonomy access the market.

That expertise is important since Thoughtonomy provides a general-purpose tool that fits multiple use cases and can automate different work activities. Channel partners are in a better position to help customers apply software robot technology since they understand the context around the customers’ business challenges.

“We are not experts in any particular domain or industry or market,” Walby said. “Partners give us the ability to do that with their own knowledge of a specific activity, market sector or specific organization.”

Source: process automation software unites vendors, partners