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

In The Mind of Algorithms: A Conversation with UiPath’s Machine Learning Team

It’s everywhere. It’s all around you. It’s in your smartphone, in your e-mail, in your Amazon and your Netflix, in your car and in your favorite supermarket. It’s in Google’s CAPTCHA, in the stock market and probably behind the recent presidential vote. It’s in genomic sequencing, in particle physics and astronomy. What is it?

It’s Machine Learning. And it’s changing the world as we know it.

You give me data, I give you (instant) gratification

Here’s a question: why would someone ever want to keep in their house a machine that collects information about them 24/7 for purposes that are arguably beyond their knowledge and control?


It’s a trade. You entrust me with your data, and in return I give you answers to your questions, product recommendations and dating suggestions tailored to your interests, optimized driving routes, spam filters, or a new credit card.


As our digital footprint deepens, most of the data we continuously generate is being collected, processed and transformed into useful products or services. Just as Google’s algorithms determine to a great extent what information you find, Amazon can largely influence what products you buy.


Machine Learning (ML) algorithms have an extraordinary capacity to process vast amounts of data and find patterns in it. And the more data there is, the more they learn. For many applications—from vision to speech to robotics, and in different areas of business—from retail to finance to manufacturing, Machine Learning is becoming the new driving force.


To give you a rough knowledge of this technology, a conceptual model to better navigate the expert field currently taking our own industry, automation, to new heights, today we’ll introduce you to UiPath’s team of Machine Learning developers. Stefan Adam, Virgil Tudor, and Dragos Bobolea are the geeks who are leading the research and development of Machine Learning here at UiPath.

Guys, what is Machine Learning?


Virgil: Machine Learning is a subfield of Artificial Intelligence (AI) that enables systems to learn from data. It has at its core Deep Neural Networks, as does most of the current state of the art AI.


Dragos: Deep Learning—the part of ML that we are using—focuses exclusively on multi-level Neural Networks. Basically it involves a network of information that takes pieces of knowledge, combines them in various ways, and finally builds them up towards sensible, high-level meaning.


In the past, AI was composed of lots of very specific algorithms invented for all sorts of problems, from finding contours in pictures to very specialized things like detecting faces. A big part of the job was engineering all this domain knowledge into the algorithms.


Now, thanks to recent advancements in Neural Networks research and hardware computing power, it became feasible to leave this reverse engineering task to a Neural Network and assist its learning process in various ways. The biggest advantage is that Neural Network training, like pedagogy if you will, is almost universally transferable across domains. For example, teaching maths is not that different from teaching chemistry (same teaching method, different curriculum). Similarly, here at UiPath we can use the same state-of-the-art methods that others use for OCR engines, speech recognition, self-driving cars, etc.


Gartner predicts that Machine Learning will reach mainstream adoption in two to five years from now:


“Machine Learning is one of the hottest concepts in technology at the moment, given its extensive range of effects on business. A sub-branch of Machine Learning, called Deep Learning, which involves Deep Neural Nets, is receiving additional attention because it harnesses cognitive domains that were previously the exclusive territory of humans: image recognition, text understanding and audio recognition.”

So what is currently embedded in our Platform in terms of Machine Learning?


Stefan: So actually in the product we have integrated different OCR components. We are using OpenCV to process images, and we also support text analysis based on Microsoft, Google and IBM components. Our image recognition engine uses powerful algorithms that are optimized to find images on screen in under 100 milliseconds. This makes it possible to automate even the most complex applications, available through Citrix and other virtual environments. In fact, it takes almost the same amount of time to build an integration that involves Citrix as it takes to automate a regular desktop application.

And what are we planning to develop going forward?


Stefan: There are three main directions. The first one is related to the way UiPath interacts with the target application—the application which we are trying to automate.

The current detection engine is based on different Accessible API’s. That’s why our screen scraping engine is strongly connected with the execution environment. We plan to incorporate ML especially Deep Learning in our product such that the system will be able to understand any screen, similar to the way humans can understand it. In this way our core detection engine will become invariant to the execution platform. This will also lead to the ability to continuously train our engine by assisting a human user.

The second direction is to offer more Cognitive activities related to natural language parsing and image processing.

And the third direction is to also offer businesses the possibility to build, train and customize different Machine Learning models for performing different tasks, mainly classification and detection.

So with all these enhancements, automation will gradually come closer to emulating and augmenting the power of the human brain.


Stefan: Yes. It has always been the specialty of humans to read and listen to words or capture images. But with the advent of Machine Learning, Natural Language Processing, Neural Networks, Deep Learning and so forth, being able to read text, understand voice and recognize images is also becoming the domain of machines. And the application of these technologies in business will open up possibilities that were previously unimagined.

One of the first, most effective outcomes of applying Machine Learning to RPA will be a newly gained ability of robots to handle complex processing exceptions autonomously. By learning from historical data, they could predict exceptions and prevent anomalies, eliminating the time, effort and cost needed to handle them. All of this will greatly extend the scope of automation to include many activities that involve human judgement.

Using learning algorithms, an RPA robot could make processing decisions contextually, while considering millions of data points from past experience and delivering more accurate predictions. In a claims processing scenario, for example, the robot would automatically review the claim file, eliminate duplicate entries, assess eligibility and then deliver adjudication decisions with human-level precision.

What sparked your interest for this domain?

“(…)In most of the computer science subfields, a scientist or a programmer without basic AI knowledge will be like a blind painter.”

Virgil: The raw power of Deep Neural Networks, the fact that it employs a lot of math and because it’s a mandatory skill for any future computer scientist. In most of the computer science subfields, a scientist or a programmer without basic AI knowledge will be like a blind painter.


Dragos: I guess the thing that awed me the most was this framework of representing knowledge. As a layman, seemingly trivial concepts like “what is a pen” were very fuzzy and ungraspable when I tried vizualizing them. There’s no obvious way of quantitatively representing a pen, so you could say, “look, this picture’s of a pen because this or that.”


Trying to make sense when your pen is just an array of numbers is even more mind-boggling. You start imagining various rules, that get very complicated very quickly, you get scared and think how could anybody ever do this? But that’s what people actually did for a long time (and are interesting on their own). So naturally, I got very excited at discovering methodical ways that can attack these sorts of problems. Also, it’s amazing that we live in a time when we can put them to good use!

The hottest job in Silicon Valley


According to Tim O’Reilly, data scientist is the sexiest job today. Machine Learning experts are rare, forming an elite category that is frenetically being hunted by the big players the likes of Google, Facebook or Amazon. The McKinsey Global Institute estimates that:


“There will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.”

They say Machine Learning is the ideal occupation, because learning algorithms do all the work but let you take all the credit. What have you got to say in your defense?

“The whole concept of learning needs a function that will measure how good or bad a prediction is. If you see a cat, it’s wrong to say it’s a dog. It’s very wrong to say it’s a truck.(…)”

Dragos: A 7 year old knows how to read, right? Imagine giving him a full fridge, and a cookbook. His beef wellington will be similar to the results of an off-the-shelf Neural Network that somebody threw data at. There are at least 3 important things that a Machine Learning developer does:


First, he has to know how different algorithms work, and why they work, so that he knows what tool to choose for the job. Second, he has to figure out how to make the best use of domain knowledge, and give possible “shortcuts” to the Neural Net—this can turn potentially unworkable problems feasible, because it heavily trims down the “number” of bad tries. This is both a problem understanding challenge, and also a technical challenge—you have to write good code for it. Third, once all the pieces are in place, you have to attend to the whole training process, because there are many more ways for it to go on a wrong path than not:

  • The whole concept of learning needs a function that will measure how good or bad a prediction is. If you see a cat, it’s wrong to say it’s a dog. It’s very wrong to say it’s a truck. This function—the cost function—turns out to be very hard to design, as it depends very much on the problem at hand (eg. the data distribution).
  • You have to find good learning rates at different stages, so that it doesn’t start out too slow, and so that it can learn fine details later on.
  • Make sure the model does not overfit the learning data, so that it will perform well in real-life situations.
  • Figure out edge cases, adversarial examples, and understand why they occur and how to protect against them.

As a final note, could you share your favorite resource on ML knowledge for all aficionados out there?

Virgil: The book Deep Learning by Ian Goodfellow, white papers from https://arxiv.orgCoursera’s deep learning course, and then the rest of the internet, of course.

Dragos: This is an emerging field, with advancements being made every few weeks. is the website where most of the research is published, and that’s what you’ll usually find the team sipping their coffee over. For maths and other bottom-up knowledge I love OCW and Stanford’s online courses. And of course, like any developer, we all have that Chrome window with 20 tabs of StackOverflow and Github threads.

Source: The Mind of Algorithms: A Conversation with UiPath’s Machine Learning Team

Top 6 Robotic Process Automation Best Practices for Maximum Gain

Where does RPA currently stand?

Worldwide use of robotic process automation has led to significant, positive impacts on business productivity. In 2017 the adoption of robotic process automation grew globally at a higher pace than ever before.

According to Deloitte, RPA has become a “top priority for global business service leaders” across the industry landscape. Sectors such as banking, insurance and financial services have led the charge with their uptake of RPA to date. Pleasingly, the range of industries now investing in or actively investigating robotic process automation is growing, including manufacturing, utilities, mining, hospitality and FMCG, to name a few.

With its non-intrusive and flexible architecture, an automation scope that encompasses such a wide range of applications and business processes, and its ability to facilitate better management of the changing labour marketrobotic process automation has become more than merely a viable option.

Indeed, extrapolating RPA’s growth trajectory into the near future would imply adoption as a necessary step that competitive businesses must take. According to the Institute for Robotic Process Automation and Artificial Intelligence Survey from June 2017, 66% of respondents were considering expansion of robotic process automation programs and 70% were allocating increased funds for investment in 2018.

Zoom in on Australia

According to Frederic Giron, research director at Forrester – quoted by Beverley Head for ComputerWeekly – one of the most ardent concerns of Australian businesses with respect to automation will be innovation.

To this end, companies will focus on sourcing bright (human) minds who can fuse the best of both worlds – human intelligence and collective experience, with the power of process automation. Such automation is expected to work hand-in-hand with broader company mandates to provide personalised products and services that provide the best possible customer experience.

In CiGen’s feature in the Australian Financial Review, Leigh Pullen, Executive Director and co-founder says of the Australian landscape: “RPA has moved to proven technology and is providing tangible benefits to the companies that deploy it”.

Let us now list some of the robotic process automation best practices that can help to lock in these tangible benefits.

Top 6 robotic process automation best practices

1. Select the processes to be automated wisely

CiGen’s Leigh Pullen states that in order to tap into the vast potential of robotics, it is essential to begin an RPA implementation with those business processes that are best suited for automation. Selection should be made based on criteria such as:

  • Mature, stable processes – processes that are stable, predictable and well documented, with operation costs that are consistent and well defined
  • Low exception rates – low rate of variable outcomes that would otherwise require complex human intervention
  • Measurable savings – cost savings and/or benefits gained can typically be expressed in terms of greater accuracy, faster response times, lower cost base, etc.
  • High volume / high frequency – these processes often provide a faster ROI

2. Understand the human resources required to build your automation projects

People are at the heart of any successful, sustainable robotic process automation program. There is no “one size fits all” approach to building an RPA team, so taking the time to evaluate your options is key. Will you look to build your team from internal talent, who may have an existing knowledge of business processes and ideal automation opportunities?

Do you prefer to hit the ground running by working with a specialised RPA consultancy, who can build, implement and manage the automation projects for you? Or perhaps a blend of both, where your internal Centre of Excellence is initially guided and trained by an RPA consultancy, before taking full control themselves? Answering these questions at the beginning of your automation journey will help drive a more successful outcome.

3. Have an “RPA sponsor” with a holistic, centralised vision of the road towards RPA implementation

It is crucial that automated processes remain compatible with all the other procedures of your business. Merging of RPA into the overall functionality is therefore a must. This is why having someone who can have a “bird’s eye view” over all the relevant aspects facilitates efficient implementation on a larger scale.

PlantAutomation-technology and ETtech take this one step further and recommend that such a person be an executive sponsor, or someone who can also handle the financial aspects of implementation. Relatedly, an RPA centre of excellence (COE) might ensure the right level of centralisation, which is likely to provide not just short-term process automation but also a coherent longer-term plan.

4. “Divide and conquer”

This piece of advice comes courtesy of Sreyans Jain, Wipro BPS. Suppose after a thorough analysis, you are faced with the reality that the process whose automation is likely to be most useful is also a very complex one. Should you go for the second-best option? Not at all.

It may be better to invest time into breaking this complex process into several sub-processes, and then implement automation only for those that do not require human-level decision making. For instance, the parts that simply gather the information needed to make a decision could be automated, and leave the decision-making itself to human employees.

5. Having a test plan and a fallback plan is critical before deploying an automated process to production

As with any automation technology, rigorous testing of an automation build is critical. It is recommended practice to be prepared with multiple environments where the build can be developed and tested, before finally being approved and deployed. We highlight some important points to consider in order to minimise any frustrations when transitioning to live production:

  • For all applications interacted with during the automation, ensure an application version match between Test/Development and Production environments
  • Sufficient test data should be made available in Test/Development environments, with the test data set being as recent as possible
  • An agreed fall back plan is in place should the automation build require re-work after deployment to Production

6. Train and educate your employees so that they can move beyond the mythology of “robots will steal our jobs”

Never miss an opportunity to explain to your employees what your automation projects are being used for, identify where the automations will assist them in their day-to-day responsibilities and how this forges a pathway towards higher value, business building projects that will tap into the true skills and expertise of your workforce.

Be very clear regarding what robotic process automation can do (and what it cannot), so that expectations are maintained at a realistic level. In doing this, you build and foster a sustainable, long-term development of RPA within the business.


Sreyans Jain makes the enthusiastic statement that “Sooner than later robots will be embraced by all.” We believe that this is the natural conclusion to be drawn, after showing how accessible software robots are in fact. If this information is corroborated with knowledge about the numerous benefits of RPA technology, one cannot but embrace Jain’s optimism about the future of robotic process automation.

Source: 6 Robotic Process Automation Best Practices for Maximum Gain

Automation Versus RPA: The Robot Wars

I’ve been selling RPA – which includes surface integration, robotic desktop automation (RDA), user interface (UI) optimization, and robotic process automation (RPA) – for over 30 years, and I’ve seen it revolutionize the way a company does business, but only when incorporated into a properly designed system. I’ve seen organizations get many times a purchase’s value in return by using RPA to bridge the gap between their systems, but I’ve also seen the opposite. Too many companies bought RPA software and incorporated it into their systems without significantly re-engineering their business process enough to receive the full benefit, despite my efforts and their internal champions’ efforts.

What Are They Missing?

Today, integration, case management, process reengineering, and newer advanced technologies like natural language processing are coming together to deliver new levels of strategic automation. RPA is a part of this growing toolset, but by itself, it will not ever deliver “end-to-end” transformation. It’s very good at the tactical replication of “manual” human work against older legacy systems. Just as an automatic car is not the same as an autonomously driving intelligent vehicle, RPA by itself is not end-to-end automation.

In fact, the word automation means something significantly bigger. It means that people are using technology to deliver transformation and simplification. Demand for change is coming in faster than ever. Real change now comes from the consumption side (either B2-B or B2-C). Buyers want to engage differently, and enterprises must change to react (and, hence, compete) with how their customers want to transact, which is at any time and on any device.

Use a Strategy that Reworks Your Workflow

Strategic automation technologies are growing at a faster pace today than tactical ones. Did you know you can start building and maintaining new strategic applications significantly faster and cheaper than you’ve ever been able to do so, often in days or weeks? You can build end-to-end journeys and processes without coding by using model-driven platforms that scale to meet IT needs while allowing for business-driven change. That spells freedom for so many organizations seeing complexity in their tools rocket their agility and usability to obsolescence.

Today, you can build and deploy on your choice of platform, be it a public cloud, private cloud, or mixed (or change your mind and simply switch later). You can deliver applications to any current or future device without even thinking about it, even engaging with new channels like chatbots and virtual assistants. Within this single platform, you have AI built in, and it’s making real-time recommendations for the Next Best Action at every interaction point, often even before your customer reaches out to you.

Wrapping AI in front or around RPA does not make RPA any more intelligent. In fact, most RPA advertised as AI or “cognitive” is simply just a repackaging of ever-evolving Optical Character Recognition (OCR) technologies. Know your market. Who actually delivers on promises today with real, functioning technology?

Make Sure You Really Do Tap RPA’s Power

RPA is powerful. It can replicate repetitive, manual tasks to make an employee’s experience better and more productive. Using AI as a feed to RPA by allowing robots to consume unstructured and structured data is a very real and very good thing. AI, machine learning, and intelligent OCR are all things that can help drive more automation of old manual tasks through RPA, but fundamentally you’re still just automating the same old manual tasks. You’re repaving the cowpath with increasingly better tar, but you aren’t building a new superhighway. However, redesigning experiences by embedding AI at the heart of every customer interaction to drive real-time change will have a more significant impact on your business over the medium and long term.

Use RPA to accelerate your transformation, to connect systems that are otherwise not connectable, or to make your employees happier and more productive. But please do not believe that RPA is your endpoint for transformation. The real work is much harder — and the impacts far more significant.

Source: pega-Automation Versus RPA: The Robot Wars

An executive’s checklist for RPA or Where to start when you’re considering an RPA strategy

For years, companies have been looking for ways to reduce the cost and burden of routine and repetitive tasks. Many turned to outsourcing and offshoring as a way to accomplish this goal, but outsourcing comes with a whole new set of issues: political, economic and cultural.

Luckily, robotic process automation (RPA) has emerged as a new technology to help free up people for more strategic and fulfilling tasks while RPA handles the routine. However, many people don’t understand or feel comfortable with the use of robots outside of manufacturing, so executives looking to adopt this technology should approach the changeover with care. Here is a checklist to help executives prepare the organization for the use of robots to automate tasks.

A Checklist for RPA

Getting an organization ready for RPA includes:

1. Educating Leadership and Stakeholders

The first step is to ensure that the entire team understands the benefits of RPA and how it can help to streamline processes and control costs. Rather than spend their days doing the same repetitive tasks, stakeholders will now be free to spend their time handling exceptions or engaged in tasks that require more judgment and independent thinking. People think of robotic arms on the shop floor, or androids such as C3PO or Rosie from the Jetsons when they think of robots. RPA is not about physical robots who jump to obey every command. Instead, the focus is on simplifying business processes by developing and automating rules so that most process steps are completed without human intervention.

2. Setting a Future of Work Vision

Just as the use of automation on the shop floor freed workers from much of the drudgery of the assembly line so they could have more autonomy to learn additional skills or take steps to improve product quality, RPA will do the same for office workers and other people involved in repetitive or routine tasks. Instead of spending their days performing the same endless tasks, people will now spend their time handling exceptions or working to provide better customer service, higher quality or new product ideas. The work will be more engaging and interesting for the people, because robots will automatically process the bulk of transactions and procedural steps that fit the norm and alert the worker of exceptions that must be handled manually. Take the time to ensure that the organization buys into the vision, because it is crucial to the success of the project.

3. Process Documentation

Once the organization has bought in to the benefits of RPA, it is time to document existing process steps. Flow charts or other process improvement techniques can help make it clear where the process can be simplified. If processes are not stable, take the time to stabilize before you start to automate.

4. Preparing IT

IT’s role will change greatly with RPA, since many RPA tools are simple enough for skilled end users to use. Rather than relying on the technology skills of the past, IT will find that their role also becomes more strategic, helping to identify processes ripe for automation. In addition, they will work with IT at customers and suppliers to enable collaboration and communication for processes that cross organizational boundaries.

5. Bring in Outside Expertise

Since RPA is an emerging technology, it is imperative to work with people who have the skills and knowledge to help guide you through the process.

6. Identify High Priority Targets

After reviewing your process documentation, you will see some high volume or extremely repetitive procedures that are good targets for the initial foray into RPA. Avoid starting with a critical business process until you have more experience with RPA.

7. Build a Business Case

If you are outsourcing the process currently, you already have a handle on a big piece of the cost, but don’t forget to add in the soft costs of delays in response, management time, contract negotiations, PO processing, travel and other business expenses.

8. Develop an Implementation Project

  1. One of the first steps should be to define the rules for the selected process. Don’t be surprised if the organization disagrees on the process steps and what the rules are. Be patient and continue working as a team until you have consensus.
  2. Once the team agrees on the steps and the process rules, work with the users and IT to develop the rules. Make sure you include how to recognize an exception and what the workflow should look like for exceptions.
  3. Run a batch of transactions or events through the new process and measure the results. Verify that the robot handled each item as expected and that it processed exceptions processed correctly, validate that the right user received the exception notice. Keep running through tests until you are certain that the RPA solution is set up correctly before you cut over.

Using people is too expensive for routine work, and people are happier when they have a variety of engaging tasks to perform. Your business transformation to RPA is the start of the future of work. What are you waiting for?

Source: executive’s checklist for RPA or Where to start when you’re considering an RPA strategy

Robotics and Cognitive Automation Required to Keep Banking From Drowning in Data

Most financial institutions realize that the volume of data and analytics required for future success exceeds current processing capabilities. To maximize the potential of machine learning, natural language processing, chatbots, robotic processing automation and intelligent analytics, new technologies will be required.

Subscribe to The Financial Brand via email for FREE!The average bank is drowning in data, from neatly structured numbers to more abstract and hard-to-capture inputs from voice, social media and mobile platforms.

IDC estimates the global generation of data will grow from 16 zettabytes (essentially, 16 trillion gigabytes) to 160 zettabytes in the next ten years, a 30% annual growth clip. And Deloitte forecasts that unstructured data – that hard-to-capture category of data; you can find a primer here – is set to grow at twice that rate annually, with the average financial institution accumulating nine times more unstructured data than structured data by about 2020.

The Reality of Data Overload

The explosive volume of unstructured data that banks are able to process every minute of every day is quickly approaching the point where it can no longer be managed by humans alone. What many banks are realizing is that technology possessing the power to mimic human action and judgment – especially at high speed, scale, quality and lower costs – is necessary in order to keep pace with the looming unstructured data surge on a number of different fronts.

In other words, all of the different technologies that encompass robotic and cognitive automation is fast becoming indispensable necessities to the industry’s data challenge. You’re going to be hearing a lot about this category in the year to come, which includes machine learning, natural language processing, chatbots, robotic processing automation, and intelligent analytics.

The industry’s growing data challenge raises a very important question: Will 2018 be the year of robotic and cognitive automation technologies’ mass adoption by banks big and small?

More Data Requires Greater Automation

The foundation is there for robotic and cognitive automation technology to grow rapidly in the year ahead. It is also being reflected in the marketplace. According to Deloitte’s 2017 “State of Cognitive” survey, 87% of cognitive-aware financial services professionals say that such technologies are important to their products and services, 88% say these technologies are a strategic priority, and just over 35% have invested more than $5 million thus far in such capabilities.

Admittedly, the large, global players in the banking and capital markets sector are in many ways ahead of the curve when it comes to experimenting with, developing and deploying robotics and cognitive solutions. We expect rapid, more democratic adoption across much larger number of banks driven by three factors:

First, banks will increasingly incorporate more information from unstructured data. Regardless of whether a bank has hundreds of thousands or millions of accounts, the rapidly expanding set of unstructured data linked to today’s customers demands that banks will need to develop new muscles to handle that data differently. On a tactical basis, executives will need to evaluate their current processes to determine how to use cognitive technologies to incorporate and sift through the large amount – and different types – of unstructured data.

For instance, banks have historically relied solely on customer-provided data and external sources like credit bureau reports in their account opening process. Today, however, banks must also have more information about an individual or company to affirm an applicant’s identity, sometimes resorting to scouring the Internet or social media for this. This could easily compute to thousands of data points for a single customer.

Second, there is a rapid increase in the level of automation of every bank process. Robotics and cognitive technologies are driving this adoption. Robotics on its own is already well integrated across many banks to complete simple rules-based tasks such as opening email attachments and completing e-forms.

However, the cognitive, analytical element of such tasks is still experimental and siloed. The coming year may be a key turning point in that we are going to see the combined power of robotic and cognitive capabilities become the de facto solution at banks for addressing business process challenges.

Simplification for Improved ROI and a Better Experience

The combination of robotics and cognitive automation could play out in more complex parts of a bank’s business and yield bigger benefits. One such example would be the repairing of payment transactions that currently require manual fixes to remediate issues ranging from the mundane (like sender/receiver information being incomplete) to the highly complex (the payment being a potential fraud case.) If, by combining robotic and cognitive technologies, an average bank could auto-clear even 50% of the original breaks, that could translate into tens of millions of dollar and significantly shorter processing time.

Finally, we believe that automation as a whole will inevitably become transformative for every business process. This will likely begin to play out in 2018. We already are seeing examples of such transformation in pockets — from claims processing completed in seconds, to retail accounts opened in minutes, to loan processing in minutes and hours. Typically, these activities take days or weeks to complete.

No matter the size of your financial institution, the business case for robotic and cognitive automation is robust. Aside from managing dizzying levels of data, it can provide a host of other benefits, including reducing costs, lowering error rates, improving customer churn by providing a markedly higher level of service, increasing the scalability of operations, and improving compliance.

Exploring and adopting these technologies will be critical in order to maintain an edge over competitors in the marketplace and to stay relevant, both next year and in the years to come.

Source: and Cognitive Automation Required to Keep Banking From Drowning in Data

Robots are replacing managers, too

A startup called B12 builds websites with the help of “friendly robots.” Human designers, client managers, and copywriters still do much of the work—but they don’t coordinate it.

That job has been given to a software program called Orchestra.

As its name implies, Orchestra conducts a swarm of workers, most of whom are freelancers, and other “robots” to complete projects. When a client requests website improvements, which B12 sells a la carte, Orchestra generates a new Slack group, identifies team members who are both available and appropriate to complete specific tasks, and hands off work to humans and automated processes in the appropriate order. It constructs a hierarchy of workers who can check and provide feedback on each other’s work.

Automation is often associated with repetitive work such as torquing a bolt or combing through contracts during an audit. Orchestra and other systems like it demonstrate that the management of that work, and even work too complex to fully automate, also involves tasks with high automation potential. According to a McKinsey analysis, 25% of even a CEO’s current job can be handled by robots, and 35% of management tasks can be automated.

The future of work may have become the hot topic, but the future of management may involve an equally drastic change.

Almost a decade of research on how to automate coordination and other managerial tasks has focused on managing crowds of freelancers, which with platforms like Amazon’s Mechanical Turk can be easily recruited from all around the world.

Employees at a company called MobileWorks (which now builds databases of sales leads and is called LeadGenius), for instance, published a paper with researchers at the University of Berkeley in 2012 describing a “dynamic work routing system” that automatically priced tasks—everything from managing a Twitter account to digitizing stacks of business cards—and assigned them to qualified workers. Multiple workers completed the same task to help check for accuracy. If they disagreed, the task was served to other workers and, if they continued to disagree, marked for review by “managers,” workers who had already demonstrated high speed and accuracy. Workers who made a lot of mistakes were assigned to practice tasks until they improved.

At Stanford, a group of researchers (including Daniela Retelny, who is now B12’s director of product) has published papers about how to coordinate crowds to complete projects that involve interdependent tasks, such as prototyping an app. One strategy, called “flash teams,” used software to automatically assemble a team of freelancers and hand tasks between them, like an assembly line. The process effectively turned napkin sketches into functional web applications and recruited users to test them—all within a single day. Another called “flash organizations,” discussed in a paper published earlier this year, placed freelancer teams into a hierarchy and allowed members to suggest changes to the organizational structure as they worked. Those teams completed prototype designs for a card game, an app for use by EMTs, and a client training portal for use by a business services company.

B12 isn’t the only company to incorporate these strategies. A startup called Gigster uses a similar system to build software and websites. Konsus, which offers business services such as data entry and PowerPoint design, has created automated workflows that hand work between its pool of freelancers and automated processes.

What all this means for the job of managing people within a company isn’t necessarily straightforward. “To the extent that we can build systems that aid coordination and awareness for teams performing routine tasks, that seems the most likely to reduce the need for managers,” says Michael Bernstein, a Stanford researcher who is an advisor to B12 and co-authored the papers on flash teams and organizations. “But to the extent that managers are providing informal and evolving coordination support, that will still be useful in my opinion.”

A Bain report published in April suggested that by the end of 2027, most of a company’s activity will be automated or outsourced.”Teams will be self-managed, leading to a vast reduction in the number of traditional managers,” the report’s authors write. “Employees will have no permanent bosses, but will instead have formal mentors who help guide their careers from project to project.”

The report suggests new types of leadership will emerge. Rather than aiming to become a professional manager (“to take expert bricklayers, so to speak, and make them managers of other bricklayers”), top talent would shift to contribute directly to a company’s service or product and communicate directly with each other rather than through managers (they should be”guilds of bricklayers”). In this new company structure, there would be multiple tracks for career advancement. “Some tracks will recognize and reward the efficient management of routine processes,” they write, “while others, just as highly prized, will value the coaching and development of apprentices as they migrate from one role to another.”

Roger Dickey, the CEO of Gigster, imagines a system that automates this type of career advancement for freelancers based on the quality of work (B12 already has some hierarchy of freelancers, as do LeadGenius and Gigster). “Leaders can oversee as many as 20 projects at a time, offering guidance to their team, recommending bonuses to people who are doing well, coaching, training and jumping in when an issue is escalated,” he wrote in a recent blog post on LinkedIn. “Companies are then able to hire an entire team of freelancers to manage a project, knowing that there is a hierarchical structure in place to support them.”

In any case, if we have truly entered a fourth industrial revolution, as the World Economic Forum recently declared, it follows that work won’t be the only aspect of an organization to see sweeping changes.

“Our philosophy is that anything that can be automated around these workflows will be,” says Nitesh Banta, B12’s co-founder and CEO. “The efficiencies are too great not to automate.”

Source: Quartz-Robots are replacing managers, too

Enterprises need robotic automation and human innovation to remain competitive

Robotic process automation (RPA), artificial intelligence (AI) and other intelligent automation technologies are the driving force in what is being called the “Fourth Industrial Revolution.” In times of such great innovation and change, we’re seeing a lively discussion being spurred on around the future nature of work.

Recently, the Information Services Group (ISG) released a report on RPA titled, “ISG Automation Index: IT Automation Driving Productivity Up, Prices Down.” This report emphasizes that RPA “is the future of work,” not the end of it, capable of streamlining tedious tasks like data input, application processing and invoicing for large organizations in highly regulated industries. According to the report, RPA enables enterprises to execute business processes up to 10 times faster with an average of 37 percent fewer resources dedicated to that particular process, leading many corporations to redeploy employees to new, higher-value roles and tasks that are ultimately more enjoyable. The benefits of automation in the workplace speak for themselves – so where does the skepticism stem from?

At Blue Prism, we’ve seen how RPA allows our customers to spend their valuable time on innovative projects instead of monotonous work. Shop Direct automated a number of tasks that delivered 450,000 hours of work back to the business in a single year. Now, with RPA, those employees have been focused on more innovative tasks related to improving the company. Xchanging (a CSC company), achieved cost savings of 30 percent with Blue Prism RPA and increased output, and Telefónica O2 – the second-largest mobile telecommunications provider in the UK – built a digital workforce that paid for itself in the first year alone.

These examples emphasize how easy it is to achieve peak efficiency and cost savings benefits, without dramatically changing your organization with automation. Automation does more to achieve new business goals and open the door to opportunities, rather than limit them. RPA allows for IT components to work seamlessly, including on-premise and cloud infrastructure, all while improving capabilities like data governance and change management.

Robotic automation enables companies and employees to work smarter, not harder, and stay competitive by focusing on innovation instead of tedious tasks. If the benefits seem like what your business needs, use this research and the successes of our customers to make a case for your team.

Source: need robotic automation and human innovation to remain competitive

7 RPA myths – busted

This article examines the validity of the most widely held misconceptions about robotic process automation (RPA).

Myth 1 – RPA will replace humans

Whenever a significant technology such as RPA software emerges, there’s always a concern that the human workforce will be replaced. In reality, by automating high volume tasks, RPA only reduces the need for repetitive human effort. Rather than removing humans from the workplace, RPA will liberate them from conducting rote and repetitive tasks and allow them to focus on more value-added work.

Humans will also gain more time to innovate and be creative – so they can seek out areas for continuous improvement and be re-deployed to higher-level, managerial or supervisory roles. There are already new roles being created in the RPA environment – where humans orchestrate, monitor and train the robots, and in time, more opportunities will emerge. In fact, as software robots increasingly work on repetitive tasks tirelessly and continuously, they are gradually being embraced by human staff as valuable team members.

Myth 2 – RPA is only about cost reduction

While cost reduction can definitely be achieved by RPA, it’s not the main reason why businesses choose it. The key drivers for purchasing RPA include; accelerating the time it takes to complete processes, liberating staff to be deployed on higher value projects – as well as generating greater predictability and higher process quality.

Another major benefit that businesses experience is better consistency – because robots always process activities in exactly the same manner. Also, as robots capture and log all activities, the degree of compliance and available reporting data for analysis, increases significantly. Furthermore, robots can easily be scaled up and down, which is very important for processes where flexibility and scalability are required. And finally, robots work faster than humans – which improves throughput times.

Myth 3 – RPA is expensive

RPA does have initial implementation costs to get it up-and-running and then to keep it operational. These include the build phase – including the provisioning of IT infrastructure such as databases, physical / virtual machines etc. and IT resource time to get RPA up-and-running. Also, additional consultancy costs from partner companies should be added. Running costs are largely time related and centre around the ongoing delivery and maintenance of processes, maintenance of underlying infrastructure and support etc.

However, RPA costs are typically not significant compared to those that accompany business process management software or enterprise resource planning implementation and traditional options such as business process outsourcing or offshore manual processing. RPA also provides rapid internal cost reduction and significant increases in ROI, making it a very appealing option for many companies.

Myth 4 – RPA software robots are always accurate.

If software robots are correctly set up they will be completely accurate. However, they are capable of making mistakes – especially as they possess no ‘common sense. This means if there’s a mistake in the instructions provided to robots, they will replicate the error that’s present in a workflow – hundreds or thousands of times – until it is spotted by a human.

These errors may necessitate that work is redone, either manually – or by re-automating tasks after the mistakes have been fixed. In order to avoid these problems, it’s important to ensure that processes are error-free before automation and the software robots are monitored by humans – at least in the initial stages of automation.

Myth 5 – RPA is not applicable for some sectors

There’s a common misconception that RPA is only productive in certain industries, but back office tasks exist in every industry. RPA can be applied to almost any repetitive, rules-based, high-volume, business activity. RPA can be used, for example, to manage claims processing in insurance, order processing in retail, and fraud detection in banking.

Myth 6 – All office work can be automated by RPA

Although RPA is perfect for work that is rule-based and has digitised inputs, there are limitations to the types of tasks that it can be applied to – specifically ones that require human judgement. RPA becomes more challenging where processes are non-standardised and require frequent human intervention to complete – such as interacting with customers or working with process variability.

Even processes that pass the RPA feasibility criteria, these still may not be the best ‘candidates’ for automation – at least not initially. For example, automating an inefficient process, can potentially only speed up the inefficiency. More benefit could be gained from either making the process more efficient, prior to automating – or by redesigning the process during the design phase of delivery.

Until artificial intelligence and machine learning catch up to the cognitive thinking skills of humans – they will still be at the heart of key decision-making. There are promising developments in the area of self-learning systems that can deal with case-based / unstructured activities but these are not typically part of RPA solutions.

Myth 7- RPA can be implemented without involvement of the IT department

Although RPA is swift to implement, and minimises the need for costly systems integration, these benefits are not always fully appreciated by the IT department. The net result is that RPA adoption isn’t normally driven by IT – but by business units instead. However, IT must be involved and fully supportive at the outset of any RPA initiative. The IT department delivers the infrastructure required and applies roles and permissions to a robotic user account – therefore no robot can operate without a PC, a user account, or access to an application.

To get the IT department engaged, there’s a growing list of scenarios that should be communicated that benefit its own internal operations. These can range from initial projects in service management and transaction processing – to resource-intensive, administrative and transactional work. As well as the obvious cost savings and efficiency improvements, in the short term, RPA has the potential to challenge the way that processes are fulfilled in IT. RPA will also provide future benefits too – allowing IT to digitise things that currently aren’t possible and provide valuable insights into forthcoming IT challenges.

Source: RPA myths – busted