How To Make BPMS And RPA Work Together

Robotic Process Automation (RPA) has been gaining traction in recent years. It has moved from being a mere buzzword to being a priority on organization’s to-do list.

Being a Business Process Management (BPM) practitioner, I am interested in exploring how a BPM software (BPMS) can work with an RPA tool. If you look at the leading BPMS vendors from different magic quadrants, you will see a trend developing. Pegasystems a BPMS vendor acquired an RPA tool last year. Appian, another BPMS/low-code vendor had RPA front and center at their annual conference.

In my opinion, RPA compliments BPMS very nicely, it can actually increase adoption of BPMS, and vice versa. In rest of this article, I am going to explore two different approaches that can be used to make a BPMS and an RPA tool work together in harmony.

Process-Driven RPA

A process cannot exist in a silo, it has to integrate with other systems in order to deliver real benefits. Unfortunately, in most organizations, a fully integrated process is only part of the vision and not reality.

Unfortunately, in most organizations, a fully integrated process is only part of the vision and not reality.

Various factors such as mergers, acquisitions, legacy software or resource constraints can stop you from building those much-needed integrations. Lack of integrations definitely has a negative impact on adoption and usability of the automated process.


Consider a basic version of the order management process. You have automated the process using a BPMS, but it lacks integration with let’s say the shipping vendor’s system.

In order to complete the Ship Order activity shown in the process above, you will need to work on multiple systems. Here is a quick list of different tasks that you might need to perform.

  • Login to order management system
  • Search and open order details
  • Login to the shipping vendor’s system
  • Copy and paste all the required data from order management system to shipping system
  • Ship the order and copy tracking number from shipping system back into order management system
  • Log out and close the shipping system
  • Mark order as shipped in order management system
  • Log out and close the order management system

If you get one or two orders a day, these tasks might not be a big deal, but if this is happening 100 times a day, then you are spending a considerable amount of time on non-value add work.


If you have similar situations in your organization, where processes have been automated using a BPMS but due to lack of integrations have resulted in swivel chair activities, then RPA tools can help you.

A swivel chair activity means that a user has to perform tasks in multiple systems in order to complete a single activity of a process. In the order management process example, Ship Order was a swivel chair activity.

The idea behind Process-Diven RPA approach is that your process keeps running inside a BPMS without any major modifications. You automate non-value add swivel chair activities by using the digital workforce (bots) provided by the RPA tool. Referring to the earlier example of order management process, once your process reaches the Ship Order activity, instead of a human doing all the tasks, a trained bot can perform all the tasks.

This approach can help you (in a way) integrate with systems that might not have been possible otherwise and more importantly free up your human resources, who can work on value-add work instead.

RPA-Initiated Process

A bot is great when you have well-defined repeatable tasks that it can perform, but what happens when there are data anomalies or errors? It is simply not feasible to train a bot with all possible exception cases (unless it is a self-learning bot, topic for another day).


To further elaborate this approach, let’s take a look at trade reconciliation process. This process usually happens at end of a trading day, and the goal is to make sure that balance is accurate in two or more systems.

The figure above shows two hypothetical systems used for trading. Here is a quick list of different tasks that an agent might need to perform for reconciliation.

  • Login to trade management system of the firm
  • Search and open the customer in trade management system
  • Login to broker system
  • Search and open the customer in broker system
  • Verify that end of day balance in both systems is same
  • Log out and close the trade management system
  • Log out and close broker system

Now consider the exception case where at end of the day, the balance was not same in both systems. In this exception case, an agent will need to perform follow-up tasks, which might require a call or email to the client and broker to find out the reason balance is not same.


In such scenarios, a bot can be trained to perform the daily repeatable tasks of checking balance in both systems, but training it on all exception scenarios, follow-ups, and follow-up actions might not be possible, and this is where BPMS comes to the rescue.

As the name suggests, the idea behind RPA-Initiated Process approach is that when a bot has not been trained to handle exception cases, it requests human intervention. The bot completes its processing, kicks off a human activity inside BPMS and moves on to the next set of work. This approach works great when a majority of the time a bot is able to complete processing without issues, but in those minor instances when they do find anomalies, a process is kicked off in BPMS so that a human can follow-up and resolve the issue.

This approach works great when a majority of the time a bot is able to complete processing without issues, but in those minor instances when they do find anomalies, a process is kicked off in BPMS so that a human can follow-up and resolve the issue. This again lets your resources focus on actual value-add work instead of spending time on mundane tasks.


In my opinion, BPMS and RPA are a great match. The two approaches discussed in this article show that if both technologies are used in harmony they can really complement each other and increase adoption.

Share your thoughts on how you are planning to use BPMS and RPA in your organization. What opportunities or challenges do you see in implementing both together?

Source: To Make BPMS and RPA Work Together 


What companies need to know when considering automation

As the hype continues around Robotic Process Automation (RPA) and Artificial Intelligence (AI), organizations are looking to invest additional efforts to better understand potential benefits and risks associated with these.

The fact of the matter: RPA and AI are already a reality and many service providers are taking an active role in the lookout for opportunities to maximize their service delivery models, profits and increased client satisfaction.

Below are some ideas and considerations for organizations prior to determine a course of action:

  • Understand the benefits beyond the hype: Organizations should have a realistic perspective on the potential benefits RPA/AI can bring to their environment. Obviously the excitement to bring those to life and all the value add innovation that can be achieved are phenomenal. Prior to executing, just make sure investments are made towards a sound business case – in which a realistic perspective of benefits and risks is presented, not the hype effect.
  • Determine demarcation points in order to maximize benefits: If service providers are already deploying RPA and AI to some of the services offered to an organization, there is a good leverage case to be used. These should translate in both financial/non-financial benefits to the services provided. In order to achieve this, it is important to determine what the opportunities are and activities that can be automated through the service provider’s capabilities. By doing so, organizations can potentially minimize capital investments, and at the same time allow RPA and AI related risks to be managed by such service providers.
  • Review your service provider’s agreements prior to adopting RPA and AI: Like other disruptors such as Cyber and Cloud, it is important for organizations to have appropriate commercial terms in place prior to entertain RPA/AI services so that the organization’s interests and risks are aligned with the organization’s procurement, outsourcing, privacy and supplier risk policies. The hype effect shall not create unnecessary exposure or challenges for the organizations that otherwise could have been prevented. 
  • Determine overlap between initiatives across the organization: It is common for different areas within an organization to work independently on their respective challenges and opportunities. In order to determine the organizations best course of action, a holistic approach aligned with the organization’s strategy should exist. This will enable the organization to identify overlaps and also promote collaboration within the organization. Another important point goes back to basic strategic sourcing principles around effective governance and economies of scale – as financial benefits and costs should be clearly stated and understood.
  • The importance of Governance and Risk Management should not be understated: I know I have written this topic before but I felt the need to reemphasize the importance to having “all ducks in place”. This is particularly important for organizations in highly regulated sectors (e.g., Financial Services, Insurance and Healthcare), for which this should be considered a top priority.        Remember that organizations should not compromise their ability to comply with their respective regulatory requirements, as this can have a significant impact to the organization’s reputation and bottom line.
  • Enjoy the excitement and discovery process but do not underestimate change management: There are a lot of “pluses” bringing disruptive technologies to an organization. Take the time to enjoy and generate the required momentum – so that change management activities are positively perceived across the different organization levels and generations. Usually organizations that pursue these through business transformation exercises tend to dig deeper on the potential additional values the organization can achieve beyond the hype. For example, the need to change processes that, although efficient, will require significant changes to support the organization’s desired future state RPA and AI. That also help the organization’s internal staff to gain valuable knowledge and experience through hands on experiences as the business transformation is being executed. In other words, the excitement and hype may help foster employee’s engagement.
  • Understand where the market is going beyond the hype: There are talks of RPA/AI organizations going public or being a target to large service providers such as IBM and CapGemini. Before entertaining a direct relationship with a specific RPA/AI service provider, it is very important to understand the potential issues of a fourth party and the implications to the organization service delivery model. For example, the Bank of New York Mellon faced significant challenges due to an acquisition of one of its main service providers by another institution back in August 2015 – The Wall St. Journal has an interesting article on it.

The conclusion: Go ahead and have fun! At the same time, do not lose sight of potential exposures for the organization. In today’s world, organizations cannot afford reputational risks / impacts to their brand and clients. Remember that the higher the benefits, the higher the risks. At the same time, organizations cannot afford to stay put, as our worlds breathes change.

A final and important update: the Wall Street Technology Conference is taking place in New York City on May 24. Further details can be found here. I look forward to seeing you there!!!

About This Year’s Wall St Technology Conference: Managing Risk & Reward in a Digital World

2016 will be the year that companies go beyond the hype, get to reality and actually invest in digital innovations that produce results and deliver ROI. It is the year that providers will go from selling buzz-ware to offering real industry-driven solutions.

Digital value chains will become a reality and organizations will reap the true power of data and insights. But along with new opportunities, there are increased risk of cyber-security hacks, data privacy breaches and regulatory issues. Financial services and insurance companies have to balance their strategic priorities with the risks, regulatory and cost objectives. The advent of Digital banking and mobile payment systems coupled with consumerization of IT, automation and real-time analytics is changing how CXOs procure and implement new solutions.


Source: companies need to know when considering automation

Robotic process automation is killer app for cognitive computing

Robotic Process Automation (RPA) is an increasingly hot topic in the digital enterprise. Implementing software robots to perform routine business processes and eliminate inefficiencies is an attractive proposition for IT and business leaders. And providers of traditional IT and business process outsourcing facing potential loss of business to bots are themselves investing in these automation capabilities as well.

While the basic benefits of RPA are relatively straightforward, however, these emerging business process automation tools could also serve as en entry point for incorporating cognitive computing capabilities into the enterprise, says David Schatzky managing director with Deloitte.

By injecting RPA with cognitive computing power, companies can supercharge their automation efforts, says Schatzky, who analyzes the implications of emerging technology and other business trends. By combining RPA with cognitive technologies such as machine learning, speech recognition, and natural language processing, companies can automate higher-order tasks that in the past required the perceptual and judgment capabilities of humans.

Some leading RPA vendors are already combining forces with cognitive computing vendors. Blue Prism, for example, is working with IBM’s Watson team to bring cognitive capabilities to clients. And a recent Forrester report on RPA best practices advised companies to design their software robot systems to integrate with cognitive platforms. talked to Schatzky about RPA adoption rates, the budding relationship between software robots and cognitive systems, the likelihood that the combination of the two will replace traditional outsourcing, and the three steps companies should take before implementing RPA on a wider scale. Where are most companies in terms of their adoption of RPA?

David Schatzky, managing director, Deloitte: RPA is a new topic to some and a well understood one to others. More and more IT leaders have heard of the term and at least know what it is in principle. Adoption thus far is pretty modest. RPA has been more widely adopted in Europe and Asia than it has been in the U.S. And even those companies in the U.S. that have adopted RPA are typically just piloting it. Why did RPA catch on more rapidly in Asia and Europe?

Schatzky:That’s due to the level of business process outsourcing that has taken place there. Asia is the hope of business process outsourcing and European companies have been eager to cut the costs of onshore operation using RPA. Also, one of the leading RPA companies, Blue Prism, is based in Europe. Why are you focusing on the potential combination of RPA and cognitive computing systems in particular?

Schatzky: I think it will help to broaden the application of RPA and increase the value it delivers to the companies that adopt it. Cognitive technology is progressing rapidly, but many companies don’t have a clear path to taking advantage of these technologies. They’re not sure how and where to put them to use.

RPA is a platform that can provide clear use cases for applying cognitive capabilities. Companies can install it to automate processes and it provides a framework or platform to integrate with cognitive systems to take automation to the next level. It’s almost the ‘killer app’ for cognitive computing.

RPA is very useful technology, but it’s not terribly intelligent technology. It only performs tasks with clear-cut rules. You can’t substitute RPA for human judgment. It can’t perform rudimentary tasks that require perceptual skills, like locating a price or purchase order number in a document. It can identify a happy customer versus an unhappy customer. Cognitive takes the sphere of automation that RPA can handle and broadens it. Where will be the most beneficial use cases for using RPA in conjunction with cognitive technology?

Schatzky: A lot of them are in the front office: classifying customer issues and routing them to the right person, deciding what issues need to be escalated, extracting information from written communication. Who tends to lead these RPA efforts—an IT leader or a business process owner?

Schatzky: It’s mixed. Sometimes it’s led by the process owner in the business. They learn about RPA and identify an opportunity to deploy it and improve efficiency. In other cases, IT has been leading the effort. It’s indicative of the broader trend of tech-centric decision being made increasingly in the business and not just IT.

Source: – Robotic process automation is killer app for cognitive computing

Making the Case for Employing Software Robots

One of the main tenets of advancing technology is to free up the time and effort workers are often required to put into relatively mundane tasks. Automating processes that once took hours for a person to complete has been a boon to a business’ bottom line while allowing IT workers to focus on tasks more central to advancing a company’s strategic initiatives. When it comes to Robotic Process Automation (RPA), Rod Dunlap, a director at Alsbridge, a global sourcing advisory and consulting firm, understands how RPA tools can positively impact workflow in industries such as health care and insurance. In this interview with CIO Insight, Dunlap expands on the RPA ecosystem, when it makes sense to employ RPA tools—and when it doesn’t.

For those unfamiliar, please describe Robotic Process Automation and explain a basic example of it in use.

RPA tools are software “robots” that use business rules logic to execute specifically defined and repeatable functions and work processes in the same way that person would. These include applying various criteria to determine whether, for example, a healthcare claim should be accepted or rejected, whether an invoice should be paid or whether a loan application should be approved.

What makes RPA attractive to businesses?

For one thing RPA tools are low in cost – a robot that takes on the mundane work of a human healthcare claims administrator, for example, costs between $5K and $15K a year to implement and administer. Another advantage is ease of implementation. Unlike traditional automation tools, RPA systems don’t require extensive coding or programming. In fact, it’s more accurate to say that the tools are “taught” rather than “programmed.” Relatedly, the tools can be quickly and easily adapted to new conditions and requirements. This is critical in, for example, the healthcare space, where insurance regulations are constantly changing. And while the tools require some level of IT support, they don’t have a significant impact on IT infrastructure or resources or require changes to any of the client’s existing applications.

What are the drawbacks of RPA?

RPA tools are limited in terms of their learning capabilities. They can do only what they have been taught to do, and can’t reason or draw conclusions based on what they encounter. RPA tools typically cannot read paper documents, free form text or make phone calls. The data for the Robots must be structured.

In what industries does RPA make the most sense?

They make sense in any situation that has a high volume of repeatable or predictable outcomes, on other words, where the same task is repeated over and over. We’ve seen a lot of adoption in the Insurance, Financial, Healthcare, Media, Services and Distribution industries.

Where does it make the least sense?

They don’t make sense in situations that have a high volume of one-off or unusual situations. To take the healthcare claims processing example, RPA is ideal for processing up to 90 percent of claims that an insurer receives. The remaining 10 percent of claims are for unusual situations. In these cases, while you could teach the robots the rules to process these claims, it’s more cost-effective to have a human administrator do the review.

If you automate a process once done by humans, and have it perfected by a robot, is it possible for the robot to determine a better way to accomplish the task?

Not with RPA. As mentioned, these tools will execute tasks only in the way in which they were taught. They can’t observe and suggest a different way to do things based on their experience, but what you are suggesting is indeed where the industry is heading.

What sort of data can be learned from RPA?

RPA tools can’t really provide insight from data on their own. They can log detailed data about every transaction they process. This can then be fed into a number of tools that will provide operation statistics. Also, they can work in tandem with more sophisticated cognitive tools that use pattern recognition capabilities to process unstructured data. For example, insurance companies have huge volumes of data sitting on legacy systems in a wide range of formats. Insurers are looking at ways to apply the cognitive tools to process and categorize this data and then use RPA tools to feed the data into new systems. Retailers are looking to apply the tools in similar ways to gain insight from customer data.

How much human oversight is needed to ensure mistakes are avoided?

The robots won’t make “mistakes” per se, but oversight is necessary to make sure that the robots are updated to reflect changes in business conditions and rules. An operator, similar to a human supervisor, can start and stop robots, change the tasks they perform and increase throughput all without worrying about who gets the window office.

Source: the Case for Employing Software Robots

4 Things Robots Need to Learn Before Working With Humans

The robots are coming. And really, in some ways, they’re already here. If you’ve ever tripped over a robot vacuum, you’ve actually waded into the fascinating frontier that is human-robot interaction. If humans are at all going to get along with increasingly sophisticated robots, we need to figure out how we’re going to interact with them, and in turn they’ll need to adapt to us.

This technological revolution is different than those that came before it. In the Industrial Revolution, the static, hulking machines required humans to fundamentally change the way they worked. But in the robot revolution, both parties have to make compromises. You’ll have to learn to communicate with a new kind of being, and that new kind of being will have to help you along as well. Subtle communications, like a robot pretending to struggle with a heavy object it’s handing to you so you’re not surprised by the weight, will be pivotal for our two species to work together without driving each other crazy.

Luckily, ace roboticists like UC Berkeley’s Anca Dragan are diving deep into the fascinating problems of human-robot interaction before they become problems. Check out the video above to see Dragan’s top four challenges with the coming robo-revolution.

Source: Wired-4 Things Robots Need to Learn Before Working With Humans

The 3 Ways Work Can Be Automated

We are at an interesting tipping point regarding how and where work gets done. As business leaders and managers, we have become increasingly capable of engaging a workforce that is some combination of virtual and on site, part time and full time, permanent and contingent. But just when we’ve sorted out preferred management routines, there is an entirely new landscape emerging with technology options central to the work and possibly your business model: work automation. How, when, and where should leaders be thinking about applying the various automation technologies to their businesses?

There are currently three technological enablers of work automation: robotic process automation, cognitive automation, and social robotics. Each technology fits a different kind of work and has different implications depending on the work to be done, as described in the chart below.

The simplest and most mature so far is robotic process automation. It can be used to automate high-volume, low-complexity, and routine tasks. It is particularly effective in automating the so-called “swivel chair” tasks, where data needs to be transferred from one software system to another. These tasks are traditionally done by humans. For example, they may involve taking inputs from emails or spreadsheets, processing the information by applying certain rules, and then entering the output into some other business systems, such as an ERP or a CRM. Creating a virtual workforce of software robots can help companies streamline operational processes as well as increase the quality and cost-effectiveness of shared services.

Nevertheless, most of the current excitement around work automation stems from systems that can replace humans in nonroutine, complex, creative, and often exploratory tasks — in other words, systems that can automate human cognition, or cognitive automation. Developments in machine learning, powered by scalable computing resources in the cloud and heavy investment in exceptional human talent by the large players in the IT industry, are making computers capable of recognizing patterns and understanding meaning in big data in a cunningly human-like way. This “recognition intelligence” is showcased in systems for voice recognition, voice-to-text, natural language understanding, image understanding, and a host of other applications that are increasingly becoming available to consumers and companies.

Companies can use these cognitive automation technologies in three ways. First, they can further automate, or completely reengineer, their business processes. Take, for example, the car insurance industry. Instead of having human agents visit cars to assess the damage, an app used by the car policy owner and powered with image recognition intelligence could process photos of the car damage, assess the degree of the damage, estimate and classify the size of the claim, and pass the information for final approval to a human, thereby significantly simplifying the claims process in terms of both time and cost. Cognitive automation like Google Glass can transform the work of a flight attendant, for example. The ability of such technology to enable traditional jobs to be disaggregated and to supplement or replace routine activities presents opportunities in efficiency, effectiveness, and impact.

The second area of opportunity with cognitive automation is for companies to develop new products and services. In the previous example, the intelligent app could be part of a new offering to car insurance clients, perhaps with added features such as a chatbot that could provide additional, on-demand advice about insurance to the policy owner.

Finally, cognitive automation can be used to gain new insights into big data. When it comes to transforming a company’s strategy around the future of work, talent analytics combined with machine learning can be a very powerful tool for analysis and prediction.

Another area that is rapidly evolving is social robotics. Unlike their predecessors, this new generation of robots is not bolted on an assembly line; they are mobile and move around in our everyday world. They can be drones that fly or swim, anthropoid robots that walk, or swarm robots that roll on wheels. They are programmable and can adapt to new tasks. This new generation of social robotics can automate routine as well as nonroutine tasks. Freed from the assembly line, the social robots can collaborate with humans in a variety of applications that were unthinkable a few years ago.

A good example is the Kiva robots that Amazon has been using to increase the efficiency of its order fulfillment process. Instead of walking the aisles to find the right packages, humans now stand on platforms while an army of social robots brings the right package to them at the right time. By reengineering the process using robots, Amazon did not replace the human workers but rather made them more productive in the same way the aforementioned app allows human adjustors to take on more cases by focusing on the “higher value added” activities while the app takes on the more routine aspects of the job.

Amazon’s employees now take 15 minutes to fulfill some orders instead of 90 minutes, an increase of 20% in efficiency; the small size of the robots also allowed Amazon to increase the size of ist inventory by 50%. Management oversees the entire fulfillment process, including the work interactions between robots and humans.

As the half-life of skills continues to shrink, the growing premium on reskilling is causing many organizations to rethink the risks associated with full-time employment in order to reduce the risk of obsolescence. The different variations of work-task automation, like the ones here, can deliver viable solutions to all of the above concerns. Selecting the right technology for automating work tasks and improving performance is therefore critical for business, as is the alignment of the selected technology with a comprehensive strategy for the future of work.

Source: Harvard Business Review-The 3 Ways Work Can Be Automated

The rise of the machines: not so doom and gloom

As technology perforates every aspect of our professional and personal lives, it is clear that a robotic revolution is upon society. While robots have been used in industries such as manufacturing and automotive for years, today’s systems are ever-improving and, in some cases, are now exceeding human limitations.

For instance, Google’s artificial intelligence (AI), DeepMind AlphaGo, is beating the world’s number one player in tournaments of Go (an incredibly complex Chinese grid game). While this represents the higher end of robots’ current capabilities, there is a growing, but unfounded, fear that with the rate of development, it’s only a matter of time before all jobs are completed by machines.

Losing jobs to technology is not simply a modern day worry. For example, the early days of the industrial revolution saw everyone believing that their jobs were at risk. In the 1930s, economist John Maynard Keynes coined the term ‘technological unemployment’, believing that the rise of technology would lead to a permanent decline in the number of jobs. In both cases, the impact was not quite as dramatic. Technology helped employees to do their jobs better, it didn’t necessarily replace them. It even created new positions.

It’s a similar story today. An accountant, for instance, is likely to use tools that automate certain aspects of their role. Data collection and report creation are both arduous time-consuming activities to complete manually, and the ever-growing deluge of information only increases the chances that something vital will be omitted. Automating the processes provides accountants with the data they need to analyse, enabling them to increase accuracy and productivity.

Many will argue that automated production lines are a testament to the fact that machines can do some jobs better, and businesses do indeed invest heavily in order to have work floors almost free of humans.

>See also: VR and machine predictions for 2017

But the belief that robots will take all jobs is wrong. Semi-skilled positions will bear the biggest brunt, while low- and high-skilled jobs (such as caretakers and data scientists respectively) will be impacted less, due to cost or the complexity of roles. In fact, the rise of machines will also help stimulate employment figures by creating new roles.

Despite having all the ‘bells and whistles’, robots can’t design and service themselves. Humans can try and create another robot to do the job, but then what happens if that one breaks down too?

The truth is, the cost of developing a machine to do such a role will always outweigh paying a few humans a salary; meaning the number of positions will increase in proportion to the number of robots. Furthermore, as machines become even more intelligent there are simply more things that can go wrong, exacerbating the need for skilled workers.

Some humans will be working for or following orders from robots. In warehouses and other logistics operations, for example, machines already tell humans what to do, including which items to select off shelves and where to process them. Robots can more quickly analyse orders and delegate the relevant responsibilities to ensure that they are fulfilled in almost real-time.

While it may seem incredibly ‘overlord’, it’s worth noting that people all already work for machines in some capacity. Every time someone uses Facebook or Google, they are providing AI systems with data and they pay people back in services. People also take their driving directions from apps on their smartphones. So, just as an employee generates value for an employer, humans are all worth something to those machines.

There will also be humans who own the machines, a role that is not just going to be filled by the billionaires developing machines today. Today’s independent lorry-driver who can operate just one lorry today will, in just a few years, be able to invest in and operate several autonomous lorries.

Ultimately, while reports will continue to conclude that robots are coming for people’s jobs, it’s often forgotten just how intensively competitive humans are. Elon Musk argues that humans will have to become cyborgs to beat machines, but people compete with other people, they don’t compete with machines. Humans will always find a way to win, even if they adopt technology from machines in order to compete with each other. Even if some jobs are replaced by robots, those affected will simply re-skill themselves, and perhaps upgrade themselves, to find another profession.

Source: Information Age-The rise of the machines: not so doom and gloom

Automation doesn’t have to be a dirty word…

Without a doubt, the impact of automation on the IT Services industry is a topic of much debate and contention. The challenge is that speculation drives much of the discussion, rather than quality data and analysis.


While the subject of automation has been discussed a few times on the blog, I feel compelled to add my experiences and those of the IT professionals I met on my travels to the discussion.

Not long before joining HfS, I spent several months presenting research on automation at events and conferences across the UK. While the research covered a broad range of topics, automation in IT services was by far the most popular. After a few presentations discussing the increased adoption of automation and the growing capability of the tooling, it became apparent where the popularity of the topic originated – fear. After each session, a small gathering of IT professionals would question me on job security, headcount decreases and how automation augered a bleak future for the industry.

It’s not difficult to see why the audience felt this way. The mainstream media and even some analyst firms have been stoking the climate of fear with considerable vigor.

So I went back to the drawing board and changed my presentation. I took a fresh look at the data to examine what was happening in the industry – did we genuinely need to worry? Beginning with an impactful quote most media outlets were running with – something along the lines of “be terrified, the robots are coming” – I started to dismantle these theories with my research data on employment trends, headcount increases, and industry perception.

While many argued that automation would lead to job cuts, my data showed the opposite. Organizations recognized the importance of technology to their businesses and were investing in the services needed to support it. The data revealed that in organizations with higher levels of automation, workers were not disappearing, they were moving to higher value areas of the support structure – taking on strategic projects or developing services.

At the end of the presentation, I concluded that the reality of automation’s impact on modern IT services was far from the bleak picture painted by other analysts and consultants.

Nevertheless, a few minutes after the session ended the same horror stories started to emerge: IT leaders facing a backlash from staff as automation projects ramp up and professionals working themselves into a frenzy over their job security if projects continued. It was frightening stuff.

Crucially, my research revealed that the cause of this panic doesn’t come directly from the automation itself – there were almost no real-life examples of automation leading to sweeping changes in any of the organizations I was working with. Without a doubt, much of the fear was generated by analysts and media outlets whipping up this distorted perception, but surely there must have been another force at work.

After a bit of digging around the real cause of the hysteria became clear. In organizations with little or no perception issues, it was clear that the leadership team had taken the time to communicate with their teams. Conversely, those with stressed and worried staff had not.

When I questioned an executive who sought advice on soothing fears in his team if he had clearly explained his vision, and what the outcome of the project would be, he replied that it was obvious what he was trying to achieve. If that were true, the perception crisis in his organization would not be there.

Successful automation projects have an engaged team working behind them. The most effective I have seen understand what will be automated and why. They know what impact it will have and, for the most part, agree it was an area of manual work they found repetitive, boring and unfulfilling anyway. They eagerly anticipated a time when they could dedicate their efforts to more meaningful and valuable work.

Under different circumstances, this committed group would be dealing with the same fear and stress as their peers in organizations with less effective communication.

In the noisy information age we now live in, it’s easy to get caught up in the hype. Business leaders have an obligation to provide clear, effective communication that outlines the vision and journey of automation projects. Without the context and understanding they provide, an engaged team can quickly turn into a stressed one. And a stressed team will undoubtedly hold your project back. It’s not hard to understand why an individual afraid of becoming obsolete may not be working towards your goals with total enthusiasm.

Source: HFS-Automation doesn’t have to be a dirty word…

RPA and AI – the same but different

For a conference run by the Institute of Robotic Process Automation (IRPA), there sure was a lot of talk about Artificial Intelligence (AI). Unfortunately, most of that talk only seemed to confuse people about this latest, and most-hyped of, technologies. There were frameworks presented which showed RPA and AI as a ‘continuum’, there were models that seemed to suggest that there was a natural ‘journey’ from RPA to AI, whilst others talked about AI being a ‘must have’ if RPA was to realise its full value. Some presenters talked about a ‘choice’ between RPA or AI. None of which really helped educate the conference attendees on the benefits of either technology. Let’s unravel each of these points so that everyone can be clear on the relationship between RPA and AI.

The RPA/AI Continuum – whilst it can be argued that RPA is the relatively simpler of the two types of technologies, they are very different beasts indeed. The key difference is that the robots of RPA are ‘dumb’ whilst the AI is ‘self—learning’. The robots will do exactly what you tell them to do, and they will do it exactly the same way again and again and again. Which is perfect when you have rules-based processes where compliance and accuracy are critical. However, where there is any ambiguity, usually when the inputs into a process are unstructured (such as customer emails) or where there are very large amounts of data, then AI is the appropriate technology to use because it can manage that variability and, most importantly, get better at it over time through its own experiences. So, if you do want think of a technology continuum, make sure you put a large gap between RPA and AI.

The RPA to AI Journey – there are a number of case studies where companies have implemented RPA and then implemented AI, but only because RPA is a more mature technology than AI. There are far more examples of companies implementing RPA and not implementing AI at all because they actually don’t need the AI. RPA does a fantastic job of delivering labour arbitrage, accuracy and compliance without AI coming anywhere near it. And, of course, some companies implement AI without RPA. It’s not a journey, just a set of choices based on specific demands.

The RPA Dependency on AI – another view that was put forward was that RPA is only valuable when it has AI in support. This is clearly a self-fulfilling view put forward by the vendors that are able to offer both technologies, but it is simply not correct. As mentioned above, many (in fact, most) companies implement RPA without any consideration or need for AI. If you want compliant, repeatable processes, and can feed the robots with structured data, then why complicate and confuse matters by introducing AI?

The RPA/AI Choice – There was yet another the view put forward (which actually conflicts with much of the above) that companies need to make a choice between RPA and AI – in other words which is the best one for them to implement that will deliver their objectives? As should be clear by now, the two technologies actually complement each other very well, for example by using AI to structure unstructured data at the beginning of the process, by using the robots to process the transactions, and then potentially using AI for decision making and/or data analytics at the end.

So, why all this confusion and mis-information? Part of it is obviously self-interest from vendors and providers to create frameworks and models that align with their own capabilities and marketing messages. And, although RPA is now pretty well defined (with that badge of maturity: its own acronym) some of the confusion surely arises from the multiple terms used to describe artificial intelligence; AI, cognitive computing, machine learning, NLP, etc. For now, it is much the best approach to think of AI in terms of how it can help your business, without worrying about what to call it. As the technology develops though a more robust approach is required, which is why at Symphony Ventures we are working on an ‘AI taxonomy’ that will clarify the different types of AI, and therefore help to explain the practical opportunities and uses for AI in our clients. We look forward to sharing this with you and de-bunking much of the confusion around RPA and AI that we have seen over the past few months.

Source: and AI – the same but different 

The Key to AI is Structured Data, BNY Says

The promise of artificial intelligence around streamlining and enhancing banking processes has enticed most, if not all, financial institutions at this point.

But before jumping to AI, FIs need to get their data straight, according to Jon Theuerkauf, managing director and group head of performance excellence at BNY Mellon.

“Forget AI, I don’t even know what it means,” he said at BluePrism World event today. “Why are we jumping on it, if we haven’t done the basics?”

The “basics,” according to Theuerkauf, is having a structured data ecosystem in place.

“We are now in a transitional phase, and are still three to five years away from integrating operating automated environment,” he said. “For example, it takes a long time to train Watson. Why? Because the data does not land itself easily to allow Watson to learn. So, there needs to be an order around that data, and we are now starting to put things together and taking the chaos out of it.”

Currently, BNY is “consciously incompetent” in terms of adopting AI and automation, and “we are really proud of that,” Theuerkauf added. “We now know what we don’t know, we know that we need ways to structure data, and are still working on how.”

BNY began experimenting with bots and automation last year. In less than 10 months, the bank went from 0 to 200 bots, according to Theuerkauf, and has thus far automated more than 100 processes, with 50 more underway.

“We have $3 trillion in payments a day that run through our shop, would you let a robot handle that process? This was actually the second process we had automated,” he said.

The efforts around structure and RPA — robotic process automation — will, eventually, lead the bank to AI and machine learning integrations. “We are figuring stuff out, putting teams together, and hopefully this will lead us to maturing into a machine learning and AI-enabled world,” Theuerkauf said. “RPA is the ‘doing’ part of automation, AI is the ‘thinking’ part.”

Source: Key to AI is Structured Data, BNY Says