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

Rise of Robotics Process Automation Bots in the energy sector

The term Bots often times elicits an intense reaction of fear that robots will rise up and achieve world domination – as depicted in the Terminator movies – but the sensationalist threat of a cyborg assassin like Arnold Schwarzenegger, while wildly entertaining, is not realistic. What is realistic, however, is the subtle movement towards Robotics Process Automation (RPA) Bots, which mimic how a user would interact with an application utilizing the same User Interface (UI) as a human. These Bots are able to execute high volumes of standardized, rules-based, repetitive tasks.

RPA Bots are probably the most mature segment of the broader emerging Artificial Intelligence (AI) and Automation market. This is where most organizations take the first step towards automation, using software robots that act on top of existing interfaces to speed up routine processes that consist of repetitive and fairly simple tasks. These automation solutions are marketed as easy to implement, with very limited need for integration to the rest of the IT architecture.

Such RPA Bots have dramatically changed the manufacturing industry and a similar transformation is now underway in the utilities industry, starting with administrative-heavy and repetitive back-office processes. There are a variety of benefits including improved quality, scalability, increased productivity/speed, agility, cost efficiency, enhanced employee experience, streamlined processes, and employee health and safety, which can all ultimately lead to a reduction in Opex. For utilities, opportunity lies in refocusing the workforce toward more skilled work and leaving the repetitive and human error-prone business processes in the hands of Bots.

Opportunity for applications in the utility sector

For now, in the utilities sector there are a range of applications for RPA Bots, which are currently suitable including rates and tariff, finance and accounting, human resources, audit, and administrative support for operations, with the exception of developing and monitoring strategy and policies, as these processes are difficult to automate due to the cognitive and creative nature of that work.

  • In finance and accounting, major business process areas ripe for RPA Bots include invoice processing, payment processing, travel and expenses, accounting, reporting, asset accounting, process receipts, and customer maintenance. This is one of the most commonly tapped departments for RPA, as processes for functions such as unbilled revenue tend to meet automation suitability characteristics.
  • In human resources, automation can support recruitment requisition, new hire and induction, training and development, change of circumstances, information and systems, leave, payroll, and benefits.
  • There is also an opportunity in procurement surrounding order & delivery processing, strategic sourcing, sourcing, supplier and contract management.

The first wave of automation in the utilities sector

The first wave of automation at utilities is underway and there are already a number of early movers taking the lead on RPA Bots, with many also building data science teams, developing digital strategies, and automating processes, as part of a broader digital utility transformation.

  • In finance and accounting, a U.S. investor-owned utility recently implemented RPA Bots to manage billing for a major commercial & industrial customer, which required 17 new employees to manage. This endeavor was successful in reducing the total number to two full-time employees – a considerable cost savings.
  • In operational processes, an investor-owned water utility implemented a workflow automation system for customer information system (CIS) updates related to customer service orders, which resulted in an increase in the percentage of service orders from 6% to 50%, equating to between three and five full-time employees.
  • In the audit function, a U.S. utility developed an employee data protection audit bot to evaluate the controls governing employee data and ensure Personally Identifiable Information and Protected Health Information are protected from compromise. The bot helped the utility to comply with various utility policies and procedures and industry regulations such as HIPPA, US Privacy Act, Identity Theft Prevention Act, as well as other regulations. The first target was 3,000 instances of Personally Identifiable Information containing social security numbers within files – they now have a fully functional bot running against numerous file type (e.g. excel, word) and file structures. Instead of a procurement process for a similar solution that would have taken months, a data scientist from the utility’s newly established Digital Analytics group built the bot in four weeks, as an existing service to the organization and no external sourcing cost.

Assessing business process suitability for automation

The risk in the industry is that automating existing inefficient processes and tasks might reinforce them, rather than tackling the problems at the core. Therefore, it is critical to put a rigorous process in place to assess automation suitability beforeevaluating RPA opportunities and performing value assessments. With the right processes in place, a value assessment can be used to evaluate an RPA opportunity in terms of both business process suitability and potential benefits necessary to feed the business case for RPA investment, which should be directly driven by business benefits. Value assessments should target areas of the business with high transaction volume, where processes can be standardized and future volumes are relatively certain.

At a high level, processes that are ideal for automation have the following characteristics:

  • High-volume transactions or activity and prone to human error, which are important for ROI (Ex. Payroll: salary payment based on time reporting data, material inventory balancing transactions)
  • Process involves searching, collating, or updating information (Ex. Human Resources: employee and payment information to maintain staff data)
  • Repetitive and rules-based (Ex. Finance unbilled revenue allocation. For work management, work order balancing/closing)
  • Accesses structured data (Ex. Finance billing exception handling)
  • Interaction with Windows or web-based platforms (Ex. IT exchange & sharepoint management)
  • Repeatable process is standardized and workflow-enabled (Ex. IT security operations, backup / restore services)
  • Three or more staff perform the process (Ex. Procurement order and delivery processing – direct purchases and supply chain)
  • Data entry/input intensive (Ex. Finance: invoice processing/data extraction from invoices)

The “Human” element

Although Bots are designed to fill traditional labor functions, it is important for utilities to develop new sets of capabilities to build and manage them. Automation in the organization will require managing this transition with the existing workforce and planning for the future workforce. It will require establishing a specialized team with deep utility business and operational expertise to explore the impact of automation on the business, a focus on staff development and training, and how to derive the maximum value from the human qualities, skills, and discretion of employees.

In many cases, utilities will need a different talent mix as job functions evolve. To this point, according to a Forrester research report, by 2025, automation will cannibalize 22.7 million, or 16%, of jobs within construction, production, office & administrative support, and sales administration roles. In parallel, automation will also create 13.6 million new jobs by 2025 in software, engineering, design, maintenance, support, training, or other specialized job area.

The human element will always be a critical asset for utilities. The kinds of skills that machines cannot acquire are those which are not easy to standardize or codify into an algorithm. These characteristics include creativity, leadership, motivation, intuition, empathy, abstract reasoning and lateral thinking, as well as skills associated with craft.

Making the case for RPA

A RPA Bots strategy, as with any strategic effort, begins with a clear vision and objectives. Developing a RPA strategy must be based on the organization’s business, existing IT strategy, and a high level RPA process analysis in order to validate a comprehensive list of processes and develop the business case to support investment.

A robust process analysis will inventory candidate business processes, which can be scored against two evaluation perspectives: ease of implementation and return on investment (ROI). These two measures will be used to build a utility’s initial cost/benefit analysis and feed the business case for investment.

PA Consulting

Early in this assessment process, ease of implementation can be determined by evaluating both cultural aspects of automation such as resistance to change, and process characteristics such as level of repeatable tasks in the process. ROI can be evaluated in numerous ways including expected cost savings, quality improvement/productivity, and revenue generation due to automation of existing processes. ROI is initially directional and can be assessed as multipliers (e.g. 1X to 10X) or net present value (NPV) estimates.

It is essential to note that as RPA applications are selected and moved to the design and requirements stage, these ROI and ease of implementation estimates must be validated and refined later in the application development process. These will be critical inputs for benefits tracking as part of the governance process. Automation value assessment is the first step in the RPA journey, which end-to-end includes the following:

  • RPA Strategy (value assessment and road mapping)
  • Solutions (design, requirements and vendor selection)
  • RPA Implementation (program management) and;
  • Post-RPA Implementation (governance, Key Performance Indictors, and benefits tracking)

Where are Bots headed?

For the right business functions, RPA can provide a solid business case for removing mundane tasks, particularly in back office and support processes. Forward-looking, more advanced combinations of RPA and AI solutions will enhance each other, which will potentially provide much deeper value and impact on core business processes.

Some organizations are experiencing decision paralysis, while others are seizing first mover advantage. Regardless of where an organization stands, AI and Automation will change or disrupt business models, and thus, understanding the implications will be a fundamental task for leadership over the coming years.

While the utilities sector is still at an early stage with Bots, it is safe to say that there will not be any kind of dramatic robot apocalypse as seen in the Terminator, but rather, automation will gradually transform every organization and every job over time.

Like the Terminator, Bots cannot be disconnected, they are here to stay.

Source: of Robotics Process Automation Bots in the energy sector

This year enterprise robotics software and services will reach $1.5 Bn

When the statement “It’s just like BPR from twenty years ago, but with tech that actually works” rang out at the recent London FORA Summit, the nods around the room were palpable.

2017 has undoubtedly been the break-out year for enterprise robotics software. We witnessed a whole new industry emerge around robotic technologies that can stitch together workflows, processes, applications and desktop interfaces to provide a genuine transformation of the digital underbelly for so many enterprises, many of whom have suffered for decades from inefficient manual workarounds and spaghetti code clogging up their ability to access data and run their businesses properly. Today, the emerging solutions available on the market do not load the enterprise transformation blunderbuss with silver bullets, but they do provide a starting point to improve fundamentally the data underbelly of an organization. And, for so many organizations, they are turning to robotics software RPA (Robotic Process Automation) and RDA (Robotic Desktop Automation) as the starting point.

Robotic Process Automation

The global market for RPA Software and Services will reach $898 million in 2018 and is expected to grow to $2.2 billion by 2021 at a compound annual growth rate of 54%.

RPA Definition:

Example use-case: automating invoice processing across multiple business applications handling rule-based exceptions. RPA is different from traditional automation software as it is inherently capable of recognizing and adapting to deviations in data or exceptions when confronted by large volumes of data. In effect, it can be intelligently trained to analyze large amounts of data from software processes and translate them to triggers for new actions, responses, and communication with other systems. RPA describes a software development toolkit that allows non-engineers to quickly create software robots (known commonly as “bots”) to automate rules-driven business processes. At the core, an RPA system imitates human interventions that interact with internal IT systems. It is a non-invasive application that requires minimum integration with the existing IT setup; delivering productivity by replacing human effort to complete the task. Any company which has labor-intensive processes, where people are performing high-volume, highly transactional process functions, will boost their capabilities and save money and time with robotic process automation. Much fr RPA is self-triggered (bots pass tasks to humans), but requires human intervention for judgment-intensive tasks and robust human governance and to make changes / improvements.

Similarly, RPA offers enough advantage to companies which operate with very few people or shortage of labor. Both situations offer a welcome opportunity to save on cost as well as streamline the resource allocation by deploying automation. The direct services market includes implementation and consulting services focused on building RPA capabilities within an organization. It does not include wider operational services like BPO, which may include RPA becoming increasingly embedded in its delivery.

Robotic Desktop Automation

In addition to RPA, the other software toolset which comprises the emergence of enterprise robotics software is termed RDA (Robotic Desktop Automation). Together with RPA, RDA will help drive the market for enterprise robotic software towards $1.5bn in software and services expenditure in 2018 (with close to three-quarters tied to the services element of strategy, design, transformation and implementation of enterprise robotics). HfS’ new estimates are for the total enterprise robotics software and services market to surpass $3 billion by 2021 as a compound growth rate of 39%.

RDA Definition:

Example use-case: automating transfer of data from one system to another. RDA is essentially surface automation, where desktop screens (whether desktop-based, web-based, cloud-based) are “scraped”, scripted and re-programmed to create the automation of data across systems. A well-designed RDA solution can automate workflows on several levels, specifically: application layer; storage layer; OS layer and network layer. Workflow automation on these layers requires equally specific technologies but provides advantages of efficiency, reliability, performance and responsiveness. Much of this automation needs to be attended by humans as the automation is triggered by humans (humans pass tasks to bots), as data inputs are not always predictable or uniform, but adaptation of smart Machine Learning techniques can reduce the amount of human attendance over time and improve the intelligence of these automated processes. Similarly to RPA, RDA requires human intervention for judgment-intensive tasks and robust human governance and to make changes / improvements:

The Bottom-Line: Automation and AI have a significant part to play in engineering a touchless and intelligent OneOffice

However which way we spin “digital”, the name of the game is about enterprises responding to customer needs as and when they occur, and these customers are increasingly wanting to interact with companies without physical interaction. This means manual interventions must be eliminated, data sets converged and process chains broadened and digitized to cater for the customer. This means entire supply chains need to be designed to meet these outcomes and engage with all the stakeholders to service customers seamlessly and effectively. There is no silver bullet to achieve this, but there is emerging technology available to design processes faster, cheaper and smarter with desired outcomes in mind. The concept was pretty much the same with business process reengineering two+ decades ago, but the difference today is we have emerging tech available to do the real data engineering that is necessary:

Click to Enlarge

In short, every siloed dataset restricts the analytical insight that makes process owners strategic contributors to the business. You can’t create value – or transform a business operation – without converged, real-time data. Digitally-driven organizations must create a Digital Underbelly to support the front office by automating manual processes, digitizing manual documents to create converged datasets, and embracing the cloud in a way that enables genuine scalability and security for a digital organization. Organizations simply cannot be effective with a digital strategy without automating processes intelligently – forget all the hype around robotics and jobs going away, this is about making processes run digitally so smart organizations can grow their digital businesses and create new work and opportunities. This is where RPA and RDA adds most value today… however, as more processes become digitized, the more value we can glean from cognitive applications that feed off data patterns to help orchestrate more intelligent, broader process chains that link the front to the back office. In our view, as these solutions mature, we’ll see a real convergence of analytics, RPA and cognitive solutions as intelligent data orchestration becomes the true lifeblood – and currency – for organizations.

Source: year enterprise robotics software and services will reach $1.5 Bn

Why A Digital Workforce Requires a New Mindset

Previously we considered the questions:

  • “What does your business do?” and “What are the goals of your business?”

To follow these, a further crucial question must be asked:

  • “Do your processes and goals align with the demands of your clients?”

Whether your customers are internal or external, you will be delivering ‘outcomes’ to them. However, your business and clients evolve. Rather than simply constructing a ‘new way’ to deliver the same outcomes, it is essential that they are re-assessed to fully understand what they need to be. Essentially, delivering an outcome that customers have always had, even if in an improved way, is not as good as delivering what they actually want and need.

For example, a cruise liner might take you from Southampton to New York in 7 days, but while this is the same perceived outcome as an airline delivering you to the same destination in 6 hours, do we truly believe this is the same outcome? Furthermore, it is critical to be aware that customers’ needs will change over time, so it is essential to keep up to date with their current state of mind and expectations.

Determine the Root Cause of The Problem

Many problems are usually identifiable, particularly when processes are well-known and understood. What is often less clear is the cause, or indeed the resolution. Consider the problem “too many errors are made ” or “customers find the turnaround too slow”. These are the symptoms of the problem; only by investigating the process, hierarchy, complexity of rules and clients’ needs can the root cause be properly identified and resolved.

As businesses grow they become complicated by the multitude of stakeholders, processes and, of course, beloved systems. This combination ends up driving how your operators work and constrains your business. When looking for process improvements, it is common to focus on how to work within the limitations we are ‘bound by’ rather than thinking through the outcomes that are required. Focusing on and addressing the root cause of the problem will allow you to build processes from the foundation up rather than from the symptom down. Be aware, there will be many limiting factors placed in your way, so always be mindful of the outcome you truly seek.

Cut Futile and Frustrating Work

With the mentality of building upwards, you consider first only what is necessary, then the things that add benefit. Anything outside this should be cut. There is no point in having a smooth-running process that adds no real worth to the business.

Cutting futile work is vital to the Future of Work mindset. As technology advances and businesses become more automated, customers will rightfully expect faster results with minimum fuss. Futile work takes up valuable resources (both human and virtual) and slows down cycle time. It also makes things unnecessarily complicated, thus providing more room for error and further exceptions for your team to handle. Streamlined businesses (no matter how complex) have flexibility and stability that allow you to develop with the changing technological landscape and changing client demands.

An important consideration is that while technology appears to give us ‘quick fixes’, it is important to avoid is configuring automation for unnecessary or overly complicated work. REMEMBER: Robots are not the answer to process problems; they are amplifiers, able to perform processes with great speed, accuracy, and agility if given the means to do so. Any unnecessary or overly complex work reduces their efficiency and potentially the benefits to your business.

Align Processes and Goals With Client Demand

As you re-structure your processes, you should absolutely seize the opportunity to do so with your client goals as well as your own objectives. Only with this 360˚ view can you determine which tasks are necessary and whether they add value to your overall business. The idea is to get the maximum alignment possible between processes, goals and demands so that they can be managed within your core business structure. Any outliers are exceptions and should be minimised to improve the fluidity and efficiency of the end-to-end process. This is shown in the diagram below:

A great example of this comes from one of our largest global clients. They wanted to introduce RPA to many of their processes, so began the initiative under a process improvement team. This meant they could look holistically at global processes, streamline systems and implement technology to align with their corporate goals and demands. It is imperative to note that they utilised (dare I say ‘leveraged’?) Future of Work technology, rather than being driven by it. RPA can absolutely assist in removing the requirement for humans to perform mundane work, but without looking at the end-to-end process, you cannot properly identify process steps that can be completely accelerated or in many instances removed completely.

Example: Is the Best Customer Service, No Customer Service?

Often seen as a beacon for challenging traditional customer service models, Amazon has revolutionized the experience of shopping online. It may be fair to suggest that it has transformed the expectations of shopping in its entirety. The ability to make an instant purchase even if delivery comes later satisfies the human need to consume and when coupled with the fact that you can browse and buy from your own home, all without having to fight trains, traffic, and parking restrictions. Furthermore, the marketplace is extended way beyond your local high-street to a global distribution network.

It really exemplifies the Martini (another brand creator) concept: anytime, anyplace, anywhere. No matter where consumers are globally, so long as they benefit from the new utility: Wi-Fi, they can be shopping. With the explosion of online payments/billing to support the marketplace, customers find shopping online quick, intuitive and satisfying. The staple preserve of physical shops has been ‘what if something goes wrong? Customers are understandably concerned that their garments won’t fit, that they won’t be allowed a refund, that their product will be faulty. The strongest e-commerce companies trust their customers first (complex algorithms tell them that most customers are honest and rapidly weed out the fraudulent ones). All of this processing and calculation is managed in the digital cloud.

While any retailers make returns onerous, returning a product through Amazon is as smooth and intuitive as purchasing. The returns process has been carefully engineered with customer experience (outcome) at its heart. All returns are managed within the Returns Support Center, and are tailored to the customer’s method of purchase and immediate needs. It is entirely fuss-free and easy. When customers have questions, the process moves into the exception path. Immediately, staff are available and ready to support, thus driving up customer and, in fact, employee experience.


There are great examples of leveraging automation technology to perform the work to which it is best suited: high volume, rules based and transactional. Work that does not fit that profile is deemed high value, judgemental work and should be where we focus our human workforce. Humans can intervene where rules do not exist or customers do not accept the rules. With the correct application of strong process rules and governance, human and virtual employees can work together in a seamless and fluid process in which the client is king! At Symphony, we believe this is the key to a successful future of work. Determining the root of the problem, cutting futile and frustrating work and aligning business processes and goals with client demand must be the first steps in a business transformation. The solution to this transformation is the combination of people, processes and technology, because only upon a foundation of well-defined and streamlined processes can a robotic solution excel.

Source: A Digital Workforce Requires a New Mindset

The Impact of Intelligent Automation Infographic

Impact of Intelligent Automation

In this infographic, we look at the impact of intelligent automation across a business — by looking at three key areas; organizations, employees and customers. The statistics illustrated in the infographic demonstrate the transformative nature of intelligent automation and how it will fundamentally impact the future of organizations, the work their employees do and the services they deliver to customers.


By combining AI, RPA & other automation technologies, intelligent automation allows organizations to truly transform the way they work. By combining human workers and virtual workers, organizations can augment their workforce – which will significantly impact the way in which they perform work and the potential value they can deliver to customers.

Organizational Stats

-64% of management and financial tasks could be automated by 2020

-80% of rules based processes could be automated

-52% sales processes & tasks could be automated by 2020


In organizations that are embracing intelligent automation, the work their employees do is changing. Instead of employees spending their time on repetitive, mundane tasks, now they are spending time adding value, being creative or interacting with customers. This not only removes them from tasks that are prone to error, but employees can now partake in more rewarding work and develop more fulfilling careers.

Employees Stats

-25% of work could potentially be automated

– 60% of jobs could have 30% of tasks automated

– IT departments spend 30% of their time basic tasks


When employees and organizations spend less time on low value tasks, they have more time to spend with customers. And, by using intelligent automation, organizations can return up to 30% of employee time back to the business, which means they can spend more time listening intently to customers and enhancing customer experiences — increasing customer satisfaction levels.

Customers Stats

-80% cut in average handling time

-90% reduction in response time to customers

– 50% increase in customer satisfaction levels

Source: Impact of Intelligent Automation Infographic

Google’s AI guru predicts humans and machines will merge within 20 years

Public perception of artificial intelligence technology, seems to lie somewhere at the intersection of existential fear and cautious optimism. Yet there’s a growing movement of people who believe AI is crucial to the evolution of our species. These people aren’t outsiders or outliers — they’re actually directing research on the cutting edge at companies like Google.

Ray Kurzweil, Google’s guru of AI and futurism, spoke last week at the Council for Foreign Relations, in an intimate Q&A session. His views on the future of humanity might seem radical to a public that’s been cutting its teeth on doomsayer headlines featuring Elon Musk and Stephen Hawking warning about World War III.

He’s quick to point out that today, right now, is the best our species has ever had it. According to him, most people don’t know that the world we live in currently has less hunger and poverty than ever before. “Three billion people have smartphones, I thought it was two but I just found out it was three. In a few years that’ll be six billion.” he says.

The deadliest war in recorded human history, World War II, ended just 72 years ago. In the time since, humanity has engaged in what feels like countless skirmishes, police actions, and outright wars. And while the US remains engaged in the longest war in its history – with no end in sight – the human species is currently enjoying the most peaceful period in the history of our civilization.

The existential fear is that AI will somehow compromise this progress and send us careening into the next extinction-level event. If technology like the atom bomb made World War II so much worse than everything before it, doesn’t it follow that WWIII will be even more devastating?

It’s more complex than that, according to Kurzweil. He believes part of the reason we’re able to coexist so wonderfully (in the grand historical scheme) for so long is because democracy has begun to take hold globally. He also believes the rise of democracy is the direct result of advances made in communication technology. According to him:

You can count the number of democracies a century ago on the fingers of one hand, you can count the number of democracies two centuries ago on one finger. The world has become more peaceful. That doesn’t appear to be the case, because our information about what’s wrong with the world is getting exponentially better.

So what’s next? He believes we’ll all be less biological, because humans are always evolving, and the next step of our evolution will be the internal implementation of technology. The human-robot hybrid won’t be a monstrosity of metal. It’ll just be a chip in your brain instead of an iPhone in your hand.

In the future it’ll be no more shocking to think about the weather in Hong Kong and get an answer than it is to say “Hey Google, what’s the weather in China?” and receive accurate information from a glowing rectangle with a speaker inside of it.

Kursweil believes “medical robots will go inside our brain and connect our neo-cortex to the smart cloud” by the year 2029.

That’s a jaw-dropper, even for a technology journalist who writes about AI regularly. It’s pretty hard to imagine people walking around with their brains connected to the cloud before Justin Bieber turns 35.

But dismiss Ray Kurzweil’s predictions at your own peril: he’s seldom wrong. When it comes to technology he’s gone on the record with hundreds of predictions, which is what futurists do, and he’s correct over 90 percent of the time.

According to Kurzweil the future is incredible, but it’s also worth mentioning that his view of the present is pretty fantastic as well. He reminds us that “just a few years ago we had these devices that looked like smartphones but they didn’t work very well,” and he’s right.

Today’s smartphones know how to respond to complex voice commands like “find all the pictures from my trip to San Francisco” and “play Star Trek The Next Generation season three, episode 16.” Today’s phones can recognize who is talking, pick out your voice even when music is playing, and execute the command without a hitch.

But just a few years back, most of us quickly gave up on using voice control regularly, because we were sick of repeating ourselves. We figured we’d wait until the technology got better. Tada! It’s better now.

The truth about AI, according to experts such as Ray Kurzweil, is that there’s no part of our lives that won’t be directly affected by it. As individuals we probably won’t notice the changes in real-time, but our dependence on machine learning will increase at exponential rates.

The law of accelerating returns is behind the artificial intelligence revolution — and Ray Kurzweil’s predictions. The very limits of what is “possible” concerning machine learning are going to require reevaluation on a daily basis going forward.

Source: The Next Web-Google’s AI guru predicts humans and machines will merge within 20 years