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My first official duty as a United States Marine was to guard the sea trial test of the world first fully autonomous weapons system: The Phalanx Close in Weapons System or CIWS: a fully automatic, self-contained, airborne threat killing machine that can be found on virtually every combat vessel in the world. The Phalanx captured my imagination immediately and permanently. Little did I realized this seed and others sown by my experience in the Military would synergize with my operations and RPA career into out of the box automation methodologies and theories that fill a 160-page Book.

Topics in “Evolutionary Database Design,” at www.martinfowler.com/articles/ evodb.html. Word, and database identifier(s). Back up/restore large data sets. Create specialized functions for especially large data sets or entire databases. Duvall.book Page 111 Thursday, May 31, 2007 9:30 AM. Free developer versions.

Marine Robotics effectively creates an Intelligent Operations as a “spin out” business model or company within in a company. With its own distinct culture to optimize the exponential productivity of Cognitive Automation. Marine Robotics doubles the productivity of Cognitive Automation. This development is necessitated by the extreme competitive pressures of asymmetrical small start-ups and other lightly structure, agile competitors. Oddly, Marine Robotics has its roots in the Pareto Principal or 80/20 rule that holds true for People, as well as, Processes. Since, Cognitive Automation can automate 80% of any Service Operations process, an Intelligent Operations only requires 20% of the previous staff. Twenty percent of employees in any organization are internally motivated to work extra hard, which is critically important, since Automation intensifies work that remains.

Twenty-Percenters on average are twice as productive as Eighty-Percenters. Marine Robotics takes advantage of these truths by an intentional business model and HR strategy that only uses Twenty-Percenters.

Technologically, Marine Robotics uses advanced analytics to quickly find automation opportunities; enhance ability to design extremely complicated, agile robots; and as part of HR’s strategy to identify and select Twenty-Percenters. Experts, who are capable of a highly adaptive RPA that creates extremely agile robots with web based control matrixes.

Effective remote control allows humans to provide robots with codeless real-time requirements updates. This greatly increases the rapid responsiveness and agility of RPA and lays the best foundation to integrate Cognitive Automation. Together, the HR and technology elements of Marine Robotics form hybrid Ops/IT Human/Robot teams that create productivity ROI of 600% plus.

Not with low hanging, simple, rules based, structured data, and stable processes that standard RPA can automate. Rather complex, judgment based, unstructured, highly fluid processes.

While Marine Robotics optimizes the use of automation in Operations, most large Enterprises have other uses for RPA based Automation. BiModal RPA with an Operation IA Ops/Dev and Enterprise IA Dev/Ops automation business model is required to meet all the automation needs of a large enterprise.

Once Cognitive Automation fully matures, Operations IA Ops/Dev and Enterprise IA Dev/Ops can be unified into one team that supports the Robot Pilot, a new type of technologist that creates autonomous automation and provide oversight. Yet, significant documentation, as well as, my own personal experience, testifies to significant barriers that exist to Marine Robotics and The Robot Pilots evolution. The Financialization of most large US businesses has led to decades of excessive M&A and Outsourcing and Offshoring activities. This leaves Leadership ill-suited for the intense change management necessary to adapt Cognitive Automation.

Financialization also destroyed the Culture of Trust necessary to fully engage and empower the number one source of successful innovation implementations: Operations Front Line Leaders and Subject Matter Experts. Financialization increased layers of Middle Management bureaucracy, which further disrupts needed innovation change management necessary for Cognitive Automation optimization. Finally, the ultimate impact of Artificial Intelligent based Automation on Humanity, Industries, Enterprises, and Individuals depends on the moral, accountable choices of Leaders and Individuals. Deliberate spiritual/moral training is needed from early youth, beside the current STEM programs, to ensure the right choices are made. Though it is not necessary to use analogies to describe or clarify complicated, perhaps new and hard to understand concepts like Marine Robotics. Since the first Industrial Revolution, large organizational business models and leadership paradigms had military roots. Today’s Fourth Industrial Revolution is no different.

For the last couple of decades, the “War on Terror” has been conducted by various types of special forces against an asymmetrical enemy. Both sides use an array of “Smart Technologies”. Nation States cannot afford to give up their large structured military forces that can project massive power globally. Yet, these types of forces are not efficient, nor effective against small, agile forces that use “Smart Weapons” to wield substantial firepower, while remaining elusive target for conventional strategies and tactics. In like manner, large Corporations face a dilemma and some are already fallen. Marine Robotics provides a solution to level the playing field!

To buy time and lay a solid foundation for what’s next – the Robot Pilot! Get your copy of Marine Robotics today! MANAGING DISRUPTIVE CHANGE: A NEW OPERATIONAL MODEL ENABLED AND BUILT BY INTELLIGENT AUTOMATION – Global Intelligent Automation Market Report (H1 2017) Before proceeding with my applied review of the above titled work, I need to explain why I think my soul was instantly riveted by its content; in a word “Ambivalence”! What? That’s right “Ambivalence”. What the heck?!?!?! In less than one week, a year and a half year ago, I wrote “The Inverse Stack Fallacy – Another Reason Innovation is Especially Hard inside an Enterprise. I reprint it now: In today’s WSJ article titled “Why Companies Are Being Disrupted”, found in the Business and Tech section on page 4 (link attached, if you are a subscriber), the author Christopher Mims got absorbed with the work of venture capitalist Anshu Sharma. Anshu believes he knows, twenty years after the watershed work “The Innovator’s Dilemma”, why Enterprises with the resource to build “the next big thing” often fail to do so.

In a nutshell, Anshu called the phenomenon the “The Stack Fallacy”. Christopher went on to define in the IT World, what the term “Stack” means; quote, “the “stack” is the layer cake of technology, one level of abstraction sitting on top of the next, that ultimately delivers a product or service to the user. On the Internet, there is a stack of technologies stretching from the server through the operating system running on it through a cloud abstraction layer and then the apps running on top of that, until you reach the user.” With that definition, the “The Stack Fallacy” then become relatively easy to understand. Enterprises routinely underestimate how hard it is to know what the User wants at the layer above where they are currently providing a product, i.e. Moving up the “Stack”. Christopher goes on to cite three examples, Oracle trying to create CRM software; Samsung, smart phones; and Google, Social Networks. Yet, moving down the “Stack” is relatively easy, i.e.

The Enterprise is the User, so they know exactly what they want; for example, Apple making their own “Chip”. What if the User is a front-line associate within the Enterprise, as is the case with RPA or at least it should be?

Then the “Stack” is turned upside down! Adding additional challenges to innovation; especially in automation and analytics, since much of the time, the real bottleneck is not technology, rather increasingly complex and fluid requirements. Don’t you see it? With RPA, it is not just that front line operators, whose functions are being automated are the best source of requirements, in the most advance applications of automation, they are also the users in newly formed hybrid teams of human and machines, i.e. Cybernetic Automation. This ownership will only become more complex when “Cybernetics Automation” evolves to “Guide Autonomous Automation”. IT and Executive Management need to wrap their minds around the fact that some of the most advance automation is internal and down the organization ladder.

That the “Grass Root” Users are the clients of RPA. The “Stack” is Inverse; turned upside down!

Need time to meditate on this one! Basic RPA: Where did Robotic Process Automation (RPA) come from; Why it is call that? The term Robotic Automation (RA) refers to the automation of industrial and clerical processes using robots. In the 90’s, RA was mostly to improve delivery accuracy & speed. In the recent past RA was widely developed and deployed in Business Process Operations (BPO). Robotic Process Automation’s (RPA) roots in RA include the use of industrial robots in manufacturing and the use of software robots in automating clerical processes in services industries.

In the latter case, the use of the term robotics, metaphorically conveys the similarity of those software products, which are produced to provide a generic automation capability configured within the end user environment to execute manual and repetitive tasks; like their industrial robot counterparts. The metaphor is apt in the sense that the software “robot” now mimics or replaces a function classically associated with a person.

RPA’s identity as such can probably be traced to the era of 2003-5. Yet, the current levels of interest contrasts with the immense amount of skepticism that was faced in those early days. The biggest question back then was – “Isn’t Robotic Automation just a patchwork?” that sits on top of an application or multiple applications to do something useful? Isn’t it more elegant to replace or enhance the underlying application itself? Mimicking user actions and doing so in an automated manner, though fascinating to watch, seemed like a perversion in the IT world. Today, few question the logic behind the technology that allows team members in a company to configure computer software or a “robot” to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems.

Any company that uses labor on a large scale for general knowledge process work, where people are performing high-volume, highly transactional process functions, will boost their capabilities, save money, time, as well as, increase quality and accuracy with RPA software; provided the wisdom contain in this paper is taken seriously. Just as industrial robots remade the manufacturing industry by creating higher production rates and improved quality, RPA “robots” are revolutionizing the way we think about business, IT support, and workflow processes, remote infrastructure, and back-office work. RPA provides dramatic improvements in accuracy, cycle time, and increased productivity in transaction processing, while it elevates the nature of work people do by removing dull, repetitive tasks. What is RPA and Why is it Needed? Business Enterprises provide products and services to society. It is Leadership responsibility to ensure this is done in a safe, ethical, cost effective way, while remaining profitable. This is a tremendous challenge in today’s fast moving, complex, demanding markets.

Business Leaders need to meet the escalating expectations of 21s century customers, while cutting cost at the same time to remain competitive against smaller, more agile companies, who can leverage new technologies quicker and more effectively. The technology of RPA can be applied specifically to a wide range of industries. As RPA brings more technologically-advanced solutions to businesses around the world, operating models that adopt automation, whether in-house or offshored, will cut costs, drive efficiency and improve quality. Generally, enterprise systems are built to support a certain industry. Each organization within the industry customizes these systems per their needs and policies.

Almost all popular enterprise applications (banking applications, ERPs, SCMs, CRMs, PLMs) perform processes needed in an organization. Often these processes span multiple roles and involve multiple individuals. Every process, like an account opening process has multiple scenarios with multiple process variations. Like an iceberg, the visible process variations that are used commonly and supported in the current application are a fraction of the total required scenarios.

There are many more that are infrequently used. Typically, the process scenarios that are used rarely are the ones that stretch the application to its utmost capability. A common automation target, such as a purchase ordering process may have 4-5 frequently used scenarios and another 20-30 variations, which are used infrequently. Complex or esoteric scenarios may cover another 15-20 situations and a process scenario may suddenly emerge that is a significant cost to handle manually. Process scenarios evolve day by day. The number of scenarios unsupported by the Application may increase to the point where the Application itself may require costly enhancements with a long cycle time.

The cost of implementing these changes may be huge for an individual organization or department. In these scenarios, RPA can play a critical role to avoid costly manual operation of enterprise systems. RPA can be built quickly to fill the gap between enterprise systems and end user and thereby minimize the cost of manual operations. To do this optimally, a RPA program should be set up strategically in alignment with the Enterprise’s Business Model and Culture; however, as will be seen later, a successful program; especially with the addition of Cognitive technologies, will require significant change management for both the Business Model and Culture. Then, the RPA Program can focus on selecting the processes best suited to automate. This is where the magic happens, tactically; at the process level.

Generally, if a process is highly repeatable and of sufficiently high volume it probably is a good candidate for automation. Besides reducing the costs of an organization’s Back Office data processes, RPA delivers multiple benefits like: 1. Avoid Human Errors 2. Streamline Process & Communication 3. Improve Process Efficiency 4.

Rapid Scalability 5. Reduced Transaction Time 6. Avoids Infrastructures and Other Load Costs 7. Avoid Capital Spend to Customize Existing or New System 8. Rapid Agility to Respond to Changing Business Processes 9. Increased Innovation and Customer Satisfaction Focus 10.

Retain SME Process Knowledge and Develop Automation Expertise RPA software consists of multiple components that capture digital data, which can include screen scrapping, digital image recognition, or the ability to access a server, or link to a website. RPA can use of Rules Engines like those found in Business Process Management (BPM) tools. However, RPA’s sine qua non (without which, not) is that RPA works at the graphical user interface layer; therefore, is non-invasive with the underlying code, greatly reducing developer and SDLC related IT cost, greatly increasing agility and speed of implementation. Six Top Processes for RPA to automate: 1.

Pre-checking – RPA filters out simple cases and leaves complex ones for manual handling. Structured and Unstructured inputs – input from a customer or supplier through the web, structured forms, or unstructured emails, letter and/or faxes, etc. With Natural Language Processing (NLP) and other forms of Artificial Intelligence. New Systems with missing functionality – when it is not possible to configure a system to do exactly what you need, then RPA can automate the gaps in a more cost effective way than a manual process.

Dirty Interface – Gartner describes this as rekeying, because there is no embedded integration between Systems, so you need to go through the User Interface. This is true where inputs need to be provided to more than one system.

OCR and Computer vision technologies are beginning to enhance this strength of RPA. Multiple Sources of Data Input – a process requires multiple inputs from different sources that is handled by a manual process, then it is suitable for RPA. Manual Checking, Decisions, and Calculations – if they follow a set rule, then it can be automated. RPA Operations The single most important leadership decision for an Enterprise wide RPA program, unless there are extra ordinary reasons, is the commitment to Business Control and Ownership. IT is a critical partner; especially during initial implementation; however, the surest way to not realize all the benefits of a RPA program is for Business not to be in the driver’s seat!

While this seems straight forward enough, for most organizations this represents a new paradigm that is hard either to accept or implement. After the decision for a Business Centric RPA program, then next most difficult and critical piece of a successful RPA initiative is to identify resource for the in-house team. These resources consist of SMEs, Business and System Analysts, Operations Experts, Developers, UAT Testers, Program Managers, etc. Who ideally have the following skills collectively: 1. Operational knowledge 2. Knowledge of IT set-up 3.

Strong process knowledge 4. Agile development experience 5. Ability to learn and understand how the RPA tool automates and then spot process automation opportunities in the Enterprises’ systems 6. Ability to engage stakeholder across all levels and areas of your business To select, train & develop these automation resources normally takes anywhere between 3-6 months. With exceptional leadership and support, shorter time frames are realistic.

From Proof of Concept to a mature RPA Team capable of continuously growing automation that can scale to other parts of the Enterprise; one year is a realistic goal. Leadership needs to focus on the following five critical areas to ensure a successful Enterprise RPA program: Processes: As elaborated on earlier, except for the simplest or most used processes, only partial documentation exists. The remaining details are in heads of different Subject Matter Experts (SME), which means process scenario inconsistencies and variations.

SMEs’ time is pure gold. This is where an experienced RPA analyst or developer is worth their weight in gold. They have tools and techniques, like screen capture tools and analytics, that greatly reduce the amount of precious SME input necessary to develop the optimal robot designs. While there are applications like Open Connects’ Comprehend analytic application specifically design to reduce reliance on SME’s for process requirements, Work Force Optimization software might prove promising here as well? RPA is rules based, yet is far more agile at updating those rules than other types of automation.

Ideally, data inputs and workflows should be stable; however, there are ways to make RPA adapt to very fluid and complex processes. This is where the beauty of RPA shines compared to other automation approaches. Its non-invasive nature and quick adaptability means that in many instances, a robot design can go into production, without it being fully validated. Production can be used safely in many instances to find exceptions and requirements that were missed in the design phase.

While this may sound reckless, it is not. Experienced RPA analysts and developer know how to work with the Business to make this a safe and very profitable technique. Sometimes it is more beneficial to do process redesign and a good RPA strategy anticipates future changes and builds adaptability into the initial robot design. Still, the beauty of RPA is how adaptable it is to unforeseen changes. This process could be done as an agile approach with scheduled sprints that pick-up changes / adaptations to the initial process delivered into production. Data: RPA is the source of rich analytics to monitor robot performance, find additional automation opportunities, or trouble shoot issues. It also provides excellent intelligence about the processes and system environment to Management and SMEs, many times faster than dedicated BI systems.

Increasingly, other data driven technologies like Big Data and AI powered analytics can interact with RPA to provide huge ROI synergies of Cognitive and even full blown AI automation. While Cognitive gets all the press these days for good reasons; there is another category of technologies that is just beginning to be recognized as a natural partner of RPA: Workforce Management software.

Designed to optimize human productivity, it should not take too much adaptation to help robots increase their productivity, as well as, integrate more advanced forms of hybrid Human/Robot operations teams. People: People are both the greatest leadership challenge and key competitive differentiator. It is no different with RPA, except for one very, very important factor, automation multiples the value gained or lost in terms of the effectiveness or dysfunction of the human/machine interface. Add Cognitive and this principal is magnified exponentially!

Optimizing the capabilities of the RPA Team and relationship to stakeholders becomes the key to realizing RPA’s full potential. Leadership needs to give as much or more attention to human resources as to technology. To realize the full impact of automation and related technologies demands innovators, tenacious problem solvers, and committed improvisers, as well as, a restructure of the Business Model and Leadership Paradigm that empowers and engages them.

Business Model: Business ownership and control of a RPA program is already an established Best Practice, unless there is overwhelming reason for it to reside with IT. Quickly becoming a Best Practice for mature RPA programs is centralization in some form of a Center for Excellence or COE. While not a Best Practices yet, many factors point to the most advanced forms of RPA deployment will be hybrid Human/Robot operations units. Not to be confused with attended, recorder based RPA in the hands of Operations employees. Rather fully developed RPA per COE governance, yet continuously adapted and controlled by Operation experts and embedded IT assets under Operation leadership who own the processes automated. Leadership Paradigm: Besides business ownership and control, two other leadership factors are a critical factor to maximize RPA’s potential: End to End Robotic and authority to automate push it as far to the end users as possible.

This will go a long way to minimize the “Wicked Problem”, i.e. Complex bureaucracy that for decades has prevented software from being optimized for the end users, which as noted earlier results in exponential, not incremental productivity loss where automation is concerned. Participatory, cross-functional teams that have the authority and resources to conduct end to end robotics for Enterprise clients should go a long way to cure the “Wicked Problem” once and for all! IT Support and Other Considerations for Successful RPA Implementation: In the implementation phases of RPA, IT Department’s support will be high. It will diminish and equalize to a minimal to moderate level, depending on the automation strategy adopted, as Enterprise Operations takes over more and more ownership.

Practically, in the early stages of an RPA implementation, overall leadership may default to the IT department. Whether this happens intentionally or not and even if it is beneficial, leadership needs to make it clear that this is just an early evolutionary stage of the RPA program and not its end state.

The Business must take ownership! What IT Department support is needed during the RPA implementation stage? Typically, Best Practice indicates that one or more application servers are needed to manage communication between the RPA robots and Database where the automated solutions are stored.

In addition, RPA Best Practice ideally require three separate IT environments to operationalize a robust automation program: • Development – where automation solutions are built or enhanced • Test – where automation solutions, original or enhanced are validated. • Production – where automation solutions once validated and signed off are migrated into the production environment. To create a RPA ready Environment, the IT Department will typically need to: • Configure the application server to allow the RPA software to create its own database • Configure window settings such as font smoothing and screen resolution settings to be able to create images necessary for creating robots • Configure any applications needed to optimize robot functionality with processes being automated. With most RPA software, this should be minimal.

• Provide full access to any application used by the business processes to be automated • Provide login credentials to developers for access to the Development, Test, and Production environments • Provide robots login credentials for access to target applications (there may be different credentials needed for different instances of the application. Some data security requirements may not allow this and it will impact ROI) Once the Environments are set up, applications configured, and access provided, IT Department support should significantly reduce. Ongoing, frequent communications between the IT Department and the RPA Business Team are needed for future changes to any of the systems the robots interact with are assessed for impact and mitigated as necessary. The following are ways that the IT Department can maximize their effectiveness supporting a RPA program: First, it is vitally important to designate a permanent IT liaison to work with the RPA Business Team. RPA is “Light IT” and constitutes a different way of working and thinking. A dedicated IT resource will better be able to coordinate and communicate the needs of the RPA Business Team to different resources within the IT Department An early decision should be whether a desktop RPA, virtual server based RPA, or both automation solutions will be used, which need different IT support.

Then IT can determine what hardware they need to provide and make sure it is available per to the automation scope. Typically, the IT Department will not be involved in the RPA software support or directly with the processes being automated.

These are the Software Vendor and RPA Business Team’s responsibilities respectively. What is key for IT to understand is how RPA accesses the various systems necessary to automate various processes. RPA team members and robot that access infrastructure through firewalls to data centers can make anyone nervous. The IT Department needs to understand the various requirements and their potential issues. The IT Department needs to do due diligence. How to avoid delays common to RPA implementations: RPA is an extremely agile automation tool that is quick and responsive by nature.

These are excellent attributes that provide leadership with many options to resolve issues and provide ROI they did not have previously. These same qualities can also generate the mentality of “how quickly can we automate the process and have it in production’’ without regard for due diligence. While requirements for governance and coordination is typically much less than non-RPA software, so that some have termed it “Light IT”; nonetheless, RPA needs to have an operating model set up with Standard Operating Procedures (SOPs) that ensure all stakeholders are informed and engaged. Otherwise unnecessary roadblocks and delays can be expected that will prevent optimal automation. The following are some SOPs that needs to be spelled out: • How will processes be selected and prioritized for automation? • What time lines will be typical for various types of automation?

• How will the automation process be governed? • What quality and security guidelines will be followed? • How will change management be handled? • How will automation exception be handled?

• Automation traceability and analytics reporting? None of these or other SOPs should be set in stone with RPA, which would be to limit its signature strength, responsive agility. All Stakeholders need to know and understand how RPA will interact with their areas and impact their ability to fulfil their organizational goals positively and negatively. A good example of this would be significant automation of work processes that reduces substantially the number of workers needed for that process. More on this later.

It is important to get Stakeholder participation in the creation of RPA SOPs. Senior Leadership needs to understand and communicate the overall vision for the Enterprise automation program. IT needs to make sure they understand the level of resource input required and set up the minimum infrastructure to start automation development and provide for enhancements. Finally, Operations, typically the biggest beneficiaries of RPA, needs to help determine what processes to automate, requirements for that automation, and provide criteria to measure automation success, and signoff on automation solution designs.

The Robot Team and other Important Control Topics The RPA Controller The RPA Controller manages the deployment of the robot workforce day to day. Depending on the size and scope of the Enterprise Automation program, the controller may or may not be a full-time position, who ideally reports to the RPA COE Manager/Director. The ideal controller isn’t particularly technical; however, knowledge of how the robots work and automate processes is helpful to identify whether an issue that develops is the Robot, the Business, or the System environment. The RPA Controller knows how to optimally schedule robots to handle fluctuating automation demands per operating policies and procedures that govern their deployment, as well as, meet the needs of their various Stakeholders. Attention to detail is a must to ensure optimal robot performance and that the flow of all appropriate reports are generated, reviewed, and forwarded. The RPA Controllers need to be able to work closely with the RPA COE Manager/Director and Operational Managers of the process the robots are automating.

Since 100% automation in most cases is not likely, coordination with Operational Managers, both upstream and downstream from robot automation is needed for exceptions handling. Finally, the Controller needs to be aware of any Disaster Recovery (DR) plan for the robot workforce. Some enterprises have a complete duplicate set of robots on stand-by in a DR environment with an SLA that within 5 – 10 minutes of an incidence, with appropriate authorization, process automation can be move to the robot workforce in the DR environment. More on the specific functions of the RPA Controller – The Control Room: Most, if not all RPA software has “Control Room” functionality built in that allows the Controller to monitor robots and assign them specific processes to automate. This activity falls into three main categories that constitutes controlling the robot workforce: 1.

Session Management – provides the Controller with a real-time inventory of what robots are available to do automation, as well as, which ones are currently automating what task and for how long. The Session Manager also generates logs of this activity that are very useful to assist with debugging when a robot terminates an automation unexpectedly. Queue Management – most, not all RPA software uses a place within the system where the processes are being automated to store the process, while it waits for the Control Room to assign a robot to work it. This is a good integration point for Work Force Management software; especially in more advance integrated hybrid human/robot workforce business models. Once the process is worked by a robot, the results are stored in a work queue. These automation result queues become the source of the most valuable analytics.

The Controller can extract productivity and exceptions data, which are valuable for metrics reporting, trouble shootings, and prospecting for additional automation opportunities. Scheduler – is used to schedule robots to work certain processes at certain times without the Controller’s intervention. This can also be done for production reporting and other analytic data. For example, there may be a scheduled “Report” process that extracts data from all the work queues outside the normal operating hours of automated processes.

In some RPA applications, one hour of Controller activity in the Control Room can equal one hundred or more hours of automated work. So, it is critical to ensure the right person is selected and trained to lead the Robot Team! RPA Developer/Analyst: Robot developers need to be trained to use the RPA software chosen by the Enterprise to automate its processes. Some RPA software; especially older applications require the developer to write code to build all the robots. Newer RPA software can write the code necessary to build the robot.

The developer in this case may not even be a programmer, rather a business analyst or SME that use some type of recorder process not unlike creating a Visio workflow diagram. Many RPA software support both methods. Depending on the method used to automate a given process, the robot developer might be an IT programmer, a business analysis or SME or combinations of these or other skill sets. If the developer is a business analysis or SME, depending on their intimacy with process being automated, they may or not require additional assistance to design and build the automation solutions.

Developer who build robots by writing code or those business analyst or SME who are not experts in a specific process being automated, need to work with those who define and refine automation requirements. It is the RPA developer’s responsibility, whoever they are and regardless which method used to build the robots, to ensure that the automation solution is tested and meets the Business’ design specification before being implemented into production. A formal, stand-alone UAT resources may or may not be utilized. For robots developed by coding, Unit testing is expected and depending on the robot’s complexity and interaction with other system, some form of integration testing might be appropriate.

The cycle can be anywhere from every week to every month. Implementation cycles longer than a month are probably not optimizing the essence of RPA. If the workload is such that implementation cycles of a month or shorter are not feasible in the existing Business/IT model, consider creating an A and B implementation teams or restructure the Business/IT model.

RPA Architect: The RPA Architect is someone that orchestrates the Enterprise’s automation program framework, advise management on new technologies, and assure that the standards and best practices are applied. He or she is also responsible to continually advance more effective and efficient RPA designs, as well as, overall Robotics strategy. RPA Center of Excellence (COE) Manager/Director: The RPA COE Manager/Director, depending on the size of the Enterprise’s automation program, most likely started out as the Program’s Champion. As the COE leader, just like any other operations, there is the overall expectation that the COE meets or exceed internal Enterprise clients’ automation service expectations. Other areas of responsibility include managing the COE’s budgets, compliance with policies and procedures, and of course making sure the COE Team is properly staffed, trained, resourced, and obstacles overcome.

Depending on the size and orientation of the RPA COE, the Manager/Director will have the following responsibilities. In smaller COEs the roles of the Controller might be performed by the COE leader and it should be an expectation that he or she should be able to take over for the Controller as a backup. While it is not necessary for the COE leaders to have either the technical background of a developer to understand RPA tool being used or an SME’s expertise of the domain being automated, increased abilities in both these areas is a huge plus in helping the leader ensure the unique End to End nature of RPA is fully realized. Because of this, Business Operations Managers and Directors often make the best COE leaders; especially if they come from one of the main domains being automated. At a minimum, the COE Leader needs to have an administrator level understanding of all RPA roles and functions, so that he or she can coordinate the most effective use of COE assets and align them with Enterprise partners such as IT and Operations to maximize impact. Regardless of the leader’s background or the exact composition of the COE, the Leader must ensure that the following primary activities are performed in an excellent manner: 1.

Operations: a) Task Scheduling b) User Management c) Reports Generation & Distribution d) Automation Productivity Tracking 2. Maintenance: a) Licenses Management b) Basic Trouble Shooting & Quick Fixes c) Issue escalation d) Robot performance monitoring 3. Staffing: a) Manage Vendor relationships b) RPA staff training and development 4. Coordination: a) Governance, in partnership with IT b) Automation prioritization per RPA Sponsor Advanced RPA Topics: Critical Robotics Process Automation Design and Development Strategies RPA Design and Development Strategies lay a solid foundation to implement a successful RPA Program and handle future Process & UI changes. Early Discovery Principals that provide the best RPA Design and Development Solution Foundation for any process: • Identify all the Requirements of the Process; whether it is part of scope or not • Identify frequency of changes on the UI of the target system • Identify and segregate UI & Business Logic interactions • Identify Scalability needs • Identify System Upgrades • Identify the Short & Long Term Goals of Target Processes • Identify Target Infrastructure deployment • Identify Security needs like HIPPA, NSA, Personal Information etc. Write your business logic using VBA,.Net, or Java Scripts. These are easy to modify in future or to reuse with different RPA tools in case a different RPA tool is needed.

When choosing RPA Tools consider following critical factors: 1. Security & Encryption Management 2. Scalability of the RPA tool 3. Easy to interact with UI layer of the applications 4.

Easy to update the future changes to UI 5. Integration with APIs 6. Supporting VBA,.Net Script, or Java Scripts Why do we need to build Business Logic outside the RPA Tool? Frequent new addition of tools to the market 2. Time-consuming to build business logic in RPA tools rather than easy to build in scripts 3.

Customize tools for specific challenges 4. Reduced overall performance to process each transaction 5. Easy to migrate to new tool in future 6. High availability of resources for languages like VBA,.Net, or Java scripts 7.

Building dynamic input to support minor changes that might arise in future What will it take to start, build, and sustain a robust, Enterprise wide RPA program? Leadership from the top that gives the initiative visible support by appointing an RPA champion to execute the program. Expert advisers or consultants to assist with software selection and installation; as well as, Business and IT alignment, and internal RPA Team selection, orientation, and training. Change Management Implementing business transformation projects are beset with many challenges. Many studies indicate that roughly only 35% of projects can deliver the original value envisaged.

Key stakeholder acceptance of the change is critical. While leadership plays an important role, the rank and file members and managers’ buy in is critical for the success of the RPA program, which is the role of the Change Management Group (CMG). Automation programs need an even more aggressive change management function than other technologies, because the objective of a lot of Automation is to eliminate or reduce the number of end users, so getting buy in from them to help automate is more challenging. The challenges rise exponentially depending on the degree of Automation. If a project yields marginal benefits or productivity gains, let’s say 10%, the resistance may not be high. However, if the productivity gains are higher than 40%, the resistance dramatically increases. Full or near complete Automation will face almost total resistance.

CMG strategies to overcome resistance needs to be figured out in advance. The Leadership Team cannot look at automation change management as another business as usual project. A robust strategy is necessary to ensure rapid adoption and success of RPA programs with high levels of process automation. Currently few complex processes are being automated.

While automation benefits are far higher in complex processes, so is dependency on end user assistance and resistance from the same for reasons already mentioned. With the potential for resistance at the point most critical to a RPA program’s success, the End User, how does an organization rapidly scale Automation?

The answer will be unique to every organization depending on their culture and values. However, some general principles can be used in every organization: 1. Make Change Management part of the strategy and implementation plan for the Automation program, which is the responsibility of the Leadership Team. Get Senior Management buy in first with some quick successes. Some suggestions on how to achieve rapid success: a. Identify processes where all the scenarios, rules, policies are documented and the current scenario is clearly understood. Identify processes whose operational unit head is a clear champion of Automation and has deep understanding of Automation and what the technology can deliver.

Identify low risk processes where automation can be rapid. Do not raise the expectations of the organization unrealistically for RPA program. Keep the RPA program as low profile as possible. Minimize over management. Ensure that reporting is linear and limited.

If possible, execute the RPA program in stealth mode. Once a significant number of automations are implemented, educate Senior Management to create active awareness. Create a separate team to execute these processes. Discover a path for individuals who are critical and whose careers might be affected if the RPA program is successful.

There should be clear asymmetric growth for these members. Identify and address the fears of members. Create a change management team that understands these challenges and works with the team members to ensure that everybody is prepared for the change. Automation cannot be driven top down in a hierarchical fashion.

It needs a more democratic approach. High ROI RPA programs need a culture of trust that truly engages and empowers frontline management and SME’s exists.

RPA’s Five Biggest Hurdles and How to Overcome Them Despite all the hype generated by RPA, the reality does not match the heightened expectations. Many implementations are excruciatingly painful and there is a common thread that run through many of these RPA programs. The following are 5 of the biggest hurdles that organizations already face or may face as they rush to get on the Automation Bandwagon. Hurdle #1- Limited scope of automation: The bulk of all the processes have a small headcount (10-20). A small number of processes have hundreds or thousands. Most processes have many use cases or variations and even in very large processes, a handful of agents (10-15) work on a single use case.

This means that a RPA implementation must focus on just 10 -15 agents that work on a specific process scenario. The implications are twofold. First, it becomes difficult to justify an implementation for just 10-15 agents and second, rapid scalability across all processes is dramatically reduced.

Hurdle #2-Lack of tools for performance measurement: Most organizations have very limited data on performance. Most is restricted to overall process performance (10 transactions per hour and so on). The exact benchmark for specific use cases are not available. Performance measurement is a science that draws knowledge from multiple disciplines, Modeling, Statistics and Simulations. Most organizations lack the skillset, tools, and experienced members who can handle such diverse areas of study and implement them to their specific environment. Hurdle #3- Lack of benchmark and data for existing process variations: Some processes are not stable enough and this is a major cause for wide variations in performance. Even for stable processes, individuals perform differently at different times.

Lack of benchmarking affects implementations in two ways. First it becomes difficult to figure out which use case needs to be automated first. Second, lack of data on current performance makes calculation of ROI for a use case a significant challenge. Availability of accurate data can help an organization to leverage more savings from automation. If an organization has benchmark data available and understands the science of performance and apply them to their organization. Hurdle #4- Long cycles of implementation: Only the simple process variation can be implemented rapidly. Most process variations take weeks or months to implement.

In many situations, knowledge on existing variations is not documented. Thus, discovery of use cases themselves become a trial and error approach. In other cases, documentation is not up to date with changes in processes. Infrastructure to maintain knowledge on AS-IS and TO-BE process variations may be nonexistent, which makes timely implementations and scale up difficult, increasing the cost of the RPA initiatives. Hurdle #5- Rapid Change management: Processes change rapidly.

Some changes are small and insignificant and some can have widespread ramifications. Small changes are typically a result of changes in business rules, change in policy, organizational changes, mergers etc. Some changes happen when the entire platform or application is replaced. In case of a platform or a large scale change the entire RPA implementation needs to started all over again. The frequency of change can put a damper on many RPA implementations. Advanced RPA Architect Methodology The RPA Architect (RPAA) ideally should engage a new customer in an initial exploratory meeting to talk about the functionality of the applications they intend to use RPA with, such as CRM, ERP, EMR, etc.

How does the application look to the operating system? If we’re talking about a PC environment, which operating systems run in production?

Which browsers and which versions need to be supported in production? If the system is a legacy application hosted in a terminal emulator, which emulator is being used?

Does the emulator have an API for screen element and remote access? Is the application being served up in a virtual environment like Citrix or RDP? The bottom line for this discovery process is not to find out about the applications’ functionality or what process need to be automated, which is the job of the Process Designer, rather, the RPAA needs to know which tricks of the trade will be required to reliably read screens, write to fields, click buttons, extract data from grids, etc. After the initial discovery session, the RPAA needs to conduct a “Screen Test”; that is spend some time with the actual applications, to get a grip on the RPA programs complexity. The Screen Test should answer the questions previously posted that will allow the creation of an automation feasibility statement and estimate for a given program. However, often there is not enough time to perform an exhaustive screen test before the customer wants to know, “Can this automation be done?”, and “What will it cost?” In those cases, the RPAA must rely on experience to render a recommendation. The following 10 factors are some of the most important points, depending to a degree on the type of RPA needed and product used if forced create an estimates and render a recommendation with incomplete information: Factor 1: Which applications are harder or easier to automate?

Browser-based and legacy applications are generally easier to automate than traditional Windows-based client applications. This is because, regardless of what the application functionally does, there is significantly less variability between browser applications or legacy applications versus traditional Windows clients. It is true that client side scripts executed in the browser or Java-based frameworks may complicate things a bit, but most applications running in a specific version of a browser or emulator look the same from the RPAA’s perspective. Browsers make available many “access points” that allow read from, and write to, screen controls. Likewise, terminal emulators are generally designed with remote accessing in mind so their APIs are usually robust in this area and the “textual” nature of green screens lend themselves to being easily scraped. On the other hand, traditional Windows-based client applications can have a lot of variability.

Factor 2: Will an application require screen OCR (optical character recognition)? Screen OCR can be an AA’s best friend or worst enemy. When all operating system screen extraction techniques fail, screen OCR is the last, best hope. When it works, it is a life saver. But used carelessly or in the wrong situations, it can be a nightmare of frustration. Most software applications share a consistent and common anchor point (upper, left-corner of the window).

Resolution and DPI can be easily determined through the operating system and differences from workstation-to-workstation can be derived from a baseline via a simple calculation. However, screen OCR has challenges: 1.

The best DPI rates used on a workstation’s display are generally lower than that encountered in low resolution fax documents. Most users have font smoothing turned on which makes it easier for humans to read screens, but poses problems for bots trying to OCR those screens. Screen font sizes tend to be smaller than fonts on printed forms. While none of these issues are insurmountable, they must be considered when screen OCR is being be used. If any of these configuration parameters are gotten wrong, results may be inconsistent which can doom a program. This is especially true if the inconsistent results are on the same field; (e.g. Sometimes the engine returns a “6” and sometimes it returns a “G” for the same character).

If screen OCR must be used, the RPAA must build in plenty of extra time for exploration and testing. Also keep in mind the OCR engine may require font “training” to achieve reliable results, and that takes lots of samples and time. Factor 3: Does the automation involve lots of data grids? If the automation needs to integrate with data grids, extra time will be required. While grids are a great UI control for humans, because they allow users to quickly view large data sets in summary, they present several challenges for bots trying to read them. For starters, most Windows client applications tend not to handle them well from an accessibility perspective, which makes them somewhat opaque to the bot. Further, grids scroll vertically and horizontally which makes it difficult to establish the grid’s state; (i.e., what part of the grid is currently displayed or has focus on the desktop?).

Finally, many grids allow users to re-order and resize columns which also creates a bit of mayhem. While this is usually not a problem when a bot is running unattended because the state of the grid can be reset each time the automation is executed, when the bot is running in attended mode on a user’s workstation, things can get tricky.

If a grid is inaccessible through the operating system, the RPAA must employ several navigation techniques to: 1. Determine which record is selected and where the user is positioned in the grid, both vertically and horizontally. Acquire data being truncated due to narrow column width settings. Select functions available from column-specific context menus. These same issues may also pertain to data tables in the browser; yet usually to a lesser extent. The good news is most applications possess a details screen subordinate to the grid that displays the selected row’s data in a consistent manner. When available, the detail screen should always be used.

Factor 4: Do any or all of the RPA involved applications run in a virtual environment? If all the applications run in a virtual environment, this is not a problem if you can run your RPA tool within that same virtual desktop. If this is not the case, then the RPAA is stuck automating what amounts to application screenshots rather than live screens, which will require screen OCR and lots of keyboard buffer stuffing. This also holds true if some of the applications run in the VM while others run on the physical desktop (we call this “crossing the virtual barrier”). If you can run the RPA tool in both environments, the project will present some challenges but nothing insurmountable.

If not, the project gets even more dicey so more time should be allocated. Factor 5: What is the data fidelity between the RPA involved applications? In a perfect organization, all systems would handle constrained lists the same way. This is often not the case. Whether we are dealing with US state names and abbreviations or general ledger codes and descriptions, very often, different applications refer to the same entities in very different ways. If this is the case, the RPAA will need to allocate extra time for coding data conversion and translation rules.

This may even involve accessing external arbitration data sources. If so, how will the sources be accessed? Via spreadsheet? Web services? Regardless of the data source access method required, the task just got more complex and requires proper planning. It is also incumbent upon the Process Designer to make sure all systems involved contain a common key that link records together.

If this is not the case, then the RPAA may have to consider employing “embedded link keys” in unused or partially used fields. Though sometimes necessary, use of embedded link keys should be used with caution. Factor 6: Will the automation involve parsing unstructured data? If the automation involves working with unstructured data such as documents and emails, the RPAA must build in additional time to get comfortable with a text parsing technology like Regular Expressions (regex).

If the only way to find something in a document (or sometimes on a highly dynamic screen), is to find a fixed, related string, regex becomes an important arrow in the AA’s quiver. However, many find regex a bit arcane and difficult to work with so extra time should be allocated. Factor 7: What is the release cycle of the RPA involved applications and how likely are they subject to changes that may impact the automation? If the applications change infrequently, the RPAA can spend less time on making the automations flexible and durable. However, if the applications change often, especially without a lot of lead time as is frequently the case with auto updates, the RPAA should spend considerably more time creating an automation that is more durable and adaptive to changes such as field placement and label changes. Otherwise, when changes occur, the RPAA will be under the gun to make the necessary changes with users waiting.

Factor 8: How difficult will it be to get an appropriate amount of test data from the users? To properly test an automation, there needs to be sample data made available that tests every rule and condition defined in the specification. Insufficient test data and improper testing is the number one reason why RPA solutions are recalled from production and refactored.

It is better for everyone involved if the automation is sufficiently tested before it is migrated into production. Frequent refactoring erodes user confidence and adds significant support costs to the process. Remember, users tend to underestimate the amount of variability there is in a process, especially when the variability happens infrequently. Having plenty of test data available will help unearth these varying conditions. Factor 9: Where will development and testing take place?

If development and testing is to be performed on live production systems, the RPAA needs to factor in additional time to handle transaction rollback and data clean-up. Further, development and testing in production systems tends to slow the process down as RPAA’s strive not to break anything and are forced to deal with maintenance cycles. If development and testing are being performed in a test environment, it is critical the test environment utilizes the same versions and configurations the applications use in the production environment. Slight differences between the environments will result in recalls and refactoring. Factor 10: The larger the number of users and variance types, the more time the AA should allocate for variance testing. As the name implies, variance testing is the process by which RPAAs detect and accommodate the variances an automation will encounter from workstation-to-workstation in production. Though applicable more so to attended automations running on the user’s workstation, a good program methodology should always incorporate variance testing as its own discreet step regardless of the bot mode.

The rule of thumb here is the more workstations an automation runs on, the greater the chance of encountering: 1. Differing performance speeds, 2. Different display configurations, 3. Conflicts with non-RPA involved software, 4. Application behavior variances due to different access privileges; (e.g., managers may encounter different screens, prompts and messages than regular users may encounter). These are not an exhaustive list of all the factors RPAAs should consider when determining how complex a project may be. Yet, it is a great place to start and demonstrate that RPA solutions can be developed and implemented faster than traditional applications or integrations, yet they still require significant due diligence that requires serious planning and therefore time.

Why RPA Implementations are Unsuccessful Banking, Financial Services and Insurance Companies have been strong adopters of RPA programs, with many advisory and analyst firms predicting huge gains. Sometimes these are tall claims, even false evangelism, yet there is no denying considerable savings are possible if RPA programs are implemented per sound designs and best practices. Apart from these, there are many reasons why RPA programs fail to have the desired effect and achieve the intended ROI. Investing with faulty expectations Some stakeholders focus on building cognitive robotic capability or artificial intelligence notwithstanding 80% of their processes are more on the adaptive RPA end of the spectrum. Business should first use adaptive RPA on processes until these opportunities run out before turning to Cognitive technologies. There are many combinations of other approaches and tools that can be use with adaptive RPA tools to resolve automation gaps with much lower investment, before turning to more expensive and complicated cognitive RPA tools.

For example, a global banking giant wanted to partner with another global service integrator to embark on a robotic automation journey. However, it was not able to make sizeable headway due to inconsistencies in its own processes, limitations of the tool, and relative maturity of RPA as a technology practice. Strangely they jumped onto the cognitive bandwagon and faced even greater issues due to inherent limitations of the Tool. Or a card services providers that faced problems reading PDF with a leading adaptive RPA tool. They went ahead with a cognitive solution when in fact the problem lay with the OCR and not with the RPA Tool itself. The problem was solved for nothing by employing an enterprise grade OCR tool the Enterprise already own.

Poor Process Selection Sometimes, the pressure to solve for critical problems is so intense that organizations fall into the trap of selecting wrong or difficult processes too early in the journey. The processes must be carefully selected and needs to undergo preliminary analysis on suitability for RPA and re-engineering effort needed. Process Re-engineering is needed when subjective manual elements exist in a process, unless it is part of the plan and ROI expectation to allow these parts to remain manual. Otherwise stakeholders should look at other methods like Process Re-engineering or perhaps Cognitive solutions. Companies should be wary of force fitting a RPA solution to problem.

A large bank in the Middle East embarked on an RPA journey and had selected processes that when looked at in its entirety, involved considerable amount of subjectivity and judgement based decision making. However, the process in bits and pieces was automatable and required re-engineering to increase the scope of automation. Because of process changes, some complex integrations had to be deployed. It doesn’t mean that these processes should not be looked at for automation; however, they should be a lower priority to processes where ROI is higher. Brittle and non-scalable Designs A robotic framework needs to conform to the best principles of robust design parameters that are intrinsic to BPMS and BRMS technologies. The design characteristics that are an absolute ‘must-have’ are: • Scalability • Maintainability • Traceability • Reusability Design is very critical to the success of an RPA program to prevent failure or exceeding budget. The designs should support addition/deletion of process steps without affecting downstream processes.

To ramp up RPA resources quickly, many companies turn to.Net developers. It is natural for these developers to write ‘custom’ codes rather than employ RPA’s advanced feature for automation solutions. Yet, these short cuts violate all four parameters of Scalability, Maintainability, Traceability, and Reusability. Access Management for Robots Robots need to have their own ID and passwords for critical systems or a multi-login ID for robots to use during processing. This maintains traceability and accountability for actions completed by robots. And these IDs must be available across different environment.

Access management for robots must be efficient or there might be considerable loss in time resulting in huge cost overruns. There are 2 aspects that one might consider under the broad heading for access management – Delay and Costs Recently a large transformation program involving RPA for a major financial services company based out of the US added a small, yet critical automation the organization wanted on a priority basis.

Considerable effort was put in analysis, design et al, yet at the time of development, it was realized that users had a common ID and password for a third-party system that cost close to USD 20,000 per annum. The automation had to be shelfed, because of the cost of another ID and password. How to ensure “All is well” It is important for organizations to carefully choose partners for RPA and be even more careful if they are embarking on the journey themselves. The partner the organizations select should have the following Partner should have a stake in the success – It is imperative the business makes partners accountable for the success of RPA. Instead of “fixed”, “time and material”, or even “outcome based” pricing, organizations should encourage partners to move to a gain share model. Some investments in terms of licenses needs to be made (specially to get a partner discount), yet any payment to customers should ideally be on a “gains actualized” basis. Scale – Once initial gains are made from RPA, the business side of organization quickly wish to capitalize on the advantage to solve key problems and drive down the costs.

The partner should have the scale to be able to meet the demands from business. Talent Pool – RPA is relatively new to the market, so it is rarely possible now for partners to have a deep talent pool very skilled and experienced in RPA. The architects, developers, and the analysts do not need a lot of time in RPA to understand RPA. What needs to be evaluated is whether their current skillsets will enable them to assimilate, understand, and deliver RPA automations with a minimum learning curve. For example, if a person has many years in BPMS and BRMS, they will probably be a perfect candidate for RPA as well. That is simply because the developer / architect / analyst will understand concepts of workflow modelling, rule analysis, and design which are key components of RPA as well.

What else can organizations do to ensure that automations are being developed in line with expectations? Pay close attention to: Governance – there should be a team that reviews completed work on a periodic basis. This review could be a part of the periodic scrum meetings that are organized periodically. This team should also resolve IS or access related issues so that the development doesn’t stop and lead to idle time, which might result in the implementation partner billing idle resources costs to the organization. Analysis and Design Sign-off – There should not be any spend on development effort if the business doesn’t sign-off and approve the functional design. Functional design should include how the automation is invoked, what are the actions completed, and where the hand-off (if any) is for a manual action and output of the automation. Business should also sign-off on any reusable components and classification of common actions that are developed as a part of the automation.

RPA as a Design Centric Approach This is going to be a deep dive into the Design centric approach to RPA. Why it this is the right approach for strategic RPA implementations in complex enterprise environments. The foundation of this research and experience are various pure play RPA tools like Blueprism and Jidoka RPA that adopted product strategies focused on the Design Centric approach to RPA. The ideas expressed in this article are applicable to these Tools and any others that align with this approach in principle.

For context, let’s look at some of the broad trends and issues faced by the RPA market today as it is going through its take-off phase and as many industry analysts are predicting- RPA market will continue to grow at a rapid pace for the next 5-7 years before it starts to level-off. As with the growth of any new technology market, there is a flood of different RPA product variants that have emerged with multiple ideas about what the customers want. As more and more RPA products are getting pushed out into the market, it’s increasingly clear this has created new products categories such as pure-play RPA- Attended or Unattended, Cognitive RPA, and AI powered RPA. Although, there are tall claims being made about RPA product offerings that create an additional 40-50% efficiency on traditional BPO costs, the demand is still in its early stages; especially in the case of Cognitive and AI powered AI, where a lot of innovative activities are going on at the moment as the products mature.

Given all the hype, an ever-growing variety of products and categories, and yet to fully mature demand, the RPA market is evolving with some products prospering and others falling apart. As more and more customers across industry sectors are trying out RPA products and conducting POCs, consumer needs are more articulate and specific. The next year or two will see a sizeable shakeout that will probably reduce the number of providers and stabilize the RPA industry. Until that happens, there are number of broad issues faced by the consumers. First – the big confusion over how to approach RPA implementations with all confusion created in the market by RPA products sales pitches.

Should you go for a record-replay product that sound cheaper and offers quick, tactical POC outcomes by automating simple processes? Or should you go for a product that forces you to look at business process automation in a holistic way across a large operational area? This may be greater investment and effort up front; however, the resultant RPA ROI gains over time will be significantly greater as well. It’s a tactical vs. Strategic choice for RPA implementation approach. As more and more enterprise customers are trying to make sense of RPA benefits; to realize acceptable ROI from their RPA program beyond the early POC stage.

Second – there is the question of proprietary development & maintenance skills? Many RPA products are claiming to offer code free development of bots that the front-line operations users can create themselves using the pre-built capabilities offered in the tool. Yet, seldom do such RPA implementation tactics work and soon customers are bringing in skilled RPA developers from outside. It doesn’t help the situation that many pureplay RPA tools don’t provide adequate support or standard software development kits (SDKs); so, getting skilled software programmers in Microsoft or Java technologies does not necessarily fix the problem. Finding resources that have been in operations and become power user in proprietary features offered by an RPA tool is challenging at best!

One solution is to have their operations teams undergo training and acquire the necessary RPA skills; however, this does not address the implementation expertise that is needed for complex implementations that produce significant ROI. It also assumes the availability of quality training programs for the RPA Tool chosen. Instead, why not chose an RPA tool that applies industry standard development techniques for building software robots and does not create skills gap?

It’s a choice between highly proprietary methodologies vs. Industry standard development practices for RPA implementations. Third – do RPA tools that push implementation approach and development technique completely operations teams, offer a silver bullet that the RPA program can be done without IT involvement? The reality is that a direct consequence of this of this approach is the failure to scale RPA beyond the POC stage. Even more serious is the level of technical quality of the solution in terms of robustness, modularity, security, scalability and maintainability.

Why not adopt an RPA tool that facilitates a collaborative effort between Operations and IT to build process automating bots? The result will be RPA implementation that deliver much-needed operational efficiency and high technical quality. In the World of Technology, where collaborative approaches like Agile and DevOps are the new normal for the Digital Transformation of Businesses, why take a step backwards when it comes to RPA by creating Operations and IT silos? It’s a choice between a collaborative vs. Siloed approach to RPA implementations. To re-iterate, there are three broad issues facing RPA implementations today: 1. Not taking a strategic approach to RPA implementation resulting in low ROI.

Not employing industry standard development techniques leading into skills gap. Not implementing RPA projects as a collaborative effort between Operations and IT teams causing implementation failures with some industry analysts citing figures as high as 60% in late 2016- early 2017.

The Design Centric approach to RPA Solution: Now that we understand the main factors that contribute to low RPA ROI or failure all together, let’s examine a Design Centric approach to RPA implementation and analyze how it resolves these broad problems facing the RPA industry. Top-Down Development Design Centric approach to RPA implementations takes a top down approach to automating business processes in an enterprise. It starts with the high-level workflow for processes to be automated in a language that is understood by the domain experts. A Design Centric approach does not dive straight into the technical implementation details by attempting to record an isolated bot in a bottom-up fashion. The processes to be automated are designed in their entirety by Operations SMEs. The high-level process flows are deterministic to ensure all possible exceptions and recovery paths are covered.

The whole idea follows the principle that Design is not Coding and Coding is not Design. Design Patterns The high-level process flows for automation are created using standard design patterns for business process modelling. The process steps are considered domain events that are labelled using a common language that describes the application domain. This lays the foundation for an object-oriented layered solution architecture. Modularity As business processes are modeled using a common domain language, the process steps that represent domain events once implemented in code provides a library of reusable automation modules or business objects that can be re-used in combination to create bots to automate numerous processes.

This reduces the level of maintenance needed once automated process are released into production. Any change to a domain event can be made only in the corresponding automated module to update all applicable bots. Scalability As processes within Operation are automated, a library of domain specific automation modules is built that provides scalability to automate an increasing number of processes within the Enterprise with minimal additional effort. Object-Oriented Implementation Implementation of process automation bots in Design Centric RPA is done using industry standard object-oriented programming techniques. OOPS concepts are applied such as building objects to model application attributes and behavior as needed for RPA implementations.

The technical implementation details are abstracted from the high-level process workflow. The technical details of object attributes (such as UI object locator attributes) can prevent misuse and duplication. Inheritance allows custom actions that extend the raw API calls provided to a RPA tool for complex implementation requirements. Layered Solution Architecture The high-level process flow represents the top layer of the automated solution. The actual implementation of the process steps as a software robot is done in one or more bottom layers.

Typically, the implementation of domain events as reusable process components is done in an application specific techno-functional middle layer that sits in between the business domain specific top layer and the technical base layer provided by the RPA tool. The base layer provides the technical API that the RPA tool uses to perform actions directly on the applications that are automated (for e.g. Web, Windows, Citrix, Excel, SAP, etc.). Extensibility The use of standard development technologies provides extensibility to utilize proven external APIs or libraries, or by building custom libraries to enhance the existing solution and fulfil specific technical implementation requirements. Collaborative Process The business process to be automated is first designed as a high-level process workflow by one or more process SMEs, who are experts in the given operational area within the Enterprise. The process workflow is created in the workflow designer provided by the RPA platform. An automation bot is then created by implementing the domain events in the process workflow as object-oriented code.

This is done by one or more automation developers in the IDE provided by the RPA tool, decoupled from the high-level process flow. The automated implementation in the form of object libraries and methods are then published to the RPA platform. Process SMEs are then able to map the domain events in the process flow to the automated components, test the automated process, and release it into production. Both, process SMEs and automation developers, work in collaboration to deliver bots that automate the Enterprises’ processes.

The automation developer ensures the technical quality of the solution, while the process SME ensures that automation bots are accurately designed to deliver true operational efficiency to the business. Agile Applied to RPA It should come as no surprise that a technology whose number one signature attribute is responsive agility, such as RPA, is an ideal candidate for an Agile approach to implementation. If the chosen the process has defined boundaries and scope, the following steps can be taken to increase implementation agility with Agile: Build an agile team The team consists of: • Business Analyst, ideally with some previous experience in Lean • Robot Developer • Product Owner • Subject Matter Experts • Agile Facilitator Ideal size of the team should not exceed 7 people, to avoid too much spent time on collaboration issues of bigger teams. Business Analyst and Robot Developer are usually full-time roles for Robot implementation programs. Product owner, Subject Matter experts, and Agile Facilitator can be a part-time.

The role of Agile Facilitator is crucial to help the Team structure the work so it can be gradually deployed in the Business and get a feedback as soon as possible. Map and Review the Process 1.

High-Level Process Mapping 2. Low-Level Process Mapping 3. Review the Process to Identifying Non-Value added Activities All decision points need to be identified, as well as, all exceptions to the process. List of possible risks should be identified. These are indispensable step for a successful robot implementation! Try not to reengineer the whole process, because it will be very difficult to automate it afterward. The manually performing of the process for some time before automation is crucial.

Only those who did the process manually can provide real insights about pitfalls and workarounds happen in the actual process activities. It is possible to fall into the trap of creating a shiny new business process that is incredibly challenging to automate. It can take much longer to automate a new reengineered process than the existing manual process. This step usually takes 8 hours (2 slots of 4 hours or 4 slots of 2 hours). Creating a Robot Journey Map Build a User Journey Map for robots that identifies activities for each step of its journey. It is suggested to use a tool such as Stories On Board or just a simple board on the wall to facilitate the story mapping exercise. However, if you use a physical board, make sure that it will be with the Team for a whole project, since it will need to be referred to every day.

Main steps of the robot’s journey are mapped across the top of the board and then create a specific User Stories to cover all activities within the Steps. Release Planning During this step, a robot’s implementation is split into releases. Alistair Cockburn’s Knowledge Acquisition Curve can be useful. Its main idea is to split the implementation into 3 phases: First Phase- exploration of the process and the proposed solution is the goal. A primary objective is to mitigate the highest risks and to check assumptions about the robot’s ability to perform a task.

For example, can the robot interact with internal systems in a way never built before? In Phase One, that piece is built to check feasibility, i.e. How fast can the robot be adapted to that System and what pitfalls might it encounter? The duration of this first phase is usually a week or a bit longer for real difficult processes or new to automation. Second Phase- After the main risks are mitigated and assumptions proven, business value can be delivered; the meat and bones of the robots. Robots are deployed to production under very strict supervision. This Phase is usually split into a few 1 week iterations of end-to-end robot developed, testing, and deploy into production.

Third Phase – fine tuning with additional controls, speed optimization, extra security for unusual situations. It is not going to deliver much business value, is the robot deployment is not robust and easy to maintain. During this Phase, training and maintenance documentation is developed and handed over to the Support and Operations teams.

The following are steps of a RPA Story Board: 1. Iteration Planning 2. Recording user steps per each activity 3. Daily standups 4.

Train the robot 5. Test the robot 6.

SMEs acceptance 7. Iteration Demo 8.

Iteration Retro Development of a robot is an iterative process. Cycle starts from Iteration Planning and finishes with Iteration Retro and then looping again until all phases of the development are completed. Iteration Planning Each iteration has a planning session to define which User Stories to release and in what order. This is necessary to identify any dependencies that need to addressed earlier in the iteration. The rule of thumb is to get end-to-end process done and dusted, so first releases cover the whole horizontal row of the Robot Journey map. Usually User Stories are roughly of the same size, however, if one looks particularly big or opaque, split it into a few separate User Stories. Record User Steps per each Activity Recording all steps of each activity in the low-level process map gives a better idea of fields the robot interacts with and how.

Free Windows PSR (Problem Step Recorder) tool records and edits the robot’s activity, which help the Robot Developer do their job faster. Daily standups Daily standups to review the progress and resolve issues for the team. A good goal is the completion of at least one activity per developer per day.

If this hasn’t happened a standup is where discussing and resolving impediments can take place. Train the robot The actual development of the robot, which involves creating a draft robot, improving it to handle many similar cases, embedding protection, and then applying it into testing environment.

Testing the robot Testing robots can involve different methods than usual software development. Sometimes it is not possible to generate test data for some cases. Tools like Message Boxes can be helpful by describing a robot’s next action and thinking. Then a SME can judge if this is the right way to do things or not.

SMEs acceptance Usually, in 1:1 session, the Robot Developer and SME run the robot in a “guiding” mode to check if the robot reasoning is correct. “Guiding” mode puts Message Box on each of the robot’s steps that describe the input data it is using and what and the action it is going to take with it. Iteration Demo The robot is shown to the stakeholders to get agreement that it is complete and ready for release. This also helps to solicit feedback from the main parties to start planning for the next release. Iteration Retro Reflection on the results of each iteration to adapt the process accordingly. RPA is still quite new as a concept to most.

There are many nuances to discover and be take into account for future releases. Deploying the Robot The robot is moved into Production using a gradual deployment, i.e. During the first 2 weeks it’s daily activities are supervised. During this period robots uses “guiding” approach. Then for the next 4 weeks, the robot performs without supervision; logging all steps into a log file. At the end of each day, the log file is reviewed for accuracy to evaluate if the robot is performing to the plan.

In addition to this overall Agile approach to RPA, it might be beneficial to have a Project Steering Committee to help the team resolving external issues, such as request for additional resources, communication with the C-Suite, negotiation with external providers and so on. A Quick Way to Evaluate an RPA Opportunity How do you identify a proper process to automate with RPA?

Here is one technique that will allow you to do it in a short time, the “90 minutes workshop”: Workshop Prerequisites: 1. Decided what part of the company, usually a department, has biggest opportunity – e.g. By number of FTEs currently deployed.

Organizational structure of the department with FTEs allocated to it 3. Have the team leaders in the room. Workshop Stages: Identify Processes (30 minutes) Start with the top-5 or top-10 processes people perform in the Department. Team leaders should have at least a rough idea.

For each process, identify the number of FTEs and main activities involved. There is no need for 100% accuracy, again, a rough idea is good enough. This step is time boxed – 30 minutes, to keep focus only on the main things. Sanity check (15 minutes) Conduct a quick sanity check by counting the number of FTEs across all processes and compare them to the organizational structure. If that number is significantly less than 50% of the organizational structure, either a few substantial processes where forgotten or there is relatively low utilization of the existing resources, i.e.

Might be slow time of year? Usually, the Sanity Check finds a few additional processes. Process Categorization (10 minutes) Per each process, there are type of input and type of activities. There might be 3 types of input for the process: 1.

Digital, structured (databases, information in the systems, electronic forms) 2. Paper or Digital, patterned (invoices, application forms, signed agreements etc.) 3.

Paper, Digital or Voice, unstructured (free text emails, letters, calls) There might be 3 types of activities in the process: 1. Rule-based (simple rules in a workflow, simple decision tree) 2. Complex rules (work based on many parameters with many outcomes) 3. Cognitive (pure cognitive activities, like working with complaints or with happy customers) Process Mapping (5 minutes) Mapping process on a canvas per the processes input and activity types. Identity Automation Opportunities (15 mins) It is possible to automate most anything with existing technologies: RPA, RPA + Vision, Machine Learning, Virtual Assistant, Chatbots. This frees our mind from narrow RPA thinking and allows us to frame a bigger picture. However, not all technologies are feasible for the particular company or specific process.

Now we are thinking through this map in 2 directions. 1) Can we change our processes to move them from left to right? Can we simplify our decisions? Can we extract what is emotional in the process and what is rule based? You will be surprised to see what you can find there.

New employee can be trained to perform this task in 2-4 weeks, this will be in the right corner of our canvas. 2) Can you change your inputs from the bottom to the top?

Can you use chat instead of voice, can you guide your customer in his questions? Can you use digital forms instead of papers? Can you get the info needed somewhere else, in existing databases or probably external ones? If you can’t do anything with the input, you might change our process to have a person extract this unstructured data into structured form and the move the rest of the process to the top. The more processes will be moved to the top-right corner or closer to it, the more efficiency gains we will achieve faster.

Deciding about next steps (15 minutes) This is basically it; a 90 minutes session resulting in clear plan of what to do now, what’s next, and what to investigate further. The data from the canvas then will be used to create a business case and start the project. You will be amazed with discovered opportunities. Experience indicates that without moving processes in the top-right corner there will be about 25% opportunities to using RPA. Additional 25% (totaling 50%) for RPA + Vision (Cognitive) + a bit of optional Machine Learning to and additional 25% (totaling 75%) for Chatbots and Virtual Assistants to investigate further. Meet the Masters: The Consultant – Deepak Sharma Deepak is a seasoned consultant with approximately 14 years of experience in UI Automation for Test and RPA; focused on Enterprise Applications such as SAP, Siebel, PLM, Custom Wealth & Asset Management platforms, Healthcare Claim Processing systems, etc.

He has worked with major global enterprises in the UK such as Barclays, IFDS, Department of Works and Pensions, RWE Npower, and in the US; such as Motorola & UnitedHealth Group. Deepak is passionate researcher, who writes about market trends, adoption challenges, and implementation approaches for the RPA Industry. He is an evangelist of a Design Centric approach for RPA based on industry standard development practices and collaborative processes between operations and IT, which holds tremendous potential for enterprise customers to reach the next level in business process optimization and to act as a catalyst for digital transformation & business model innovation. Deepak holds a bachelor in Computer Science & Engineering degree and a Masters in Strategy & Innovation from the SAID Business School, University of Oxford where He secured a distinction in Innovation Strategy focused on market evolution & implementation of nascent technologies such as Robotics, 3D printing, AI and Big data.

The Entrepreneur – Joe Labbe Joe Labbe is the Co-founder and CEO of RPA software company, RatchetSoft, based in Manhasset, New York. Labbe is a seasoned sales and systems integration specialist with over 25 years’ experience designing and developing solutions for clients of all sizes.

In his current role at RatchetSoft, Joe is responsible for defining corporate strategy, product direction and heads up the firm’s professional services team. Prior to co-founding RatchetSoft, Joe was the CEO of web services management company, Primordial, where he designed and oversaw the development of one of the industry’s first web services management platforms, WSBANG. Prior to Primordial, Joe was a Senior Partner with Internet consulting company, USWeb, where he managed the New York office and ran sales for the Northeast region.

Labbe came to USWeb through the acquisition of systems integration firm Synergetix which he co-founded in 1989. At Synergetix, Joe was responsible for sales and professional services, while also presiding over the firms document management and web development practices. The Evangelist – Lee Edwards Lee is a true All-Star of RPA delivery and implementation.

From strategic visioning to hands-on involvement across multiple organizations, he uses his experience in business strategy, process identification and re-engineering, design and build, infrastructure, and delivery to create a structured, consistent methodology that produces quality automation solutions. He is the Winner of Call Centre Management Association’s – ‘Back Office manager of the year award -2013’ for the delivery of the Robotic Process Automation solution for Capita PLC Local Government Division. Lee is an accredited Lean Six Sigma green belt with extensive Process Improvement, Business Transformation, and Operational Management experience, with a proven track record of leading and delivering Operational excellence initiatives. Consultancy experience in multiple sectors that span back and front office as well as Contact Center Operations and Shared Service Centers. He can operate effectively at all levels from process SME’s up to board level using a blend of hands on activities and key senior stakeholder management support to ensure successful delivery of RPA program objectives. The Strategist – Nirmalya Shome Nirmalya is a part of a rare breed of management consulting individuals who has worked across the value chain of the IT & BPS industry (front- back). He brings to the table over 10 years of international experience across the BFSI domain that spans the globe.

He has worked extensively on projects using various RPA Tools (Robotics Process Automation), Decision Management (Rules and Events Engines) – IBM ODM, IBM DSI, Blaze Advisor and other decision management tools. His clients include P&C, Life insurance providers and administrators, retail banks, payment service providers, retail chains and shipping corporations. Nirmalya has help them realize their business imperatives. He has a BA in Business Communication and Accounting, as well as, a MBA in Finance.

His strong business acumen, excellent presentation and communication skills enable him to build key relationships amongst clients. Putting it all together, Nirmalya is a specialist in Robotic Process Automation, Decision Management, Business Rules Management Systems (BRMS) Modelling, Design and Analysis. The Agilest – Pavel Gimelberg Pavel Gimelberg is an Agile and RPA Evangelist with over 15 years of experience in the field of Business Process Automation. He has been involved in Software Engineering since the early ‘00s and made his career path from a Systems Analyst in pharmaceuticals to the Head of IT Applications in a federal mortgage agency. Pavel has broad experience transforming businesses through automation using 21st century concepts and tools. He constantly pushes the limits of the achievable by the tenacity of an innovative mind. Pavel creates strategic competitive advantages for his clients and he is eager to share his knowledge and experience.

With a Computer Science degree with honors from one of the best Russian Technical Universities and an honors MBA graduate with distinction, specializing in IT Process Management; Pavel is one of the authors of Managing Distributed Teams online course from Berkeley X and is the founder and one of the leaders of Robotic Process Automation community in Australia. The Master – Phani Kumar Chandu Phani is a RPA Lead Architect with fifteen-year experience with Cognizant and Accenture, specializing in automation solution for BPO processes. A true technology thought leader and innovative problem solver, who takes the initiative to handle any automation challenge. Highly organized, with excellent communication skills, and experienced in many software languages and RPA tools, Phani can see the big picture while paying attention to small details, to create unique automation design solutions that he can implement from the inception phase to deployment. He also has management experience and skills in: Account, Application Support, Delivery, Engagement, Program, and Transition Management; managing P&L, Project/Net Margin, Resource Utilization, attrition, and creating Value Framework, Managed Service Model, C-SAT Plans, with an expertise in handling Integrated Service Delivery (ISD) and Multi-Service Delivery (MSD) accounts across F&A, HRMS, Pharma, Supply Chain, Engineering Manufacturing, Retail, Apps and other horizontals; specializing in large offshore accounts. With a BS in Electronic and Communications Engineering and graduate training in executive management, Phani is a Domain Expert in F&A, Engineering Manufacturing and Retail automation. The Innovator – Ravi Ramamurthy An IIT graduate from Chennai, and the youngest professor to be invited to IMT Ghaziabad, Ravi Ramamurthy’s achievements are varied.

He is a member of the executive committee of Science Olympiad Foundation, the largest Olympiad in the world. He is also the Chief mentor of Aashwasan, a unique global spiritual science organization. Ravi is the CEO and Founder of a software product company called Epiance, which has clients all over the world. He has 30 years of experience in the corporate arena. Ravi was one the of the early creators of the foundations for automation. The first automation product that he created in 2003 was the basis for the Robotic Automation product that is becoming very popular today.

Ravi is responsible for a host of software, technology and product innovations. He has multiple patents to his name and is the author of eleven books on physics. Ravi’s success as an entrepreneur and a business leader lies in his ability to transform these innovations into pioneering and successful products that are globally sought after and used by the world’s best performing companies.

The charitable association Aashwasan is his passion. The Editor – John Slagboom (not one of The Seven) Managed Health Care (MHC) Automation and Analytic Architect Technology Innovation / Robotic Automation/Data Mining & Analysis/Organization Development. Over twenty-five year’s Claim/Operations/Automation/Analytics experience with a proven record of innovation leadership. I possess an aggressive ability to adapt new technologies to the front-line processes through comprehensive personnel development, organization and work system redesign: specializing in Robotic Process Automation (RPA) to automate MHC claims.

An honor graduate of UCLA and University of Redlands with a Master in Management in addition to various military, volunteer, and operations leadership assignments, gives me the theoretical frame work and practical ability to envision and construct advanced generation of robotics and analytics capable of fully automating MHC claims processing. I seek to partner with a major MHC to sponsor a three-year project to develop the world’s first hybrid Operations/IT robotic claims processing unit, along with the leadership, training, and management system via a doctoral program that begins at the end of February 2016. Historical Perspectives Part # 1 – Robotic Process Automation By Ravi Ramamurthy, CEO of Epiance When we came out with the first process automation tool way back in 2003, there was a sense of WOW and deep interest from business units and operation heads. Unfortunately, that did not translate into actual buys for many reasons. There was stiff resistance from IT groups, because they felt that this technology intrudes into the actual application. Also, robotic playback looked more like a band aid solution rather that a root cause fix (this remains true today).

Two trends accelerated the rise of this technology • Stabilization of BPO industry: As the BPO industry matured, it needed technologies that could increase productivity and provide it with key competitive advantage to win over new customers and retain existing customers. • Acceleration in process change: Overall change-business environment, competition, demand for new products have all accelerated change and at an increasing pace. This has forced organizations to become agile and continuously adapt processes to be in tune with a volatile environment. Changing applications is costly and cannot be done frequently. Also, many organizations are run their core processes on legacy applications.

The only alternative is to find ways to make this happen from the outside without the changes to the underlying applications. Given the hype of Robotic Process Automation (RPA), it is easy to get carried away and focus all energies on automation. While automation is, a key enabling technology for BPOs and Shared Service organizations, one should be mindful of the following: • An out of the box approach to Automation technology will not work – the automation platform that one chooses need to be flexible. • Automation goes hand in hand with process improvement – process improvement needs an out of box thinking approach. Some of the most brilliant ideas can dramatically change the process and completely alter the process of automation. • The degree of automation is directly proportional to the sphere of influence –(see graph attached). For example, the sphere of influence can be purely internal in which case certain amount of productivity gains can be achieved.

However, if the sphere of influence can be extended to the customer, the possibilities of automation increase. If this is further extended to the customer’s customer the automation possibilities dramatically increase. Extending this further if the sphere of influence is global, the automation possibility is maximum and in certain cases, processes which seemed to be manual can be completely automated.

Obviously, many organizations have only limited sphere of influence (in most cases within the organization and in some cases to the customers’ organization). However, this is where an organization can take a leadership role by evangelizing change across the industry. An organization which manages to do so gains leadership position with obvious advantages. Historical Perspectives Part # 2 – Selling RPA By Joe Labbe, CEO of RatchetSoft When my company started selling our Ratchet-X RPA platform twelve years ago (long before RPA had a name), we had a tough time marketing the solution for the following reasons; a) RPA is a technology that every organization can use, but apparently, it is very difficult to market to, well, everybody, and b) prospects had become so accustomed to software applications not communicating with each other, they couldn’t understand how what we were proposing was possible. We thought we were clever branding our solution as a “desktop application integration platform”, but that only left prospects asking; “Great, what the heck is that?” Although the term RPA has helped describe what it is we do, we need to appreciate the term is still foreign to most prospects (at least the prospects we encounter).

Further, those who are familiar with RPA have vastly different understandings of the term as we vendors, predictably, have tried try to make RPA mean all things to all people. So what’s the point? Well, despite the fact analysts tell us RPA is poised for enormous growth, and RPA vendors’ sales are keeping their boards quite happy, we still find ourselves doing a lot of evangelizing and educating. Though obvious to those of us who live in the RPA bubble, understanding what RPA does, the cost savings it can yield, the new business opportunities it enables organizations to seize, and why it is frequently preferred over traditional integration methods, is a bit more elusive for prospects just beginning to get their minds around the concept. So while we are still evangelizing and educating, when dealing with end users, we need to get out of the clouds and come down to ground level. No doubt, RPA using organizations need to understand how the technology works and what are the technical requirements. However, that is not what we should lead with.

The quickest way to get an organization started with RPA is to frame the discussion in terms of point solutions rather than an enterprise-wide automation platform. If we communicate that message in terms users can understand and then deliver on our promises, it won’t be long before the user asks; “Where else can we use RPA in our organization?”. Historical Perspectives Part # 3 – Is Robotic Automation just a Patchwork? By Ravi Ramamurthy, CEO of Epiance While RPA can probably trace back its roots to the era of 2003-5 the current levels of interest contrasts with the immense amount of skepticism that we faced in those early days.

The biggest question that we were always confronted with was – “Isn’t Robotic Automation just a patchwork?” If it sits on top of an application or multiple applications and does something useful, is it not more elegant to replace the underlying application itself? Mimicking user actions and doing so in an automated manner, though fascinating to watch seemed like a perversion in the IT world. Today very few question the logic behind this.

In fact, it seems fashionable to perform processes this way. This question however will be asked and need to be answered to ensure strong theoretical foundation for this burgeoning field of RPA technology.

Convenience of adopting such a solution is just one aspect. The theoretical elegance and necessity needs to be well established.

Almost all popular enterprise applications (banking applications, ERPs, SCMs, CRMs, PLMs) operate on processes that need to be performed in an organization. Often these processes span multiple roles and involve multiple individuals. Every process, for example an account opening process has multiple scenarios and we can call this process scenarios or process variations. An example of a variation to an account opening process could be – opening a savings bank account or opening a fixed deposit account, opening a current account etc.

Inside these variations are multiple variations or scenarios. A client may or may not be a citizen of a country. The process for opening an account may vary between these individuals. A typical process has multiple scenarios much like an iceberg. The visible Process variations that are used currently or supported in the current application is a fraction of the total required scenarios. There are many process scenarios that evolve out of a customer need or arise because of a change or evolution in the business.

There are also vast scenarios that come up on a frequent basis and these are the process scenarios of the future. Even in the visible scenarios, few of them are commonly used and a few are uncommon or infrequent. Many of the process scenarios stretch the application to its utmost and used very rarely.

A typical and a common process such as a purchase ordering process may have 4-5 frequently used scenarios and another 20-30 variations, which are used infrequently. Complex or esoteric scenarios may cover another 15-20 situations or a process scenario may suddenly emerge.

Process scenarios are therefore not static as one imagines, but evolving day by day. The number of scenarios unsupported by the application may increase to the point where the application itself may have to be changed.

In addition, End Users work based on process scenarios; however, applications do not. Applications are built on modules and screens. A typical process scenario spans multiple screens or modules and require a certain number of features in a module or a screen. Some process scenarios may traverse through the same screen or module and have features in common; while other scenario may require additional features. An application works based on features and functionalities and multiple processes reuse these features and functionalities.

We are therefore looking at incompatible paradigms. Humans work efficiently based on process scenarios. Applications work based on common code base or screens that are used by multiple process variations.

And this does seem to be the most efficient way of developing an application. However this manner of developing an application leads to tremendous human inefficiency. We studied a few processes in SAP and IFS (ERP application) and the table below illustrates the situation: The automation possibility in such application is a phenomenal 66%. This means that because of the clash of the paradigms, End Users are performing tasks that can be largely automated. It is this mutual incompatibility of paradigms that makes RPA. The tasks, activities and process variations keep evolving and it is necessary for users to navigate not just one application, rather multiple applications.

The growth rate and ad hoc nature of such need makes it impossible for create new applications by integrating existing applications. Processes that span multiple organizations is another reason that supports RPA. Robotic Automation is therefore an integrator of all IT resources that are available. The impact that it can have on businesses is far reaching. A few interesting trends will lay the path for the future of this incipient technology.

Some of them are outlined below: • New tasks, process variations and scenarios will grow at an incredible rate. End Users will discover new ways of doing things that will create a need to create robots on the fly. Creating robots rapidly, building them on existing robots, being able to do so without scripting knowledge will become an essential requirement. It should also be possible to integrate these robots into a larger framework, where such creations can be shared in a controlled manner with a larger population. • Robots should be able to interact with the external world by written or electronic mail, phone calls, or faxes. Natural Language Processing today that focuses more on understanding the grammar of the language in the future may need to infer the intent of the mail.

• Decision making will increasingly become a significant part of a robot. It will be required to perform more intelligent tasks. While there may be some initial rules, the robot should be able to learn based on its current and past performance.

Big Data analytics will play a key role in uncovering new rules. If analytics can provide new rules based on the success and failures of the past, then it will become possible for robots to codify these rules and apply them in the future. The entire adaptive process will need be moderated to so the new rules sensible, feasible and can be applied. Being able to create robots that have this type of decision capability can have dramatic ramifications for the future of organizations. • It essential that robots are interoperable. One should be able to build robots on top of existing ones. There should be seamless sharing of data between these robots.

Robots will perform all kind of tasks on User Interfaces, as well as, web services. A rich ecosystem of robots interacting in the virtual marketplaces will proliferate Automation. Looking back, it is indeed difficult to believe that what started as a patchwork has grown into something which could have widespread ramifications in every sphere of business activity. First Things First Part #1 – RPA vs. Native Integration By Joe Labbe, CEO of RatchetSoft For years, I have told our integration partners and customers; “Don’t let RPA become the hammer that makes every integration project look like a nail.

If you can natively integrate applications through APIs, data exports and imports, or direct writes to backend databases, that is the way you should integrate.” While I still believe this in theory, hundreds of real world projects have educated me on the delicate balancing act most organizations engage in when integrating systems. More often than not realities on the ground lead us down the path of IT practicality rather than IT purity. So, what are the factors we should consider when trying to decide whether to go with an RPA or native integration solution?

Let’s start with availability. Well this seems kind of obvious.

In order to integrate applications natively, native interfaces need to exist. However, the definition of the term “exist”, can vary based on the eye of the beholder. It is one thing for a vendor to provide product native integration interfaces, but another thing entirely as to whether those interfaces can be used in a given situation. Here are the factors I consider when trying to decide which way to go: 1) Is there a cost associated with using the interfaces? While many vendors license its APIs as part of its product license, many do not. In fact, in some cases, those additional licensing fees may be quite high. Further, there may also be additional runtime fees associated with any solutions you create using a given vendor’s API.

Make sure you read fine print in the EULA before you decide. Does my organization have the requisite skills to use the interfaces? While web services has diminished some of the issues associated with language dependent interfaces, the using organization still needs to have access to a developer who knows, or can learn the interfaces, and write to them given his skillsets and tooling.

Can my organization scale to support multiple integrations using multiple vendor interfaces or does it make more fiscal sense to learn one RPA framework and use that for each integration, thus creating a scalable integration framework? This is even more important for system integrators who, by design, must support multiple applications for many customers. For system integrators, employing a scalable integration framework is critical success factor in preserving margins. 3) Do the native interfaces support all the functions required? Native interfaces come in all shapes and sizes, some good, some not so good.

When evaluating native interfaces, it is critical that the API or import/export facility accommodates all the transactions you want integrated. Don’t assume a product’s native interfaces will support every feature you see in the application’s user interface. Serial Postgresql Hibernate. It is my experience that they rarely do.

What about writing directly to the backend database? Though a valid option, it is the option most fraught with peril. I say this because writing to backend databases requires an intimate understanding of the database model.

If you miss one entity relationship or are unaware of a specific trigger or stored procedure, your integration could be doomed. This becomes even more complicated if you are trying to write to commercial application databases which tend to be larger and significantly more intricate than home grown application databases. One other thing to consider regarding direct database writes has to do with data validations. Well-designed applications should relegate all data manipulations and validations to the data tier. However, many applications do not follow this rule often including validations in the presentation tier.

If this is the case and you attempt direct database writes, it is quite possible you will bypass important application validations and write data the application was not designed to handle. These kinds of insidious validation sidesteps can cause significant application problems and may be difficult to detect. This does not happen when you integrate through the user interface. You are sure to catch every downstream application process regardless of which tier performs the process. The last consideration when making the RPA vs.

Native integration decision has to do with monitoring and transaction rollback. Once an integration is moved into production, the majority of the user’s time spent on the process is monitoring transactions, determining causes when exceptions are thrown, and correcting data and/or rolling back transactions. I contend that the easiest way for an end user to deal with these issues is by having them presented in a way that is familiar to the user. In most cases, the most familiar way is through RPA because RPA mimics the process the user already understands.

For example, in our Ratchet-X platform, a user can monitor a given bot’s processing in any, or all the following ways: 1) View interactive log data in the form of steps where they user can see what specific action is being performed and the input and output data states. The user can drill down into these steps and view the natural language translation of the step defined in the automation’s specification document. In other word’s the user can view the step in the “user’s voice”. Trittico Botticelliano Program Notes Band.

2) If the transaction is still in process, the user can view screen shots of the bot desktop at any step along the automation processing route. Here, the user is seeing the integration in a “language” he already understands – the language of screen shots. 3) If the automation is complete, the user can view a recording of the automation transaction. Even a techno neophyte can detect where an errant transaction went wrong by viewing the playback of a familiar transaction. In the case of many API integrations and imports/exports facilities, the user’s only recourse when things go awry is to pour over cryptic log data in order to decipher which transaction abended and why.

Worst yet, the process is even less opaque in the case of direct database writes. However, if your platform can show you where the problem occurred, display the state of the data, tie it back to the specification document and allow the user to see a playback of the actual transaction, I believe the user will have a much easier time managing the integration in production. Suffice it to say, when it comes to selecting the right integration solution, it is often not our training and instincts that guide us towards the answer. Rather, the right solution is the one that best fits the task at hand. While we should not let, RPA become the nail that makes every project look like a hammer, when the project is nail, we should pick the most appropriate hammer.

First Things First Part #2 – Critical Factors to be considered in an Automation project By Ravi Ramamurthy, CEO of Epiance Implementing business transformation projects are beset with many challenges. According to many studies, roughly only 35% of projects are able to deliver the original value envisaged. Key stakeholder acceptance of the change is critical.

While leadership plays an important role, the rank and file members and managers’ buy in is critical for the success of the project, which is the role of the CMG. Automation projects need an even more aggressive change management function than other technologies, because the objective of a lot of Automation is to eliminate or reduce the number of end users, so getting buy in from them is more challenging. The challenges rise exponentially depending on the degree of Automation. If a project yields marginal benefits or productivity gains, let’s say 10%, the resistance may not be high. However, if the likely productivity gains are higher than 40%, the resistance dramatically increases. Full or near complete Automation will face almost total resistance. CMG strategies to overcome resistance need to figured out upfront.

Leadership team cannot look at automation change management as another business as usual project. A robust strategy can ensure more rapid adoption of automation projects in an organization. Currently very few complex processes are being automated.

Benefits of automation are far higher in complex processes; however, these will also face much tougher resistance for the reasons already mentioned. Taking resistance to change into account, how does an organization rapidly scale Automation? The answer will be unique to every organization depending on their culture and values. However, some general principles can be used in every organization: • Make Change Management as a part of the strategy and implementation plan for the Automation project, which is the responsibility of the leadership team. • Get Senior Management buy in first with some quick successes.

Some suggestions on how to achieve rapid success: • Identify processes where all the scenarios, rules, policies are documented and the current scenario is clearly understood. • Identify processes whose operational unit head is a clear champion of Automation and has deep understanding of Automation and what the technology can deliver. • Identify low risk processes where automation can be rapid • Identify processes where clear operational champions can be identified.

• Do not raise the expectations of the organization unrealistically for this project. Keep the project as low profile as possible.

• Minimize over management. Ensure that reporting is linear and limited. • If possible, execute these projects in stealth mode. • Once enough projects have been implemented, educate the senior management and create an active awareness. • Create a separate team to execute these processes.

Discover a path for individuals who are critical and whose careers might be affected if the project were to be successful. There should be a clear asymmetric growth for these members. • Identify and address the fears of the members. • Create a change management team which understands these challenges and works with the team members to ensure that everybody is prepared for the change.

Automation cannot be driven top down in a hierarchical fashion, but needs a more democratic setup. High benefit projects need a higher dose of egalitarian culture to succeed. I have seen change and many disruptions in organization and this is yet another indication that no matter what technology is riding the wave, human factors are fundamental. Organizations that pay heed to these factors, plan for it, and build a human change management implementation paradigm succeed, others fail to make the transition. First Things First Part # 3 – Create a Robot or Change Underlying Application? By Ravi Ramamurthy, CEO of Epiance With the advent of RPA rapidly growing popularity, most organizations look at RPA as a silver bullet and a catchall solution for most of the changes/issues related to IT applications.

RPA is ultimately a patchwork and a quick fix solution that can yield dramatic benefits when used wisely. In some cases, it may be better to change the underlying application or migrate to a new application. Some of the parameters that will govern this decision are: • Pace of Change: Does the application cater to front end or customer facing processes? Do the rules and policies change rapidly? RPA solutions are applicable in a fast-changing environment. If the pace of change is slow, it is usually preferable to change the underlying application.

• Level of Application Integration: Some applications interact with many external application or end users may need to navigate multiple applications to complete a process. In some cases, the applications are managed or are under the control of third parties. It may not be feasible to change all the applications as a response to a change in requirement.

If degree of integration is high, RPA solution is preferable. Grounds up development is preferable if the application is monolithic in nature. • Age of Application: It is estimated that more than 1/3rd of all applications are older than 2 decades. Non-availability of documentation or availability of skilled resources might make it infeasible to change the application.

In such a scenario, it might be preferable to use RPA as an interim solution, while the organization looks at moving over to a new product to replace the legacy one. • Complexity of application: Complexity of an application is proportional to the number of use cases it serves. If a single screen in an application is designed to serve multiple use cases, the complexity of the application increases.

Time to learn the application, errors, productivity is directly related to the complexity. Complexity can be expressed as C= (N cases)^2 NCases: Number of use cases using a common real estate (such as a screen) As an application becomes more complex, the design complexity of the application also increases and the number of steps that a specific user must go through for a specific use case or process variation also increases. The most sensible thing to do would be to simplify the application for a specific user and specific use case.

Obviously, this will increase the number of applications dramatically and reduce the viability. In such scenarios, it would be ideal to create a broad-based application that can serve as many scenarios and process variations as possible and the create a robot for every use case scenario. Best Practices #1 -Key Best Practices for successful Implementation of Automation By Ravi Ramamurthy, CEO of Epiance Many organizations approach Automation and RPA in a very tactical manner.

A strategic approach towards automation is necessary to yield the desired benefits. For a strategic approach to succeed, one should not look at just a department, rather the entire organization. The vision must be broad even if the implementation is narrow. Just like any IT project, RPA is beset not only by the normal project challenges, it also faces immense resistance from end users.

Key best practices need to be followed. Most of these best practices are new to many stakeholders in an organization and it will require time before an organization can gain competency and proficiency in these areas. Stricter adherence to best practices yields systematic, sustained, and consistent benefits. If done properly projects can run smoothly and it will be easier to implement projects in a scalable manner. Failure to do so may make every project a heroic implementation project and prevents RPA from being implemented in a scalable manner. • Know your existing processes: Most the processes in an organization are complex in nature with multiple process scenarios/use cases/variations for every process. The number of variations are dynamic and change based on the business rules or underlying change in the application. Many of these processes are not documented and reside in the heads of experts in the organization.

RPA implementation demands accurate availability of all variations and information. Organization need to document all the variations of a process. The documentation process itself cannot be a onetime activity since the process undergoes regular changes. A robust change management process needs to be instituted. The process documentation, captures of the process need to be stored in a central repository with necessary access controls and version control procedures in place. Any change must be recorded in a regular and consistent manner. The process documentation and content should be distributed to all the stakeholders and end users. • Benchmark all process variations: All process variations should be benchmarked and an organization must have data on Transaction time (TAT), error rate and other key metrics. Most organizations have rudimentary data on their processes-i.e.

Average data for an entire process or the process major variations. However, data on time to complete a particular process variation across end users is essential before commencement of a RPA project. The frequency or the number of times a process variation is executed, error rates or key metric data for every process variation is also important. A robust automated process for gathering and modeling this data will make the entire process seamless.

• Understand the key challenges faced: The key objective of automation and expectations need to be articulated and defined upfront. Change of expectations at a later date while the project is midstream can be catastrophic. Decide mix of Automation: Is the RPA implementation going to be a complete automation of the process or is the objective only to automate it partially? While a small minority of processes (.

The Robot Consultant is the third in the RPA Expert Series, which can be found on my Blog site under that heading. “The Robot Master” is an edited compilation of seven technically oriented articles by a seasoned RPA lead. “The Robot Architect” is an edited compilation of approximately thirty blogs covering a full range of topics necessary to operationalize an RPA program. “The Robot Consultant” is the collection of white papers and articles by RPA and related industry experts to give a big picture context to the world of RPA and Cognitive.

I have categorized these pieces under the headings: Strategy, Cognitive/AI, Avoiding Pitfalls, and of course my favorite, Automation Theory. Strategy: Everest Group – Practitioners Perspective – A Conversation with Simon Munter and Pankajam Sridevi, ANZ Global Hubs Leadership ANZ Bank is a celebrated RPA case study of a 10,000 employee Asian bank that began to use Automation Anywhere (AA) in February 2015. They emphasized a fast adoption of RPA. Specifically choosing AA because it did not require programing knowledge. Also, they did not mind automating sub-optimal processes with RPA, because it is inexpensive and in their own words didn’t mind if the Robots worked extra! They adopted a multi-tiered, decentralized team approach, because they believe that the experts in the functional areas are in the best position to decide what needs to be automated. Their A Team has 40 power users in a centralized organization available to help the B teams of a couple hundred users in smaller teams spread throughout the organization who can automate medium difficult processes.

They are planning to create C teams that can automate simple processes, made up a thousand or even two thousand people. At the time of the interview, ANZ Bank had achieved 40% automation of their processes with RPA and are looking to add Cognitive for an anticipated rate of 70 to 80%. Automation Anywhere – Count Down to a Digital Workforce – A Conversation with Automation Anywhere Shail Khiyara, CMO of Automation Anywhere in a smooth as silk, upbeat, positive interview and I might add hype-free, makes a persuasive case for the probable future that Cognitive Automation is bringing to the world of business; especially in terms of employment. While massive job loss is a possibility in certain segments of the job market, overall, augmentation instead of elimination of jobs is the most likely outcome in most cases.

The reason I use the term “smooth as silk” is because the way the Author does things like effortlessly laces together cutting edge terminology like “Digital Workforce”, “Digital Worker”, “Digital Bots”, and “Digital Work Platforms” or how He emphasizes the need for an enterprise Center of Excellence. He finishes this interview with perhaps the best use case possible to illustrate what He discussed: ANZ Bank. HfS – Defining the Digital Organization: The Intelligent Digital One Office I feel a kindred spirit with Phil Fersht, the Founder of Horse for Sources and by that I do not mean to imply I am in his league, rather He is an out of the box, maverick thought leader, whose ideas are ahead of their times.

However, do not get comfortable and think you can ignore or put off what He has to say. Being ahead of your times these days, mean you might have enough time to actually do something strategically intelligent about the massive, rapid, disruptive innovation that overwhelms businesses at an ever increasing magnitude. Phil’s stuff is unique and I wouldn’t know where to start. I can’t do Him justice with a summary.

You need to see for yourself! JDSCyberbots – The Vital Few This is the first of a series of articles that explores from an RPA experience/expertise and graduate academic perspective, the radical implications of RPA and Cognitive disruptive innovation technologies on the organization business model, leadership paradigm, and HR policies of Service Operations. Let me be blunt, established organizations are experiencing what is intrinsically asymmetrical warfare. Their traditional competitive advantage that came from scale and deep pockets is being systematically chipped away by smaller, lightly structured organizations that are able to quickly adapt the heavy firepower of smart technologies.

If these larger, traditionally structured organizations have any hope of survival in the face of this onslaught, they will need to do what the US military did in the face of a long term, worldwide asymmetrical war on Terror; emphasize the adoption of smart technologies and embrace the special operations subcultures necessary to wield them optimally. The Vital Few: Cognitive/AI: Cognizant – “Intelligent Automation: Exploring Enterprise Opportunities for Systems that Do, Think, and Lean” Matthew Smith’s concise, six-page (content) work has no peers when it comes to communicating to Management the way that Management needs to be communicated to about the essential content and urgency of Cognitive Automation. His “Do-Think-Learn” that evolves to “Think-Learn-Adapt” captures the intrinsic essence of the RPA Cognitive revolution and He does not fail to mention that it is just that, referring to the “Fourth Industrial Revolution”. While his ability to communicate in Manager is peerless, the practical, actionable content is rock solid and balanced. He does a great job explaining RPA and Cognitive in plain language and lays out Cognizant’s detail plan how an Organization can execute an Enterprise wide automation initiative, while the very next page exhorts the need to get right in there with ambitious “Fail Fast” and “Learn Quick”. I personally love this maxim: Automate First, Automate Ambitiously, and Automate with Purpose. Matt would have made a great Automation Marine Commander!!!

Beyond Digital: Intelligent Revolution – Model of Intelligent Systems Development & Reference Architecture Technical article on Cognitive Architecture. This is deep, not light stuff! Excellent graphics and illustrations. Personally, I am not qualified to evaluate the contents.

However, after reading this stuff every day for the past two years, my gut tells me this a superior article on the subject. You be the judge. One thing is for sure, there is science here necessary for “The Robot Pilot” to become a reality. The fourth in this Expert Series?

ICG (Internal Consulting Group) – Smart Process Automation (SPA) -Critical Components for a Successful Digital Transformation Disrupt or be Disrupted is the opening premise based on a 2016 McKinsey report that predict 40% of existing business will be disrupted and put out of business because they did not adopt disruptive innovations such as Cognitive Automation or did not do so aggressively enough. This paper is full of data that quantifies increased business success for those that have adoptive digital technologies; explains in more detail that any white paper what is Cognitive, the different levels of it, how it works (uses Sylvan Design material largely for this); that ultimately the business model will be radically shifted from People driven process aided by technology to technology driven process supervised by People. Other key features, cites shortage of experts, 80% automation ultimately, and how that breaks down, i.e. What part RPA and what part SPA. Full of very useful graphs and table. Yahoo, finished my second Doctorate Class “Leadership- Theory and Practice” and treated myself to my weekly movie – Matrix Revolution! Not particularly a fan; however, it was the best thing on the Tube, so why not? I woke up the next day and realized I had just watched one of the best non-military analogies on Cognitive RPA’s power to radically transform business as we know it and its greatest obstacle. More on that in a minute. If you have been following me, you know the primary reason for my enrollment in the Walden Program is to figure a way to translate my Cognitive RPA automation business model and leadership paradigm theory from military analogies to ones more accessible to Business. And of course, to dig deeper and wider into theory and management literature to add more substance.

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