Robotic Process Automation (RPA) - Best Practices

I work at an executive level and as part of our organization strategy, we decided to focus on RPA as it was an upcoming technology that could help customers automate many manual and repetitive tasks. RPA is an exciting technology with very high growth potential over the next 4–5 years. The consulting side of business is expected to grow at CAGR of 40%+. The technology will help many companies to automate mundane, repetitive manual tasks. When the RPA hype stabilized in 4–5 years, there will be millions of digital assistants helping humans in mundane tasks so that humans can focus on interesting tasks. Large corporations have a lot of technology debt as they have multiple systems that do not talk to each other, the systems are outdated but can’t be replaced because of dependency on them, system limitations resulting in many tasks being performed manually etc. RPA proponents claim to reduce this technical debt in short time span and with quick Return on investments. But is this really true? We have worked with many customers and delivered RPA projects using Automation Anywhere platform and there are a lot of learning that we have acquired over those years. This article is an attempt to share these learnings’ with readers.

RPA is not a magic wand - RPA is not a magic wand and “one solution fits all”. RPA can solve some business problems, but not all of them. It may not be possible to automate complete process using RPA and some steps may need manual interventions. So customers should set their expectations accordingly. Set up RPA Centre of Excellence (CoE) - Instead of running the RPA initiatives in silos across the organization, it is recommended to set RPA CoE which drives the RPA initiative, sets up long term goals and targets for the initiative, sets best practices, helps aligns operations team and IT, work with RPA vendor and external RPA development partner etc. Identify a RPA Champion - It is recommended that customer should allocate a senior leader as RPA champion. This person would drive the RPA initiative, alleviate employee concerns, if any, make sure that support is available from executives for the initiative, work with RPA CoE. Set Realistic Project Timelines - Customers expect quick ROI when they start the RPA journey. It should be noted that RPA project is like any other software development project and needs sufficient time for development, testing & stabilize to the bots. Customers/RPA team should plan development timelines accordingly. Customer Commitment to RPA & Return on Investment (ROI) Expectations - RPA initiative should be a strategic initiative for customers and they should be committed to RPA journey for long term to get positive ROI. It is a known fact that the ROI is faster in RPA project versus other IT projects, but higher & sustained ROI can be achieved only if multiple processes are automated and existing bots are modified on a regular basis to meet the updated applications & process.

Build Enterprise Architecture for RPA projects - It is recommended to think of RPA as enterprise initiative and hence build enterprise architecture at the start of the engagement. This includes ● Building layered designs ● Breaking process into sub-processes and creating separate bots for sub-processes ● Creating common components/meta-bots which can be used by other bots ● Diving processes into attended and unattended ● Defining coding standards ● Process to track bot infrastructure utilization so that the infrastructure can be optimally used ● Use of database instead of MS Excel ● Audit a& error log methods that can be consumed by all the bots ● Process master bot that managed effective scheduling of all bots ● Scripts that check if the RPA servers are running or not ● Automatic communication mechanism when bots fail

Time availability of process Subject Matter Experts (SME) - One very critical reason why RPA projects are initiated is to automate existing manual process. Hence it is very important for the RPA development team to understand existing process in entirety, understand all possible deviations in the process and actions that existing team takes for such deviations etc. It is very critical for the customer process SME/domain expert to work closely with RPA development team. Customer should plan & allocate sufficient time of process SME during project life-cycle, especially during Requirement analysis and User Acceptance Testing phase. Focus on Process Improvement - We have observed that ‘As Is Process’ has many activities that can be removed/optimized when automating the process. RPA development team should recommend all such Customer should be ready/willing to accept process improvement suggestions. Environment Homogeneity - The Development, UAT and Production environments should be of the same configuration for the bots to run successfully. This also includes screen resolution, MS office versions, driver versions etc. Database Vs MS Excel to maintain configuration data and logs - Many RPA projects rely on MS Excel to manipulate transnational and temporary data for the process. Using MS Excel as database is alright for processes that are not heavy on data. But for processes where the bot needs to process large volumes of data, MS Excel slows the process and also reduce reliability of automation. Hence it is recommended to use database in such scenarios. It is also recommended to use database for managing bot schedules, execution logs, error logs etc. This way the data is secured, can be easily queried for dashboard and reporting purpose. Audit Log and Error Log are important - Logging at every event/action is necessary to provide more insight to the team through status dashboard. It also helps in analyzing the BOT behavior & reason for error when bots are deployed in production. Automation Anywhere provides detailed logs related to the execution of BOT. But the RPA development team should create application functionality related logs. Implement Feedback loop for the Process - How much ever care RPA development team takes to make sure that all exceptions are handled in bot, bot will encounter unforeseen scenarios in production. If such scenarios occur, the bot should send an email to support team or automatically create support team to bot support team. Smooth execution of the BOT can be achieved by taking care of various points of failure like System not responding, BOT taking more time to execute than estimated, dependencies are not anticipated, access permissions not granted to carry out certain tasks using BOT ID, Image Recognition failure, etc.

AS RPA is a relatively new technology, the RPA tools will continue to undergo lot of changes over the next few years before they mature & stabilize. Therefore it is very important for RPA consulting companies to set and implement best practices mentioned above so that the change in RPA tools has low to minimal impact to customers.

Decision Intelligence With AI

Decision Intelligence (DI) is the commercial application of artificial intelligence (AI) to business decision-making processes in all areas. It is outcome-driven and must meet commercial goals. Organizations use Decision Intelligence to optimize every department and improve business performance.

What is the significance of Decision Intelligence?

Decision Intelligence allows businesses to use artificial intelligence (AI) and data to make quick, accurate, consistent decisions and address specific business needs and problems. It enables the collection and modeling of data using machine learning to predict accurate outcomes for optimal commercial decision-making. What Decision Intelligence is not is the complete removal of humans from the decision-making process. It's about empowering humans with AI and a more holistic, accessible view of all of their business data so they can make the best decisions possible. It enables businesses to process and predict data in order to make more informed decisions at all levels of the organization and gain better visibility into their operations while driving game-changing commercial outcomes.

What exactly is the distinction between Decision Intelligence (DI) and Artificial Intelligence (AI)?

AI is the theory and development of algorithms that can perform tasks that were previously only performed by humans, such as decision-making, language processing, and visual perception. Decision Intelligence, on the other hand, is a practical application of AI to the commercial decision-making process. It suggests actions to address a specific business need or to solve a specific business problem. Decision Intelligence is always commercially focused and powers large-scale business decision-making for organizations across multiple industries.

Benefits

Decision Intelligence is uniquely positioned to assist in making sense of massive amounts of data, especially when a clearly defined outcome or metric is measurable. The following are the primary advantages of decision intelligence: ● Automate and accelerate the discovery of insights in order to deliver actionable recommendations. ● Continuously uncover hidden drivers of business change to keep a pulse on KPIs without hours of manual analysis, allowing an organization to act in real time to capitalize on opportunities and address problems. ● Make number-intensive data and business analytics metrics understandable to non-expert analysts. ● Contextual intelligence is used to make data more understandable and useful by explaining how KPIs and other data are relevant to end users. ● Allow for better-informed, more data-driven decision-making that is also faster than traditional BI. Allow users to drill down to see more granular data to support a user's decision and its impact, while also providing them with actionable insights and recommendations for decision-making.

Challenges

When it comes to decision-making, it can be difficult not only to make the decision but also to live with the decision if it does not turn out well. The following are some common decision-making challenges. ● Incomplete Information: A lack of available information places decisive leadership in a sea of uncertainty, making it impossible to interpret that information and make the best decision. ● Information Overload: Traditionally, most people believed that having more information and data at your disposal would allow you to make better decisions. ● Time Constraints: Time constraints can put additional pressure on you to make a business decision faster than you anticipated. ● Uncertainty: Another challenge for organizational decision-makers is uncertainty. Even though organizations face uncertainties on a regular basis, it can be difficult for even the most experienced leader to overcome uncertainty about the future.

Conclusion

Using AI-powered business intelligence solutions allows businesses to make better and faster decisions within critical business processes. Companies can thus not only reap the full benefits of being data-driven but also consider the widest range of relevant information when deciding on the next step.

How RPA Can Help You Move From Tactical To The Strategic Task

RPA stands for Robotic Process Automation. RPA is a type of business process automation that attempts to mimic human activities in relation to digital systems. It uses the combined capabilities of artificial intelligence (AI), machine learning (ML), computer vision, and automation to automate high-volume routine operations. Transformational technology creates and deploys software robots that are developed with business logic in mind. These bots, also known as software robotics, execute tasks by carefully watching, comprehending, and applying the process. That is, they ● First, observe human digital behaviors. ● Understand what is displayed in the graphical user interface (GUI) of the program or any digital system, and ● Then assist you in carrying out a specific digital job by effortlessly merging user interface interactions, client servers, mainframe connections, or APIs.

What Are the Different Kinds of Robotic Process Automation?

To automate business processes, robotic process automation systems primarily provide two deployment options: supported automation and unassisted automation. Their combined automation is known as hybrid automation. Let's take a closer look at these sorts. 1.) Assisted Automation: Also known as attended automation, software robots work alongside human workers to complete tasks. The bots operate on the users' desktops and assist them in completing a task in less time. 2.) Unassisted Automation: Also known as attended automation, software robots work alongside human workers to complete tasks. The bots operate on the users' desktops and assist them in completing a task in less time. 3.) Hybrid Automation: Also known as AI-assisted bots, hybrid automation enables human employees and RPA bots to execute back office and front office processes simultaneously.

3.) Key Market Drivers:

The following are key market drivers in the RPA market ● Most firms have optimized their digital transformation initiatives through the use of RPA in the workplace. ● Ability to automate a wide range of ordinary chores even in a complex unstructured ecology ● Close system integration gaps and implement remote desktop automation ● Adoption and integration with other new technologies like as AI, cloud computing, and so on. ● Increased demand for RPA services in the BFSI industry ● Increased emphasis on lowering the load of human resources in industries such as healthcare. ● Increased use of robot-based solutions in major corporations

RPA as part of the strategic transformation: six elements to consider

● Strategic coherence

It is critical to integrate program objectives with the overall DT strategy when establishing an RPA program.

● Administration

According to Wagner, RPA and intelligent automation have been marketed as being easy to scale, but in fact, require effective governance and a plan for managing huge software bot fleets.

● Stability of the system and process

RPA performs best in a consistent process and system environment. Enterprises must evaluate RPA's feasibility in the context of their overall DT plan and avoid systems and processes that are undergoing considerable near-term transformation."

● Management of organizational transformation

One of the most prevalent reasons RPA deployments fail is a lack of appropriate change management planning and implementation. RPA and intelligent RPA can enable whole new ways of working for people who have been doing their tasks in the same way for a long time.

● Specific success metrics

It is vital to establish and measure the tangible advantages expected from an RPA deployment Stages of automation maturity Automation maturity is divided into four stages: ● Tactical Horizontal Most firms will begin at this level, where they are primarily concerned with reducing personnel in horizontal operations such as finance and human resources. This is an excellent testing ground for automation projects and gives motivation (confidence, experience, financing) to go to stages 2-4. ● Tactical Vertical This is comparable to stage 1 in many aspects, except that the procedures to be automated are more client-centered. This means that the drivers and advantages of the business case shift away from productivity and toward things like customer retention, customer happiness, and competitive advantage. ● Strategic Horizontal A company should strive to achieve an effective and efficient degree of automation that is suitable for its purpose. As a result, it is critical to clearly define the company objectives and design a plan. ● Strategic Vertical This is process excellence, with practically endless potential advantages. Automation and accompanying technologies are no longer employed in isolation but as part of an integrated process excellence strategy.

Intelligent Process Automation

(IPA) – RPA + AI

Process Intelligence Automation is a subset of a larger technological shift known as Automation as a whole. From driverless cars to self-driving drones, automation is helping to make seemingly futuristic technologies available today by developing and deploying new forms of intelligence. Whether it's automated tasks across an enterprise or customer communications in the form of desktop assistants, automation is changing the way we live and work. Organizations must orchestrate their complex operations through automated processes in order to deliver services based on this automation.

What is the primary goal of Intelligent Process Automation?

Intelligent Process Automation (IPA) refers to a set of technologies that are used to manage, automate, and integrate digital processes. Digital Process Automation (DPA), Robotic Process Automation (RPA), and Artificial Intelligence are the primary technologies that comprise IPA (AI). DPA refers to an agile set of intelligent process automation technologies that have evolved from their BPM roots. It allows you to manage the flow of data throughout your organization, making it easier to identify areas for improvement and implement agile changes. RPA, on the other hand, brings speed and efficiency to the table. The use of robots that mimic human actions aids in the reduction of highly manual, labor-intensive tasks such as re-keying data from one system to another. AI then adds tremendous intelligence and decision-making power to the mix. This adds another level of thought to automation because AI can analyze data in ways that humans cannot, recognizing patterns in data and learning from previous decisions to make increasingly intelligent decisions.

Benefits of Intelligent Process Automation

Human and robot orchestration - Rather than simply deploying technologies like RPA in silos and relying on them to complete individual tasks, Intelligent Process Automation can aid in the coordination of work between robots, humans, and systems. ● Employees can be released from labor-intensive tasks - By RPA and assigned to work in more efficient areas. You can be confident that the right decision is being made by combining DPA and AI. ● Ensure proper governance and risk minimization - You can reduce the risk of errors such as incorrect data entry by automating end-to-end processes. ● Visibility of processes and the customer journey from start to finish - When individual automation technologies are deployed, it can be difficult to see the overall impact. ● Agility and speed of process change - IPA not only speeds up end-to-end processing but also makes it simple to make agile changes to processes and the technologies that support them.

Challenges of Intelligent Process Automation

Some of the most common obstacles to implementing IPA in business include: ● Scarce skilled labor to implement IPA. ● Staff retraining and reskilling to use IPA technology is difficult. ● IPA technology integration with legacy software solutions. ● Human workers' opposition to rightsizing and layoffs ● Lack of cybersecurity in dealing with hacker threats. ● Technological advancements enable ongoing innovation, particularly new design patterns. ● Improve your machine learning abilities to aid predictive and prescriptive analytics. This can significantly alter complex actions or processes and improve decision-making.

How Do AI and RPA Interact?

There are many similarities between artificial intelligence (AI) and robotic process automation (RPA). They're both becoming more popular, with enterprise installations increasing year after year. They both promise to transform businesses pursuing digital transformation. And, until now, they have both been relegated to organizational silos, requiring highly skilled - but scarce - practitioners to successfully deploy them. When AI and RPA technologies are combined, they produce intelligent automation that enables rapid end-to-end business processes and much more.

Conclusion

With ever-increasing competition in mind, the demand for advanced work automation tools has increased and is expected to multiply in the coming years. They are moving beyond the traditional boundaries of business process management. Intelligent process automation software is intended to help processes do more than just managing operations. Starting with identifying and removing performance bottlenecks. Smart process automation software employs advanced analytics. This aids in analyzing overall performance, understanding ever-changing market dynamics, and developing appropriate strategies. In response to the ever-changing needs of tech-savvy customers.