Month: July 2022

Enterprises today still rely on the human workforce to extract data from a plethora of documents, which is becoming tedious and time-consuming. Companies thus need to adapt their operating models and enhance processes intelligently to survive the competition by shifting their gears towards automation. One such solution that immediately comes to our mind is Process Discovery.

Process Discovery – What is it, and how does it work?

Process discovery is a machine learning-based tool and technique to define, map, and analyze an organization’s business processes and transforms the dynamics of acquiring an accurate and comprehensive mapping of business processes.

These machine learning-based solutions facilitate identifying business processes and capture process variability through logical reasoning, besides providing recommendations for automation. Consequently, planned workflows can be expedited and made more cost-effective. Also, an inverted level view drives the Business Process Analysis (BPA) when automated business process discovery steps into the scene.

Automated Process Discovery tools assemble data and change this into structured, usable datasets for diagnosis, either by AI or experts. User repetitive procedures are contextually arranged into events, and an analytical process model is devised. The bots operate passively in the background, collating multi-app data to monitor employee activity. Advanced machine learning algorithms analyze this, creating automation workflows for later interpretation by automation tools. This forms the basis of intelligent process discovery.

Digital strategies rely on process discovery’s prompt, efficient & dependable nature to promote an ideal working environment. Subsequent tasks can then also be automated. This curbs manual effort for scalable process identification and paving all variations plus exceptions. It also creates a robust automation blueprint, driving transparency & eradicating subjectivity. Now errors & drawbacks can be dodged with more competent exception handling.

Benefits of business process discovery

Enhanced quality and performance: Process discovery can accurately prioritize & update process workflows with deep learning & process optimization. In addition, it gives a clear picture by automatically identifying, analyzing, and determining tasks and priorities for automatable processes with any human labor.

Visibility: With process discovery, visibility of ownership for business processes is lifted, mitigating risk with better access control, and thus companies can find new inroads and future automation opportunities.

Lesser risks: Risks can be reduced considerably if fewer people are provided access to process information.

Cost efficiency: Process Discovery application avoids needless recurrences or other inefficiencies as it works without a human workforce; hence, the costs get reduced significantly.

Increased scalability: With the insights engendered via Process Discovery, companies can make intelligent choices on which processes to automate next. It unlocks further growth potential and performance with marginal use of resources and time.

Top 5 use cases for process discovery are:

Telecommunications: Process discovery in the telecom industry helps identify the workflow processes to be automated. Mostly, these include interactions with onboard customers by updating them with the latest service options. It also steers them through other processes and fulfills customer service needs, such as updating contact information and addresses.

Accounting: Legacy systems can be time-consuming, tedious, and error-prone. Process discovery cuts down the cycle time by finding ways of automating the process of handling payrolls, balance sheets, filling tax forms, expenses, and making sure that invoices are paid on time. Process discovery solutions can analyze turnaround time to guarantee that resources are used optimally and make up-to-date decisions to improve each process. Whether you’re looking to automate invoicing or create error-free financial reports, process discovery can help.

Insurance: Claims processing involves the confirmation of required information to verify the authenticity of claim requests. For instance, claims processing is generally data-centric, time and labor-intensive task, susceptible to human errors when handled manually. Therefore, the application of process discovery tools can finish this validation effortlessly.

Finance: Automated business process discovery is used extensively in the financial domain. The biggest use case in this sphere is regulatory compliance besides mandates like ‘Know Your Customer.’ Process discovery, combined with RPA, guarantees that these requirements are completed on a timely basis without any error.

Human Resources: The HR domain is another use case of process discovery. It is particularly effective with processes related to interview invitations, background checks, screening resumes in addition to sending job offers. It also assists in leave requests and health insurance requirements, and many more.

AssistEdge Discover – Realizing the automation potential through process discovery

AssistEdge Discover, an AI-powered solution, helps unlock hidden process insights to accelerate enterprise-wide automation. It gently captures human-machine interactions and leverages AI for actionable process insights.

It cannot be denied that process discovery can do wonders for business operations when implemented. However, the challenge lies in its execution, as many existing conventions are peppered with data blunders, inadequate details & questionable integrity. Therefore, a tool can only churn out decent results depending on the quality of data fed into it in the first place.

So, with good data, one can observe better cost savings & willingness to reinvest. Additionally, with the advancing standards of organizations and their customers, it has become vital to have a well-devised business process discovery approach. Therefore, it is always advisable to consider your business data carefully and refine any questionable assumptions before executing any such solution for analysis.

The post-pandemic period has seen a rapid transition of businesses to digitization, and Robotic Process Automation probably played the key role for many. But RPA’s scope is restricted to rule-based, repetitive tasks. For businesses to imbibe automation as their corporate culture, they need to identify processes that need immediate Automation attention, a capability that is not part of what RPA does. Hence, they turn to Process Discovery tools to complete the identification journey effectively.

What is Process Discovery and how does it work?

Process Discovery is a set of tools and techniques used for defining, mapping, and analyzing business processes for a given period. Process discovery utilizes Machine Learning capabilities to record all possible variations existing in each process and make recommendations for Automation.

Process Discovery methods identify use cases that can be automated and distinguish them from the rest of the processes. Further, it helps design automation workflows, map, plan, and implements automation faster and more efficiently than manual methods could construe.

Process Discovery can be termed an investigative process incorporating the best business process management and data mining. It identifies, monitors, and improves real processes by analyzing data captured from user keystrokes in enterprise systems. The tools run on employee machines silently in the background, mapping the workflows without hampering daily work. It collects data on how employees use various applications to complete their key roles. The information thus captured is transformed into structured datasets for further diagnosis with the help of AI technology. Repetitive actions from users are grouped into meaningful events, and a process model is proposed for analysis.

Obviously, Process Mapping and Mining for information takes time and cannot be completed overnight. Hence, Process Discovery tools and bots monitor each machine for a specific period to arrest any slight variations and subtle changes.

Following that, advanced ML tools are utilized to analyze all the data captured to narrow down specific workflows that can be moved to an Automation tool to complete the transition journey.

Process Discovery: Benefits and challenges

Process Discovery fuels digital transformation and is one of the important steps toward scaling automation to other critical business use cases. The opportunity cost of wrong process selection is enormous. If enterprises wish to extract the full value out of automation programs, they should determine the right processes to automate.

However, in order for Process Discovery solutions to work adequately, they need comprehensive real-time data. Manual approaches to data collection fall short of complying with the exact requirements, causing numerous challenges and hurdles along the way. These challenges can be either or all of the following:

To address the above-mentioned challenges, enterprises should opt for automated Process Mapping to mine accurate and granular data from business processes to ensure the success of the Process Discovery. Hence, the Process Discovery tool is deployed across all user workstations at once to ensure the data collected is accurate and in real-time.

Let’s explore the benefits of Process Discovery:

Improved quality and performance: With the help of empirical data, Process Discovery tools provide clarity by automatically identifying, analyzing, and determining tasks and priorities for automatable processes. It doesn’t depend on how employees think the tasks should be performed. This more accurate approach offers up-to-date insights into process workflows, deeper understanding, and enhanced process optimization. It leaves no room for guesswork and human bias.

Visibility: Process Discovery guarantees visibility of ownership for specific process steps and an overall process across the organization. This helps enterprises to identify new pathways and future automation opportunities easily.

Fewer risks: Process Discovery solutions restrict user access to critical business information to minimize risks.

Cost efficiency: It enables a measurable way for business improvements to avoid unnecessary repetitions or other inefficiencies. And, with fewer human resources involved, the overhead costs go down significantly.

Improved scalability: The insights generated through Process Discovery help organizations with intelligent decision-making to ensure the gradual evolution of automation solutions and unlock further growth using minimal resources and time.

Maximized ROI: Empirical data-based business process maps prove helpful when enterprises navigate their automation journey from value creation to realization. The user keystrokes captured for process mapping cover all process exceptions and variations. This improves process visibility and provides options to scale, thus maximizing the expected ROI from the automation program.

Conclusion

Enterprises should start small and then scale up to gain early experience, and with the help of the right Process Discovery tools, this task will be realized in no time. Process Discovery is an essential step and a more reliable and valuable tool for enterprises looking to scale automation projects faster to maximize their value and increase ROI.

Robotic Process Automation or RPA bots help organizations automate labor-intensive and repetitive, simple tasks so their human resources are optimally utilized. And it can be considered a true harbinger of innovation because its success made enterprises look for different ways to scale automation and bring more intricate processes under its roof. Nevertheless, implementing Robotic Process Automation comes with its challenges. In this blog, we will study each of them and also discuss remedies to negate those bottlenecks.

And, to begin the journey, let’s first understand:

What exactly is Robotic Process Automation (RPA)?

Robotic Process Automation or RPA, as commonly called, helps automate recurring rule-based tasks that require minimal human intervention. These monotonous labor and time-intensive workflows are integral to any business organization and help employees to focus on more value-added roles requiring their skills and expertise.

With Robotic Process Automation solutions, companies can:

Robotic Process Automation implementation challenges and remedies

Lack of understanding and acceptance

A lack of acceptance stems from a poor understanding of how RPA works and what it can do for your business. That non-acceptance mindset of the employees and the management together impairs the smooth deployment and implementation of RPA solutions.

Unavailability of required skills

RPA is currently booming because its requirements have increased since. However, the matching skillsets are falling short of fulfilling enterprises’ expectations to bring more workflows under the RPA umbrella. Procuring resources during the RPA implementation and backfilling a key resource in the event of attrition becomes a major hurdle. Also, RPA professionals are not exactly cost-efficient resources. Hence, a lack of necessary RPA experts is another challenge companies face today.

Poor IT infrastructure

Technical ambiguity and poor IT infrastructure make bringing everyone on board with the change one hell of a job. That leads to unnecessary complications and creates a disjointed culture in the organization. In the absence of proper infrastructure and a centralized team, speeding up the process of RPA implementation can be a cumbersome task with no guarantee of success.

Unclear expectations

Having high expectations from RPA projects before they are deployed leads to disappointments and disengagement from the project itself. The reason is that reality is always far away from expectations. Without a proper plan and a roadmap to follow, inadequate infrastructure and technical ambiguities can result in the total failure of the project, which was implemented without any specific objective in mind.

Choosing the wrong vendor

There is nothing like a one-size-fits-all solution when outsourcing the implementation of Robotic Process Automation tools. When RPA investment precedes expectations, it leaves the organization with no choice but to go with whatever tools they have or discard the whole process and consider the investment one of the poorest choices made in the history of bad choices.

Humongous implementation costs

RPA projects are usually not cost-effective. However, if one considers the return on investment relatively short, the upfront cost might look less significant than earlier.

Security and scalability issues

Security must be a major concern when deploying a new software solution or outsourcing RPA implementation to outside vendors. If RPA bots have to access sensitive company data, it is imperative to build a robust security infrastructure to prevent any future cyber threats.

When scaling RPA solutions as your business grows, you have to ensure that the tools can be scalable or that they are easily configurable to avoid future challenges.

Poor integration, monitoring, and support

Seamless integration can seriously challenge organizations attempting to sync the new tool with their existing systems and applications. Without a centralized team, monitoring RPA bots’ performance and timely maintenance remains a never-happening reality. And the owner has to bear the brunt of the RPA project failure with negative ROI. Also, a support tech team must constantly assist in resolving issues as and when they arise for businesses to optimize the asset fully.

Conclusion

In order for businesses to fully optimize their investments in Robotic Process Automation, they should consider the challenges mentioned above and take remedies for them accordingly. The RPA tools alone cannot fetch the desired outcome unless proper infrastructure and technical and skills support are not provided during deployment.

Robotic Process Automation or RPA bots help automate repetitive, rule-based processes.

However, Intelligent Automation or cognitive automation combines AI and RPA technologies and helps automate more complex business processes, from enhancing overall customer experience to dealing with large volumes of data. Nevertheless, RPA is the first step toward improving process efficiency and accelerating digital transformation.

Here, we will explore the key differences between RPA and Intelligent Automation.

What is Robotic Process Automation?

Robotic Process Automation or RPA refers to tools or bots designed to automate recurring tasks that require minimal human intelligence but are mostly time and labor-intensive. These bots can work silently in the background automating rule-based tasks and completing them at record speed without incurring errors or requiring downtime. Furthermore, they can emulate human-machine interactions as closely as possible and complete them with the utmost accuracy without human intervention. Hence, the human workforce is free of lesser-value but time-intensive roles and can contribute to business areas requiring expertise and experience.

There are usually two types of RPA, such as:

Assisted RPA: Here, RPA bots are deployed on an individual desktop so that the human worker carries out certain intricate aspects of the task while the bots carry out more cumbersome and technically fewer complex parts of the process.

Unassisted RPA: Unassisted RPA bots work independently when deployed on a centralized server, allowing manual control. These bots automate end-to-end tasks and workflow scheduling from a central control point.

Benefits of Robotic Process Automation
RPA implementation services have unique advantages besides automating repetitive tasks and workflows. For instance, RPA tools like AssistEdge RPA by EdgeVerve helped Royal Philips, a global leader in healthcare technology, enable end-to-end automation across the finance operations domain, realizing the following benefits:

What is Intelligent Automation?

When advanced technologies are coupled with unique capabilities, the scope of RPA broadened to give birth to Intelligent Automation, commonly called, Connected Automation. It drives enterprise digital transformation to foster end-to-end business process automation.

Intelligent Process Automation empowers enterprises to seamlessly connect data, processes, and people, opening new horizons for transformation. Intelligent Automation capabilities include process discovery, automation blueprinting, RoI calculation, automation studio, and process orchestration, accelerating enterprise-wide automation while infusing intelligence and insights at every step. Furthermore, this solution is imbibed with advanced technology solutions like AI, ML, NLP, and Intelligent Document Processing to simulate human intelligence when catering to high-functioning tasks entailing reasoning, judgment, decision-making, and analysis capabilities.

Like RPA, Intelligent Automation (IA) helps save time and human effort while performing more intricate tasks alongside humans without involving the latter in the process.

A few IA examples would include:

Benefits of Intelligent Automation

RPA & Intelligent Automation: Key points of difference

RPA has evolved into IA to accommodate comprehensive models for complete end-to-end process automation.

Intelligent Automation capabilities include key functions of RPA bots, but RPA does not need IA capabilities. The latter is more comfortable handling time and labor-intensive, rule-based, repetitive, but relatively simple tasks faster and more accurately than humans. But there is so much an RPA bot can do beyond which its capabilities fall short of handling intricate tasks that are not strictly rule-based and require human intelligence.

This is where IA steps in, allowing the system to complete complex processes using AI reasoning and decision-making techniques.

Another difference between the two lies in the fact that IA can efficiently work with both structured and unstructured data. With its varying tech capabilities, the former can handle exceptions and process complexities and continuously learn from various data patterns to enhance operational efficiency and productivity.

Agreed, RPA bots are tasked to improve productivity and effectively optimize a company’s time, cost, and resources. But, to scale automation in more complex business use cases, companies need to deploy IA.

Conclusion

Even though RPA was the first enabler bringing automation to business processes, IA across enterprises is increasingly becoming core to their business strategy. The former has matured over time, and its adoption is widespread to improve the efficiency and productivity of human workers and reduce errors by automating repetitive, rule-based processes. However, to scale and be more strategic with automation, enterprises are trying other avenues to bring more complex processes, which RPA alone cannot accommodate, as mentioned earlier. But RPA and Intelligent Automation can lay the foundation for Connected Automation, which most enterprises aspire for.