Intelligent Automation and Change Management — Key steps

Intelligent Automation or the combination of Artificial Intelligence and automation has grown substantially over the years and across various industry sectors including, healthcare, banking and insurance, retail, and manufacturing. According to Gartner, RPA is the fastest-growing segment of the global enterprise software market. With such tremendous growth, enterprises are taking advantage of the opportunity, spearheading RPA initiatives like never before.

What then is needed to implement Intelligent Automation? How can enterprises overcome the resistance to change? What change management strategies can enterprises implement? In this blog, we will take you through some of the key steps or checklist needed to implement Intelligent Automation Change Management across all levels of an organization.

What is Change Management?

Change is inevitable in life and enterprises are no exception. Implementing IT projects require enterprises to adopt new automation solutions, gear up for a data-driven market, and address the change management challenges head-on. Despite the popularity, enterprises are hesitant to adopt disruptive technologies such as RPA and Intelligent Automation. What is stopping enterprises from embracing digital technologies?

SSON’s findings summarized in the 2019 State of the Global Shared Services Market Report states that insufficient change management is one of the top reasons an enterprise’s IA/RPA project has run into trouble. The IA Global Market Report 2019 also highlights that digital transformation will largely succeed or fail based on effective planning and execution of change management. The report also emphasizes that all transformation programs have critical areas that require a change management framework — IT Change Management (ITCM), Business Process Change Management (BPCM), and Organizational or People Change Management. These three disciplines have a profound impact on effecting enterprise-wide transformation.

It is not only crucial for enterprises to embrace new automation solutions but also necessary to obtain buy-in from all stakeholders involved — employees, clients, partners, and overcome the resistance to change by adopting new, digital tools.

Intelligent Automation defined

Robotic Process Automation is the automation of rule-based, repetitive tasks whereas Intelligent Automation applies Artificial Intelligence and related technologies such as Computer Vision, Cognitive automation and Machine Learning to make intelligent decisions, thereby improving accuracy and eliminating errors.

At EdgeVerve, we believe that enterprises will have to transcend from ‘Deterministic Automation’ to ‘Intelligent Automation’ and to ultimately ‘Human-empowered Automation’. And, traversing this journey requires a synergy of people, process, and technology, which is essential for implementing change across an enterprise.

This is certainly an exciting time for enterprises. Harnessing the power of automation helps them stand out from the competitors, heralding a future of innovation, human-digital worker collaboration, and intelligent decision-making.

Here are a few steps to a successful Intelligent Automation implementation:

Embrace change across all levels of an organization: Is your organization ready for intelligent automation? Is your organization ready for change? Answering these questions and working towards digital transformation is an important first step that enterprises need to take. It’s indeed a mammoth task to win over your employees and partners to support change. Change in mindset and across an enterprise does not occur overnight. It’s a gradual process and one that involves the synergy of people, processes, and technology.

Develop an automation strategy and identify best use cases: What processes should you automate? As we know, RPA is a platform that helps automate repetitive, tedious tasks, relieving the employer to focus on higher-value work. From developing an automation strategy that outlines and identifies the use cases that are appropriate for automation to ensuring your automation strategy aligns with the overall business vision is critical. While identifying the use cases, it’s also necessary to understand the capabilities of a human worker and whether the use cases can scale and sustain.

Appoint leaders to drive change: In addition to collaborating with IT teams, partners, and customers, it’s also imperative to assign change champions. These change champions are well-versed with the automation strategy and go the extra mile to spearhead RPA projects across all levels of an organization. They work on rewiring the enterprise by introducing new applications, new operating models, and entirely new approaches that facilitate change.

Reskill your existing talent: Will humans merge with machines? Will robots replace human workers? As enterprises gear up for human-empowered automation, existing employees must be trained on new automation tools and technologies in order to leverage the full potential of intelligent automation. Organizational readiness should go hand-in-hand with training and upskilling existing talent for the future workplace. Enterprises today are introducing upskilling programs to prepare for an AI-driven future that’s impacting all industries and sectors.

In short, we can say that, from developing a clear automation strategy to selecting the right tools/processes, these factors drive change across an enterprise, ensuring change management implementation successful. Download the IA Global Market Report 2019 report to learn all about change management and the three disciplines.

What is Computer Vision?

In the previous blog, we learned about what OCR is, its history and the importance of moving to Computer Vision. In this blog, we will throw light on what is Computer Vision, its advantages and, why does it matter in the age of intelligent automation.

Computer Vision: The concept

According to SSON’s report on: Computer Vision and Cognitive Automation, Computer Vision refers to the ability to see, read and recognize specific objects or data within an unstructured format and is frequently associated with the field of artificial intelligence. Computer Vision emerged in the 1960s as a result of research into artificial intelligence and mimics the human visual system as a means of endowing machines with so-called intelligent capabilities.

Computer vision is a field of computer science and is closely linked with deep learning and artificial intelligence. It makes understanding the visual world/digital images a breeze.

Computer vision uses neural networks to make sense of the image and recognize specific objects in the digital image. It surpasses human abilities and makes up for what a human eye will forego. Applied across industries, from manufacturing and healthcare to insurance and security — preventing breakdowns in manufacturing to helping leaders explore analytics for effective legislation, CV is changing the way we synthesize and interpret information.

How does Computer Vision enable data collection and extraction?

Are you struggling to process visual information? Are you trying to keep errors to an absolute minimum? Computer vision enables your computer/machine to see and process information in any scenario.

Let’s take an example of the healthcare industry — Once the MRI scans of a patient are obtained, doctors may take weeks to analyze the report, causing a delay in treatment. With the help of deep neural networks, CV processes information faster, more accurately, thus facilitating better patient care. Advanced image analytics, machine learning and artificial intelligence integrate data, thereby improving decision-making, avoiding complications and enabling faster treatment.

With the help of AI patterns, data in the form of PDFs and scanned text images can drive faster insights for patient care. By leveraging Computer Vision, classification, indexing, retrieval, and management of images is no more a mammoth task, rather a giant step in the journey towards building a truly intelligent enterprise.

Extracting information from the huge volume of unstructured data is nearly impossible. Computer vision involves several techniques, including image segmentation, facial recognition, pattern detection, image classification, image restoration and object detection to interpret information from digitally captured images. However, there are challenges in data extraction that enterprises need to take into account — the format and structure of images, PDFs, use of foreign languages, infographics, and special characters, among others.

Advantages of Computer Vision and why do enterprises need it?

With futuristic technologies growing at an unprecedented pace, enterprises are looking to tap into the power of computing more than ever. Mobile technology with built-in cameras and new algorithms and neural networks are bringing about disruptive transformation in the field of computer vision.

Here are a few advantages of Computer Vision that you cannot ignore:

Computer Vision has seeped into many areas of our life and has been successfully applied, from driverless cars to tagging friends on Facebook. An integral component in artificial intelligence, enterprises are looking to invest in CV to automate data collection, extraction and understanding of images and videos. It’s an interdisciplinary field that helps in the extraction of unstructured data. For enterprises to succeed in the age of intelligent automation, embracing AI-related technologies will bring about change in unexpected ways.

If you own a retail store or are in a lifestyle business, Computer Vision has real-time applications across many industries. It’s time businesses not just jump into the technology bandwagon, but successfully rollout its applications — reading vast pool of data from PDFs or a sequence of images.

The role of Computer Vision in the intelligent automation road map for enterprises

With the explosion of image data, enterprises are looking at Computer Vision, a groundbreaking technology to automate a myriad of tasks across industries. Computer Vision is not just about extracting data — it’s far more powerful than that. Many enterprises are already deploying CV — from using image analysis in predictive analytics to forecast customer behavior to the application of biometrics and facial recognition in security.

AI-related technologies like cognitive automation are putting data at the heart of an enterprise’s digital journey. From transforming data to actionable insights to enabling businesses to maintain a competitive edge, Computer Vision possesses endless possibilities. Intelligent automation synthesizes vast amounts of information, automates processes and workflows, and enables decision-making, thus changing the way we work. Intelligent automation powers a new generation of enterprises and is the core factor for driving change, compelling business leaders to reevaluate strategies.

Final thoughts…

With Computer Vision gaining popularity by the day, enterprises across industries are stepping up their AI game as they do not want to miss out on the huge opportunities that CV throws. And this is just the start!

Download the SSON report — Enabling Intelligent Automation using Computer Vision now.


Personalized Bot Monitoring and Security

What are personal bots?

“Personal bots work on employee’s machine, mostly in attended form and perform tasks for the employee — pulling data from multiple sources to create reports, storing client contact data and even creating regular presentations.

Personal bots can be looked at as a digital concierge for employees in an organization. Through advanced mobile interface and virtual assistants, employees can interact with these personal bots installed on their office machines/systems. The personal bot triggered on-demand or scheduled by the employee, can perform tasks on behalf of the employee, even in his/her absence. Just like a digital concierge, this personal bot on the employee’s machine will be well-equipped to take requests and execute.”

The management of security risks is a top-priority issue for the development of RPA. When it comes to personalized bots in RPA, one of the serious concern is to ensure that the confidential data is not misused due to the actions of a personalized bot.

Some generic processes compliant in a personalized bot would be regular business procedures such as file transferring, data processing, process related to payroll, etc. All these require that the automation platforms have access to confidential information (inventory lists, addresses, financial information, passwords, etc.) about a company’s employees, customers, and vendors.

Consequently, the issue of security can be broken down into two highly inter-connected points:

As a result, strong monitoring and security measures of the personalized bot becomes evident.

How can a personalized bot ecosystem be secured?

In a personalized bot, the starting point would be Architecture and Authentication. In a personal bot scenario, one of the issues could be ‘individual authentication’, wherein the robots use employee log-ons and, it becomes increasingly difficult to distinguish human activities from those of the bot. Security is given a boost when each robot is given its own individual log-in, and its own permissions from the system.

The below approach can help effectively monitor and secure the RPA platform:

Integrity: A person needs to ensure the results/data coming from the bot has not been modified/altered.

What monitoring and security features does the enterprise edition offer? What is needed in a personal bot?

Currently, the enterprise editions of most of the well-known RPA tools have robust monitoring and security features, which allows organizations to have full control over the security features of the bot. But, when it comes to a personalized bot, the features have to be customized to meet the needs accordingly. For example, AssistEdge 18.0 Enterprise Edition has ‘Control Tower’ component, which has monitoring and security features covered. A component of the Control Tower, i.e. the credential manager ensures the safeguarding of credentials, and end-to-end monitoring of robots can be seen through the dashboard component. Also, all the details related to each transaction executed by the bot are available in the process view component, but for personal bots, all these components will need a re-look.

One might say that by using the features mentioned above in AssistEdge 18.0, the bot is visible, traceable and secure — then why is a re-look needed? Well, automated logins are entirely traceable. The challenge arises when one part of the process is administered by humans, then handed over to robots, then back to humans. The problem here is really about simplification, and lack of straight-through processing or integration, where a single sign-on allows continuous workflows without the stop-start that characterizes handovers. Also, in case of monitoring, the enterprise concept of a dashboard might not be essential, rather personalized information for the individual handling the bot should suffice.

Fundamentally, it comes down to a thorough homework and preparation while handling personal bots to ensure a more robust and secure ecosystem. Today, with enormous opportunities in RPA, the market is exploding with providers who promise to offer quick and easy implementations in cases, where processes can be replaced by a bot. But, what needs to be looked into is how much success will the concept of ‘Robot for every person’ bring.


Licensing models for Personalized Bots

When choosing an RPA technology, it’s important to think about licensing. License models in RPA are tremendously diverse across the vendor landscape, making it impossible to compare on a similar basis.

When we talk about licensing from AssistEdge 18.0 point-of-view, the licensing concepts can be broadly classified under the below categories:

Named User: This type of license is mainly for attended robots (Robots which are triggered by a user on need basis or need a human intervention). It enables him or her to register any number of robots on any machine, as long as the same active directory username is present on all of them. The user is not allowed to use multiple robots simultaneously.

Concurrent User: A type of license that helps users that work in shifts, as licenses are consumed only when you in fact want to use a robot. Multiple robots can work on the same license provided that there is only one bot running at a given instance.

Number of Bots: This type of license is mainly for unattended robots (Robots that run independently based on the schedule, mostly on virtual machines). Primarily, only 1 robot can run on 1 license, and it can run any number of times. For simultaneous execution, multiple licenses would be required based on the number of bots that are running simultaneously.

Most of the leading RPA providers offer the below licensing models:

What do we mean by personal bots?

“Personal bots work on employee’s machine, mostly in attended form and perform tasks for the employee — pulling data from multiple sources to create reports, storing client contact data and even creating regular presentations.

Personal bots can be looked at as a digital concierge for employees in an organization. Through advanced mobile interface and virtual assistants, employees can interact with these personal bots installed on their office machines/systems. The personal bot triggered on-demand or scheduled by the employee, can perform tasks on behalf of the employee, even in his/her absence. Just like a digital concierge, this personal bot on the employee’s machine will be well-equipped to take requests and execute.”

When thinking of licensing models for personal bots, the below models can be considered:

Pay-as-you-go Licensing Model: A Consumption-based pricing model, that is, you pay for what you use can be considered as the most suitable. A mix of named user and concurrent user licensing would be an ideal prospect for personalized bot licensing model i.e. to calculate the runtime hours of a robot and charge on per hour basis. In case more than 1 personal bot is required to run concurrently, then charge on per hour basis for each bot. Since a personal bot is not expected to run unattended or run 24 X 7, it’s only meant to do tasks such as pulling data from multiple sources to create reports, storing client contact data and even creating regular presentations, which require it to work for a specific period of time.

Per bot/per user Licensing Model: In per bot licensing model, charges are based on the number of bots utilized to execute a process i.e. 1 license for 1 personal bot whereas, in per user licensing model, license is allocated based on the number of users, in which a user can use any number of bots provided, multiple robots do not run simultaneously.

Enterprise Licensing Model: An enterprise-wide license for personal bots can be considered as one of the models. For illustration purpose, let’s say a client is interested in implementing personal bots at a large-scale but is not sure about the number of personal bots required. In such an instance, an enterprise-wide license can be purchased by the client, and he can up-scale as per requirement at no extra cost.

Cloud-based Licensing Model – The cloud service offers a quick and convenient way to begin using robotics. Introduction of a pre-built and optimized cloud service is easier and faster than using an on-premise solution. If the client already uses public cloud, the next step is to take the RPA service also to the public cloud, in which case, the cost level will further decrease. This functionality can be extended to personal bots as well. When a client starts using the cloud service, the personal bots will be quickly available at the user’s service with some pre-built functionalities. Also, personal bots can be used even if the person is offline or wants to run it remotely from a mobile device, provided the data is exposed to the cloud.

Also, everyday work may vary each day for a person. Sometimes the workload can be high, which might result in a need for scaling up the number of bots, and other times, there could be a lower workload, which might result in low utilization of the bot. The licensing model should be such that it should cover the above-mentioned aspects as well. The model should provide flexibility to scale up and scale down the bot usability, and the license charges should be such that the person should pay only for the time he has utilized the bot.

Deep thought should be given to the cost of the license for the personal bots. A lot of factors need to considered when calculating the cost, given the fact that it’s a personal bot doing tasks on employees machine and not residing on an application server like the traditional digital worker which is developed under enterprise licensing cost.

We should remember that the personal bot is not to replace the human but to replace the simple repetitive and manually intensive tasks that a human performs in day-to-day life.


Why does change management matter in ensuring an enterprises’ digital transformation journey?

According to McKinsey, 70% of change programs fail to achieve their goals, mainly due to employee resistance and lack of management support. The IA Global Market Report 2019 states that digital transformation will largely succeed or fail based on effective planning and execution of change management. As new solutions present themselves and the nature and mode of work shifts, effective change management plays a critical role in successful implementations.

In this blog, we will shed light on why change management, in particular, organizational and people change management is a key enabler of digital transformation. Read on.

What is Change Management?

Change management is an interdisciplinary approach that calls for process change and a change in company strategy and policies. The IA Global Market Report 2019 outlines three disciplines of change management — IT change management, business process change management, and organizational and people change management.

Prosci defines change management as “the process, tools and techniques to manage the people side of change to achieve a required business outcome”.1

In other words, technology, people and processes are the foundational blocks on which change management framework is built and deployed. In this era of digitalization, enterprises are looking to streamline processes and achieve end-to-end automation across business functions. Using an appropriate change management model contributes to making digital transformation a reality.

Change Management focuses on people and helps them transition and adopt new technologies and processes, whereas Project Management focuses on managing the project scope, budget, and timeline.2

How are enterprises traversing the automation journey? How can enterprises address the enormous automation challenges head on?

We are all aware that change does not happen in siloes. It requires syncing of organization structure, roles, processes, and leaders that form an integral part of change management, empowering enterprises to embark on a successful automation journey.

Here are a few questions that enterprises should address to succeed in the age of automation.

Undoubtedly change management plays a crucial role in driving digital transformation process, enabling enterprises to understand the problem, respond and adapt to change faster, and create a customized plan that requires your existing workforce or people to adapt to change management methodologies. Factors such as sponsorship and buy-in to communication and readiness, empower enterprises to implement a successful change management framework, thereby dramatically improving productivity, innovation, and efficiency.

Here are a few points that organizations need to focus on to implement organizational and people change management.

Change leader

Not only is having the right mindset and behavior important but also having the right leaders to lead change and own the enterprise automation program. The IA Global Market Report 2019 emphasizes on two key change leaders — The VP/ SVP executive charged with owning the enterprise automation program, and the executive responsible for the part of the operation experiencing an automation transformation. These leaders are tasked with inspiring the entire organization, the IT and audit teams to support the overall strategy, budget, and skills.


Are your employees worried about losing their job to automation? Training and educating your existing workforce to experience change with regular feedback supports change practices and readiness that was unthinkable in the past. Automation is indeed gaining a lot of traction across industry verticals; educating your employees about the benefits of automation, encouraging adoption and training the team on embracing change is the way ahead for enterprises. Devising an effective change management strategy depends on people — aligning the organization’s values, culture, and people will go a long way in successful RPA implementation.

Training your staff to perform higher-value work that requires decision-making ability helps you to look into the future and co-create the future workforce — the human-digital twin.

Involve all stakeholders

Establishing an RPA Center of Excellence helps take RPA capabilities to the next level – from gathering people and leaders to ensuring the goals are aligned with the needs of the organization, RPA CoE brings about a paradigm shift in the way your business works, thus fostering collaboration with all the stakeholders, and enabling automation program see the light of the day.

Download the IA Global Market Report 2019 report to learn what change management means.


[1] “Definition of Change Management,”,

[2] “The Importance of Change Management in AP Automation Transitions,” Mediusflow,

Personalized bots in the IoT environment

RPA market trends have witnessed an ever-evolving trend of constant innovation on how the solutions address critical business problems. What started as a solution that automates repetitive rule-based tasks through unattended/attended automation, now boasts of an intelligent platform which plunges itself from the core ‘deterministic’ offering to a ‘cognitive’ one. AssistEdge is already aiming for the stars with Automation Singularity by providing a scalable and secure offering, right from identifying the appropriate use cases through our in-built Process Discovery tool to seamlessly automating the identified processes and orchestrating this entire gamut of complex activities through a digital-human concierge.

While AI and RPA solutions are already making strides and are the most recognized buzzwords for businesses attempting to catapult their efficiency into the next orbit; Internet of Things (IoT) will be the next big wave which will work in tandem with automation. With increased data sharing brought in by IoT, organizations are now able to experience seamless and efficient streamlining of business processes. This makes IoT a major candidate amongst emerging technologies to pair with RPA. But, before we jump into that bandwagon, let’s understand how these are related in modern-day businesses.

To understand how all these emerging technologies will work in sync, let’s dissect how we humans function on a day-to-day basis. The reason we are choosing a modern-day human as a reference is because at the end of the day the purpose of these technologies is to replicate what humans have been doing for the past decade or two and ensure that an organization’s productivity is prioritized in the right place. A human body works in a fascinating way that we almost take for granted. We use our mind to analyze a situation and make decisions real-time; our limbs to react to the analysis and enact the decision that the mind has made. Every moment of our life, the human body is performing three steps: observation, decision and action. Enterprise operations are fairly similar operationally where organizations need to analyze what situation they are in, make a decision accordingly and take actions based on the decisions.

With data being ‘the new oil’ in the current century, Artificial Intelligence and Machine Learning are helping entities make complex decisions day in and day out. Not that a human can’t perform the same task but something that a human workforce won’t be efficient in w.r.t ‘time’ being one of the most important metrics for business success. Artificial Intelligence is nothing but a machine’s mind which analyzes data patterns, makes guesstimates with minimal error and prescribes a plan of action. But, someone needs to execute what the AI/ML offering suggests. This is where RPA comes into the picture. RPA can pick up action triggers based on events and execute pre-programmed repetitive tasks with ease. RPA and AI will always work in tandem, similar to how our limbs and mind are connected. But, for them to make a decision and enact on it, these technologies are reliant on another aspect of the mind — Observation. Data needs to be fed into an AI model in order to gain insights and take subsequent actions. This is where IoT comes in handy. Internet of Things is a concept of intercommunicating devices which can identify each other, transmit data and pass triggers for pre-programmed business/personal needs. In other words, if AI is the mind and RPA the limbs of an organization, then IoT is the eye and ear, which helps an organization observe events around their universe.

According to GE, IoT will contribute roughly $10-15 trillion to the global GDP by the next decade or two. Global consulting giant McKinsey and Company has visualized IoT transformation involving interconnected devices into two broad categories:

A simple example of ‘Information and Analysis’ would be how IoT has already made its presence felt in supply chain with industrial automation. With the help of sensors and actuators, organizations can track products in the supply chain lifecycle and monitor health of the industrial equipment, etc. IoT Gateway devices are the communication bridge between the endpoint sensors, and the Cloud server helping democratize the information. ‘Automation and Control’ is where IoT is making rapid strides with the advent of ‘Home Automation’ and ‘Smart Living’ intelligent infrastructure. Users can now have every home electronic devices connected, controlled by a standalone application on a personal mobile phone. This is where personalized bots in RPA can help revolutionize the industry as a whole as it receives actionable insights from IoT sensors and transform them into meaningful engagements as per business/customer needs.

RPA and IoT will primarily elevate operational efficiencies by providing innovative ways to capture business information and leverage it with the help of personalized bots managing it. Autonomous responses to events will experience a transformative journey where personalized bots will be configured to take instantaneous actions without any human intervention whenever an IoT device signals a trigger. This will drastically improve the quality of output for both home and industrial automation scenarios. The future lies in interconnected devices exchanging gazillion data points every second and it needs an ecosystem of products and solutions to ensure a seamless transformation from today’s as-is state. The future lies with IoT, AI and RPA coexisting and forming a holistic solution suite to make daily lives of an enterprise and its staff hassle-free, thereby channelizing the productivity levels on other pressing needs.

Building a Cohesive Platform for Automation

A product firm believes in developing a solution that addresses a customer’s pain point as well as ensures that the solution is widely adopted within an organization to realize the product benefits. While a majority of product firms focus on addressing a specific pain point of an entire use case, developing a cohesive platform is one approach, wherein an organization builds solutions for each section of the entire lifecycle of a transaction.

Microsoft, for instance, can be seen with products such as Server, Database, Vision, OCR, and Business Intelligence, trying to address the complete line of requirements with its cohesive platform.

On the flip side, how often do we see enterprise implementation consisting of a cohesive platform? What stops enterprises to venture into a cohesive platform?

When it comes to automation, can a cohesive platform be built for robotic automation? Will the industry accept a cohesive platform?

Building a cohesive platform requires deep insights into the entire lifecycle of a use case. Enhanced collaboration and understanding across multiple entities involved in the lifecycle of the use case is essential to build an offering that addresses the entire line of pain points to achieve the ultimate goal of developing a cohesive platform.

For instance, when we talk about the automation industry — while most of the top product vendors in the recent Forrester RPA Wave rely on collaboration with other vendors to address an entire use case involving process discovery, automating using intelligent automation capabilities, EdgeVerve decided to venture into the creation of a holistic solution for automation use cases.

At EdgeVerve, we offer a solution starting right from shortlisting the eligible process for automation using Discover, seamlessly automating majority types of application via AssistEdge, to even facilitating monitoring of digital workers of competitive vendor products over a unified dashboard — Orchestrator. EdgeVerve took this cohesive platform as a challenge after witnessing numerous use cases, where either wrong processes were picked for automation, citizen developers faced challenges in automating processes or an organization implemented more than one RPA vendor offering.

Although, the industry insights helped EdgeVerve to design the right business offering, mastering each of the product capability has been an arduous journey which was well-captured in the recent Forrester and Gartner Wave, wherein EdgeVerve is ranked in the leader’s quadrant. EdgeVerve applied a staggered approach towards the development of a cohesive RPA platform:

Industry Perspective:

Mike Beecham, CIO International Society Bank, wishes to implement RPA to achieve efficiency and save cost; however, statistics showing the failure percentage of RPA implementation has made him apprehensive of the investment. He is looking for a solution that covers his use cases and guarantees him forecasted benefits.

His best bet would be on a cohesive platform that takes over from process selection to robot monitoring. The EdgeVerve team applies Discover’s automation blueprint methodology to select the right use cases and auto-automation capability to feed inputs from Discover to AssistEdge to automate shortlisted use cases. iPad version of Control Tower allows Mike to control AssistEdge robot execution, while Orchestrator manages all robots deployed across International Society Bank.

The harmonized communication format across the cohesive platform facilitates seamless process automation and monitoring. The cohesive automation platform hugely benefits in terms of turnaround time and RoI.

Microsoft and EdgeVerve are successfully able to create a cohesive platform offering, applying their industry insights. Here are a few other essential properties that drive a firm to venture into the platform approach:

Seamless Intelligence Sharing Across a Cohesive RPA Platform

Not long back, Robotic Process Automation (RPA) was still being considered a standalone automation tool. Eventually RPA matured from handling simple deterministic use cases to solving complex use cases with ‘intelligent automation’ and ‘human-empowered’ automation. This advancement opened RPA to a host of new business tasks and processes. As is always the case — necessity demanded new inventions. Enterprises realized the need for a Cohesive RPA Platform, which, in addition to simplifying process automation, also helped them in:

Although various products provide these capabilities in bits & pieces, the actual value is realized when all these components are cohesively knit together. AssistEdge provides a holistic suite which enables enterprises in their end-to-end automation journey.


Fig: AssistEdge RPA Platform

The foremost advantage of such a cohesive RPA platform is intelligence sharing across the platform with information generated from one component being fed into the other components. In this article, we will look at the insights that can be garnered through intelligence sharing across these tools in a cohesive automation platform.

Intelligence from Process Discovery

The two major aspects of a process discovery tool are:

This information allows you to easily chalk out the automation roadmap at department and organization levels. Process discovery also gives a complete landscape of the business applications being used within the organization and across different departments. This information is useful to determine which applications need to be configured for automating each process. Additionally, with the enterprise-wide view, it is easy to identify similar tasks/processes within different departments and reuse the automation processes configured through RPA.

With the same platform discovering the automation candidates and automating these processes, auto-automation comes into the picture. The same underlying technology being used for identifying controls and for automating them helps to achieve this. The task map generated through process discovery can be transformed into an automation workflow in the RPA design environment with a few clicks that can help save a large amount of manual effort required to design and configure the automation processes.

Intelligence from Core RPA

Intelligence from Orchestrator

In the case of an enterprise opting for different tools for discovery, automation and orchestration, integrating each of these components becomes a full-fledged IT project in itself and entails overhead costs for development, management and support. Whereas, information dissemination becomes seamless in a cohesive RPA platform. The individual components have been developed and already tested to work impeccably. This makes a knowledge-driven, cohesive RPA platform the ideal choice for an enterprise which is focused towards automation maturity.

Automation Singularity

Digitization of core banking

Truly digital is all about transformation spanning experience, engagement and business engine layers. Focusing only on enabling new channels or touch points is not enough. There are a set of basic characteristics that a Core banking solution must demonstrate to enable banks to achieve the intended results:

This article describes some of the most critical basic characteristics across three dimensions for core transformation.

Before getting into the details, let us look at the change of context w.r.t

In short, it can be referred to as “A.I.M”. (Access, Infrastructure and Mass customization)

Accessing banking services

In the first phase of automation banking services were accessed

In the next phase, which can be referred to as the “Access” phase, banking services were made available as

Phase Where When Who
Automation Designated Places Designated Time Designated People
Access Any where Any time Any one

Hence there is a fundamental shift of context from “Designated*” to “Any*”. This clearly carves out the path for the digital engagement layer to take care of personalized data, control and engagement and at the same time clearly emphasizes the significance of core banking.


There is a considerable change in infrastructural aspects for computing power, storage, network, monitoring, IaaS, PaaS etc. Considering that transaction volumes are growing exponentially (both read and write), solutions like Core banking should leverage such infrastructures.

Mass customization

Due to various factors and predominantly change in customer expectation, there is a shift from mass production to mass customization. From the bank’s perspective, this can also be referred to as “What I have” to “What you need”. It is a well-known fact that personalization is the key to achieving mass customization. Let’s have a look at how it plays out:

While personalization for interaction, convenience, control can be dealt with at the digital engagement layer, product, services and functionality-level personalization should be dealt with at the business engine level as well.

Based on these changing paradigms, business engines like core banking should have some critical attributes that are mapped below to the three dimensions of (A.I.M):

Digitization core banking


To realize the benefits of digital transformation, digitization is required across experience, engagement and business engine layers. For supporting digitization of core, it should exhibit a set of characteristics across three dimensions (A.I.M). To mention a few:

Is your core banking ready for Digital age? Does it exhibit these characteristics? Do share your views in the comments section below.

Prepare your data for demand planning: Your step-by-step data-transformation guide

Not all data is ready to give you insights. But it can be. In this blog post, we draw from our experience working with a global FMCG player to shed light on the processes that can power your data transformation.

To put simply, demand planning is the process of forecasting the demand of a product—which is then used to inform the manufacturing and distribution strategies of said product. Among retailers, demand planning is an integral part of supply chain management. Demand planning, when done well, has multi-fold benefits: It helps save money, manage product lifecycle better, improve marketing effectiveness, even heighten customer satisfaction.

But in today’s scale of operations, volume of data that is available, and the need for real-time insights, it is impossible to perform demand planning manually—over 54% of the companies surveyed in 2018 by the Sourcing Journal say that they frequently experience inventory imbalances. In fact, 13% say they do so ‘all the time’.

Technologies like artificial intelligence and machine learning can offer great value for demand planners, by crunching numbers meaningfully and at scale. It enables demand planners to work on real-time data and react to real-time market forces. But, given the maturity of AI tech today, it’s best to approach with caution.

In this blog post, we’ll outline the things to keep in mind while developing demand forecasting models for your AI engine.

Consolidate your data

The first step to building a demand forecasting model is to bring all your data to one place. Identify all data sources—explore all dimensions across geographies, channel partners, third party consolidators, products.

Once you’ve identified where you can source your data from, understand their tech maturity. Are all sources capturing data digitally? Even among digital data capture systems, there is a wide range from spreadsheets and emails to ERPs and CRMs. If your data is disparate, consolidate them. You might be able to leverage APIs and data pipes to achieve this.

Standardize your data

Once you’ve collated your data, you need to make sure the information is standardized. Procurement officers surveyed in the 2019 CPO survey by Deloitte complain that “poor master data quality, standardization and governance are the biggest problems to master digital complexity”.

To standardize your data, you need to identify the variance in data formats and use a unifying taxonomy like the United Nations Standard Products and Services Code (UNSPSC), Harmonized System (HS) or Central Product Classification (CPC).

Validate your data

Once you have your data standardized, but before syncing them, perform a thorough master data validation. Perform data cleansing first—for both historical and real-time data—without this, analysis would be tedious and possibly inaccurate as well. Once the data is validated, map them to your product hierarchy.

Synchronize your data

Set up clear, actionable processes for your data sync. For instance, outline what level of data (geo, region, distributor, store) needs to be synced, at what frequency does data need to sync with the master data, how the data is to be transferred (AS2, sFTP, XML) etc.

Set up data extraction and reporting

Once your data is synchronized, probably in a data lake on the cloud, enable reporting. Identify what kind of reports are necessary and build corresponding dashboards. For instance, if you need a demand planning dashboard, identify all the data points you need extracted, and pull them into your dashboard. For instance, for one of our clients—a global FMCG player—we brought data points across retail execution, e-commerce, inventory, secondary sales etc. to build their demand planning dashboard.

Some products like TradeEdge Market Connect have built-in dashboard templates that you can customize instantly.

Expand your data footprint

Once the first set of data is ready for reporting, go back to the drawing board and explore other sources you might collect data from. Onboard all your channel partners into your demand planning system—hire the right onboarding partner to achieve this at high-accuracy at scale.

If you have all bases covered, identify other data dimensions you may have missed. Or see if you can increase the frequency or data flow. Study if there are any external factors—like seasonal changes, competitor promotions etc.—that can have an impact on demand for your products; include data from there too.

Remember that your insights are only as good as your data. Gathering, cleaning and optimizing your data is an important part of improving your demand planning.