The Need for a Large Enterprise Grade Automation Software Platform

Characterized by the spread of business and operations across geographies, a large number of employees and partners, complex management hierarchy, legacy systems and a huge volume of data, large-scale enterprises are a complex beast. Large and complex IT systems are but inevitable, and with it come problems of improving workflows, aligning systems and processes to serve customer journeys, optimal allocation of tasks, and driving dynamic business operations.

Large enterprises today are looking to scale up their operations, streamline business processes, accelerate growth, and make data-driven decisions more than ever. This calls for an enterprise-grade software platform with a new set of capabilities that can drive a new wave of transformation. The advent of RPA (Robotic Process Automation) and convergence of that with AI have assured a new set of possibilities. Can RPA open up opportunities for a large scale enterprise to become agile? Can it take the large enterprise to the next level of optimization?

In a short time, RPA has proved to be a game-changing asset, moving beyond its obvious benefits of eliminating human errors and taking over repetitive rule-based tasks to enabling enterprises to make data-driven decisions, amplifying human capabilities more than ever. It has demonstrated the necessary ingredients needed to affect enterprise-wide transformation. As a matter of fact, a recent survey by Forrester states that 43% of organizations globally consider RPA initiatives a strategic priority.

The primary drivers of increasing RPA adoption

To understand the current state of RPA adoption and the future needs of large scale enterprises, we recently co-conducted a survey with Forrester, where we surveyed around 300 professionals from large enterprises across Australia, France, Germany, Japan, US, and UK markets who were responsible for their organization RPA strategies. The study highlighted that large enterprises globally are serious about expanding and scaling their automation efforts for the multitude of benefits it can provide for their human workforces and their business outcomes. As RPA expands, enterprises will require enhanced systems and capabilities to govern, manage, and optimize its value.

From our experience of working with over 360 customers across the globe, we have found companies broadly approaching this in two ways. Looking at RPA for simple task automation, i.e. automating rule-based, repetitive tasks that extract low-value human labor; or leverage RPA as a change agent to drive end-to-end automation with a broader objective of achieving scale and efficiency. The latter would be combining RPA with futuristic capabilities such as Machine Learning, Computer Vision and text analytics that help redefine operations.

There is also an ever-increasing need for large enterprises to augment their existing workforce capabilities to gear up for an AI-driven future. With RPA becoming disruptive as a force, we will see the human and digital worker collaborating closely than ever before — where the aspect of creativity and empathy from the human and predictability and consistency from the bot will bring about a paradigm shift in the way enterprises think and work, transforming how enterprises deliver their services to the stakeholders. This will compel enterprises to relook at their overall workforce management strategy, starting from what the future workforce looks like to what role the human workers play.

So, what is it that large enterprises need?

With the enterprises’ growing demands for an end-to-end automation solution, having an enterprise grade scalable automation platform along with a robust framework helps enterprises map themselves in the whole Automation maturity curve, starting from basic automation going all the way to autonomous operations.

Just recently, we’ve elaborated on the concept of Automation Singularity, that refers to a highly customer-centric and agile oriented state of constant improvement and optimization through the future workforce of humans and bots. It is a journey that has fundamentally three stages — Deterministic Automation, Intelligent Automation and Human-empowered Automation. As companies traverse through this evolution of Automation Singularity, they will move from:

Enterprise Grade Software Platform and Automation Framework — The way forward

Large enterprises need a cohesive and scalable platform that will successfully cater to their complex requirements and enable them to move forward in the journey towards Automation Singularity. To bring upon this change, they need to focus on building capabilities across three dimensions — governance and risk, people, and technology.

That’s where an end-to-end platform like AssistEdge comes in. With a robust framework, AssistEdge empowers large-scale enterprises to reach the next level of optimization. As mentioned before, a successful RPA implementation requires scalability and agility — the key drivers that bring about a disruptive transformation across the enterprise. Having deployed over 49,000 attended bots for one of our enterprise customers, we have realized that scalability is an aspect that large enterprises cannot overlook.

AssistEdge is a cohesive, enterprise-grade platform with offerings in the discovery space, catering to attended and unattended automation along with its ability to traverse the intelligent automation journey, bringing about orchestration and accelerating the onset of the future workforce. This, combined with the AssistEdge Marketplace and AssistEdge Academy enables large enterprises to make intelligent automation pervasive in their environment.

Join us as we enable and empower large enterprises to navigate their automation journey with a holistic, enterprise-grade software platform that is second to none.


[1] “Evolution of The Enterprise Workforce in The Age of Automation,” EdgeVerve,

Prevent Customer Churn through Exemplary Customer Experiences

Lending Enterprises- like any other business organization- are driven by the objective to boost their revenues. Regulators play an important role in balancing this objective by ensuring that the customer interest is not sidelined. Lending organizations must have a customer-centric approach towards acquiring and retaining customers if they wish to have an edge over competitors. To do that, lenders need to adopt Artificial Intelligence (AI) and digital technology to significantly enhance operational transparency for customers and proactively understand their needs to provide hyper-personalized solutions.

FinXEdge Lend uses AI to enhance customer experiences, predict and prevent customer churn and better channel management. Here are a few ways how:

Predictability in Loan Closure timeline

In last two decades, we have witnessed numerous claims of shortening loan funding cycle time from days to hours to minutes. In practice, a large portion of such loan applications are funneled through exception and do not meet the claimed SLAs. A significantly more pragmatic approach for customer-centric lenders would be to use Machine Learning (ML) to predict the closing cycle time and let the customer know upfront if they can be fast-tracked or not. In addition, complete transparency can be ensured by progressively predicting the remaining time to closure.

Incentivizing Quality over Quantity

According to the U.S Department of Labor, the cost of hiring is approximately 30% more than the employee’s annual earnings. Assuming the annual compensation for a Loan Officer(LO) is $90000, the cost of hiring one will be about $30000. This loss is significantly higher when a star performer resigns. There is a need to create an objective way of measuring the LO’s performance based on the quality of loans (risk to default) that they can convert.

Creating a framework that links the performance of the channel to the forward-looking quality of the loans can help lenders in:

Valuing the High-Value Customers

LOs traditionally focus on converting as many leads as possible. There is no way to differentiate leads based on either the risk of rejection or the risk of customer fall out. This might result in a high rate of rejection or worse, higher downstream risk of default. With FinXEdge Lend, there is a defined framework that helps LOs to prioritize valuable customers who have a high risk of fall out.

Hyper-personalization of Solutions

FinXEdge Lend helps in personalization to the customers, so that they are not just offered an off-the-shelf loan, but a well-thought-through, customized solution.

Adopting Effective Marketing Strategies

FinXEdge insights combine the historical trends of the performance of the region, channel and product along with the projections for the next quarter. This helps sales teams to realign their marketing investment on strategic products and channels.

Want to know more about FinXEdge Lend? Speak to our experts.

How AI is Solving Complex Data Challenges to Enable Smart Business Decisions

For people in the technology industry, Artificial Intelligence(AI) is the new buzz word, though its true potential or value is yet to be understood and realized.

Facebook shutting down its robot chat program because the robots devised a language of their own, while learning from humans to negotiate, was a huge buzz. For all the noise it is making, the application of AI in everyday life is still limited. The term AI itself can be expanded across different levels of maturity

Domain-wise examples of AI usage to solve complex business challenges

The application of AI in various domains makes for very interesting reading.

Human Resources Management

Using its tools internally, IBM has cut its HR department by almost a third and can now better identify employee skillsets and skill gaps. This has helped the tech firm become more transparent about career paths and opportunities for employees. Another example is of algorithms helping in filtering job applications. The machine is trained to look for specific keywords and skillsets in the applications to shortlist candidates.


The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. Medical institutions such as The Mayo Clinic, Memorial Sloan Kettering Cancer Center, Massachusetts General Hospital, and National Health Service have developed AI algorithms for their departments.

Large technology companies such as IBM and Google and startups such as Welltok and Ayasdi have also developed AI algorithms for healthcare. Additionally, hospitals are looking to AI to support operational initiatives that increase cost savings, improve patient satisfaction, and satisfy their staffing and workforce needs.  Companies like Hospital IQ are developing predictive analytics solutions that help healthcare managers improve business operations through increasing utilization, decreasing patient boarding, reducing length of stay and optimizing staffing levels.


In the retail space, AI can help multiple stakeholders. It can help shoppers sift through tons of merchandise by recommending the most suitable product based on their purchase/ browsing histories, it can help CXOs plan more effective promotions and events. It can help HR managers reduce the headcount in customer service by automating the frequently asked questions, it can help business to predict the CFR (Cost and Freight) for orders.

Edgeverve’s TradeEdge suite has a few retail-specific AI-ML offerings –Trade Promotions Optimization, Perfect Order Measurement, Suggested Ordering to name a few.


Legal analysts are being gradually replaced by algorithms that scan contracts and documents and interpret them as programmed. So, in a way, the case outcome could be partly dependent on a machine.

Financial Markets: Sentiment analysis is an interesting field where machines scan the web to understand the perception of companies based on online content. Trading decisions are based on these sentiments.

The Not-So-Visible applications

Large organizations invest a good percentage of their funds towards promotions planning and execution. However, considering the vast number of internal and external factors that influence buying behavior, it is very hard to justify or apportion the promotion costs to sales. AI is being used nowadays to track promotion effectiveness so that more pointed promotions can be planned and executed.

Another application closer home is the use of AI/ ML-based data harmonization tools in master data management to de-duplicate data, enrich attributes and align master data from multiple sources with a global master.

Other use cases where AI/ ML is being explored are recommendations engine, suggested orders, and perfect order measurement.

In Conclusion

These are just samples of how AI is helping in complex decision-making by unlocking the potential of the huge data volumes that businesses have been storing over the years.

There are quite a few naysayers who warn about the pitfalls in over-reliance on machines for everyday operations as well as critical tasks. However, the reality is that AI is here to stay. If leveraged well and with human supervision, it can ensure great strides towards the future.

The curious case of ‘Online Banking’

Are mobile apps making online banking obsolete? Statistics paint a different narrative.

Today, we live in an extremely connected-world; one in which smartphones are near-ubiquitous, and access-to-internet is considered an essential-service. Almost all digitally-active individuals of the banking-population seem to own and use a smartphone.

So, in this Mobile-First era, should banks persist with ‘Online Banking’ service?
Who really uses it? Why not migrate every account and functionality to the mobile app? What justifies the continued investment in sustaining the Internet/Online Banking platform?

My curiosity drove me to objectively evaluate the trends, statistics and forces influencing the adoption across the Mobile and Online channels. As I delved deeper, a different narrative shaped up.

Over the years, Bank of America (BofA) has championed serval innovative digital-initiatives. In June 2018, BofA launched their chatbot – Erica, and made it available to all their mobile users. In about a years’ time, Erica has become one of the most popular and rapidly adopted chatbot in the Financial Institution (FI) ecosystem. Now theoretically, given the best in class mobile app and chatbot that BofA offers, all of their digi-savvy customers must have migrated entirely to mobile-apps.

At least, that is what we can expect, right?
However, statistics indicate otherwise. As of June 2019, BofA has about 27 million active mobile users and about 10 million ‘online banking’ customers who do not use the mobile-app.

Among BofA customers, adoption of mobile-apps has not meant the abandonment of Online-Banking.

Kindly note that these 10 million are internet-aware and tech-savvy customers and in all probability have and use a smartphone. BofA understands and acknowledges what is happening here. A third of it’s digital-customers have a clear channel preference – Online Banking. It needs to be respected. So, in the near future, BofA plans to extend their award-winning bot, Erica to its ‘Online Banking’ customers as well.

Among the end-customers, the adoption of Mobile-Apps has not come at the cost of abandoning the Online-Banking channel. The lesson for FIs then, to not ignore the online-banking platform in pursuit of mobile-first design/approach.

The relevance and continued-usage on Online-Banking platforms can be explained by the 3 Perspectives (3P) and 3 Advantages (3A) that favor them.

The three perspectives (3P) are,

These considerations are valid realities, and strong use-cases is support of Online Banking platforms.
The three advantages (3A) of Online-Banking over Mobile-Banking platforms,

In short, the Online Banking platforms still has several compelling use-cases and is expected to remain relevant for a long time. The trends indicate that the FIs across the world are leveraging the platform to mine analytics/insights, and personalize their offerings. A new functionality is easily prototyped/tested on the Online Banking platform to asses it popularity and adoption. For example, the calculator-tools (such as interest calculators, Loan/EMI calculators etc) are often found in the Online Banking platform and not in the mobile apps.

The focus of the FIs is to expand the customer base, mine insights about existing customers, upsell them the right product at the right time, and deliver superior transaction experiences.

In summary, I think the Online Banking platforms need to be innovated further. They still have a vital role in meeting the customer requirements and also furthering the banks’ vision.

Why are cognitive solutions important? — The need, importance and benefits

Intelligent Automation, Cognitive technologies, and Big Data — In today’s digital era, these buzzwords are hard to ignore. This blog explains what cognitive automation means, the need and importance of cognitive solutions in the digital world, and how can enterprises benefit by leveraging new, algorithmic technologies.

What is Cognitive Computing?

Often enterprises find themselves with limited information sources, resulting in gaps in data collection and insights. With cognitive solutions, enterprises can power a new generation of intelligent machines that collaborate with humans to perform wider tasks.

Cognitive solutions facilitate self-learning by leveraging machine learning models, business intelligence, NLP and neural networks. With a voluminous amount of unstructured data growing exponentially, from documents and emails to images and videos, enterprises are looking to make data-driven decisions more than ever.

How can businesses deal with the huge amount of data being generated? How can data play a major role in creating personalized experiences? Can data change the face of your business forever?

The answer indisputably is yes!

The need and importance of Cognitive Automation Strategies

Cognitive technologies are changing the way we perceive data, augmenting human intelligence and unleashing capabilities that we’ve never imagined before. The cognitive systems use Big Data analytics to analyze, interpret and learn over time, leading to breakthrough improvements in performance and scalability throughout the enterprise.

With extensive data analysis being a challenge, cognitive solutions can make use of algorithmic capabilities in Artificial Intelligence, Machine Learning and Deep Learning to open up new opportunities for scaling and driving innovation. These futuristic technologies are ushering in a new era of intelligence across industry sectors including, banking, healthcare, media and entertainment, the public sector, retail, technology, and agriculture, among others.

If you have large datasets which require collating information, reports, and data from disparate sources, cognitive computing can help. It identifies emerging patterns from the massive amount of unstructured data out there and helps business and IT leaders make better-informed decisions.

Keeping up with the ever-increasing flow of data can be daunting. With Robotic Process Automation, enterprises begin their automation journey automating repetitive tasks. With data analytics, businesses today can harness the power of cognitive computing, delivering incredible value and driving significant change enterprise-wide. As digital transformation makes greater inroads into our lives, bringing about a quantum shift in the way we think and act, enterprises are soon capitalizing on the human-machine collaboration. This human-machine collaboration will co-create the future workforce. Read what Automation Singularity as a concept means.

Benefits of Cognitive technologies

According to our Report On: Computer Vision and Cognitive Automation, modern Computer Vision and Cognitive learning solutions are critical in supporting enterprise digitalization. From supporting RPA in order to run automated solutions to using data to improve customer experience, cognitive solutions are benefitting enterprises in more ways than one.

Below are a few benefits of cognitive technologies:

Enterprises must draw out a clear strategy where leaders and employees at all levels embrace the new wave of technology, orchestrating end-to-end processes, streamlining business processes, and driving digital growth.

As per SSON’s 2019 Survey, the cognitive tools will not only help overcome the limitations of RPA (unstructured data) but also help to scale up its application (expand the possibilities of tasks/processes to automate). These are clear signs that change is in the air. Enterprises that employ a wait-and-watch approach fear the risk of falling behind and those that embrace the change will push ahead in the automation journey.


With a wide range of industry verticals investing in cognitive and AI solutions, process discovery, and data mining gaining prominence, it’s no surprise that enterprises are embracing digital transformation.

Our Report On: Computer Vision and Cognitive Automation states that where traditional OCR fails to accurately extract specific content from image-based documents, Computer Vision’s enhanced approach overcomes this gap by accurately detecting and narrowing down target objects within image-based documents. And that’s where AssistEdge comes into play.

With digital transformation taking place at an unprecedented rate, AssistEdge’s capabilities have been designed to power enterprises’ Cognitive Automation strategies. AssistEdge drives the automation experience forward with a host of other cognitive features, including advanced PDF controls and OCR capabilities to extract information from PDF documents faster and with a 15-20% improvement in quality and accuracy compared to other RPA tools.

Download our report — Enabling Intelligent Automation using Computer Vision to learn more about Computer Vision and Cognitive Automation.

Factors That Ensure the Success of Your Automation Transformation — Change Management Being One Key Factor

The Intelligent Automation Global Market Report 2019 focuses on how to manage change so Automation sticks. The report reflects on how enterprises can support digital transformation through effective change management. As per the 2019 survey, lack of effective change management support is the leading case of automation failure. The report further explains the three disciplines of change management that will ensure the success of Intelligent Automation programs — IT Change Management (ITCM), Business Process Change Management (BPCM), and Organizational and People Change Management.

Let’s delve deeper into each of the factors and the importance of change management in achieving Intelligent Automation.


Business and IT teams must collaborate, enabling enterprises to properly scale Intelligent Automation program across the organization. The Intelligent Automation Global Market Report 2019 states that enterprises must draw out a clear plan to beef up existing operational processes and systems to identify use-cases, configure automation, and re-implement work with a new balance of redesigned human and digital labor. The IT teams must work with the Automation CoE — stakeholder and representatives, all of whom will participate in the review of proposed changes and can decline or propose different success criteria for a change to be approved.

Not only is selecting the right automation tool crucial but also understanding the application — its features, how it works, scalability issues, and how can it ensure effective automation is all the more imperative. The role of technology in change management is enormous. Effective change management requires IT support teams to familiarize themselves with CRM, ordering, invoicing etc. thereby expanding their skills and ensuring technology supports the overall business process. A change in employee mindset about the adoption of new technology empowers enterprises to gear up for a data-driven market.

Technology is no doubt a game-changing asset but do business and IT leaders know how to put it to use? How can enterprises employ these new technologies that result in human-machine collaboration?

Working in sync with a dedicated Automation CoE will improve the decision-making process and will enable the process to evolve gradually, empowering the human-digital worker.

In short, IT plays a crucial role in:

Technology drives change — bringing different teams together that were once working in siloes, leading to business and IT collaboration, resulting in improved scalability and agility and driving growth across the enterprise. Doing away with legacy systems is the norm today; leading to a transformative potential, interoperability and human-digital collaboration.

Business Process

Business Process Management (BPM) is where management and information technology meet, and it sets out methods, techniques and tools to allow the business to design, enact, control, and analyze its operational business processes. These operational business processes are made up of people, organizations, applications, documents and other sources of information.

According to the Intelligent Automation Global Market Report 2019, Business Process Change Management (BPCM) is a new discipline that combines process awareness with the detection and management of non-software-based process change. In the age of digitization, it’s essential to keep a close eye on the overall working of a process and identify individuals who can work with new skills — skills to engage in process discovery or reengineering at the level needed for automation.

The report emphasizes on:

Planning for change management requires business and IT leaders to draw out a clear roadmap that includes the scope and objectives, costs, the people and processes involved and the steps with in-depth metrics and analysis.

Organizational and People

Success or failure of an automation program largely depends on people. Understanding end-to-end processes, implementing new work, and monitoring compliance open up endless opportunities in automation. In today’s digital world, there is greater emphasis on human-digital collaboration. Hence, the first step is to train and educate the existing workforce on the new automation tools, addressing the skills gap and inspiring the entire organization to embrace change. As automation gains more ground, relieving employees’ to perform higher-value tasks, leaders should focus on upskilling existing employees to manage the new hybrid workforce.

Involving IT support teams, audit teams and stakeholders in the change management program is an enormous challenge. From developing solutions jointly to selecting outcomes that align with stakeholder objectives is pivotal in organizational change management. Communicating the change — from identifying and planning to executing the change management plan will pave the way for successful implementation of IA down the road.

Transitioning to new automation technologies is no mean feat. It requires a robust product/business roadmap, revamping IT process, training and educating existing employees, assigning new roles, looking into resistance management and working on continuous improvement plan based on stakeholder feedback.

Download the Intelligent Automation Global Market Report 2019 report to learn what change management means, the three disciplines of change management and how enterprises can create a robust Intelligent Automation strategy.

[1] “Business Process Management and Change Management,” Michealaxelsen,

Digital concierge – Embracing the future of RPA with personal bots

RPA or Robotic process automation has been the enterprise operations automation technology of choice for the last few years. The technology has achieved great returns on investment by automating repetitive manual tasks and improving the efficiency of the back-office and front-office operations. Organizations have used RPA to streamline their operations primarily in finance, accounting, supply chain and HR groups. RPA has quickly moved from a pilot program in a few business groups to an enterprise-wide IT strategy and boardroom conversation.

But, does RPA have a life beyond being an enterprise tool?

We believe RPA can go beyond its capabilities and become a personalized tool for every employee in an organization. It’s possible due to the three key trends that are shaping the future of RPA.

So, how do these three trends bring about RPA transformation?

The simplicity of use, more options for attended automation and built-in intelligence means that RPA is no longer limited to unattended back-office scenarios. Any employee can use the power of RPA to reduce the repetitive tasks from his/her daily job and concentrate on value-add activities. An average employee in an organization can now identify opportunities within his/her daily work that can be automated by RPA bot and even configure such a bot.

What exactly is a personal bot?

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.

However, the utilization of RPA as personal bots raises multiple questions. Enterprise RPA has specific management, governance, monitoring, licensing, pricing and systems integration practices. In a personal bot scenario, all of these practices will need a relook. E.g. Personal bots may mean a user-based license for enterprises, akin to a Microsoft Office or equivalent. The personal bot platform will need robust inter-personal collaboration and communication capabilities than the current Enterprise RPA.

In this series of articles, we will cover these aspects of RPA as a personal bot, a “digital concierge” for employees.

Redefining Enterprise Intelligent Automation Goals with Automation Singularity

According to a recent report by IDC, worldwide spending on cognitive automation and Artificial Intelligence (AI) systems are forecasted to reach $77.6 Billion in 2022. With new technologies mushrooming across a plethora of industries, enterprises are gung-ho about creating a brave new digital world, where enterprises traverse from a pure deterministic phase to intelligent automation and finally to human-empowered automation.

How can intelligence help accomplish complex enterprise goals? How can enterprises create Intelligent Agents? How can enterprises create the future workforce — a human-digital twin?

What is Intelligent Automation?

Intelligent Automation, an advanced form of Robotic Process Automation or RPA, has evolved from a mere screen-emulation tool to a one that enables software bots to make intelligent decisions across business processes and functions.

The origin of RPA dates back to the early 2000s when screen emulation, workflow automation and Artificial Intelligence were disrupting the manufacturing industry. Repetitive and deterministic processes were automated, enabling employees to focus on more strategic and higher-value tasks. RPA brought about a massive transformation across back-office functions, relieving the employee off clerical tasks. With the advancements in breakthrough technologies such as Big Data, Machine Learning and Artificial Intelligence (AI), Intelligent Automation is revolutionizing the way businesses think and operate. Ideal for finance, HR, procurement, insurance and accounting activities, it does not replace humans but seeks to usher in a new era of automation, where innovation and productivity are no longer seen as pie-in-the-sky goals.

Preparing enterprises and leaders to embrace the future of Automation

Remember Good Ol’ Fashioned AI (GOFAI) or Symbolic AI that aimed to focus on symbolic reasoning and logic? It failed to live up to its promise since it was challenging to set up, patterns were spoon-fed into the system, and it focused on creating software that was as intelligent as a human. That’s when game-changing technologies or non-symbolic AI such as Deep Learning powered applications came in, bringing in a sweeping transformation and enabling organizations to unlock potential opportunities for achieving an intelligent system that can make better-informed decisions.

Hence, enterprises must redefine their Intelligent Automation goals and create a new standard — Automation Singularity that is poised to challenge enterprise-wide digital transformation. ‘Singularity’ as such is predicted to create quite a stir where adoption of AI and automation will transform the existing workforce, leading to a fear of job loss.

At EdgeVerve, we believe a seamless collaboration between the human workforce and the digital workforce will help co-create the future worker, bringing about ‘Human-empowered Automation’.

What is Automation Singularity?

Automation Singularity refers to a highly customer-centric and agile oriented state of constant improvement and optimization through the future workforce, opening up an expanded horizon of possibilities. Human specialists drive customer orientation using their creativity and empathy and are complemented by digital workers with extreme productivity and consistency.

It has the potential to bring humans and the evolved digital workers closer than ever. This unified human-digital workforce will represent a powerful blend of advanced capabilities that will drive the enterprise of the future.


Enterprises will have to transcend from ‘Deterministic Automation’ to ‘Intelligent Automation’ and to ultimately ‘Human-empowered Automation’. Traversing the journey from rule-based automation to a state of human-empowered automation requires a synergy of people, process, and technology.

Creating a new human-empowered workforce

Training your existing workforce and educating your employees on how RPA and other technologies can simplify customer interaction, analyze Big Data, provide insights that help improve business processes, enabling enterprises to ride the Automation wave. With a seamless collaboration between the human-digital twin, there is a considerable improvement in business agility — where enterprises can transform and adapt to rapidly evolving market needs, respond instantly to emerging threats, and seize new market opportunities.

The road ahead for enterprises

Are you prepared to embark on a journey towards Automation Singularity?

RPA is seen as one of the key pillars of Intelligent/Cognitive Automation. By leveraging Intelligent Automation, enterprises can move from RPA to machine-based learning to cognitive capabilities such as AI. RPA has come a long way from being just a piecemeal approach to an enterprise-wide strategy, transforming the way organizations work.

We believe Automation Singularity is a journey wherein enterprises traverse from attended and unattended automation to Intelligent Automation and ultimately to human-empowered automation that touches every process, employee, and system in the enterprise. With our key disciplines — Discover, Automate and Orchestrate, we provide enterprises with the right platform to traverse the journey towards Automation Singularity.

Redefining intelligent automation goals with Automation Singularity requires enterprises to establish a Center of Excellence. This CoE should be a combination of business and IT leaders who will spearhead automation capabilities in the right direction. Automation Singularity should not be regarded as just an end state or from a technology perspective. It also represents business views as business teams have an equally vested interest in making Automation projects successful. At EdgeVerve, we envision an organic change and a unified platform where Automation, AI and ML capabilities all work in sync with each other and that it is not deemed or construed as just an incremental change. Hence, enterprises should draw out a clear automation roadmap or blueprint, which focuses on having a unified view of how they are going to evolve over a period of time in the journey towards Automation Singularity.

Change management is another aspect that enterprises cannot afford to overlook. Enterprises need to prepare their existing workforce and leaders to get better acquainted with new, futuristic technologies that will change the way organizations think and operate. Establishing a hub or a center which has a profound understanding of Automation and AI technologies and how they can be adapted to overcome challenges in the journey towards Automation Singularity is the way forward.

In the whitepaper, ‘Powering a new horizon of possibilities with Automation Singularity’, we have tried to articulate what Automation Singularity means for enterprises, and the changes enterprises need to implement to achieve this end state. Download the whitepaper.

Avengers of the Banking Ecosystem

Superpowers, special weapon and smart-moves to earn customer loyalty and a larger share of wallet

At a family gathering, I asked my 14-year-old niece, who her favorite Avenger was?

She paused, and replied, “I like Shuri for the smarts and Natasha for the fighting skills”.

I smiled, and told myself, “kids these days, can’t relate to, or remain loyal to a single character for long. Even when presented with so many good-choices, they are still want to handpick traits across several characters”. I moved on from that conversation. Or, so I thought, until it hit me a little later.

Our Banking Ecosystem isn’t very different from the Marvel Cinematic Universe (MCU).

Seriously, think of it. Both of them have several good protagonists to root for. In both cases, fans/customers are spoilt for good-choices. And yet, customer’s loyalty keeps shifting from one bank/protagonist to another.

Customers today have different Go-To banks for specific use-cases

I wondered if the plethora of choices in the market has made the customer feel enabled, or lost?

To get some perspective, I asked Samuel, a friend, what his footprint across the Banking Ecosystem was?

A typical person’s footprint across banking Ecosystem
Bank Name Product / Service Availed Why choose the particular bank?
Bank A Salary Account Employer insisted
Bank B Saving Account & UPI Better interest on FD and RD
Bank C DEMAT & Savings Account Ease of navigation & self-help
Bank D Home Loan Lower interest rate
Bank E Retirement Fund/Corpus Govt. backed plan (Legacy Bank)
Bank F Meal Vouchers Vendor insisted
PayTM, GPay, Amazon Pay Wallets, Cashbacks, Coupons Cashback

No doubt, Samuel has carefully and thoughtfully distributed his loyalty across the ecosystem.

He is no anomaly. A similar spread of portfolio is common among a large section of people.

My interest in all this is to find out the discernable traits that are consistently co-related with customer loyalty. And then, leverage this insight to influence outcomes.

In other words, what does it take our Banking Avengers to win customer-loyalty and sustain it?

I think, it takes a super power, a special weapon and some smart-moves!

Superpower – Customer Experience/Engagement (CX/CE)

I believe, the secret superpower needed by a bank to transform its customers into evangelists is CX/CE.

An engaged customer is likely to buy often, remain loyal and recommend your solution among his peers.

Statistics are in agreement too.

A study by Kantar showed that U.S. retail banks can potentially grow their share of deposits by about 16.5% through improvements in their customer experience.

Special Weapon – Digital Engagement Hub (DEH)

A sound strategy and noble intentions are important; but they do not complete a superhero. Even the mighty Thor and Iron-Man needed a hammer and the suit respectively. Sometimes, how well your plans get executed depends on the weapon you wield.

In banking parlance, a deeper/personalized customer engagement means having an IT system built on a scalable architecture with a capability to facilitate channel administrations, and manage content/campaign management. The engagement hub must seamlessly integrate with both the business-applications such as core banking, trade finance, and payments – as well as channel-applications such as mobile banking, branch solutions, and chat bots to provide a seamless experience to the end-users. A good example is the Digital Engagement Hub by Infosys Finacle.

Smart Moves – Contextual Campaigns

Let’s not forget that over 60% of the population in 2019 comprises of Gen-Z and Millennials. Much of the banked-population will involve this demographic. Needless to say, mobile-first campaigns are no longer just an option, but the new-normal.

Statistically, 91% of consumers are more likely to shop with brands who recognize, remember, and provide relevant offers and recommendations (source). The ideal customer-engagement campaign must be personalized for everyone, and NOT offend anyone. A good way to improve customer loyalty and engagement is to shortlist your top 3 features for each target-group (TG) and make it truly relatable to every individual.

A good campaign must balance these three objectives,

While these ideas/principles are universal, they may be slightly more relevant to the Retail Banking sector.

Also, this cannot be a one-time exercise. Winning the customer loyalty and sustaining the trust, both take equal efforts.

The superpower of ‘customer engagement’ must be put to work every day.
And then, remind yourself, as Captain America does, ‘I can do it all day long!’