Transforming Your Complex Business Processes with Speed and Agility
Businesses, like yours, are experiencing increasingly complex scenarios, externally as well as internally. To succeed in your digital transformation efforts, it is critical to address complexities effectively and create an operationally efficient and strategically agile organization.
Join Craig Le Clair, Vice President and Principal Analyst, Forrester Research Inc., Prashant Gandhi, Senior Director, Digital Experience and Business Transformation, Infosys, and Anurag Shah, AVP & Head of Digital Products, Newgen, as they discuss why digital transformation of complex processes in an agile manner is imperative for organizations.
- Factors that make mission-critical processes complex
- How to simplify complex processes with low code
- Case study: leading US-based annuities company
- Introduction to Newgen’s low code digital automation platform
Gunjan Kalita: Just give it a minute as the attendee list is being … All right. Good morning and good afternoon, everyone. Welcome to the webinar titled Transforming Your Complex Business Processes With Speed and Agility presented by Newgen Software, a leading provider of a unified digital transformation platform with native process automation content services and communication management capabilities, with a multitude of implementations across the globe. I’m Gunjan Kalita, and I shall be your host and the moderator for this webinar.
Today, we have Craig Le Clair, vice president and principal analyst from Forrester Research, and Prashant Gandhi, senior director, digital experience and business transformation from Infosys. They are joined by Anurag Shah, Newgen’s EVP and head of digital products.
At the end of the presentation, we will be having a question and answer session. We request you to type your questions in the question window of your GoTo Webinar anytime through the session. I shall take them up at the end with our experts. With that, I hand it over to you, Craig.
Craig Le Clair: Well, thank you very much. Were you going to introduce anyone at this point, or should I just start?
Gunjan Kalita: Please, you can go on.
Craig Le Clair: Okay, great. Well, thank you, Newgen, for having me talk about process improvement and the need for speed and agility. What we found in the pandemic, particularly in the beginning, that we weren’t very able to do. That we had a lot of processes that depended on face-to-face interaction that couldn’t adapt very quickly to a systemic risk, so there’s been quite a surge in automation.
If you look at this simple chart, we had this progress towards digital transformation. That was really caused by the mobile explosion, when in 2010, the iPhone really got going and everyone needed a mobile app. We saw companies, traditional companies disrupted by new ways of doing things with social and mobile, by startups. There was a lot of talk about digital transformation, but really it didn’t kick in until the pandemic. Now, we had to do it. Now, it was a matter of surviving almost, and for many it was.
We had a surge in all things automation. The pandemic winners were many of the companies like Newgen that have been helping companies improve processes, make them more digital for years, but all of a sudden, it became much more of an imperative. Here’s where the roadmaps for companies really changed. You see that acceleration zone in the upper-right. All of a sudden you had to support remote workers. Some 25, 30%, in a hybrid way, will not be returning to work. In other words, a lot of policies are settling on coming in two or three days a week.
We project there’s going to be a big battle, I think, between people that have built work-life patterns that they want to keep, and companies that want the control of their employees like they had previously. On top of that, of course, we have particularly in the services area, the lower wage workforce. We have just a lot of people quitting, four or five million a month in the U.S., and people have gotten used to working in a different way. In support of that, digital automation, digital assistance, digital workers, all the things that we’re talking about here, are very important in supporting the new way of work, however that finally settles.
Then, of course, we had to remote business. If you were a small bank or mid-size bank and you could not do a mobile deposit, well, this was a real problem during the pandemic, so you had to fix that. Areas like electronic signature, even remote notarization, all forms of electronic delivery, all of these things really accelerated or are in that acceleration zone in the upper-right.
Now, losing momentum, it’s worth mentioning that a lot of the more visionary AI projects, just in the face of a lot of companies trying to survive, moved off the table a bit, so a lot of this very tactical, very pragmatic automation, what we call intelligent automation, really accelerated during that time. Now, when you look at what this means for the software market, so this is a view of what the intelligent automation market really looks like, a snapshot in a couple years.
You see a lot of that 32% are the alpha pure plays in the RPA market, robotic process automation market, which as you probably, unless you’ve been in asleep at the wheel in following technology, has been a very high-value and growing market. They’re only going to take about a third of the overall application development business for process improvement because the intelligent automation suites, which are companies like Newgen, a lot of the BPM companies, software platforms that have always been about process improvement have tucked digital worker and RPA into their portfolios. Now they provide a breadth of necessary intelligent automation technology, some of which are AI-based like machine learning, decision management, or maybe chatbot integration, to their portfolio, so they’re really geared towards this sort of more pragmatic type of automation that were occurring.
The market’s changed a little bit because of that, but one thing has remained the same, that if you’re going to do well … I apologize for the silly bouncing bullets here. If you’re going to do well in automation, you have to figure out where different types of tools are best used. When you look at RPA, it has really been designed, its design point were relatively short tasks, very tactical, not transformational, not end-to-end process improvement. If you follow these simple rules, then you’ll keep RPA in the box that it was designed for. Look for processes that have five or less decisions. In other words, there’s no real decision management that’s occurring.
Every decision has to be scripted very discreetly, deterministically in a bot. Think of the applications that are being manipulated by the bot. When you get to a certain number, it becomes just mathematically certain that you will have a disruption of the bot because the applications change. They are altered, they’re upgraded and so forth, so you need keep the applications down and look for those short tasks that are 500 clicks or less. That’s really what RPA was designed for, but a lot of companies are finding that they need to do more than basic digitization, which is the stage one here on this graph, which is limited rules and deterministic. Just what I just talked about, that really, they want to look at extracting structured fields from unstructured content, from invoices, from email, from loan documents.
They want to look at the ability to orchestrate the end-to-end process and do work orchestration, which is stage three, where you’re really managing the end-to-end process using digital process automation, which I’ll talk about, using some elements of AI. Basically, stage three is more transformative than two or one and that’s really, coming out of the pandemic, I think, what companies are going to be looking at, This is the path that we’re seeing towards intelligent automation, which was rooted a lot in that excitement around task automation with RPA but it really, it needs to go a lot further.
Let me dig into that little bit more. Firstly, if you look at the 19 different software markets that are part of, that all participate in improving processes, this is what intelligent automation does. It improves a process. This is our buying pattern guide that we publish routinely in different areas. Basically, it tells you where to invest today, where to just maintain the capability, where to divest. There’s some older technologies you really need just to plan to remove, and where you should be experimenting because that’s the next set of technology that’s going to help you restructure your business.
When you look at intelligent automation in this way, firstly, you see it’s a lot of things. You do have some RPA scattered there, but you also have digital process automation, and you see the wide aspect of that and the deep aspect of that. Now, the deep aspect of that, which is on the maintain area are for more complex processes, so this would be claims management and insurance. This would be a complex mortgage origination process.
The invest area right now is what’s called DPA-wide, which is, think of this as your more low-code platform that’s really designed for the business developer. The idea is that there’s far more automations that are needed in a company that can be done centrally with professional developers. You really need to take advantage of the growing technical skills in the business and start to use these tools that are coming out and getting better all the time, that really allow those business developers to do automation.
Now, this requires an operating model that has the right governance and so forth. That’s a topic for another day. This is the trend in the industry and we support that trend. We think there’s far too much automation that needs to be done without the help of the technical capabilities that are growing in the business. Of course, with experiment, you really need to start to look at domain-specific robots, look at chatbots that are employee and customer-facing, and so forth.
Now, but you have a challenge. There’s so many of these. You look at all these different automation tools, so forth, how do I align them with the right use case? One way to help think about that is with this pyramid of different process patterns. This gets a little bit confusing for some to try to think about how these patterns are different. “How do I understand where’s the best pattern or use case to apply a certain automation to?” You see, at level one, you have software controlled that are rules-driven. Now, this is the kind of workflow and process we’ve been doing for decades, and decades, and decades. That’s where 81% of processes have that type of pattern. That might be an automation that’s in a core banking system. It doesn’t have to be done with a DPA platform.
Most of these are workflow process layers that are built to really affect and improve a process. About 10% or 12% are actually starting to have digital workers, which where you take advantage of the enhancements in conversational intelligence to be able to have an employee ask a question or direct an automation. Of course, with customer self-service, customer-driven automations are extremely popular, and they became real popular in the pandemic as there were no people to do anything. These one and two level process patterns are deterministic. They’re not using any AI components. They’re what most companies have today.
Where we’re going, levels three and level four, where we’re going to start to introduce machine learning, and statistics, and predictive analytics, and all of these wonderful things that AI presents an opportunity for, and we’re going to start to drive process. You see level three is semi-autonomous, where you have a human very strongly in the loop, but the machine might be making decisions based on predictive analytics and say, “Pick one of these.” Or, “This is the best thing to do,” and the human will make the final decision. A lot of opportunity in that because with the human as a default, it takes some of the pressure off the perfect algorithm, and pristine data, and a lot of the governance, and things that we’re concerned about when you go to level four, which is autonomous, where the machine is doing everything.
Of course, the human built the machine, but once it’s built, the process pattern is closed loop. It’s where the car is driving itself, the delivery vehicle is delivering itself, and there’s a way to combine these patterns in different ways. Think about where your company is today. You’re probably, like most, dominated by deterministic types of process areas, but think about where you have to go. Because there’s going to be a lot more processes that are going to go in that level three and level four, and if your company doesn’t take advantage of them and doesn’t move towards them, you’re not going to be as adaptive and resilient, or as competitive going forward, and that’s really the point.
The level one and two patterns, they use DPA to go deep and wide. I mentioned this before, but I think one of the most interesting things in the market in the last few years has been how these lower-code principals and primitives have the ability to have template-driven, have simple IDEs for development of processes, and how they’re taking off in the business. That’s what half of my inquiry calls are around, companies trying to understand how do this from a governance and operating model basis, but it’s a very, very important trend.
The deep area, you have a lot of … A small number of apps very much led by the automation center of excellence, very much concerned because there was interaction with core systems. IT has to ensure the diligence, and a lot of focus on cost reduction and compliance. On the wide side, you can focus on outcomes more. You can focus on individual automations, so it’s a very, very good transition. You see it’s the long tail. It’s, again, this point that there are so much automation to be done in companies that you just can’t do it in the IT zone.
You have a few highly controlled and really critical apps that are using probably the more complex automation, those level three and four patterns, but there’s a ton in that business developer zone. There’s a done of application work that can be done that, when properly governed, can really add to productivity. In fact, I would say that most of the productivity will come from the right side of this chart and not the left. That’s really the core of digital transformation.
Now, in my last five minutes, let me try to make this a little crisper. BPM or DPA can work very, very well with digital workers or RPA bots. There’s no conflict between them. As I said earlier, a lot of the intelligent automation suites now include RPA and DPA. They work together. Instead of routing work to a human, you’re now routing it to a bot that’s doing the same things a human was doing, so there’s really no issue there. These are some of the digital workers that we’re seeing developed. Now, the pandemic just really accelerated the employee self-service for HR. Instead of … You just couldn’t walk into the office and go down to the HR department and ask about a benefit question, so you came in through a chatbot and the workflow for the conversation had been built with those questions that are most asked, and they link directly to your HR human capital management system that had the answers, and this became very, very popular. Also, help desk, IT service management.
This whole notion of having employee self-service is really a growing area for digital workers. Document specialists being able to look through tables and contracts. You couldn’t go in the office and shuffle paper anymore. We’ve been talking about the elimination of paper for decades and decades. The pandemic has really moved it forward quite a bit, and we’ll see that just continue. These are just a smattering of some of the categories of digital workers that we’re going to see. A lot of document extraction. Now, with a lot of the more advanced machine learning and natural language processing that basically have zone-free extraction from documents, and emails, and so forth. This opens up the door to moving a lot of unstructured data into structured files that then can be used by RPA bots, or DPA, or APIs to be able to get work done. They’re a very, very bright area.
Now, when you try to tell the difference among these automations, we thought that if we could figure out the nine, or 10, or five most important attributes of an automation that could distinguish them, then you would help the enterprise architects in a company align the right automation tool with the right use case, and so that’s the goal here. The goal here is not to either over-stretch with RPA, apply an AI-based application where it’s not needed. Try to align. These are the nine dimensions. There are some that are very process-oriented. There’s some that are more enterprise-oriented, like governance and auditability, and there are some that are people effect, because you have to keep people at the center of all of this automation. If you don’t, you’re going to risk a degradation in employee psychology and employee experience. As you see right now, you have many, many employees deciding they’re not going to work for that company anymore. Now, that may change. We’re not quite sure why, but right now, you want to have a real focus on employee and on the people side of your automation projects.
The process side, if you think about data acquisition, some automations natively deal with unstructured content like speech, or OCR words, Microsoft Word documents, PDFs, and others don’t. Others just handle structured data. Some have actual learning competence, real AI. They can take in new data. They can have refreshed data that will then recalculate probabilities or other statistics, and they will then change their outcome. This is the essence of AI. Now in the future, what a lot of the conversation about AI is, based on that refreshed data that the internal models and algorithms, and how it’s making decisions will change and alter, that introduces a lot of the issues of explainability, transparency, opacity, all these great new terms that we have. Comprehension is something to be aware of in the automation tool that you’re applying and then, how deterministic is it? That’s the ability of a process to make changes within the process without human intervention.
When you do a standard DPA workflow application, every step that the software is doing is 100% articulated in the code. There’s no altering of those steps by the software itself, so that’s highly deterministic. Nondeterministic, and even areas like case management, that’s a derivative of DPA. You do have some nondeterministic aspects of that because during the case, the case worker might do some research or make some decisions that will change and alter the process. They may bring in new participants in the process. They may alter a snippet of work, so that has an element of nondeterminism. Really, it’s about the ability of those models, based on new data, in an AI context to become nondeterministic. Then that’s where it gets really interesting and the potential gets really good. The enterprise effect, what is the operating effects? What is the balance of the business maintaining this versus having it be IT-maintained? That’s human effect.
When you look at these, you can take a look at different automations, this is an example of RPA, and just plot it based on these dimensions. This is a graphic form. If any of you have played in a band or been in the back of a concert hall, there’s a mixer that’s controlling the microphones on the amps that the musicians are playing through. Each amp will have a microphone, and the volume for that microphone is controlled by this device, this mixer in the back. We thought it was an interesting way to show how the dimensions are affected. Here in RPA, it only handles structured data, so it’s very low on that. It has no learning or no AI component. It’s really deterministic. You can go through, and it’s federated in that the RPA design studios like the DPA. Low-code design studios are being built to support the business developer, so it has an element of federation about it that needs concern and potential. It also has some issues of transparency, but not tremendous.
Now when you compare that to others, this shows DPA-wide and case managed, I talked about. You basically have different characteristics. Not to get too detailed here, but basically you can take any of these automation tools. You can understand the profile, and that can really help you align it in the best way. Here are some other equalizers. That’s what we call this type of charting for chatbots where you see it handles unstructured data really well for digital decisioning platforms. You see how nondeterministic that is because it’s using AI to create new decision points.
That’s pretty much the bulk of what I wanted to present. I just have a couple more quick slides. When you think of all of this, where work is going, the kind of platforms that are going to emerge have to understand integration at a broader level. It’s not just traditional data integration. It’s also UI integration with RPA. You have to have our concept of AI integration because you’re going to be drawing intelligence from the cloud. You need to have bundled AI components to be able to help you do better process improvement. You have to have a concept of human in the loop design. That’s where the pyramid comes in. That’s where you have level one, level two, level three, and level four. You have to think about the role of humans in each of those layers. You need task automation, and you need the ability to think about customer self-service, employee robots, and so forth.
This is the future of work. The future of work is going to have digital workers, basically automations, and it’s going to have humans. How that work is orchestrated is going to be critical because that’s the way work’s going to look as we go forward. To make all of this work, you need a concept for an operating model. You need governance. A lot of the leading companies have built centers of excellence for automation. They’ve built what we call strike teams, which is just our term for it. That term may not catch on for you, but it’s basically trying to show that it’s a more agile construct than say previous centers of excellence that tend to be a little bureaucratic. They may exist in the business.
Actually, 52% of these automation strike teams report into the CIO or the CTO, but 48% report into the business, so it’s not clear … They can be different areas, but what their goal is, is to develop a template for governance, a control framework, or automations built in the business to jumpstart and evangelize automation. “Here’s the license. Here’s the templates. Here’s Sally, who knows how to do it, done it before.” This is what the tracking, the pipeline management, this is what you need to scale automation, and a lot of companies are realizing that and developing these.
The bottom line, my last slide. You have a tremendous opportunity with all this practical automation, this intelligent automation we’re talking about to make your company more agile, to make it more adaptive, to prepare it for probably the next systemic risk that we face, and just from a pure economic value. Forget those higher level strategic advantages. $130 billion of potential economic value. If you look at different work personas in your organization, cubical workers, that’s back office, front office, the coordinators, the middle management level, different levels of knowledge workers. You can support them with automation and lift hours out of their lives and provide them. Now, what you do with those hours is up to the company, obviously. You can do stock buybacks, but you can also invest in better training for your employees that are dislodged.
You can return some of that profit to stakeholders, but you can build more automations. We hope that companies will take a balanced approach to that and provide a much better employee experience, which automation can provide, but it has to be a proactive effort on the part of companies to try to provide that better experience and not just think of it from a pure business standpoint. Of course, a better employee experience is important from a business standpoint. We want to keep those employees. With that, I will make one short, shameless plug for my book on this whole area of automation. This is from the workers’ side, it’s from the people side, employee experience side, called Invisible Robots in the Quiet of the Night. With that, I’ll turn it over. Thank you very much for your attention.
Gunjan Kalita: Thank you, Craig. That was truly insightful. As you said, intelligent automation platforms look like the way to go for enterprises to accomplish their digital transformation goals. Now, I invite Prashant to take us through how Infosys is enabling businesses to go digital and address their complex process needs with speed and agility. Over to you, Prashant.
Prashant Gandhi: Right. Thanks, Gunjan. Let me know if my screen is visible.
Gunjan Kalita: There it is here.
Prashant Gandhi: Good. Okay, great. Thanks. Thanks, Craig, for that insightful session. A quick introduction about myself, I’m Prashant Gandhi. I am a part of the Infosys digital services organization. I got about 20 years of experience in the industry and have been helping our customers through similar solutions across the various generations of those platforms as they’ve evolved. We’ll today speak about how we made such a digital transformation come to life taking a platform-based approach for one of our key customers. Before I get into that, I am just going to give you a one-minute pitch on how Infosys looks at helping its organizations go digitally native. There are a lot of organizations, as we speak, who are pivoting and trying to compete with their digitally-born competitors. This case study really highlights one such example. This is a quick view of how we help our customers.
Being a part of this digital services organization, we deal a lot on the customer experience layer, helping our customers retain and attract a lot of their clients, help them elevate their brand, and as an extension, create systems and platforms that are highly efficient for executing work inside the organization. The associate experience is one of the key drivers to achieving that outcome of keeping your customers satisfied. The key message on this slide is, it’s a very broad lens that you need to look at for turning into a successful digital native organization. A few key pillars, I’m going to quickly go through here.
It is obviously important to make the right choices, and have right product mindset, and take a platform-based approach that is capable of evolving over time. If you look at a three-year term or a five-year term, that itself is a very long-term strategy in today’s world, but there are certain other very important aspects that you have to look at. A few key ones I’m going to highlight here. Okay. Sorry. My screen just vanished.
Gunjan Kalita: I think, probably, you can stop it and then-
Prashant Gandhi: Is my screen still visible though?
Gunjan Kalita: It’s on the same slide.
Prashant Gandhi: Okay. Give me a second. It just vanished for me. Okay, all right. It is about also changing the ways of working. To sustain a platform on an ongoing basis, digital, agile, fail fast approaches are very important, and that is a progressive capability-building that we’ve seen in the organizations. While it is very important to have a strategy to succeed in day one, we do see that equally important is an approach to sustain the platform and continuously innovate, and create that engine for innovation as far as the organization is concerned, and as far as the technology is concerned. All that has to be in place. While doing so, the stakeholders, this could be your focus groups of your clients, or your key internal stakeholders, they have to be continuously engaged, not at the end of a cycle, but on a continuous basis in the evolution.
We see all of these coming together and working in cohort as very important aspects of turning an organization digitally native. Now, with that, this case study is about an annuities organization. The parent organization spun off this business to create its own independent growth engine. I’ll speak about the outcomes, looking back over the last 12 to 18 months, that has been achieved. That was the primary agenda that this divestiture had. With that, there was a total separation of systems that was required, and it had to be done in an expeditious fashion, in a very time-bound, aggressive timeline that was given to go live. In doing so, there is a whole bunch of legacy technology that was at play as part of the prior organization. Somewhere there were also about 40 terabyte of customer critical content critical for operating the business on an ongoing fashion. A lot of them were proprietary formats.
We’ve seen these kind of systems have been around for 40, 50 years, in some cases. This was a great opportunity to go to more of a web-ready, open kind of a standard. Those were the key outcomes that the customer was looking for. They were a complex set, 120-plus business processes that had to be brought on onto this platform with a view that the customer had of simplifying. Because it had been, by the time we engaged, we realized that there was a huge potential to simplify and run this business much smoother, much efficiently Those were the objectives that we set out with, which started with, essentially, an assessment of how we define the future. “What does future look like? What do you want in our day one kind of a platform when we go live, and how do you see yourself evolving over time?”
When we started digging down, initially, what became clear was we are talking about, essentially, a business platform that will help turn this organization digitally native, and help them achieve productivity and efficiency, which was nonlinear. There was a clear agenda to multiply, triple, quadruple, over a period of three to five years, or even more. The customer did not want a linear growth in staff to support those operations. That was one of the primary agendas. Then, of course, we’re getting rid of legacy, all the tech that had accumulated over the years, those complex systems, oftentimes, having been built over multiple years and decades and such, in some cases. There was very little known of how something works, so we had to deal with that aspect as well and move from proprietary to nonproprietary formats of content that I was talking about.
We started laying this all out as far as key capabilities are concerned, and very quickly realized that we’re going to need a much broader kind of a system. We’re not talking about just a workflow system, or imaging and workflow as it is called in some of our insurance clients. This really was a digital business automation platform, which had very specific outcomes that they expected on day one, but very different than progressively improving outcomes that they wanted to achieve going forward. With that objective, we went about doing pilots on two or three different platforms. It was after throwing very specific business requirements at the shortlisted solutions, and the ease with which we were able to achieve and demonstrate the capabilities of platforms that led to the selection of Newgen iBPS platform that was selected. We led that initiative, and now then came the … This was done, mind you, as I was telling you, about eight to nine months is what we had, overall, to go live. Within that, when you’re making a pilot kind of a phase, it has to be really expedited, so that’s what we ended up dealing with.
In the end, we were able to go live for the day one objective, which is a fully functional system able to support operations for our customer, for hundreds of their back office users. They were doing about 120-plus business processes, serving their clients across the policy life cycle for annuities. We were able to go live in a SaaS-based platform in slightly over seven months. This was done iteratively. All along, we followed principles of agile functionality, and evolution was continuously demonstrated, signed off, feedback taken. In a very iterative fashion, in a show-and-tell kind of a fashion, we went live and achieved great objectives for our customers.
Somewhere there, we also migrated about 200 million documents, which were coming from a legacy system, which needed to be transformed from a legacy proprietary format into an open format. We chose PDF, one of the more open formats, and we used our tools and accelerators to achieve that outcome of content migration from legacy to the target platform. This is one of the examples of the tools and accelerators that we continuously build to help achieve outcomes by working with several of these technologies, being a larger systems integrated and a digital service organization.
What did we achieve? We went from being a single channel, a mail room-based organization, where the only source of input was information that arrived mostly on paper and was processed through the mail room into the workflow. At the end of it, in long cycle times, achieved the outcome. That’s how it was to actually, on day one, being live with mail room, of course, we checked the box on that, introduced automation right from day one, inbound emails, touchless processes, where information arrives in that channel and can be processed quickly and efficiently. We added that channel, and we set the stage for a future evolution, which I’ll speak about in just a minute here.
We went live with all of that, with a significant amount of functionality which exceeded the current capabilities, or the historical capabilities that they had within the platform. One of the other key requirements that we achieved was regulatory compliance for cloud-based SaaS platforms. Section 17a-4, which was the FINRA standard that was mandated to be complied with, Newgen has a platform supports that fully and we were able to use AWS’ implementation of section a-4 compliance and integrate that with the Newgen platform to help achieve that compliance for our customer in this case, so that’s also something that we achieved.
Now, what’s important is the journey here. Day one, it was a lot of simplification. Went down from 120 disparate processes into very sophisticated process fragments, where we brought the complexity of the system down dramatically by means of its rules engines, by means of configurable user interfaces, and rules-based experiences that were delivered to the back office users based on the context. We didn’t have to go about building one queue for one process, and then replicate those 120 business processes as is onto the target platform, so that was a great outcome that was achieved, a significant amount of simplification.
Right after that, within about six-odd weeks from going live, this was hooked up to the customer experience platforms, which is a customer portal. We hooked the system up. All the customer documentation became available to our customers with lightweight API-based integration for direct access by the end customers. That in itself reduced a huge amount of call volume, so another huge step in making sure that we don’t have a linear growth as the business expands. Move forward another four to five months, and this became really a digital operations kind of a platform layer. A lot of self-service that the customer had intended to enable for their clients. Instead of somebody having to fax in something or email in something, they simply could go online, use responsive self-service capabilities where requests could be generated and straight through processing could be enabled.
We set up that framework and beyond the first seven months, within the next three or four months, we progressively started delivering on those capabilities. It has turned into a back office-based workflow solution, into a really customer experience elevation platform, so that’s what we’ve achieved. Progressively, at the back end, we went from a lot of screen-based data entry, to using RPAs, to now API-based integrations for posting into the core systems. It’s like a seamless hook into the target platforms in this case, policy admin systems that directly hook onto these kind of solutions to achieve the outcome.
A wide variety of request types, as far as policy-servicing are concerned. These are the business processes that were automated for this customer. You see a quick view of this. This cuts across from financial and nonfinancial kind of use cases, to claims and other aspects of it. Some of you more familiar with insurance and annuities as a domain would relate with this, but you’d see that a lot of this involves a back office OCR. A lot of this involves financial instruments that flow along with the request that have to be processed and information has to be posted, so all of that was automated as part of this solution. It’s the sweet spot of all these moving parts together that made this all possible. Another key aspect that we’ve realized is, these are centers of excellence for a lot of our customers. Oftentimes, these run as independent pockets. The key here is, this needs to work together for achieving outcomes and needs to work. The closer they’re able to work together, the faster is the outcome and the better is the evolution roadmap that you’re able to achieve.
When we started breaking this down, so there’s a quick view on key capabilities that we see are essential for digital business automation. Over the years, we’ve built solutions, which are similar, with varying levels of complexity for our customers. You can achieve these capabilities using six different systems. Use APIs to integrate together. Use, pilot it for three months, four months, and then get to the part where you start solving business problems, or with the right choices you can choose to go with a solution that is built, basically, to expand and evolve, and has many of these moving parts as part of a common platform. Comes pre-integrated so if not day one, week two, you start focusing on solving business. Content-centric use cases, integration capabilities across process automation, content management, collaboration. Collaboration is a key part. It means different things to different people. Collaboration could mean two people working on a document together, or it could mean a contract life cycle kind of a use case where you collaborate with third parties and your partners in achieving terms that work for both the parties. That is also collaboration, or somebody, workflow process reaching out to a specialist and asking for some expert comment. That is collaboration too, so all aspects of that.
It is about how many different moving parts can be brought together as quickly as possible to solve the business problems as part of a platform that is extendable, doesn’t lock you in, has APIs available for integration with anything else that is outside across all these tiers that we feel is very important to achieving the outcome in an expeditious fashion. You could take two years for achieving this outcome, or you could take seven months, six months or seven months to achieve this outcome with continuous show-and-tell. It is the number of different systems that you work with that ultimately is going to determine that. With that, I’m going to hand it back to Anurag to take this forward.
Gunjan Kalita: Yes. Thank you, Prashant. A brilliant case study that’s a best testament to the solid Infosys, Newgen partnership. With that, I invite Anurag to give us a glimpse into Newgen’s digital transformation platform and how it is aiding this motion. Anurag?
Anurag Shah: Yeah. Let me … Why is it not showing my video? You see my screen, but what is happening here? You see me now?
Gunjan Kalita: I do.
Anurag Shah: Okay. Okay, we’re good to go. All right. Thank you so much. The insight from Craig and Prashant were great. I will quickly take you through … By the way, I’m Anurag Shah. I manage the products and solutions for Newgen. With about 22 years of industry experience and being with the company, have seen the evolution of what we are calling low-code, no-code, intelligent automation. In last couple of decades, I’ve seen that evolution. I’ll quickly take you through, how do we leverage this intelligent automation for creating these responsive business processes?
If we really see, what is slowing you down? Three things, and Craig and Prashant both touched upon these things. First, in the engagement that you have for your customers, are you in the context with respect to the content, with respect to the process, with respect to the communication that you’re doing? The information systems that you are dealing with, which are contributing, which are helping you do things, what you need to do, are those disjointed living in their own silos, and you are running around from one to another? The expectations coming from the customers, are those changing? Are those demanding? Are you able to see those? If you really see, what we are trying to do here, let us see. Prashant talked about making life simple for the insurance company, that the case study that he talked about from more than 120 different processes, how to simplify, and rationalize, and making it smoother.
Look at NewgenONE as a digital transformation platform to enable your customer journeys from end-to-end, starting from initiation, all the way up to the end, irrespective of the industry and the vertical, using components, which includes local development and deployment capabilities with three key outcomes. Quick to market. Again, you saw that in the case study Prashant talked about. Just quick time to go live, be able to start seeing show-and-tell things from week one, week two. Not really wait for three and four months to see the first outcome, and be able to consistently deliver the customer experience across the channels, be it your portal, mobile, contact center, in person. How can you maintain the consistency of the customer experience? Then, what Craig really beautifully laid it out, closing the loop around the intelligent automation. Technologies like artificial intelligence, delivering those predictive analytics, delivering those machine learning, natural NLP kind of thing, natural learning, processing capabilities. Achieving those all things into a closed loop and getting that intelligent automation.
Let us see how all of these solve in a Newgen way. We have seen that more of these business needs are becoming more and more dynamic. It is requiring more robust, comprehensive, and deep digital transformation platforms. If you look at the business initiatives, of course, the customer experience and simplification of the customer experience is really at the top, combined with the internal employee experience as well, who are serving these customers for different business use cases. At the same time, other aspects, rapid and expeditious … Prashant talked about expeditious digitization of your business applications supported by the IT architecture, which is not monolithic, which is not legacy. Being modern about it, and at the same time, providing those secured, uninterrupted, continuous and remote operations. We lived through the COVID and pandemic year and we all know what is the importance of this remote, uninterrupted, continuous, and at the same time, secure way of working.
If you really look at a unified platform, which really is required to meet these kind of business initiatives, it includes a few things. It may be just a reiteration of what Craig and Prashant would have talked about in a different landscape, but just to put it into a Newgen kind of a platform perspective, technologies like mobile portal collaboration, enabling the customers for self-servicing. Having those low-code application development framework with three key capabilities, the content, the process, and the communication with deployment flexibility. Being able to go cloud if you have applications running on-prem, how can it interact with them? Maybe live with hybrid model technologies like artificial intelligence, machine learning. Leveraging those microservices, DevSecOps for deployment with more and more enriching of the integration, adapters, and catalogs, web services, open APIs. All of these with enhanced monitoring for the entire platform for high availability and continuous uses.
With that, if I just had to introduce NewgenONE as a digital transformation platform, it starts with the core layer of content and communication at the bottom, if you see here on the slide. On top of which is their business process automation, which combined with business process automation, content and communication, these three things gives you a low-code application development framework and brings in all of those technologies, AI, the ML, the analytics, bringing in the intelligence and automation into this entire platform with the multi experienced user interfaces.
If you look at the business applications, I’m going to talk about it in my next slide, but if you look at multiple verticals, banking, insurance, manufacturing, shared services, how are you able to create those user experiences for these different business applications serving, of course, customers being the central part of it? How do you serve the customer, provide that end-to-end journey? Provide that experience, but at the same time, how can you give an enhanced experience for your employees who are serving those needs of the customers, and the partners who are participating into your processes, into your customer engagement, either directly with customer or through your employees in the whole context? Of course, with an integrated ecosystem of the information systems of the applications, of the core systems and on cloud.
If you really see a unified platform, cloud-hosted, scalable, extensible, highly available, and this platform is used by enterprises to deliver those quick and critical business application, mission critical business applications, performing millions of transactions, handling billions of documents, and those hundreds and thousands of employees, providing those remote access to them. This platform has been recognized by the analysts, the leading analysts, Forrester and Gartner, for each one of its three components, the content, the process, and the communication. All of those are individually, and in combination, recognized very highly by these analysts.
To conclude my presentation, I just wanted to give a quick view of the indicative solution areas, use cases this NewgenONE platform has been deployed for, has been used by our system integrators like Infosys and our enterprise customers, starting from banking, be it deposit account opening, or lending in consumer, lending for commercial, wholesale trade finance, compliance, sales. If you look at insurance, the digital policy-servicing case study you saw in the presentation by Prashant. In addition to that, electronic code generation, policy issuance, underwriting. Creating those workbenches for different use cases and user personas. If you look at cross-industry enterprises, the shared service centers, the optimized processing in those departments like accounts payable and voice processing. If you look at the governments, how do you deliver those eGov office automation processes, citizen-centric services? It really cuts across all.
If you really see where we stand out is, Prashant talked about, we can look at having multiple systems, three, four, five systems, stitching those together, combining those together, integrating those together. Or you can look at one such natively and organically-built unified platform without any acquisition, all sharing the same code base, same technology landscape, same user persona, same experience. Delivering those productivity for your professional and IT developer for agile and iterative application configuration and development. Comes packaged with certain industry domain expert components in financial industry, in insurance, and delivered by our global system integrators like Infosys for all your needs.
In the end, I would like to conclude here saying that all of these we have been doing at Newgen since last 30 years, even before the low-code, the terms like low-code and the intelligent automation were coined. All of these are being delivered for about three decades, since 1992. With that, I will turn it over back to our host, Gunjan, and see if we have any questions to take.
Gunjan Kalita: Thank you, Anurag, for that comprehensive overview of Newgen’s digital transformation platform. We do have … In the interest of time, I’ll just take four of these. The first question that came in was for Prashant. Are you there?
Prashant Gandhi: Mm-hmm, yeah.
Gunjan Kalita: The first question is that, going into this specific case study that you presented, when did you realize that their needs are for an end-to-end intelligent automation and not just a localized one?
Prashant Gandhi: Yeah, so this became apparent right upfront in the discovery phase. The customer had a really, they had a directional view of what they wanted done on day one, and the evolution path for the next six to nine months, and even beyond. The selection and the choices of technology were made keeping that in mind. This was something that we had come to know in the first six to eight weeks of engagement.
Gunjan Kalita: All right. Thanks, Prashant. There is a follow-up to that. Any tips for enterprises to identify and prioritize such projects in their organizations?
Prashant Gandhi: Yeah. See, that ties to … There’s a few factors to consider. Every program is different, and the objectives, medium to long-term or it’s a short-term, are different in most cases. We always recommend, take a value-based approach. Focus on functionality that can be rolled out quicker, touches the maximum number of users, and is not the most complex to implement. It’s about showing-and-telling, and proving out things as you go along. We would typically recommend that kind of an approach, unless there is, obviously, a business priority to grapple with, the most daunting or the complex task at hand first. We recommend starting somewhere in-between as far as complexity is concerned, and progressively demonstrate as we go along.
Gunjan Kalita: Thank you, Prashant. Next up, there is a question for Anurag. How do firms handle scenarios of multiple content sources/repositories while automating document-heavy processes?
Anurag Shah: Yeah, that’s a great question. In fact, relating with the industries like insurance, where they are managing these customers and these documents for like 30 years, 40 years, for the life of the policies and the life of the policy-holders, it has become almost existential that the repositories and the so-called document management systems from the last couple of decades, two, three decades would be existing. This is where the federation and the intelligent crawling into those and bringing all of … I mean, of course, you can look at migrating all of these, but more than that, if you want to do progressively, if you want to do the digitization, like Prashant also talked about, what is your day one objective and how do you want to evolve going forward?
Day one, you can look at federation. You can look at certain content, which are not really into a structured repository, bring those into the content services platform, and then you start working through the microservices to crawl into specific repositories, bring those taxonomies and metadata into the content services platform for that unified search. You should be able to do a comprehensive unification of the search, bring the result seamlessly from multiple repositories across into the context of the process for the users. Over a period of time, it always will make sense to see, when is the time for you to migrate from those monolithic repositories into more modern, maybe cloud native, maybe modern repositories? It does not need to be in one place. It could still be federated, but over a period of time, modernization would be the key.
Gunjan Kalita: Brilliant. Thank you, Anurag. Just in the interest of time, I’ll just take one final question. Probably, Anurag, you can answer this. How does Newgen’s low-code process automation platform handle exceptions, especially when RPA is involved?
Anurag Shah: Yeah. Craig touched upon it during one of his slide presentation, that when you move from … If you recall, his slides, stage one, two, three, four, when you move from stage one and two to three and four, the work which was being done my human, when the RPA came in, the work will transfer from human to bot. What started happening there is maybe 80% of the transactions were handled smoothly and straight by the bots, but there are those 20% which failed in the bot because of multiple reasons. That is where the artificial intelligence, that is where the business rules management system, that is where those OCR, all of those technologies came in, predictive analytics came in, and it allowed the entire foundation of the intelligent automation. Those all contributed in finding out, what is next? What has to be done? Machine then took a decision on that exception and presented it to a human.
Ultimately, the human would have taken the decision, but in the time which is significantly and multiple-fold less than what it would have taken if the human had to do all of those research and analysis. If a human would have taken, in a traditional way, maybe couple of hours to take that decision, with all these intelligent automation and the components coming in place and contributing into the next best thing suggestion, the time for human has come from couple of hours, two, three hours into minutes, and even less than a few minutes.
Gunjan Kalita: Thank you, Anurag. With that, we will wrap the session. Thank you, Craig, Prashant, and Anurag. We really appreciate your time and your valuable insights that you brought along. Newgen will send across a copy of this recording to all the attendees and the registrants. Those who were not able to share their questions today, please feel free to send them over via email. Thank you again, and have a great rest of the day ahead.
Anurag Shah: Thank you.
Prashant Gandhi: Thanks. Thank you.