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Amardeep Modi

Amardeep Modi is a member of the Service Optimization Technologies team and assists clients on topics related to technologies that directly impact global services, with an emphasis in Service Delivery Automation (SDA) technologies.

Blue Prism’s Acquisition of Thoughtonomy: Does 1+1 =3? | Blog

By | Automation/RPA/AI, Blog

As a reader of this blog, you likely know that we’ve been researching and analyzing the RPA market in-depth for more than five years and have conducted multiple RPA technology vendor PEAK MatrixTM evaluations in the same time frame.

Starting in 2015, Blue Prism earned a Leader’s spot in in our assessment because of its extensive features and strong market presence. Thoughtonomy made it into our Leader’s group starting in 2016 for its Software-as-a-Service (SaaS) offering, and for combining RPA and AI for unstructured data processing.

Because it is a public company, Blue Prism’s strong growth over the years is a matter of public record. Thoughtonomy has also grown strongly, gaining around 77 direct clients and another 200 indirect through its service provider partners.

Against that backdrop, we believe that Blue Prism’s announcement earlier this week that it is acquiring Thoughtonomy for a total consideration of £80 million is a positive move for three reasons.

First, Blue Prism gains several hundred mid-sized direct clients in an instant. Second, and more importantly, its ability to deliver intelligent automation through a SaaS delivery model gives it the opportunity to much more easily sell into the mid-market. Third, this is a strategic move by Blue Prism. Right now, adoption of RPA on the cloud is in the early stages. At the same time, many AI solutions are offered on the cloud to enable access to computing power on demand, and many work with RPA in combination when needed. Having both RPA and AI on the cloud could help companies realize the full potential of intelligent automation and achieve higher scalability. Blue Prism is becoming cloud-ready with this acquisition.

But there is more.Blue Prism Acquires Thoughtonomy

What Thoughtonomy Brings to Blue Prism

Thoughtonomy was set up in 2013 to provide a cloud-based intelligent automation platform. At its core, it is a cloud version of Blue Prism’s RPA, combined with other capabilities that Thoughtonomy has developed over the years, including:

  • Features for human-in-the-loop automation (Self-Serve), including next-best-action recommendation – These features will help Blue Prism with attended automation that is typically used in the front office. Currently, Blue Prism offers human-in-the-loop through its technology partner, TrustPortal, which provides the UI for this capability
  • Built-in AI / machine learning within the platform to optimize workload distribution and robot performance
  • Natural Language Processing (NLP), sentiment analysis, and chat interface to automate processes using chat as a channel
  • A web-based interface for controlling and monitoring robots – While Blue Prism offers a central console for controlling and monitoring robots, it is not web-based. This will help improve the accessibility of its console
  • Wireframer, an intelligent coding quality tool – Blue Prism currently has an automation methodology, but not a coding quality tool
  • Use cases in IT process automation – This will help improve Blue Prism’s value proposition for IT use cases, which are growing in demand

In addition, Thoughtonomy will help enhance Blue Prism’s presence in some verticals, such as healthcare and government & public sector, where it currently has limited market share.

With Blue Prism at the heart of Thoughtonomy’s SaaS platform, the job of integrating the two product sets should be relatively straightforward.

All in all, we believe in this case that 1+1 does add up to more than 2. Is it a 3? Maybe not, but it is a solid 2.5.

The challenges of SaaS, selling to the mid-market, and targeting the front-office market

Blue Prism’s model includes a minimum licensing requirement that can make it expensive for smaller companies to get started with its RPA offering. Thoughtonomy was absorbing these requirements. Blue Prism will no doubt clarify how it will handle licensing for its SaaS offering.

The addition of Thoughtonomy’s human-in-the-loop interface will help boost Blue Prism’s attended automation value proposition. But if it intends to target this segment – which primarily consists of front-office and contact center use cases where thousands of robots might be required – it will need to adjust its pricing to reflect large orders. Additionally, it will need to deliver more desktop-based features in order to outshine established attended automation vendors such as NICE and Pega. As this doesn’t appear to be a high priority segment for Blue Prism, we may not see those additional features in the near future.

The market outlook

With this move into SaaS, Blue Prism has captured a competitive edge. We expect other companies will quickly follow suit. Several RPA vendors are cash rich thanks to recent private equity investments, as well as good organic growth, and they may well have their eyes trained on potential acquisitions. Other RPA technology vendors and other companies that provide complementary technologies, like chatbots, could well be either acquirers or acquisition targets. AI-based automation vendors, e.g., those with NLP or intelligent virtual agents, could make acquisitions of their own to complement their products. And we wouldn’t be surprised to see large software vendors acquiring RPA vendors, just like SAP did last year with its acquisition of Contextor, an RPA vendor that we positioned as an Aspirant in our 2018 RPA Technology Vendor PEAK MatrixTM Assessment several months before SAP made its move.

This is just the beginning of the consolidation phase of this expanding market, and we have no doubt there is more to come.

Everest Group will be publishing its 2019 RPA Technology Vendor PEAK MatrixTM Assessment in the next few weeks. In the meantime, please check out our recent service optimization technology-focused publications, including Intelligent Document Processing (IDP) Annual Report 2019 – Let AI Do the Reading

Why Invest in Artificial Intelligence (AI)? | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog

“Facebook shuts down robots after they invent their own language.” This headline was splashed across myriad news outlets just a few weeks ago. And although the story itself made the event seem like just a normal science experiment, this type of alarming tone in media reports is becoming the norm and is sowing seeds of doubt, fear, and uncertainty among consumers and even some businesses.

However, behind the vendor hype and the media fear mongering, there are real, bona fide reasons for organizations to invest in artificial intelligence (AI).

Humans can perform various expert tasks with relevant training and experience. For example, a research analyst trained for and with experience in market research, can predict future market size and growth with considerable accuracy. Using machine learning, a system can be trained to perform the same task. Yet, with their enormous computational power, such expert systems/machines can beat humans’ speed, accuracy, and efficiency in this and many other tasks. This is the reason why many organizations are investing heavily in developing and creating AI-enabled systems.

Narrow AI

Have you ever encountered a situation where you’re talking to a customer service executive over chat, and wondered if you’re actually talking to a real human agent or a virtual agent/computer program?

I recently attended IPsoft’s Amelia 3.0 launch event. Amelia is an AI-powered virtual agent platform that uses advanced machine learning and deep learning techniques to get progressively better at performing tasks. In one of the more interesting demonstrations, Amelia went head-to-head with a real person in answering questions posed to it in natural language, by real-time processing of unstructured data from natural language documents such as Wikipedia pages. It was fascinating to see how Amelia could answer questions with considerable accuracy.

Such domain-specific expert systems that can simulate human-like capacities and even outperform human expertise in specific domains are called Narrow AI.

While most AI vendors typically focus on building Narrow AI systems for a specific purpose such as virtual agent capabilities, some large vendors such as IBM, under its Watson brand, offers multiple individual Narrow AI systems to cover a wide range of use cases.  For example, it is being used at several top cancer hospitals in the U.S. to help with cancer research by speeding up DNA analysis in cancer patients. In the finance sector, DBS bank in Singapore uses Watson to ensure proper advice and experience for customers of its wealth management business. And in retail, an online travel company has created a Discovery Engine that uses Watson to take in and analyze data to better link additional offers and customize preferences for individual consumers.

True, or General, AI

Artificial intelligence with multiple and broader capabilities is called True, or General, AI. When it comes to developing General AI, which has the ability to generalize and apply learnings to unlimited new domains or unexpected situations – something that humans often do – I think we are just scratching the surface. Primary barriers to achieving General AI are our lack of understanding of everything happening inside human brain and the technical feasibility of creating a system as sophisticated, complex, and vast as the human brain. As per a survey of 352 researchers published in 2017, there is a 50 percent probability that General AI will happen by around the year 2060.

Current lay of the land – A world of opportunities

Despite the many evolutional, ethical, and developmental challenges researchers and technology developers continue to face in making artificial intelligence more capable and powerful, I believe that even existing AI technology presents unique opportunities for organizations. It enables them to improve the customer experience and operational efficiency, enhance employee productivity, cut costs, accelerate speed-to-market, and develop more sophisticated products.

To help its clients understand the AI technology market better, Everest Group is researching this field with a lens on global services. Although early in our research, one fascinating use case is how AI is automating decision making with complete audit trail in the heavily regulated financial services industry. The research will be published in October, 2017 as part of our research program, Service Optimization Technologies (SOT), that focuses on technologies that are disrupting the global services space.

RPA is Free, so Let’s Discuss Cognitive Now | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog

In the midst of increasing RPA adoption in global services, WorkFusion, a technology vendor that focuses on delivering smart automation solutions, has taken a bold move to offer RPA for FREE. While trial or community editions of RPA tools are available for free from RPA technology vendors such as UiPath, WorkFusion’s RPA Express is the first ever scalable and enterprise-grade RPA product to be made available for free and for all.

The adoption of RPA has been increasing rapidly, but it has been skewed toward a few industries and large-sized buyers. WorkFusion’s disruptive move of offering RPA for free will make the business case for it extremely favorable, and will significantly accelerate the adoption of RPA across all industries and buyer sizes. It makes RPA accessible to many, and accelerates trials and proofs of concepts by those that have not yet adopted it.

As organizations realize the benefits of RPA, they are likely to turn to cognitive technologies as the next natural step in their enterprise automation maturity. Availability of free RPA will consequently speed up organizations’ journeys to adoption of cognitive automation. WorkFusion is clearly looking to accelerate those journeys, and transition clients from its free RPA to its integrated RPA and cognitive automation platform, while putting commercial pressure on competitors who do not have a fully-fledged cognitive option to offer.

We believe this to be a very smart move. We think the message here is pretty clear that RPA is becoming table stakes, and the real deal is cognitive automation technologies. Also, it’s worth noting that this seems to be happening much faster than many of us would have predicted, and is a clear example of the “law of accelerated returns” and the exponential evolution of technology we talked about earlier this year in our blog: “Artificial Intelligence: How far or how close?”.

It will be exciting to see how WorkFusion RPA Express compares with the leading RPA technologies that are available in the market on a paid basis. Interestingly, we have conducted an in-depth analysis of WorkFusion’s full RPA capabilities, and overall, they stood up very well to relative assessment against better known RPA technology vendors. The technologies assessed included Automation Anywhere, Blue Prism, Kofax Kapow, Kryon Systems, NICE, Redwood, Softomotive, Thoughtonomy, and UiPath.

To read our complete assessment and analysis, please see our newly published report, “Robotic Process Automation (RPA) – Technology Vendor Landscape with FIT Matrix Assessment – Technologies for Building a Virtual Workforce.”

Artificial Intelligence: How far or how close? | Sherpas in Blue Shirts

By | Blog

Progression of technology

Technology has advanced to the extent that the sci-fi stories have come close to becoming reality. Whether it is the humanoid AI from “Ex-Machina,” Skynet from “Terminator,” or JARVIS from “Iron Man,” most people might likely agree that nothing seems impossible to achieve. The debate lies on how far are we from getting there. And here’s why we are probably much closer than most of us might actually think.

Ray Kurzweil – the American author, inventor, futurist well known for his predictions about artificial intelligence and the human race – suggests in what he calls “the law of accelerating returns” that the rate of change in a wide variety of evolutionary systems tends to increase exponentially. This includes the evolution of technology as well. Kurzweil suggests that this exponential technological growth is counter-intuitive to the way our brains perceive the world, as our brains were biologically inherited from humans living in a world that was linear and local. Due this exponential nature of growth, all predictions made based on past and present growth rates would lead to massive underestimations of the future, which in turn would lead to great skepticism in our future projections. If Kurzweil is correct, the level of advancements we would experience by jumping to 2035 would be equivalent to what a person from 1750 would experience in 2016.

So, advances are getting bigger and happening more quickly than before. This suggests some pretty dramatic things about our future, right?

This theory is well demonstrated in the case of business services (business process and IT services) in terms of the adoption of Robotic Process Automation (RPA) – or rather, the lack of it. The role of technology (RPA in this case) in delivering services has evolved at a very fast rate, faster than what one would have naturally perceived, and the skepticism in future projections resulted in most enterprises’ seeming unpreparedness for robotic automation. The economic downturn that started toward the end of the last decade should have been the perfect trigger for RPA uptake in business services. RPA adoption seemed like a natural corollary as enterprises concentrated feverishly on cost savings, and trimmed their support functions in the wake of the recession … but that didn’t happen. A very important technological development has been presenting itself in the form of RPA, and most enterprises were clueless about the impending disruptions. It was not until recently that enterprises started to take a serious look at RPA. Even now, action is lagging the hype, though the upward trajectory for RPA adoption from here onward should be exponential.

Era of Cognitive Disruption – the road to Artificial Intelligence

Cognitive disruption and its usage in business services is an extension of RPA’s story. Many people are confused about the term Artificial Intelligence, mainly because it’s a very broad subject with diverse set of applications ranging from smart phone apps to self-driving cars to something much more dramatic in the future, and because of the way it’s portrayed in popular media.

Almost all the AI and cognitive platforms that have been developed to date, ranging from iPhone’s Siri to Google’s self-driving cars to more sophisticated systems such as IBM Watson and IPSoft Amelia, are examples of what are called Artificial Narrow Intelligence (ANI) systems – AI that equals or exceeds human intelligence in specific areas. The next generation of AI, Artificial General Intelligence (AGI) – AI that is as smart as a human and can perform any intellectual task that a human being can – may still sound like science fiction; but it could suddenly become very real due to rapid advancements in technology. We keep coming across claims made by various AI developers about how close they are to achieving the next level of cognitive intelligence. For example, IPSoft claims that its artificial intelligence platform, Amelia, is close to achieving “near human cognitive capabilities” and we are going to hear more and more about AI in the coming months and years. However, many such claims are still met with great skepticism, and understandably so.

AI-enabled automation of knowledge work could cut employment costs by $9 trillion by 2020, according to estimates by Bank of America. This depicts the huge potential for near-complete automation of core and repetitive businesses functions in the future. Many enterprises missed the early adopter bus for RPA, in a perfect example of the law of accelerated returns. The question is, “Will they repeat the same mistake for AI?” Or, more correctly, “Are they ALREADY REPEATING the same mistake for AI”?

Future-ready enterprises

Being reactive is certainly not the best way to realize full benefits of important technological advancements, especially in highly competitive markets. In order to be future ready, enterprises need to cut through the web of skepticism, and proactively take necessary steps to align themselves to what is about to come. Due to the counter-intuitive nature of the progression, this is probably even more challenging than it actually sounds.

Business services is inherently an area which is seemingly laggard in any types of innovation. Most enterprises are reluctant to doing untested innovation in back-office processes. The primary challenge is the chicken-and-egg problem… getting budget allocation without demonstrable benefits out of using AI, and vice-versa. Enterprises can move past this problem by starting small with a small seed budget, creating liquidity in a small segment of the market, getting the virtuous circle to work for them, demonstrating some benefits, getting stronger buy-in, obtaining a bigger budget for bigger AI projects, and extending to adjacent areas.

Several companies, such as Baidu, Black Knight, Facebook, Google, IBM, Hitachi, and Microsoft are investing heavily in AI. And they are not only at the forefront of the latest technological developments, but are also laying the groundwork for the future developments.

Technological advancement is progressive, and organizations need to prepare for this journey, rather than seeing it as a destination. Enterprises are already in the midst of robotic revolution, and with Kurzweil’s law of accelerating returns in mind, it is time they start embracing the cognitive era.

Cracking the Code of Automation in Shared Services | Sherpas in Blue Shirts

By | Blog

We have been wondering how service providers will ultimately tap into Service Delivery Automation (SDA) technologies to support their standardized and industrialized shared services offerings. Most have multitudes of in-house developed automations, including macros and the robotic varieties. Some have developed automation routines that can be shared across engagements for either specific processes (e.g., accounts payable) or for common purposes such as login & credentials management. The challenging part for shared services is to get a view across all automations, whether they are provided by macros, robotic or cognitive tools, as well as across clients.

Service providers are approaching this problem differently. Some are happy to just tap into the individual automation tools control panel, while others are looking for a controller of controllers capability. We are starting to hear from more and more service providers that have built the capability – Capgemini and Xerox being among them.

In a recent briefing with Capgemini, we heard about its solution to this problem. Capgemini has developed its own business services automation platform that will ultimately work with most automation technologies, including UiPath and Celaton, two of most recently announced Capgemini SDA partners. The platform is already operational and soon will clock up over a million transactions processed through it and UiPath.

Why a business services automation platform?

The majority of off-the-shelf automation software allow the user to manage and control automations from a centralized feature. This is fine and dandy for that piece of software, but most organizations use several automation tools from different vendors and need to have oversight of operations across all of them. With shared services, there is the added requirement of monitoring and, possibly, metering automated processes that are fulfilled through the shared capability. These features would aid operational quality assurance and transaction-based and volumetric pricing by providing process intelligence through role-based dashboards and reports. This kind of capability takes time to develop. We note that Capgemini’s platform offers the monitoring and analysis capability currently but not fully automated metering.

A central capability to manage, monitor, and measure the performance of automations, no matter where they’re run in the world, is fundamental for shared services. The advantages are clear to see:

  • Helps an automation Center of Excellence (CoE) to reuse and share robot codes across engagements as befits a shared services environment
  • Replaces a typically uncontrolled mess of home grown automation routines that are classically hidden among different engagements’ project assets and artefacts and consequently:
    • Enables IT to keep track of what is running where and implement full version control and asset management best practice
  • This allows service providers to have sensible discussions with clients about changing to transaction-based pricing, gainsharing, baselining volumes, benchmarking, and measuring performance against SLAs
  • The capability allows the service provider to see what is available and what needs to be added, e.g., add agent assist capability to the mix of RPA for back-office and AI document processing

For the controller of controllers to work, service providers will have to keep interfacing more and more automation technologies with it so that they can manage all of them no matter when and where they are used. Capgemini, for example, is working to connect more automation tools to its control platform. These include virtual agents for answering repetitive questions and smart search.

The shape of things to come

Whether they like it or not, service providers face the prospect of having to do away with much of manual processing of repetitive work and have staff, who will be exception managers and teachers of AI platforms, and will take care of higher value and complex work instead. The outlook for the future is as much straight-through automated processing as possible.

It is good to see how service providers, such as Capgemini, are making good progress on SDA. For many, previously, automation was part of the bigger digital picture, but today having teams of automation excellence specialists has become an imperative.

Ease of system integration and achieving higher transaction integrity at reasonable cost are key parts of driving higher benefits from automation.

We expect to see more announcements by service providers, as they enhance and scale up their automation capabilities.