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Sarah Burnett

Sarah Burnett is a member of Everest Group’s European research as well as Service Optimization Technologies (SOT) teams and assists clients on topics related to European sourcing market, trends and developments, and Service Delivery Automation, including RPA and cognitive technologies. Sarah serves European clients across Everest Group’s global services research areas and leads its Service Optimization Technologies (SOT) offering globally. To read more, please see Sarah’s bio.

Appian’s Jidoka Acquisition Sets the Scene for the RPA Market in 2020 | Blog

By | Automation/RPA/AI, Blog

Appian announced its intent to acquire the Spanish vendor Novayre Solutions SL and its Jidoka RPA platform on January 7. With this acquisition, Appian, best known for its low-code process management and orchestration software, will be able to offer extensive automation capabilities natively, while it did so previously with partners’ software such as Blue Prism and UIPath.

So, what does the acquisition mean for the market?

Why the acquisition?

Our estimates show that the RPA third-party software market is expected to grow by 80 percent to reach $2.5 billion this year. With this phenomenal growth rate, it’s not surprising that non-RPA companies want a slice of the pie.

Appian has been active in this market for a while and has benefitted from many new clients thanks to its partnerships with RPA vendors. It is also a reseller for Blue Prism and has experienced growing demand for RPA first-hand through that channel.

In addition, technology giants are increasing their activities in this market. SAP acquired Contextor back in 2018. And most recently, Microsoft announced UI flows to add RPA capabilities to Microsoft Power Automate (previously Microsoft Flow). It combines digital process automation (DPA) via APIs with UI-based automation. Pega is another competitor that has also invested in this market; it took over OpenSpan back in 2016.

Why Jidoka, and what about the partners?

We have assessed Jidoka as part of our RPA Technologies PEAK Matrix for a number of years and most recently positioned it as a major contender in our 2019 assessment. Jidoka is a Java-based platform where robots are designed and managed by a web-based console. There is a design studio for workflow and orchestration of robot operations. A console centralizes monitoring, audit, and exception handling features along with secure user permission and authorization capabilities. It has proprietary image recognition technology, Hawk Eye, to support Citrix automation. The platform offers capabilities such as auto-scaling of robots, a secure credentials vault, roles-based access controls, execution logs, audit trail, robot performance analytics, and ROI calculator. It also offers a chatbot capability that is available from the console. Real-time human-robot collaboration is provided via chat interface from the console (and Google Home,) the Jidoka mobile app (voice and chat,) and via IoT devices.

Appian intends to rebrand the product as Appian RPA. It will turn it into a low-code environment and integrate it with its own solutions to be offered on the cloud on a competitively priced subscription basis. While growing in Spain and Latin America, Jidoka has limited presence in other geographies. This is something that Appian can address with its presence in major tech markets.

As for its partnerships, Appian is keen to keep them going and offer clients choices. It remains to be seen how partners such as Blue Prism and UiPath will react to this news. It is not unusual for partners to go for co-opetition. For example, last year Blue Prism announced an Intelligent Document Processing (IDP) solution called Decipher, but has maintained its partnerships in the IDP segment, e.g., with Abbyy.

What does it mean for the market?

We have been expecting M&A activity in this sector to increase with market maturity and as RPA becomes a key tool for process efficiency and productivity. RPA is also commoditizing, and the fact that Appian is acquiring a very small vendor shows that entry into the market is not expensive. The news of this acquisition could encourage other tech companies, particularly those in the process management and orchestration space, to act too. There are many small RPA vendors with good offerings. The big RPA players with their current large valuations could suffer if a wave of acquisitions materialized and bypassed them; but at the same time, they have an awful lot of customers and a huge global footprint among them. Furthermore, private equity investors continue to invest in the market, as evidenced by Automation Anywhere’s last round of funding. This market remains buoyant and dynamic.

With Microsoft getting into the RPA business, all vendors have to up their game to remain competitive.  As for the RPA scale challenge that many enterprises are facing, vendors are working on this with new, improved offerings in the areas of robot management and controls, ease of use, and increased robot resiliency. With its existing and new capabilities, Appian will be well placed to address the scale challenge to make RPA adoption and operations smoother and, in so doing, edge ahead of the competition.

What’s Your Company’s Digital Ethics Score? | Blog

By | Automation/RPA/AI, Blog, Digital Transformation, Uncategorized

I marvelled at the passion demonstrated by the London Extinction Rebellion activists while I attempted to make my way to the Digital Agenda Power & Responsibility Summit at the British Library on 9 October.

During the Summit itself – while listening to presentations delivered by eminent speakers including Matt Warman MP, Minister for Digital and Broadband at DCMS; Sana Khareghani, Head of UK Government Office for AI; Russell Haworth, CEO, Nominet; Cheryl Stevens MBE, Deputy Director for Trust & Identity at DWP; Jacqueline de Rojas CBE, President, techUK; and Caroline Criado Perez OBE, award-winning author of Invisible Woman and activist – it struck me that consumer disillusionment with unethical applications of technology could lead to its own type of activism in the form of product and service boycotts.

Read my blog on Digital Agenda

In AI We Trust, Thanks to AI Checking Software | Blog

By | Automation/RPA/AI, Blog

The increasing popularity and uptake of Artificial Intelligence (AI) is giving rise to concerns about its risks, explainability, and fairness in the decisions that it makes. One big area of concern is bias in the algorithms that are used in AI for decision making. Another risk is the probabilistic approach to handling decisions and the potential for unpredictable outcomes based on AI self-learning. These concerns are justified, given the implicit ethical and business risks, for example, impact on people’s lives and livelihood, or bad business decisions based on AI recommendations that were founded on partial data.

The good news is that the software industry is starting to address these concerns. For example, last year, vendors including Google, IBM, and Microsoft announced tools (either released or in development) for detecting bias in AI, and recently, there were more announcements.

IBM

Last year IBM brought out:

  • Adversarial Robustness 360 Toolbox (ART), a Python library available on GitHub, to make machine learning models more robust against adversarial threats such as inputs that are manipulated to derive desired outputs
  • AI Fairness 360, an open-source toolkit with metrics that identify bias in datasets and machine learning models, and algorithms to mitigate them

Last month, IBM further augmented its offerings with the release of AI Explainability 360, an open source toolkit of algorithms to support the understanding and explainability of machine learning models. It is a companion to the other toolkits.

Cognitive Scale

Cognitive Scale recently unveiled the beta of Cortex Certifai, software that automatically detects and scores vulnerabilities in black box AI models without having access to the internals of the model. Certifai is a Kubernetes application and runs as a native cloud service on Amazon, Azure, Google, and Redhat clouds. Cognitive Scale also unveiled the AI Trust Index. Developed in collaboration with AI Global, it will provide composite risk scores for automated black-box decision making models. This is an interesting development that could grow to become a badge of honour for AI software, and a differentiator for those with the most trusted rating.

The Reality of Bias

While these announcements and those made last year are good news, there are aspects of AI training that will be difficult to address because bias is all around us in real life. For example, public data would show AI that there are many more male CEOs and board members than female ones, leading it to possibly conclude that male candidates are more suitable for shortlisting for a non-executive director vacancy than women. Or public data could lead AI to increase mortgage or auto loan risk factors for individuals living in a particular zip code or postcode to unreasonably high levels.

It is the encoding and application of these kinds of biases automatically at scale that is worrying. Regulations in some countries address some of the issues, but not all countries do. Besides, the potential for new threats and risks is high.

There is still a lot more for us to understand when it comes to making AI fair and explainable. This is a complex and growing field. As demand for AI grows, we will see more demand for solutions to check AI as well.

Open Source “Robin”: A Disruptor in the RPA Industry? | Blog

By | Automation/RPA/AI, Blog

The RPA world just got a bit more exciting with the early release of Robin, a Domain Specific Language (DSL) designed for RPA and offered via an Open Source Software (OSS) route. A brainchild of Marios Stavropoulos, a tech guru and founder and CEO of Softomotive, Robin is set to disrupt the highly platform-specific RPA market. Robin is not the first attempt to democratize RPA, so will it succeed at this feat?

The Robin advantage

RPA democratization isn’t a new concept. Other OSS frameworks, such as Selenium, have been used for RPA. But they weren’t designed for RPA and are best known for software testing automation. And there are other free options such as WorkFusion’s RPA Express, and Automation Anywhere’s and UiPath’s free community licenses. These have certainly lowered the barrier to RPA adoption but come with limits, for example, the number of bots or servers used.

When demonstrating his software environment, Stavropoulos explains that the principles he has applied to Robin are to make RPA agile, accessible, and free from vendor lock-in. This could be very powerful, for example, an RPA DSL could provide more user functionality. Not having to rip out and replace robots when switching to a different RPA software is tremendously appealing. And, availability of OSS RPA is likely to boost innovation as it will make it a lot easier to develop new light programs that simply collect and process data, such as RPA acting as a central data broker for some functions.

What’s in it for Softomotive?

There are four main reasons for an RPA vendor to invest in an open source offering.

First, Softomotive will become the keeper of the code. And while it will not charge for the software, not even other RPA vendors that start to support it, it will charge for Robin support and maintenance should customers wish to pay for those services.

Second, many other OSS vendors grew on the back of this model and got acquired by bigger companies. For example, JasperSoft, the OSS reporting company was acquired by Tibco for US$185 million in 2014, and Hitachi Data Systems acquired Pentaho for a rumored US$500 million in 2015. I’m not at all hinting here that Softomotive is looking to be acquired, but these are compelling numbers.

Third, if Robin is successfully adopted, the user community will contribute to the development of the environment and modules to a community library. There will also be community-led support and issue resolution, and so on.

Finally, Softomotive will still have its own products and will continue to generate revenue based on the solutions it wraps around Robin.

Robin success factors

Of course, while Robin is a great idea, Stavropoulos needs to ensure it is quickly and widely adopted. For it to become the de facto language of RPA, other RPA vendors must support it. And the only way to get them to support it is by forcing their hands with widespread adoption.

There are two ways Stavropoulos can make this happen; via free online delivery and through classroom-based training in key RPA developer hubs such as Bangalore. He is lucky to have a lot of existing users in small- to medium-sized companies. The developers in those companies are likely to try out Robin and give Stavropoulos a flying start.

Getting Robin onto a major OSS framework is also very important.

An RPA DSL on an OSS ticket is an exciting proposition that could significantly disrupt the market. But success depends on adoption and on Stavropoulos playing his cards right.

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

Do We Really Need a Robot Per Employee? | Blog

By | Automation/RPA/AI, Blog

When I started researching the RPA space five years ago, vendors were working hard to position themselves in the unattended automation space, where robots ran on servers in the data center, according to schedules, typically delivering back-office functions.

This was a departure from attended automation that for some years had boosted (and still does) agent efficiency in the contact center.

Today, the market has come full circle, with a focus on helping other office workers, not just contact center agents, increase their productivity. A robot per employee is a marketing message we are hearing increasingly frequently, boosted by the concepts of lo-code software and citizen developers who can build their own robots with little help from tech developers.

Examples of automation vendor activity in this space include:

  • NICE’s NEVA, an avatar for NICE’s attended automation, to help all office workers automate their repetitive tasks
  • Softomotive’s People First approach, which intends to democratize automation in the enterprise. This applies to both attended and unattended automations, but puts the power in the hands of employees
  • UiPath, which is putting out a robot per employee messages in addition to its Automation First campaign. It has even showcased robot-based consumer apps at its event.

One could argue that going full circle back to attended is because unattended automation is proving tough to scale. That does not diminish the potential opportunities that the concept brings to the enterprise and its employees. But it is not immediately obvious what attended robots could do for the average office worker.

Here are a couple of examples.

At the recent Pegaworld event in Las Vegas, a healthcare payer company showcased several examples of how it is using attended automation, including logging employees in to half a dozen systems, a task they need to perform every morning, through what the company calls “start my day,” and changing passwords on those systems on behalf of the employees, at the frequency dictated by the corporate IT policy. Another is helping with repetitive sales administration tasks, e.g., the robots update daily sales information for reporting purposes.

The big question is, do these kinds of examples, good as they are, justify the investment in desktop/attended automation robots by the thousands? True that attended robot licenses typically cost much less than unattended ones, and vendors are likely to offer good rates for bulk orders. But overhead costs, such as training employees to code their own robots and for the enterprise to support them, also come into play, as do robot performance: how fast can they run on those desktops, and can employees get on with other work while the robots are running?

It is early days for a robot per employee model, but it is high time that we boosted office worker productivity again. It has been decades since the advent of personal office software led to the last productivity revolution.

Personally, I am looking forward to seeing attended automation evolve and become really useful. I cannot wait to “robot-source” some of my daily routine work. First though, we (office workers) have to try attended automation for ourselves and see what works and what doesn’t. Lessons learned in the contact center can help us with this, but hands-on and trial and error is the best way forward.

Process Mining for Automation Gold | Blog

By | Automation/RPA/AI, Blog

The process automation market is evolving in more ways than one. Many organizations are taking the next step of complementing Robotic Process Automation (RPA) with Artificial Intelligence (AI) solutions such as virtual agents and intelligent document capture. Others are looking deeper into their business functions with process mining and discovery software to scale automation and capture more returns from them.

Process mining and discovery solutions automate a part of automation itself. This is effectively mining processes for elusive gold opportunities for automation.

Process Miners

Process mining software has been around for a while and can be used for many purposes, but several vendors have made a name for themselves in the automation space, e.g., Celonis and Minit. These types of solutions use application logs to reconstruct a virtual view of processes. They discover business process flows and models, and provide process intelligence analytics. They can even suggest how to change a process using smart capabilities. The result is information that allows organizations to decide what process to automate next.

Some service providers have developed their own capabilities in this space as well. An example is Accenture, which uses process mining for automation as a competitive differentiator.

Valuable as it is, however, process mining also has its drawbacks. For example, it requires a lot of data. And if you want to find opportunities among processes that go across enterprise systems, you need to integrate the logs from these systems, e.g., build a data warehouse. Those of you who have built data warehouses know what a massive pain this can be.

Process Discoverers

While process miners can also do process discovery, several RPA vendors – including EdgeVerve, Kryon, and Nice – are offering new solutions. They’re using their desktop automation and action recording capabilities, complemented with AI, to capture and reconstruct what the human worker does, and then map and analyze the actions to identify opportunities for automation. Process discoverers do not require a load of application data, but they do come with their own challenges. For example, a recording may not capture the full set of relevant steps. And employees may have concerns around privacy.

The Art of the Possible

So, is it worth it to use process mining and discovering solutions despite their downsides and flaws? Yes, absolutely. But curb your enthusiasm, set expectations at the right level, and go for the art of the possible.

For example, there are many opportunities for automation within individual applications, without having to include processes that go across systems. And, you can use human intelligence to manually fill in the gaps and augment the findings of an automation discovery tool, even though doing so is going out of fashion.

With yet another category of software coming to the fore, enterprises would be right to feel that they are on a technology investment hamster wheel – there is no end to the cycle. After all, in recent years we have had the huge wave of RPA adoption. And today, in addition to competitive pressure to invest in AI-based automation, enterprises are having to evaluate process mining and discovery as well.

The good news is that automation can generate significant returns on investment. Our research and interactions with enterprises have shown this to be the case time and again. Process mining is another piece of the jigsaw, and it can help you find more automation gold.

Everest Group will be publishing a detailed viewpoint on process mining and discovery very soon. Be sure to keep an eye out for it, so you can mine it for gold.

Thanks to RPA, “Integration” is No Longer a Dreaded Word | Blog

By | Automation/RPA/AI, Blog

Many enterprises that have used Robotic Process Automation (RPA) have seen the power of digital transformation, even if only in a small way through a few automated processes. The transformational value they experience is often a tipping point that whets their appetite for even more automation and deeper levels of application integration. But, this creates a quandary about how to maintain the array of automations. Ultimately, their success depends on the scope of the centers of excellence (COEs) that maintain their automations. Let’s explore further.

Getting the Wheel Spinning – Getting that Old-time Integration Religion

I believe that RPA has helped companies that previously held back from adopting newer technology solutions see the value of a digital mindset. These converts are now finding more opportunities for automation, and greater conviction in moving to digital-first operating models.

In short, something comparatively simple like RPA helps inspire confidence and vision.

The Ironic Corner to Turn – Moving beyond what Initially Made RPA so Enticing

Once this passion is unleashed, organizations come to fully appreciate that RPA is only one tool for automating operations. Many desire to transform their high volume, fast processes, and must confront the reality that surface-level RPA integrations are often not sufficient. The next steps towards more powerful automations often include integration via connectors and APIs.

The following exhibit reflects the diversity of systems which may now need to be integrated in a digital-first operating model world. (Spoiler alert: we’ll be writing a lot more about the Digital Capability Platform in the upcoming months.) And there are many ways to go about creating the needed integrations.

 

Digital Capability Platform

 

Some enterprises have cast aside the promise of surface-level RPAs, and now use their RPAs more through APIs. This is a bit ironic and worthy of a discussion by itself, but let’s get back to what happens as the types of automations proliferate.

Holding it Together – not Firing and Forgetting

One thing that all integrations – surface, APIs, or connectors – have in common is that they need maintenance. With surface-level RPA, you need to do a lot of robot maintenance when application layouts change. But all integrations, RPA included, require maintenance for other reasons as well. The biggest is the need to resolve data ambiguities, e.g., common customer names (think Jane Smith) with similar account types requesting a temporary address change. Which record should be updated? How can this correctly propagate across all the relevant systems and processes?

This is why a COE should be responsible for all types of automations, whether through surface or other integration methods. By looking across all automations, a COE can not only more accurately maintain the automations, but also identify anomalies and conceive new ways to structure interdependent automations. Of course, adding AI-based tools into the mix adds even more API connections to manage. But AI connections are far from the only ones that will need to be managed; the landscape will become more complicated before it simplifies (yes, I’m trying to be optimistic here.)

I can hear some of you saying that the COE should be an overall digital center of excellence. My answer is a big “no.” Digital is a far broader field that often involves major legacy transformation projects. Automation is clearly a part of digital, but it is operationally focused on the practical realities that come from modernizing processes that still primarily run on legacy systems.

This is a different mindset and a different set of competencies. As a result, it is best to keep a separate automation COE focused on the details of operational processes, while separately working towards the corporate digital objectives in a broader digital office. And that automation COE’s remit should be bigger than just RPA – it must deal with the combination of all types of automations that are enabling the operating processes.

Investing Big in RPA is Not a Fool’s Game | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog

The news of another big round of funding for UiPath, US$225 million series C, and a valuation of US$3 billion created a lot of excitement and amazement in the market. It followed on from Automation Anywhere’s whopping series A funding round of US$250 million in July, which valued the company at US$1.8 billion, and which surpassed UiPath’s earlier series B funding of US$153 million and a valuation of US$1 billion in Q1 2018.

These valuations are phenomenal. In UiPath’s case, the rise from US$1 billion to US$3 billion in less than six months is, I believe, unprecedented. You might think that investors are living on a different planet than us ordinary folks, and that this kind of valuation is plain wrong. I beg to differ.

Investing in the Future of RPA

My case rests on the rapid increase in market adoption and the huge investments that vendors are making in their platforms. As much has already been said about the fast rate of enterprise adoption, there’s no need for me to repeat it again here. Jumping to the second part of my case: RPA today is not the RPA that launched this market three to four years ago. The original developments lacked many of the features that we see today, e.g., computer vision to pick objects on the screen and robust control panels. Similarly, tomorrow’s RPA will be superior to today’s.

As someone who assesses RPA technology on an annual basis, I see a fast rate of product development, not just year on year, but in some cases quarter by quarter.

Everest Group’s “RPA Virtuous Circle” highlights the continuous cycle of developments in the market.

Virtuous Circle w title - Investing in RPA blog

Much has been said of organizations struggling to scale their deployments. I completely agree with this, and for a while I’ve been asking vendors to do something about this issue. I am delighted to see that they have been listening and are investing in features for scaling. These include enhanced robot run time control and management features including intelligent control systems for dynamic workload balancing, auto-scaling, and even identifying processes for further automation. Another major stream of development is turning RPA platforms into the glue that holds together business process management systems (BPMS), different varieties of machine learning, and narrow artificial intelligence. These will ultimately be integrated and will combine seamlessly to provide end-to-end process automation.

While vendors do their bit for scale, organizations should also examine their deployment models for RPA and take a more programmatic approach. Automation is going to be a serious competitive differentiator, and a programmatic approach would significantly speed up organizations’ adoption and realization of desired outcomes. Everest Group’s RPA Pinnacle study highlights some of the approaches that organizations have taken to achieve excellence in RPA.

Related: 2018 RPA Vendor Technology Landscape PEAK Matrix™ Preview

Of course, these enormous investments in RPA do carry some risks. There is the possibility of tech giants bringing their own RPA solutions to market, in turn pushing out the current RPA vendors. But that wouldn’t be easy to do, as the existing vendors have gained a lot of hard to emulate know how in the past few years. And any one of the existing RPA vendors could be acquired in a major acquisition, but then the investors would get the handsome returns they anticipated…just in a different way.

Taking the Manufacturing Model to Business Processes

Another reason for my optimism about the recent investments in RPA and vendor valuations is that I recently got a glimpse into the future of business automation by looking at manufacturing. On a visit to Siemens Digital, I saw how the concept of digital twin and simulation of manufacturing processes is helping speed up production times and efficiency, even in manual/human processes.

For years, corporate global services functions have attempted to copy manufacturing principles, e.g., adopting Lean and Six Sigma methodologies. Today, they have moved on to automation, which manufacturing adopted decades ago. Having started on automation of global services, enterprises are not going to turn back. They will continue to follow manufacturing’s lead.

Leading organizations are already giving their processes version numbers with supporting documentation, having taken each step through a rigorous Lean Six Sigma methodology.  On the automation front, while the focus has been primarily on tactical needs, it will increasingly move to outcomes and the finished “product,” as in manufacturing.

We will see enterprises develop digital twins of their processes or robots, and run complex functions end-to-end in virtual reality before committing to the final model for deployment in the real world. Future versions of RPA will have to support these requirements, and that is where some of the millions of funding will be spent; on product development and advanced features.

Today’s RPA products are paving the way for a far bigger change in automation of global services than we have seen to date. They are the building blocks of the platforms of the future for an inevitable automation journey that every organization will have to take sooner or later. That is why the current group of vendors are so attractive to investors. They are betting not just on today’s growing revenues, but what is to come.[/vc_column_text][/vc_column][/vc_row]

Are Colleagues Electric? | Sherpas in Blue Shirts

By | Blog

“Max, please send our new terms and conditions’ letter to all our Prime current account holders,” said Louise, a customer contact manager in a retail bank.

“I will ask Alf to do it. Is there anything else I can do for you today Louise?” Asked Max, the personal virtual helper on Louise’s desktop computer.

“Yes, please tell Alf to update Elsa.”

You may have guessed that Alf and Elsa are robots too – one processes letters for mailshots, the other makes records for regulatory compliance.

Is this scenario hype or reality?

Are colleagues going to be electric?  Everest Group data indicates that by 2021 there will be as many Robotic Desktop Automation (RDA), attended robots running on users’ desktops, assisting agents, and employees, as there are people currently delivering contact center outsourcing services globally; that means about three million attended robots by 2021.

There will also be a huge rise in the number of virtual workers or unattended Robotic Process Automation (RPA) robots, running on servers in data centers and delivering end-to-end process automation without the need for employees to activate them. Exhibits 1 and 2 highlight the projected rise of both attended and unattended robots through to 2021. These estimates are for robots purchased on license from independent third-party RPA software vendors. They exclude robots provided by vendors at no charge for proof of concepts, and training, etc.

Exhibit 1 – Attended robots

 

Exhibit 1 - Attended robots blog

Exhibit 2 – Unattended robots

Exhibit 2 – Unattended robots blog

Methodology

Our calculations are based on data from multiple Everest Group databases including but not limited to:

  • Revenue, average license costs, and growth of 18 RPA vendors projected out to the larger market
  • Numbers of people currently working in contact center outsourcing services, in Global in-house Centers (GICs), also known as shared services centers, in both front- and back-office functions globally

Everest Group analysis indicates that many colleagues will indeed be electric by 2021, a shift that will impact enterprises, not only in operations but also in terms of HR policies, recruitment, succession planning, process knowledge and other skills development, process and program document management, IT investment, management and maintenance, and business and IT continuity.

Sarah Burnett will be discussing this topic and other RPA trends during her talk at Symphony Venture’s Robotic Operations Centre Launch in Krakow, Poland on June 27.