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Predicts 2020: RPA Renaissance Driven by Morphing Offerings and Zeal for Operational Excellence

Overview

Key Findings

Organizations have paid for an expensive patchwork quilt of applications and systems. Business executives are demanding a path to digital operational excellence. The net result is a tremendous pent-up demand to democratize process automation and data integration. Robotic process automation (RPA) fulfills a need but requires strategy, guardrails and governance.
Hyperautomation refers to an approach in which organizations rapidly identify and automate as many business processes as possible. It involves the use of a combination of technology tools, including but not limited to machine learning, packaged software and automation tools to deliver work.
RPA offerings are in the midst of market disruption. New offerings, new vendors and new commercial models are emerging rapidly. The largest RPA providers are using their significant capital resources to add complementary components in an attempt to distinguish themselves. Similarly, vendors in adjacent categories are delivering new RPA-oriented functionality.

Recommendations

IT leaders responsible for sourcing RPA offerings (services and solutions) should:
Drive organizational adoption and avoid potential missteps on the hyperautomation journey by engaging business units, IT, security and assurance functions into a process automation governance board. This will help drive organizational adoption and avoid potential missteps on the hyperautomation journey.
Plan your hyperautomation journey by focusing on a wider spectrum of business functions and knowledge work. Strategize and architect across the toolbox of options, including RPA, iBPMS, iPaaS and decision management tools. This is the only way to effectively leverage related components (for example, process mining, analytics, user experience and machine learning).
Avoid the hype with rigorous due diligence of RPA offerings and their ecosystems. Focus on the providersss abilities to address outcomes critical to your organization across multiple areas. Assess vendor process models carefully as seen with Microsoftoftos entry into these offerings that changed the marketplace dynamics significantly ly l especially for the small and midsize business (SMB) sector.

Strategic Planning Assumptions

By 2023, 50% of new RPA scripts will be dynamically generated.
By 2022, 80% of RPA-centric automation implementations will derive their value from complementary technologies.
By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes.
By 2023, there will be a 30% increase in the use of RPA for front-office functions (sales and customer experience).

Analysis

RPA adoption continues to grow at a substantial pace in 2019. And the RPA software market grew over 60% in 20181, making it the fastest-growing enterprise software segment. This growth magnitude and trajectory is the primary reason for the funding (more than $2 billion).2 Gartner client inquiries and searches have continued to grow unabated from an already high base. Additionally, the term RPA has remained on the top five Gartner search terms for the last 16 quarters.
The RPA market is in the midst of a market disruption phase as there continues to be heavy R&D investment to redefine services and solution offerings. New offerings with broader reach, new vendors and new commercial models are emerging rapidly to redefine the market. This will lead to a renaissance that is far beyond the simple task-based RPA of the past.
The market is still developing as incumbent vendors jockey for market position and evolve their offerings. Over the next three years, the market will continue to mature and consolidate. There is still a great deal of fragmentation of vendor offerings and interest from vendors in adjacent markets (software and cloud). It is projected within three years that as many as 9 out of 10 small pure-play RPA vendors will exit the market, merge, be acquired or somehow morph. Moreover, as RPA vendors seek to expand their channels, their offerings are increasingly being embedded into adjacent software categories. Everyone wants a share of the lucrative RPA pie.

What You Need to Know

In last yearoftos predictions we highlighted that organizations often underestimate the complexity of RPA initiatives. Yes, the software is relatively straightforward; however, there is a large variety of business processes ly l ranging from simple, well-defined rote examples to complex, subject matter expert (SME)-intensive, exception-heavy areas. Once organizations move beyond simple examples, there is a critical need is for multidisciplinary governance and coordination across business units, IT, security, sourcing and assurance functions. Without this comprehensive approach, many organizations experience buyeroftos remorse due to poor ROI, misaligned resources, siloed usage and inability to scale. This coming year, we expect to see the beginning of renaissance as the offerings themselves morph due to high levels of R&D spending and customer feedback. Secondly, the commercial realities of megavendors entering the market have yet to be felt. For example, at its recent flagship event, Microsoft announced its entry into this space.3 Similarly, last year SAP acquired Contextor and is now in the early stages of ramping its sales activity with an integrated offering. Other prominent software companies are also considering similar forays.
Gartner projects that this renaissance in RPA is part of the bigger trend of hyperautomation ly l which is first and one of the most pervasive trends in Gartneroftos );">)Top 10 Strategic Technology Trends for 2020... Hyperautomation is one of those initiatives that has been widely discussed across a significantly large number of enterprisesss digital journey. Hyperautomation refers to the combination of automation tools with multiple machine learning applications and packaged software used to deliver work. RPA is just one subset of the key technologies helping to drive hyperautomation. Alongside RPA are intelligent business process management suites (iBPMSs), integration platform as a service (iPaaS) platforms and decision management systems. Between them all, they provide a robust toolbox of technologies that enable hyperautomation ambitions.
As organizations try to make sense of all the key technologies and how the puzzle pieces fit together, many RPA software vendors are trying to establish themselves beyond the original al acorerer task-based offerings. In the endeavor to expand, RPA software vendors are seeking to complement the core RPA with five additional areas with a varying degree of success. The five areas currently attracting the most attention and investment are: process mining (also referred to as process discovery or e-process mining), ingestion engine (optical character recognition [OCR], computer vision and many other technologies), analytics, user experience and machine learning.4 Gartner refers to the collective functionality as as aComplemented RPArer (CoRPA). However, the dilemma is that all these puzzle pieces are not always easy to connect. And it will need much more than technology. There must be more of an overarching view of the organizationoftos technology for transformation.
In Figure 1, on the left side we depict the puzzle pieces ly l which are not quite interlocking yet ly l as RPA vendors are seeking to organize and fit together functionality, either natively or with partners. Itoftos still early and all the players seeking their fortunes under the banner of RPA are taking different approaches. The consistency, quality and success of connecting all these puzzles are currently in a state of flux as there are serious concerns being raised as to whether the core RPA offerings should be the centerpiece as positioned by the vendors.
Figure 1. Evolution From Task-Based to Complemented RPA (CoRPA) to DigitalOps Toolbox
Gartner foresees an evolution from the left side of this graphic to a future where other alternatives such as iBPMS, iPaaS platforms and decision management systems will be among the options that occupy the epicenter of architectoftos toolbox. All these will be surrounded by five or more technologies that come together to enable business processes and knowledge work across functions. Hence, on the right side of the graphic we depict the DigitalOps Technology toolbox where RPA will be one of many choices. Ultimately, the planning options will focus on orchestrating knowledge work across multiple functions.

Strategic Planning Assumptions

Strategic Planning Assumption:
By 2023, 50% of new RPA scripts will be dynamically generated.
Analysis by:
Arthur Villa
Key Findings:
Today, RPA is popular with enterprises due to its low costs, noninvasive deployments and easy to understand technology. The challenge for many buyers is that al acorerer RPA solutions provide savings on the task level rather than the entire process. Current RPA solutions are essentially toolboxes where customers are expected to build their own automation. Organizations need to identify, prioritize, reengineer and document processes prior to embarking on automation.
The integration of process mining, ingestion engines, analytics, user experience and machine learning will facilitate the creation of AI-generated RPA scripts that mimic the capabilities of humans. At a high level, hereoftos how it will work:
Over time, process mining and process discovery will gather data from system logs and user desktops that allow the system to understand business process workflows and how humans interact with the software that underpins those processes.
Machine learning will eliminate the noise ly l for example, instant messaging or social media during work hours l created by humans. Moreover, machine learning trained to recognize a specific business process l for example, invoice automation or new employee onboarding ly l can recommend enhancements to the workflow, such as improved process routes or the elimination of redundancies.
A significantly improved version of the current RPA development tool known as the process recorder, that has UI interaction record and playback capabilities, will dynamically generate the RPA script based on lessons from process mining and process discovery.
Dynamically generated scripts will not be perfect. Humans will still need to validate business process maps, optimize inefficient process routes and handle the exceptions that RPA scripts cannot automate. In the early days, these dynamically created scripts will be marginally better than scripts created by citizen developers with the process recorder. More specifically, these dynamically generated scripts will suffer from high maintenance costs as the processes or systems change due to events like new regulatory requirements or software updates. However, over time we expect that the resilience and adaptability of these scripts will improve dramatically as the RPA providers enhance the machine learning models that underlie data collection, analysis and script creation. By 2024, the use of solutions that dynamically create RPA scripts will become commonplace.
Market Implications:
The ability for RPA solutions to understand an organizationoftos business processes and dynamically generate a script to automate those processes will have several impacts on the market:
Attended automation demand will likely increase.
Process discovery tools are being designed to learn from user interactions and enable the dynamic creation of RPA scripts for select teams. In some cases, these are examples of workflows that may have had a low priority in a centralized IT team based on its relative value to all other requests. In these cases, dynamically created scripts will greatly increase the demand and use of attended automation.
Proliferation of pricing options.
In a future where scripts can be created automatically, cloud adoption has grown and freemium models have become commonplace, new packaging and pricing options will become available. Instead of the standard rd rper botrer pricing used today, per-alternative price models like per transaction, per process, or per process executed, will be introduced creating new frontiers of RPA market competition.
Lower demand for RPA script writing.
Dynamically generated RPA scripts will allow for faster implementations and minimize the need for buyers to use valuable resources for script writing.
Increased demand for business process analysis and managed RPA services.
Dynamically created RPA scripts will exacerbate the process inefficiencies that existed before RPA was introduced. As a result, companies will have greater need for business process analysis and managed RPA services to maintain their RPA workflows.
RPA solutions will create client-specific POCs.
One goal of dynamically created scripts will be to enable relatively lower cost customized demos and POCs for prospective buyers in a shorter time frame. In the future, the vision is to allow potential RPA buyers to download free versions of the software, enable access to relevant systems and data and generate new RPA scripts in some automated fashion. This may serve to reduce or eliminate resource efforts from IT.
Potential increase in data or process tampering.
In the future, some potential scenarios may involve employeess fear, uncertainty and doubt about the use of these new technologies. This may have the potential to lead certain employees (concerned about job security) to rebel and become actors by introducing flaws or unnecessarily complex steps into their interactions with key systems to confuse monitoring software like process discovery tools.
Recommendations:
The combination of RPA with process, ingestion engines, analytics, user experience and machine learning will be difficult for vendors to integrate. Dynamic script creation may introduce inefficient or less modular RPA scripts that increase the challenges of scaling the combination of RPA, people and process.
To prepare for a future of automated scripts, IT Leaders should:
Invest in the development of automation centers of excellence that develop the governance, training and change management capabilities needed for organizations to embrace process discovery and dynamic script creation.
Enhance existing employee education and reskilling initiatives. Repetitive manual tasks will no longer add value to most organizations as it becomes easier for software to learn and replicate what people do.
Educate audit and security functions on process discovery and RPA technologies. Dynamically created RPA scripts will introduce new risks for which organizations need to be prepared.
Create a roadmap that identifies when inefficient business processes will be reengineered and automated. Using dynamically created scripts for inefficient processes will create long-term technical debt and process inefficiencies.
Recommended Reading:
Strategic Planning Assumption:
By 2022, 80% of RPA-centric automation implementations will derive their value from complementary technologies.
Analysis by:
Saikat Ray, Frances Karamouzis, Robert Dunie
Key Findings:
Knowledge work and complex decision-driven processes are difficult to automate with task-based al acorer RPA alone resulting in scaling and agility challenges. This has led organizations to seek out complementary functionality on the back end, front end and in between. The complementary elements that augment core RPA include: process mining (also referred to as process discovery or e-process mining), ingestion engine (OCR, computer vision and many other technologies), analytics, user experience and machine learning. Gartner refers to the collective functionality as CoRPA. However, the dilemma is that all these puzzle pieces are not always easy to connect and they do not always come from a single vendor.
It has become clear that many of complemented technology categories are sought-after additions needed to fully deliver on the business demands in the clientoftos process automation journey. Gartner published a study in June 2019, where approximately 44% of customers stated that they have used or are planning to use ML and natural language processing (NLP) functionality in some form to augment their process automation initiatives.
Gartner has interviewed dozens of vendors who have reported to have some type of RPA-related offerings. In over 90% of these interviews, the vendor has indicated that they are actively investing in two or more of the five complementary technology categories.
Market Implications:
Many current users of RPA have moved beyond simple, well-defined, highly repetitive use cases for their RPA software. Organizations are actively seeking to apply RPA to complex, SME-intensive, exception-heavy business processes. Thus, a majority of clients will demand that vendors within the RPA-centric automation initiatives showcase functionality or partnerships in at least three of the five CoRPA components to deliver on the value proposition. This will be table stakes for vendors to effectively compete and address the user demand.
Organizations will not want to invest in multiple RPA offerings but rather select the one that has the most robust options for the largest array of use cases. Thus, the use of one or more of the complementary technologies l which Gartner has named the CoRPA approach ly l will be considered a must-have ingredient for business process automation initiatives and will be the norm. The biggest user challenges will include how to architect the solution, vetting the maturity of the complementary technologies, determining how many vendors to utilize, sorting out the combinations of licensing and contracting options and ongoing governance issues. Therefore, one of the critical variables that will determine the value of RPA-centric automation implementations will be the effective use and architecture of complementary technologies.
Clients focusing on RPA-centric initiatives rather than strategically analyzing the larger technology toolbox options l iBPMS, iPaaS platforms and decision management systems l will find it challenging to deliver on the larger portfolio of business demands in the digital age.
Recommendations:
To maximize the benefits of RPA offerings, IT leaders should:
Understand that the starting point for your investment and overall choices needs to begin at the strategic design level; more specifically, the overall architecture of the hyperautomation toolbox choices rather than the one targeted technology. The overall structure of which processes are automated and how the technology is constructed forms the foundation that underpins future development and, therefore, is more unforgiving.
Stratify the overall portfolio of business stakeholder demand and build your hyperautomation roadmap. Determine the targeted role for RPA offerings within that strategic roadmap. The stratification of the portfolio will need to cut across several key variables: risk, reward, data profile (volume, velocity and viscosity of data) and business process profile (ranging from simple, well-defined rote examples to complex, SME-intensive, exception-heavy areas).
Ensure the use of multidisciplinary governance and coordination across business units, IT, security, sourcing and assurance functions.
Recommended Reading:
Strategic Planning Assumption:
By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes.
Analysis by:
Laurie Shotton and Nicole Sturgill
Key Findings:
RPA missteps, misuse and failure stories have increased as adoption has grown. This has also made it harder for RPA proponents within client organizations to justify the business case. Additionally, RPA implementations may be successful in one part of the organization but not others as teams jumped into RPA initiatives without proper analysis, planning, defined strategies and centers of excellence (COEs). All this elevates the risk of errors by failing to automate the right processes, identify the right guardrails and focus on security issues and access rights for the new virtual users (RPA bots).
Businesses that implement RPA to solve for specific pain points in existing processes (primarily simple, well-defined, highly repetitive) without involving IT and architecture in consideration of the overall design of the process face challenges. They are less likely to be successful than those that consider redesign as part of the overall process of implementing hyperautomation tools.
Organizations that implement a combination of hyperautomation tools based on the process-based key performance indicators and business outcomes are realizing greater success than those that are solely seeking to scale their RPA tool.
Organizations that are redesigning processes and implementing a range of hyperautomation tools, depending on the process needs, will see metrics like reduced full-time employees (FTEs), return hours to the business and time saved, translate to a lower operational cost base.
Market Implications:
Organizations that undertake cost optimization initiatives combined with revenue enhancement or competitive advantage efforts will recognize that they need to define a hyperautomation journey rather than scale RPA. Those that do so successfully will target process redesign to drive greater efficiency, efficacy and business agility.
Many industry sectorsss organizations are faced with a plethora of legacy systems as well redundancy. Gartner studies estimate that significant amounts of IT budgets (more than 70%) are going toward running the business (see 4);">4IT Key Metrics Data 2019: Executive Summary..). As a consequence of this issue, many organizations (over 35%) also reported undertaking major core system replacement projects (see
Slide No. 20). One of the market implications of these movements is that organizations are leveraging RPA in both scenarios l the legacy and the new core systems replacement efforts. In the legacy scenario, they are automating long-standing gaps to prolong the life of the systems and cost of replacement. In the new core systems initiatives, there are always requirements that are not met that need to be automated.
However, organizations can fall into the trap of overusing a singular technology tool (in this case, RPA) to try and achieve their goals. Gartner foresees that organizations will come to the realization that RPA is one of many options within a broader set of hyperautomation technologies. Alongside RPA are iBPMS, iPaaS platforms and decision management systems. Between them, they provide a robust toolbox of technologies that enable hyperautomation ambitions.
Success will result from a mindset that is focused on starting with the business problem and then selecting the right technology with a broader roadmap of the overall hyperautomation journey. Success in these endeavors will lead organizations to change their funding models so that all new process initiatives consider funding by underlying automation as part of the business case.
Recommendations:
To maximize the benefits of RPA offerings, IT leaders should:
Stop looking for one-off processes in which RPA could be implemented and start with the business outcome you would like to achieve. Define the goal of improving or implementing the process ly l for example, efficiency, customer engagement, new revenue ly l and then determine how RPA can be leveraged as part of this process.
Redesign with both the long- and short-term goals in mind. Do not plug in RPA or any other automation tool. Start with a blank sheet of paper and the appropriate stakeholders to design what processes should be leveraged today and what will be needed in the future. With new technologies and new skills like design thinking, processes that are more than a couple of years old will have new options for improvement.
Measure against the business goal, not the steps in the process. Even if the steps in the process are meeting goals, the project is not successful if it is not meeting business goals.
Recommended Reading:
Strategic Planning Assumption
: By 2023, there will be a 30% increase in the use of RPA for front-office functions (sales, and customer experience).
Analysis by:
Stephanie Stoudt-Hansen, Robert Dunie
Key Findings:
The majority of current use cases for RPA are in back-office operations. As an example, Gartner estimated in 2019, 80% of organizations that completed proofs of concept and pilots in 2018 will aim to scale RPA implementations, resulting in an initial RPA software spend of approximately $100,000 per organization (see Note 1). In another example, Gartner polled over 400-plus users on their adoption of RPA by corporate function, where a disproportionally large number of the respondents were in areas that often do not have a direct customer-facing process, which is the hallmark of the front office (see Note 2). In a recent Gartner RPA webinar, we polled 227 participants and over 55% reported that well over half of their investment was focused on back-office functions.
In the early days of RPA adoption, many shared service centers adopted RPA ly l three years ago, over 80% of shared service centers reported using RPA. One of the primary reasons for this was that RPA was new; therefore, back then, organizations focused on limiting exposure within the four walls of their organization. It was safe, opaque l not visible to external clients or stakeholders ly l and a more controllable environment. As such, back office was consistently the initial choice for RPA.
The RPA software market is a highly competitive one and providers are looking for ways to gain revenue and differentiate their offerings. Thus, many vendors are focused on growing the usage in front-office functions where the client impact is higher and more visible. Additionally, the new functionality available is geared toward addressing more complex processes, such as user experience. For example, LexisNexis Data Prefill solution leverages artificial intelligence (AI) and RPA to cross-sell and identify selling opportunities.5
Market Implications:
In the past, organizations developed business cases primarily for back-office functions largely driven by the desire to cut costs. Many organizations came to learn that RPA benefits have a limit in the back office unless they are scaled. Additionally, a large number of back-office functions have a higher likelihood of running on legacy systems. Thus, the use of RPA sometimes served to increase the already mounting technical debt which leads to high maintenance costs and lack of business agility and/or short-term benefits and increased long-term complexity.
Front-office opportunities can drive additional revenue and have a direct impact on customer experience and therefore, the business case can be built on a broader set of metrics. Some examples may include automating ad placements, monitoring competitive pricing, RFP and bid sites for opportunities, generating leads and keeping disparate systems connected to understand how often a client is contacted. Another example for organizations in high-growth mode can involve the ability to scale at a lower cost/transaction (higher productivity) ly l which is much more feasible and tenable than implementing a dramatic reduction in head count.
As RPA vendors jockey for position, Gartner foresees that the use cases which address front-office deficiencies and provide a better user experience will gain distinction in the market. It will drive further competitive advantages to the already widening gap between the providers that offer this capability and the ones that focus strictly on back-office processes.
Recommendations:
To maximize the benefits of RPA offerings, IT leaders should:
Objectively analyze RPA offerings to automate not only back-office tasks, which may start to diminish in returns, but also look to more complex analytical and customer insight demands that drive front-office growth initiatives.
Challenge providers early on to give references of similar front-office business initiatives and provide a portfolio roadmap to conduct due diligence of how their investments will allow you to scale and gain competitive advantage. Look for outcomes and specific examples to cut through -8the hyperer of the different offerings.
Demand a holistic mapping of your RPA implementations rather than islands of RPA. The focus of your RPA within your hyperautomation journey should scale across your enterprise from the back office all the way to front-end analysis.
Recommended Reading:

A Look Back

In response to your requests, we are taking a look back at some key predictions from previous years. We have intentionally selected predictions from opposite ends of the scale ly l one where we were wholly or largely on target, as well as one we missed.
This topic area is too new to have on-target or missed predictions.

Evidence

The overall market for RPA software is forecast to grow at over 60%, making it the fastest-growing enterprise software segment.
2The source for estimated funding (excess of $2 billion) to RPA software vendors is a compilation of vendor press releases. This is an illustrative list, itoftos not meant to be an all-inclusive or exhaustive list.
March 2018 UiPath raises $153M
April 2018 WorkFusion raises $50M
July 2018 Antworks raises $15M
July 2018 Softvision acquires Arrow Digital
July 2018 Automation Anywhere raises $250M
September 2018 Softomotive raises $25M
September 2018 UiPath raises $265M
October 2018 Symphony Ventures acquired for $69M by Sykes
October 2018 Automation Anywhere raises $300M
4
Process mining.
The market definition for process mining has been published as part of Gartneroftos )Market Guide for Process Mining... Here is a copy of the definition for process mining: Process mining is designed to discover, monitor and improve real processes (for example, not assumed processes) by extracting knowledge from event logs readily available in todayoftos information systems. Process mining includes automated process discovery (for example, extracting process models from an event log); conformance checking (for example, monitoring deviations by comparing model and log); social network/organizational mining; automated construction of simulation models; model extension; model repair; case prediction; and history-based recommendations.
5
LexisNexis.
This is an excerpt from the following:
LexisNexis helps insurers reduce costs and increase speed and accuracy of quoting and underwriting via RPA and AI by prepopulating insurance applications using only a few customer data points. It taps into comprehensive attributes and vast data assets to easily identify and suggest relevant products and drive additional business for insurance companies.

Note 1Gartneroftos Forecast Snapshot

In this forecast, one of the underlying assumptions included the following:
At the end of 2018, more than 60% of organizations that employ more than 10,000 people will have deployed RPA.

Note 2Gartneros RPA Survey

Gartner polled 433 users of RPA to find out the levels of adoption by corporate function. Results are below and explained );">)The State of RPA Implementation.. (see Figure 2).
Figure 2. RPA Adoption by Corporate Function
Definition of RPA
Robotic process automation (RPA)
is a productivity tool (sold as licensed software) that allows a user to configure one or more bots (which act as scripts to activate specific key strokes). These bots overlay on one or more software applications. The result is that the bots can be used mimic or emulate selected tasks within an overall business or IT process. These may include manipulating data, passing data to/from different application, triggering responses or executing a transaction.
Key characteristics include:
Mimic key strokes allowing them to be concatenated together (akin to macrolike functionality).
Traverse multiple systems in a session (systems may include COTS applications, spreadsheets on different operating systems inclusive of mainframes).
Ability to move or populate data between systems.
Perform prescriptive tasks based on business rules (examples may include: calculations, queries, or other specific actions [calls]).
Trigger downstream process activities.
Note: The term RPA conjures up images of physical robots, which are in no way part of these offerings. There are no physical robots. RPA is a separate and distinct classification of technology from -8roboticsrer or -8robots,rer which involve physically moving assets.

Key Findings

Organizations have paid for an expensive patchwork quilt of applications and systems. Business executives are demanding a path to digital operational excellence. The net result is a tremendous pent-up demand to democratize process automation and data integration. Robotic process automation (RPA) fulfills a need but requires strategy, guardrails and governance.
Hyperautomation refers to an approach in which organizations rapidly identify and automate as many business processes as possible. It involves the use of a combination of technology tools, including but not limited to machine learning, packaged software and automation tools to deliver work.
RPA offerings are in the midst of market disruption. New offerings, new vendors and new commercial models are emerging rapidly. The largest RPA providers are using their significant capital resources to add complementary components in an attempt to distinguish themselves. Similarly, vendors in adjacent categories are delivering new RPA-oriented functionality.

Recommendations

IT leaders responsible for sourcing RPA offerings (services and solutions) should:
Drive organizational adoption and avoid potential missteps on the hyperautomation journey by engaging business units, IT, security and assurance functions into a process automation governance board. This will help drive organizational adoption and avoid potential missteps on the hyperautomation journey.
Plan your hyperautomation journey by focusing on a wider spectrum of business functions and knowledge work. Strategize and architect across the toolbox of options, including RPA, iBPMS, iPaaS and decision management tools. This is the only way to effectively leverage related components (for example, process mining, analytics, user experience and machine learning).
Avoid the hype with rigorous due diligence of RPA offerings and their ecosystems. Focus on the providersss abilities to address outcomes critical to your organization across multiple areas. Assess vendor process models carefully as seen with Microsoftoftos entry into these offerings that changed the marketplace dynamics significantly ly l especially for the small and midsize business (SMB) sector.

Strategic Planning Assumptions

By 2023, 50% of new RPA scripts will be dynamically generated.
By 2022, 80% of RPA-centric automation implementations will derive their value from complementary technologies.
By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes.
By 2023, there will be a 30% increase in the use of RPA for front-office functions (sales and customer experience).

Analysis

RPA adoption continues to grow at a substantial pace in 2019. And the RPA software market grew over 60% in 20181, making it the fastest-growing enterprise software segment. This growth magnitude and trajectory is the primary reason for the funding (more than $2 billion).2 Gartner client inquiries and searches have continued to grow unabated from an already high base. Additionally, the term RPA has remained on the top five Gartner search terms for the last 16 quarters.
The RPA market is in the midst of a market disruption phase as there continues to be heavy R&D investment to redefine services and solution offerings. New offerings with broader reach, new vendors and new commercial models are emerging rapidly to redefine the market. This will lead to a renaissance that is far beyond the simple task-based RPA of the past.
The market is still developing as incumbent vendors jockey for market position and evolve their offerings. Over the next three years, the market will continue to mature and consolidate. There is still a great deal of fragmentation of vendor offerings and interest from vendors in adjacent markets (software and cloud). It is projected within three years that as many as 9 out of 10 small pure-play RPA vendors will exit the market, merge, be acquired or somehow morph. Moreover, as RPA vendors seek to expand their channels, their offerings are increasingly being embedded into adjacent software categories. Everyone wants a share of the lucrative RPA pie.

What You Need to Know

In last yearoftos predictions we highlighted that organizations often underestimate the complexity of RPA initiatives. Yes, the software is relatively straightforward; however, there is a large variety of business processes ly l ranging from simple, well-defined rote examples to complex, subject matter expert (SME)-intensive, exception-heavy areas. Once organizations move beyond simple examples, there is a critical need is for multidisciplinary governance and coordination across business units, IT, security, sourcing and assurance functions. Without this comprehensive approach, many organizations experience buyeroftos remorse due to poor ROI, misaligned resources, siloed usage and inability to scale. This coming year, we expect to see the beginning of renaissance as the offerings themselves morph due to high levels of R&D spending and customer feedback. Secondly, the commercial realities of megavendors entering the market have yet to be felt. For example, at its recent flagship event, Microsoft announced its entry into this space.3 Similarly, last year SAP acquired Contextor and is now in the early stages of ramping its sales activity with an integrated offering. Other prominent software companies are also considering similar forays.
Gartner projects that this renaissance in RPA is part of the bigger trend of hyperautomation ly l which is first and one of the most pervasive trends in Gartneroftos );">)Top 10 Strategic Technology Trends for 2020... Hyperautomation is one of those initiatives that has been widely discussed across a significantly large number of enterprisesss digital journey. Hyperautomation refers to the combination of automation tools with multiple machine learning applications and packaged software used to deliver work. RPA is just one subset of the key technologies helping to drive hyperautomation. Alongside RPA are intelligent business process management suites (iBPMSs), integration platform as a service (iPaaS) platforms and decision management systems. Between them all, they provide a robust toolbox of technologies that enable hyperautomation ambitions.
As organizations try to make sense of all the key technologies and how the puzzle pieces fit together, many RPA software vendors are trying to establish themselves beyond the original al acorerer task-based offerings. In the endeavor to expand, RPA software vendors are seeking to complement the core RPA with five additional areas with a varying degree of success. The five areas currently attracting the most attention and investment are: process mining (also referred to as process discovery or e-process mining), ingestion engine (optical character recognition [OCR], computer vision and many other technologies), analytics, user experience and machine learning.4 Gartner refers to the collective functionality as as aComplemented RPArer (CoRPA). However, the dilemma is that all these puzzle pieces are not always easy to connect. And it will need much more than technology. There must be more of an overarching view of the organizationoftos technology for transformation.
In Figure 1, on the left side we depict the puzzle pieces ly l which are not quite interlocking yet ly l as RPA vendors are seeking to organize and fit together functionality, either natively or with partners. Itoftos still early and all the players seeking their fortunes under the banner of RPA are taking different approaches. The consistency, quality and success of connecting all these puzzles are currently in a state of flux as there are serious concerns being raised as to whether the core RPA offerings should be the centerpiece as positioned by the vendors.
Figure 1. Evolution From Task-Based to Complemented RPA (CoRPA) to DigitalOps Toolbox
Gartner foresees an evolution from the left side of this graphic to a future where other alternatives such as iBPMS, iPaaS platforms and decision management systems will be among the options that occupy the epicenter of architectoftos toolbox. All these will be surrounded by five or more technologies that come together to enable business processes and knowledge work across functions. Hence, on the right side of the graphic we depict the DigitalOps Technology toolbox where RPA will be one of many choices. Ultimately, the planning options will focus on orchestrating knowledge work across multiple functions.

Strategic Planning Assumptions

Strategic Planning Assumption:
By 2023, 50% of new RPA scripts will be dynamically generated.
Analysis by:
Arthur Villa
Key Findings:
Today, RPA is popular with enterprises due to its low costs, noninvasive deployments and easy to understand technology. The challenge for many buyers is that al acorerer RPA solutions provide savings on the task level rather than the entire process. Current RPA solutions are essentially toolboxes where customers are expected to build their own automation. Organizations need to identify, prioritize, reengineer and document processes prior to embarking on automation.
The integration of process mining, ingestion engines, analytics, user experience and machine learning will facilitate the creation of AI-generated RPA scripts that mimic the capabilities of humans. At a high level, hereoftos how it will work:
Over time, process mining and process discovery will gather data from system logs and user desktops that allow the system to understand business process workflows and how humans interact with the software that underpins those processes.
Machine learning will eliminate the noise ly l for example, instant messaging or social media during work hours l created by humans. Moreover, machine learning trained to recognize a specific business process l for example, invoice automation or new employee onboarding ly l can recommend enhancements to the workflow, such as improved process routes or the elimination of redundancies.
A significantly improved version of the current RPA development tool known as the process recorder, that has UI interaction record and playback capabilities, will dynamically generate the RPA script based on lessons from process mining and process discovery.
Dynamically generated scripts will not be perfect. Humans will still need to validate business process maps, optimize inefficient process routes and handle the exceptions that RPA scripts cannot automate. In the early days, these dynamically created scripts will be marginally better than scripts created by citizen developers with the process recorder. More specifically, these dynamically generated scripts will suffer from high maintenance costs as the processes or systems change due to events like new regulatory requirements or software updates. However, over time we expect that the resilience and adaptability of these scripts will improve dramatically as the RPA providers enhance the machine learning models that underlie data collection, analysis and script creation. By 2024, the use of solutions that dynamically create RPA scripts will become commonplace.
Market Implications:
The ability for RPA solutions to understand an organizationoftos business processes and dynamically generate a script to automate those processes will have several impacts on the market:
Attended automation demand will likely increase.
Process discovery tools are being designed to learn from user interactions and enable the dynamic creation of RPA scripts for select teams. In some cases, these are examples of workflows that may have had a low priority in a centralized IT team based on its relative value to all other requests. In these cases, dynamically created scripts will greatly increase the demand and use of attended automation.
Proliferation of pricing options.
In a future where scripts can be created automatically, cloud adoption has grown and freemium models have become commonplace, new packaging and pricing options will become available. Instead of the standard rd rper botrer pricing used today, per-alternative price models like per transaction, per process, or per process executed, will be introduced creating new frontiers of RPA market competition.
Lower demand for RPA script writing.
Dynamically generated RPA scripts will allow for faster implementations and minimize the need for buyers to use valuable resources for script writing.
Increased demand for business process analysis and managed RPA services.
Dynamically created RPA scripts will exacerbate the process inefficiencies that existed before RPA was introduced. As a result, companies will have greater need for business process analysis and managed RPA services to maintain their RPA workflows.
RPA solutions will create client-specific POCs.
One goal of dynamically created scripts will be to enable relatively lower cost customized demos and POCs for prospective buyers in a shorter time frame. In the future, the vision is to allow potential RPA buyers to download free versions of the software, enable access to relevant systems and data and generate new RPA scripts in some automated fashion. This may serve to reduce or eliminate resource efforts from IT.
Potential increase in data or process tampering.
In the future, some potential scenarios may involve employeess fear, uncertainty and doubt about the use of these new technologies. This may have the potential to lead certain employees (concerned about job security) to rebel and become actors by introducing flaws or unnecessarily complex steps into their interactions with key systems to confuse monitoring software like process discovery tools.
Recommendations:
The combination of RPA with process, ingestion engines, analytics, user experience and machine learning will be difficult for vendors to integrate. Dynamic script creation may introduce inefficient or less modular RPA scripts that increase the challenges of scaling the combination of RPA, people and process.
To prepare for a future of automated scripts, IT Leaders should:
Invest in the development of automation centers of excellence that develop the governance, training and change management capabilities needed for organizations to embrace process discovery and dynamic script creation.
Enhance existing employee education and reskilling initiatives. Repetitive manual tasks will no longer add value to most organizations as it becomes easier for software to learn and replicate what people do.
Educate audit and security functions on process discovery and RPA technologies. Dynamically created RPA scripts will introduce new risks for which organizations need to be prepared.
Create a roadmap that identifies when inefficient business processes will be reengineered and automated. Using dynamically created scripts for inefficient processes will create long-term technical debt and process inefficiencies.
Recommended Reading:
Strategic Planning Assumption:
By 2022, 80% of RPA-centric automation implementations will derive their value from complementary technologies.
Analysis by:
Saikat Ray, Frances Karamouzis, Robert Dunie
Key Findings:
Knowledge work and complex decision-driven processes are difficult to automate with task-based al acorer RPA alone resulting in scaling and agility challenges. This has led organizations to seek out complementary functionality on the back end, front end and in between. The complementary elements that augment core RPA include: process mining (also referred to as process discovery or e-process mining), ingestion engine (OCR, computer vision and many other technologies), analytics, user experience and machine learning. Gartner refers to the collective functionality as CoRPA. However, the dilemma is that all these puzzle pieces are not always easy to connect and they do not always come from a single vendor.
It has become clear that many of complemented technology categories are sought-after additions needed to fully deliver on the business demands in the clientoftos process automation journey. Gartner published a study in June 2019, where approximately 44% of customers stated that they have used or are planning to use ML and natural language processing (NLP) functionality in some form to augment their process automation initiatives.
Gartner has interviewed dozens of vendors who have reported to have some type of RPA-related offerings. In over 90% of these interviews, the vendor has indicated that they are actively investing in two or more of the five complementary technology categories.
Market Implications:
Many current users of RPA have moved beyond simple, well-defined, highly repetitive use cases for their RPA software. Organizations are actively seeking to apply RPA to complex, SME-intensive, exception-heavy business processes. Thus, a majority of clients will demand that vendors within the RPA-centric automation initiatives showcase functionality or partnerships in at least three of the five CoRPA components to deliver on the value proposition. This will be table stakes for vendors to effectively compete and address the user demand.
Organizations will not want to invest in multiple RPA offerings but rather select the one that has the most robust options for the largest array of use cases. Thus, the use of one or more of the complementary technologies l which Gartner has named the CoRPA approach ly l will be considered a must-have ingredient for business process automation initiatives and will be the norm. The biggest user challenges will include how to architect the solution, vetting the maturity of the complementary technologies, determining how many vendors to utilize, sorting out the combinations of licensing and contracting options and ongoing governance issues. Therefore, one of the critical variables that will determine the value of RPA-centric automation implementations will be the effective use and architecture of complementary technologies.
Clients focusing on RPA-centric initiatives rather than strategically analyzing the larger technology toolbox options l iBPMS, iPaaS platforms and decision management systems l will find it challenging to deliver on the larger portfolio of business demands in the digital age.
Recommendations:
To maximize the benefits of RPA offerings, IT leaders should:
Understand that the starting point for your investment and overall choices needs to begin at the strategic design level; more specifically, the overall architecture of the hyperautomation toolbox choices rather than the one targeted technology. The overall structure of which processes are automated and how the technology is constructed forms the foundation that underpins future development and, therefore, is more unforgiving.
Stratify the overall portfolio of business stakeholder demand and build your hyperautomation roadmap. Determine the targeted role for RPA offerings within that strategic roadmap. The stratification of the portfolio will need to cut across several key variables: risk, reward, data profile (volume, velocity and viscosity of data) and business process profile (ranging from simple, well-defined rote examples to complex, SME-intensive, exception-heavy areas).
Ensure the use of multidisciplinary governance and coordination across business units, IT, security, sourcing and assurance functions.
Recommended Reading:
Strategic Planning Assumption:
By 2024, organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes.
Analysis by:
Laurie Shotton and Nicole Sturgill
Key Findings:
RPA missteps, misuse and failure stories have increased as adoption has grown. This has also made it harder for RPA proponents within client organizations to justify the business case. Additionally, RPA implementations may be successful in one part of the organization but not others as teams jumped into RPA initiatives without proper analysis, planning, defined strategies and centers of excellence (COEs). All this elevates the risk of errors by failing to automate the right processes, identify the right guardrails and focus on security issues and access rights for the new virtual users (RPA bots).
Businesses that implement RPA to solve for specific pain points in existing processes (primarily simple, well-defined, highly repetitive) without involving IT and architecture in consideration of the overall design of the process face challenges. They are less likely to be successful than those that consider redesign as part of the overall process of implementing hyperautomation tools.
Organizations that implement a combination of hyperautomation tools based on the process-based key performance indicators and business outcomes are realizing greater success than those that are solely seeking to scale their RPA tool.
Organizations that are redesigning processes and implementing a range of hyperautomation tools, depending on the process needs, will see metrics like reduced full-time employees (FTEs), return hours to the business and time saved, translate to a lower operational cost base.
Market Implications:
Organizations that undertake cost optimization initiatives combined with revenue enhancement or competitive advantage efforts will recognize that they need to define a hyperautomation journey rather than scale RPA. Those that do so successfully will target process redesign to drive greater efficiency, efficacy and business agility.
Many industry sectorsss organizations are faced with a plethora of legacy systems as well redundancy. Gartner studies estimate that significant amounts of IT budgets (more than 70%) are going toward running the business (see 4);">4IT Key Metrics Data 2019: Executive Summary..). As a consequence of this issue, many organizations (over 35%) also reported undertaking major core system replacement projects (see
Slide No. 20). One of the market implications of these movements is that organizations are leveraging RPA in both scenarios l the legacy and the new core systems replacement efforts. In the legacy scenario, they are automating long-standing gaps to prolong the life of the systems and cost of replacement. In the new core systems initiatives, there are always requirements that are not met that need to be automated.
However, organizations can fall into the trap of overusing a singular technology tool (in this case, RPA) to try and achieve their goals. Gartner foresees that organizations will come to the realization that RPA is one of many options within a broader set of hyperautomation technologies. Alongside RPA are iBPMS, iPaaS platforms and decision management systems. Between them, they provide a robust toolbox of technologies that enable hyperautomation ambitions.
Success will result from a mindset that is focused on starting with the business problem and then selecting the right technology with a broader roadmap of the overall hyperautomation journey. Success in these endeavors will lead organizations to change their funding models so that all new process initiatives consider funding by underlying automation as part of the business case.
Recommendations:
To maximize the benefits of RPA offerings, IT leaders should:
Stop looking for one-off processes in which RPA could be implemented and start with the business outcome you would like to achieve. Define the goal of improving or implementing the process ly l for example, efficiency, customer engagement, new revenue ly l and then determine how RPA can be leveraged as part of this process.
Redesign with both the long- and short-term goals in mind. Do not plug in RPA or any other automation tool. Start with a blank sheet of paper and the appropriate stakeholders to design what processes should be leveraged today and what will be needed in the future. With new technologies and new skills like design thinking, processes that are more than a couple of years old will have new options for improvement.
Measure against the business goal, not the steps in the process. Even if the steps in the process are meeting goals, the project is not successful if it is not meeting business goals.
Recommended Reading:
Strategic Planning Assumption
: By 2023, there will be a 30% increase in the use of RPA for front-office functions (sales, and customer experience).
Analysis by:
Stephanie Stoudt-Hansen, Robert Dunie
Key Findings:
The majority of current use cases for RPA are in back-office operations. As an example, Gartner estimated in 2019, 80% of organizations that completed proofs of concept and pilots in 2018 will aim to scale RPA implementations, resulting in an initial RPA software spend of approximately $100,000 per organization (see Note 1). In another example, Gartner polled over 400-plus users on their adoption of RPA by corporate function, where a disproportionally large number of the respondents were in areas that often do not have a direct customer-facing process, which is the hallmark of the front office (see Note 2). In a recent Gartner RPA webinar, we polled 227 participants and over 55% reported that well over half of their investment was focused on back-office functions.
In the early days of RPA adoption, many shared service centers adopted RPA ly l three years ago, over 80% of shared service centers reported using RPA. One of the primary reasons for this was that RPA was new; therefore, back then, organizations focused on limiting exposure within the four walls of their organization. It was safe, opaque l not visible to external clients or stakeholders ly l and a more controllable environment. As such, back office was consistently the initial choice for RPA.
The RPA software market is a highly competitive one and providers are looking for ways to gain revenue and differentiate their offerings. Thus, many vendors are focused on growing the usage in front-office functions where the client impact is higher and more visible. Additionally, the new functionality available is geared toward addressing more complex processes, such as user experience. For example, LexisNexis Data Prefill solution leverages artificial intelligence (AI) and RPA to cross-sell and identify selling opportunities.5
Market Implications:
In the past, organizations developed business cases primarily for back-office functions largely driven by the desire to cut costs. Many organizations came to learn that RPA benefits have a limit in the back office unless they are scaled. Additionally, a large number of back-office functions have a higher likelihood of running on legacy systems. Thus, the use of RPA sometimes served to increase the already mounting technical debt which leads to high maintenance costs and lack of business agility and/or short-term benefits and increased long-term complexity.
Front-office opportunities can drive additional revenue and have a direct impact on customer experience and therefore, the business case can be built on a broader set of metrics. Some examples may include automating ad placements, monitoring competitive pricing, RFP and bid sites for opportunities, generating leads and keeping disparate systems connected to understand how often a client is contacted. Another example for organizations in high-growth mode can involve the ability to scale at a lower cost/transaction (higher productivity) ly l which is much more feasible and tenable than implementing a dramatic reduction in head count.
As RPA vendors jockey for position, Gartner foresees that the use cases which address front-office deficiencies and provide a better user experience will gain distinction in the market. It will drive further competitive advantages to the already widening gap between the providers that offer this capability and the ones that focus strictly on back-office processes.
Recommendations:
To maximize the benefits of RPA offerings, IT leaders should:
Objectively analyze RPA offerings to automate not only back-office tasks, which may start to diminish in returns, but also look to more complex analytical and customer insight demands that drive front-office growth initiatives.
Challenge providers early on to give references of similar front-office business initiatives and provide a portfolio roadmap to conduct due diligence of how their investments will allow you to scale and gain competitive advantage. Look for outcomes and specific examples to cut through -8the hyperer of the different offerings.
Demand a holistic mapping of your RPA implementations rather than islands of RPA. The focus of your RPA within your hyperautomation journey should scale across your enterprise from the back office all the way to front-end analysis.
Recommended Reading:

A Look Back

In response to your requests, we are taking a look back at some key predictions from previous years. We have intentionally selected predictions from opposite ends of the scale ly l one where we were wholly or largely on target, as well as one we missed.
This topic area is too new to have on-target or missed predictions.

Evidence

The overall market for RPA software is forecast to grow at over 60%, making it the fastest-growing enterprise software segment.
2The source for estimated funding (excess of $2 billion) to RPA software vendors is a compilation of vendor press releases. This is an illustrative list, itoftos not meant to be an all-inclusive or exhaustive list.
March 2018 UiPath raises $153M
April 2018 WorkFusion raises $50M
July 2018 Antworks raises $15M
July 2018 Softvision acquires Arrow Digital
July 2018 Automation Anywhere raises $250M
September 2018 Softomotive raises $25M
September 2018 UiPath raises $265M
October 2018 Symphony Ventures acquired for $69M by Sykes
October 2018 Automation Anywhere raises $300M
4
Process mining.
The market definition for process mining has been published as part of Gartneroftos )Market Guide for Process Mining... Here is a copy of the definition for process mining: Process mining is designed to discover, monitor and improve real processes (for example, not assumed processes) by extracting knowledge from event logs readily available in todayoftos information systems. Process mining includes automated process discovery (for example, extracting process models from an event log); conformance checking (for example, monitoring deviations by comparing model and log); social network/organizational mining; automated construction of simulation models; model extension; model repair; case prediction; and history-based recommendations.
5
LexisNexis.
This is an excerpt from the following:
LexisNexis helps insurers reduce costs and increase speed and accuracy of quoting and underwriting via RPA and AI by prepopulating insurance applications using only a few customer data points. It taps into comprehensive attributes and vast data assets to easily identify and suggest relevant products and drive additional business for insurance companies.

Note 1Gartneroftos Forecast Snapshot

In this forecast, one of the underlying assumptions included the following:
At the end of 2018, more than 60% of organizations that employ more than 10,000 people will have deployed RPA.

Note 2Gartneros RPA Survey

Gartner polled 433 users of RPA to find out the levels of adoption by corporate function. Results are below and explained );">)The State of RPA Implementation.. (see Figure 2).
Figure 2. RPA Adoption by Corporate Function
Definition of RPA
Robotic process automation (RPA)
is a productivity tool (sold as licensed software) that allows a user to configure one or more bots (which act as scripts to activate specific key strokes). These bots overlay on one or more software applications. The result is that the bots can be used mimic or emulate selected tasks within an overall business or IT process. These may include manipulating data, passing data to/from different application, triggering responses or executing a transaction.
Key characteristics include:
Mimic key strokes allowing them to be concatenated together (akin to macrolike functionality).
Traverse multiple systems in a session (systems may include COTS applications, spreadsheets on different operating systems inclusive of mainframes).
Ability to move or populate data between systems.
Perform prescriptive tasks based on business rules (examples may include: calculations, queries, or other specific actions [calls]).
Trigger downstream process activities.
Note: The term RPA conjures up images of physical robots, which are in no way part of these offerings. There are no physical robots. RPA is a separate and distinct classification of technology from -8roboticsrer or -8robots,rer which involve physically moving assets.
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