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Tag: robotic process automation

The Barriers to Robotic Process Automation, Part 1

It sounds alluring enough: Intelligent bots step up and do the menial tasks humans would rather (probably) not be doing–or employers would rather they not, in order to slash costs. But streamlining business processes with robotic process automation (RPA) isn’t as easy as setting and forgetting a software bot as if it was an automated rotisserie.

Still, many enterprises forge ahead as if that’s the case. This rush forward is likely one of the top reasons, as consultancy Ernst & Young found, as many as 30% to 50% of initial RPA projects fail.

This isn’t a reflection of RPA technology or its capabilities. It’s generally the result of organizations rushing forward without doing the right upfront work necessary to prepare for heightened software autonomy and automation. To find answers, we reached out to experts to get their thoughts on the most common barriers to the effective enterprise deployment of intelligent automation.

“The number one success factor for intelligent automation is visible sponsorship,” said Jesse Tutt CEO at mattress company Gotta Sleep. Tutt claimed having visible sponsorship is essential because the biggest barrier to RPA success is often resistance from both management and staff.

“Management often have little capacity to allocate time to improvement and secondly resist intelligent automation as they have a perception it may negatively impact staff satisfaction,” said Tutt.

Tutt said the second barrier is the lack of staff with broad skillsets in data, analytics, software development and RPA necessary to succeed. Good data is necessary to support the analytics that will drive the automation.

In addition to data and analytics skills, automation teams also need to be good software developers. Typically, most automation projects are accomplished through direct database or API interaction, Tutt explained.

“Lastly, in cases where systems are antiquated, are only accessible published applications, or which require mouse or keystroke interactions, robotic process automation skills are required,” said Tutt.

Salvatore Stolfo, founder and CTO at Allure Security adds another barrier: the elusiveness of the AI-powered minimum viable product (MVP). Stolfo said, like any complex application, the difficulty is driven predominately by the complexity of the problem, business considerations and computational constraints.

“To achieve an MVP, the performance of the machine learning engine must achieve accuracy levels that make the application work well enough for the intended market solution. There is no magic bullet,” said Stolfo.

Niraj Patel, managing director, artificial intelligence practice at enterprise technology consultancy DMI, said enterprises are floundering when it comes to RPA because they spend insufficient time understanding the customer experience and therefore the ultimate value derived from automation; they lack clean data sources which gets in the way of generating the intelligence; and legacy applications that don’t lend to plug-play of newer modules such as RPA and machine language components; and unrealistic expectations when it comes to robotic software processes.

In part 2 of this story, we will tackle how organizations can beat the barriers to robotic process automation.

— George V. Hulme

August 21, 2019
Micro Focus Lays DevOps Foundation for RPA

Micro Focus has announced the general availability of Micro Focus Robotic Process Automation (RPA), which sets the stage for employing DevOps processes to construct highly automated business processes.

Thanks to the rise of artificial intelligence (AI) and digital business transformation initiatives, interest in RPA has increased significantly. However, rather than merely using RPA to build bots to automate a narrow set of static processes, RPA is now leveraging machine and deep learning algorithms to optimize a wider range of dynamic business processes and adjust more easily to changes within an application.

Travis Greene, senior director for IT operations management ay Micro Focus, said new capabilities such as object recognition technology included within Micro Focus RPA enables the platform to detect and automatically adapt to changes to the user interface.

Other new capabilities now include the ability to visually or programmatically create workflows using a tool that records screen actions, the ability to more easily combine graphical and programmatic workflows spanning multiple platforms and the ability to deploy hundreds of thousands of bots as needed. A centralized dashboard manages the bots, all of which have unique IDs and encrypted, role-based credentials.

As RPA and other forms of AI continue to be integrated and embedded with applications, Greene said it’s a matter of time before DevOps practices are extended to RPA platforms. Organizations will seek to train bots continuously as more data is made available, which in turn will drive updates to applications that are dependent on those bots. In most cases, depending on the complexity of the task, the bots will be built using a mixture of application programming interfaces (APIs), command-line interfaces (CLIs) and graphical tools. Organizations will need to define the processes to determine how and when each organization will opt to invoke one interface instead of another, either in isolation or combination, added Greene.

Historically, organizations have been able to apply RPA to static processes with mixed success. With the rise of AI, the scope of those efforts has become more ambitious. Business leaders are too inclined to believe that just about every business process can be automated using a combination of AI and RPA. However, AI and RPA still are confined to relatively narrow use cases involving highly predictable outcomes.

That’s changing, however, and RPA and AI are becoming more pervasive across the enterprise. DevOps teams will need to find ways to update both their applications and RPA/AI models without breaking one or the other. Most of the current RPA/AI models are highly sensitive to any change made to the application environment they are monitoring. And, unfortunately, the data scientists who are largely responsible for constructing those RPA/AI models don’t yet tend to have much of an appreciation for best DevOps processes. There will, however, come a day soon when these two worlds will collide.

— Mike Vizard

July 23, 2019
Workflow Automation Poised to Accelerate DevOps

Automation is taking the market by storm. Research firm Gartner predicts that by 2020, more than 40% of data science tasks will be automated, and that is just the tip of the iceberg. Businesses are turning to automation to reduce costs, increase productivity and gain a competitive advantage. Add DevOps to the mix and it becomes readily apparent how DevOps teams can benefit by leveraging a little workflow automation. After all, the DevOps process is framed by repeatable workflows, wherein the agile manifesto dictates progress or other frameworks define how change moves through the development and deployment cycles.

“Most development projects are hindered by numerous manual steps and the lack of defined collaborative communications methodologies, leaving far too much room for error,” said Raj Mehta, CEO of Raj Technologies. “Defining a workflow and bringing automation into the picture could go a long way toward reducing errors.”

The DevOps community has come to rely on many manual processes and numerous tools to keep the pace of rapid application delivery and change management. Those solutions often have points of failure, where important tasks can slip through the cracks. Even so, many developers are resistant to change and embrace the processes they have already established. Yet, intertwining manual processes with automation and management tools has been demonstrated as rather inefficient, slowing the perception that workflow automation can actually achieve the productivity promises claimed.

Perhaps the challenge comes from the fact that many DevOps teams require assistance to achieve any type of automation goals. That indicates there may be a lack of qualified professionals to bring forth workflow automation. “You have to crawl before you can run, and that means you must become an expert in designing workflows, before deploying one, or at the very least, turn to an expert,” Mehta noted.

A workflow is a fundamental process that dictates the steps to achieve a goal. Workflows have been used for centuries by businesses and have become digitized over the last few decades, with numerous workflow tools coming from leading vendors.

Many technologists may have first encountered the workflow ideology back in the days when Microsoft’s Sharepoint first came on the scene, which offered rudimentary tools for routing content through various steps. Of course, the workflows in use today have become much more complex, requiring designers to not only know how to script actions but also understand the overall goal of those actions.

That’s a simple truth understood well by Workfront, a Lehi, Utah, based vendor of workflow solutions which is looking to better service various organizations, as well as DevOps professionals, to create workflow automation opportunities. The company’s primary product goes by the name of Workfront as well and uses a platform-based approach for creating workflows that can be integrated across multiple technologies, bringing centralized workflow orchestration to many use cases.

Tools like Workfront fit well into the automation equation by creating the foundation of workflows. However, most DevOps professionals need more than workflow design to achieve improved automation. Other elements must be considered, such as demand management, resource management, project templates, policies and scripted actions. Efficient workflow tools should also incorporate task lists, collaboration capabilities and reporting.

Of course, Workfront is only one of many workflow automation companies on the market. Many other vendors are established in the market, with Market News Reports mentioning leaders such as Pegasytems, Tibco Software and Sourcecode Technology Holdings dominating the market.

HNY Research projects workflow automation market size will grow to $16.58 billion by 2023 from $4.68 billion in 2017, at an estimated CAGR of 23.48%. Key factors such as ease in process with the installation of workflow automation tools, convergence of workflow automation with traditional business processes, focus on streamlining business processes and cost efficiency through workflow automation are driving the workflow automation market.

“Business leaders must carefully analyze different processes and identify which workflows can automated. Therefore, business leaders should avoid rushing into workflow automation adoption and analyze the various potential use cases and choosing which vendors best fit in their organization,” said Naveen Joshi, founder and CEO of Allerin.

Many businesses are leveraging workflow solutions on their path to digital transformation and bringing once-manual processes into a world where automation can be applied. While some may argue over the intricacies of digital workflows and how those workflows fit into business process management (BPM), one truth remains evident: The goal is to digitally transform and introduce automation, which in turn indicates robotic process automation (RPA), becomes the next logical step.

According to Grand View Research, the global RPA market is expected to register a CAGR of 31.1%, from $597.5 million in 2018, driven largely by demand for business process automation via artificial intelligence (AI) and software robots.

RPA paired with workflow design may prove to bring full digital transformation to DevOps, and potentially grow beyond just a niche in those challenging environments. Workflows, BPM, BPA and RPA are becoming increasingly important to DevOps organizations seeking to lower error rates, boost productivity and improve communications.

— Frank Ohlhorst

July 12, 2019
7 Big Digital Transformation Trends

As enterprises continue their rush to digitally transform their organizations, they’re investing big in retooling their software development pipelines, new artificial intelligence (AI) and robotic process automation (RPA) technologies and low-code development platforms, as well as putting considerable money into customer engagement software. Yet, the extent of success following these investments remains unclear and varies significantly from industry to industry.

To shine a light on the digital transformation efforts currently underway, as well as the degree of their success, we’ve collected a number of fascinating trends in the most essential areas of digital transformation.

Investment in AI, Machine Learning and RPA Technologies Hitting an Inflection Point

According to the consultancy KMPG, enterprise investments in AI, machine learning, and RPA will rise to $232 billion by 2025 from $12.4 billion in 2018. “Nearly two-thirds of respondents indicate plans to fully implement RPA within three years. Another 18% plan selective use. As for cognitive automation, nearly half noted intentions to use these approaches at scale within 3 years, while 29% indicated selective reliance on cognitive automation capabilities. Some 10% said they would launch pilots and proof of concept projects,” according to the report.

DevOps is Just Getting Started

While we’ve been writing about the enterprise move to DevOps for years now, the investment in DevOps is just setting itself up to truly soar. According to the market research firm Market and Markets, the DevOps market will grow 24.7% annually, to $10.3 billion by 2023 from $2.9 billion in 2017. “The demand for DevOps solutions and services among enterprises is expected to gain huge traction, due to the increasing need for fast application delivery with high quality,” the firm said.

Digital Transformation Spending to Top $1 Trillion This Year

In research firm IDC’s “Worldwide Semiannual Digital Transformation Spending Guide,” global digital transformation spending will reach $1.18 trillion this year, rising nearing 18% from 2018. “Worldwide DX technology investments are expected to total more than $6 trillion over the next four years,” said Eileen Smith, program vice president with IDC’s Customer Insights and Analysis group.

For 2019, the two industries expected to invest the most into digital transformation are discrete manufacturing and process manufacturing, which will invest roughly $222 billion and $124 billion, respectively. According to IDC, the top digital transformation spending will be in smart manufacturing, which will be supported by autonomic operations, manufacturing operations and quality. The retail, transportation and professional services industries will follow closely behind.

IT and Business Leadership Chasm Widen

According to research firm consultancy Capgemini, there appears to be a growing disconnect between the CIO and senior business executives. Capgemini’s research found that while in 2012, 65% of organizations believed that the CIO and senior business executives possessed a shared understanding of the role of IT in their organization. In their most recent survey, that figure dropped to 37%. Additionally, Capgemini found, 59% of enterprise respondents in 2012 believed that the CIO and senior business executives had a shared understanding of how IT could be used to increase organizational productivity. That figure also declined precipitously, hitting 35% in 2018.

Low-Code Development Continues to Gain Higher Ground

The demand for enterprise software seems to have no bottom. And no matter how hard they try, developers can’t code software quickly enough. This is why more enterprise software needs are being met by low-code development platforms.

According to research from ResearchandMarkets.com, the global low-code development platform market will grow to about $27 billion by 2022, up from $4 billion in 2017 and at an annual compound growth rate of 44%.

Digital Transformation, Customer Engagement Software Grow

Digital transformations don’t mean much if they don’t reap increased revenue, land new customers and improve relationships with existing customers. And how enterprises engage with their customers is one of the most rapidly evolving aspects of enterprise technology today, from AI and machine learning to IoT devices and ubiquitous adoption of mobile devices. Customer engagement software ranges from staid CRM systems to customer self-service software. This class of software was a nearly $13 billion market in 2017, but will likely reach $22 billion by 2023, an annual growth rate just shy of 10%, according to market research firm Mordor Intelligence.

Digital Transformation Success Grows Harder to Come By

The success rate for digital transformation efforts is persistently, and dismally, low. According to research from McKinsey and Company, over time fewer than 30% of digital transformations succeed.

And that failure rate appears to be growing worse, not better. McKinsey’s 2018 survey results show only 16% of enterprises believe their digital transformations will improve organizational performance over the long term. “Even digitally savvy industries, such as high tech, media and telecom, are struggling. Among these industries, the success rate does not exceed 26%. But in more traditional industries, such as oil and gas, automotive, infrastructure and pharmaceuticals, digital transformations are even more challenging: success rates fall between 4% and 11%,” McKinsey found.

While enterprises are clearly investing into their digital transformation efforts and the desire to succeed is high, the high digital transformation failure rate says enterprises either aren’t taking the right approach to these efforts or they are not executing properly.

Whatever the reasons for the high rate of failure, with the vast amount of business capital on the line and the increasing risks upstart businesses pose to those established in their markets, more enterprises better figure out soon how to get their transformation efforts right.

— George V. Hulme

May 9, 2019
5 Practices That Will Help Drive Successful Robotic Process Automation Deployments

Enterprises have begun to invest considerably in robotic process automation (RPA) in recent years. RPA is one of the critical technologies that enterprises now are leveraging to automate their business processes to make their businesses more agile and efficient. When properly deployed and managed, RPAs can automate everything from mundane tasks (such as organizing files) to more significant functions, such as user provisioning to resources and more.

While RPAs do promise to help businesses move more quickly and with more agility, a company’s RPA strategy needs to be executed effectively across the organization. In my recent discussions with CIOs and others implementing RPA, the following practices repeatedly were mentioned as essential to RPA success.

Understand the Process Being Automated and How RPA Impacts Other Operations

One of the most common mistakes made is not thoroughly understanding the process being automated and the potential implications of automating the process. An example would include a business process that is time-sensitive and automated, which throws off the workflow timing. It’s possible the automation moves things along too quickly.

Another example would be not taking into consideration the nature of the data being accessed. If it’s regulated or sensitive data, there are security implications that must be managed. If the bot potentially will process sensitive or regulated information, the bot should be controlled appropriately. RPA technology is an IT function, and at the very least IT and security should be made aware of and potentially involved in the oversight of the bot. It’s much better to inform them and bring them in early than them finding out later about an army of bots managing and manipulating information.

Consider Refining the Process Before Automating

After the process being automated is understood, evaluate the process for potential optimization. Sometimes processes that have been underway in organizations for years are more than just inefficient; they’re actually antiquated. Look for ways the process can be made more effective; consult all stakeholders when necessary or warranted; and consider refining the process if possible. There’s no sense in automating an inefficient process.

Document, Test and Catalog Your RPAs

After the RPA is built, make sure to document its functionality and how its code works and examine its functionality. The RPA is now ready to be used. It’s important to continually monitor the RPA for potential abuse or something gone wonky. Send all the documentation relating to the bot to the IT department.

As the Bot Owner, Know You’re Responsible for Your Bot

Should the bot do something unexpected or unwanted, prepare to answer for it as the bot, or business process, owner. Make sure the bot is managing risk within its acceptable scope and that it’s not given too high a level of access. While you as process owner may have administrative rights to an application, does it make sense for those rights to be transferred to the bot? Perhaps it does, and perhaps it doesn’t. It’s a risk management call and will vary from situation to situation. Just be aware that, as the owner, you also own the risk.

Remember Your Bot May Live Beyond Your Tenure

In one, two or five years from now you may not be in your current job position. It’s essential that the bot is constructed for this eventuality. Beyond the proper documentation and cataloging of the bot mentioned previously, put into place the appropriate identity and entitlement management of the bot. Down the road, someone may want to enhance the bot with machine learning or AI capabilities. For when this happens, it’ll be essential to have the bot well-documented as well as its entitlements well-defined.

Consider Centralizing the RPA Deployment Process

While some enterprises have chosen to undergo organizationwide automation efforts, others are dipping their toes in tepidly with small RPA pilots. Others still haven’t decided on how to incorporate bots within their organization, so it’s happening organically and haphazardly as individuals and departments decide to make a go of it on their own.

If an organization is large enough, it may make good sense to put into place an RPA center of excellence. It will provide a repository of policies and strategies for RPA deployment, help catalog bot capabilities and dependencies and help drive overall RPA efficiency as the organization becomes more and more automated.

These bots are here to stay—in fact, they are going to grow in numbers. Technology research and advisory firm Gartner expects global spending for RPA to reach $2.4 billion in 2022, up from $680 million last year. That’s an increase of 57 percent annually. RPAs help drive efficiencies and remove mundane, low-risk tasks and workflows so that humans can focus on higher value and higher cognitive tasks.

— George V. Hulme

March 18, 2019