Preparing for the third wave of innovation with intelligent automation

At the core of this third wave of innovation is a new value proposition between the work that is done on a day to day basis and the people responsible for it—the digital workforce, where knowledge workers work alongside managing software robots—and the technologies that support this work. McKinsey summarizes the opportunities inherent in “Intelligent Process Automation” in this way:^[11]

At its core, IPA is an emerging set of new technologies that combines fundamental process redesign with robotic process automation and machine learning. It is a suite of business-process improvements and next-generation tools that assists the knowledge worker by removing repetitive, replicable, and routine tasks. And it can radically improve customer journeys by simplifying interactions and speeding up processes.

IPA mimics activities carried out by humans and, over time, learns to do them even better. Traditional levers of rule-based automation are augmented with decision-making capabilities thanks to advances in deep learning and cognitive technology. The promise of IPA is radically enhanced efficiency, increased worker performance, reduction of operational risks, and improved response times and customer journey experiences.

Legacy systems of record and systems of engagement perform mission critical functions and represent an enormous investment, particularly for organizations operating at significant scale. While organizations recognize the need to modernize their information infrastructures, they can’t just turn these legacy systems off and start over.  Ninety-two percent of organizations believe that something needs to change and that they must modernize their information management strategy.^[12]  The reason why RPA is getting so much attention is that it is an important “bridge” technology as organizations seek to modernize their legacy information infrastructures to drive greater automation.

Robotic Process Automation (RPA) has the potential to offer key benefits—improved speed & accuracy, enhanced customer experience, and reduced cost, among others. Moreover, this value is realized fairly quickly, as deployments are rapid and low risk due to the fact that integration is typically non-invasive and easily remediable. As a consequence, many enterprises and global service providers are investing in RPA. However, RPA is a burgeoning market with technologies that are relatively new to many potential buyers in terms of solution features, deployment models, supporting frameworks, and commercial aspects. The technologies are also evolving, with an expanding feature set and increasing richness of functionality.^[13]

In its early stages, RPA is rules based—robots are used to extract and interpret existing applications for the purpose of automating rules-driven transactions. As implementations mature, robots are able to understand unstructured content and apply it to process automation. The ultimate objective of Intelligent Automation is to integrate intelligence into tasks involving intuition, judgement or problem solving and augment the capabilities of their human counterparts—a true digital workplace.

Major RPA vendors (both specialists and those with RPA capabilities in a broader software platform) include the following companies. In addition, IT and BPO service providers such as Cognizant, Conduent, Sutherland, SyntelTech, and Mahindra provide RPA capabilities.^[14]  Forrester now counts 38 vendors with RPA offerings and another 50 big and small service companies spawning automation businesses with RPA built in.^[15] Gartner estimates that over 10,000 companies have already piloted and/or implemented RPA and are now moving to the next level, expanding use cases across business units and business functions.^[16] 

Forrester anticipates this progression in the market over time:^[17]

True AI solutions will slowly enable digital workers to move into these categories of cubicle work. This leads to a market that will:

Show strong growth from a modest base. Forrester gathered revenues for the top 11 providers. We then estimated that to be 60% of the total market. We eliminated service revenue. The result was a market of $250 million in 2016. Vendor pipeline information showed a 50% growth rate.

Grow to $2.9 billion through gradual integration of cognitive. Office and administrative tasks will be fertile ground for RPA. AI will lift RPA to make decisions required for nonroutine tasks, find and memorize patterns, and support them moving forward.

Still be a small fraction of the overall AI "cubicle" market spend. New business applications that embed AI will evolve to disrupt jobs in management and professional categories and provide the foundation for human-machine interfaces through chatbots and NLP. This will lead to a market that grows to $48.5 billion in 2021.

As many organizations deploy their initial RPA and machine learning initiatives and drive toward the type of progression defined above, they are finding that while automation of many rote, repetitive, data-intensive processes has yielded immediate gains, something is missing in terms of fully optimizing these platforms. 

What is needed in order to fully capitalize on this enormous emerging opportunity to improve the efficiency, satisfaction and effectiveness of the digital workforce?  The answer is simple in concept, but not so well understood in practice. And its roots go back to the first two waves of innovation in the content management space.

At the center of an expanded value proposition for RPA is the ability to integrate unstructured information into RPA and intelligent automation engines.  RPA automates processes across the enterprise using software robots. To have a truly Intelligent Digital Workforce, organizations must be able to turn all of the vast, growing, and incomprehensible (at least to machines) troves of unstructured information into actionable intelligence that digital robots can easily consume.

With enterprise organizations beginning to start and scale out their RPA operations into different business groups and processes, a key focus for many organizations is around automating content-centric processes. A big reason is the opportunity to deliver significant value to an organization by eliminating manual work that involves the processing of content.  As Sarah Burnett at Everest Group points out, “It is the capability to automate content-centric processes that makes AI an ideal complementary technology to RPA. Using a combination of the two, organizations can automate processes end-to-end, e.g., take in documents using AI, parse, classify, and understand meaning or sentiment and pass on the required action to RPA.”

The wide variety of unstructured inputs is clearly a challenge for RPA engines—and an opportunity for those focused on turning unstructured and semi-structured information into structured data.^[18] 

• Forms remain a problem for many RPA engines (e.g., invoices, claims, bills of lading, purchase orders), as does unstructured content (e.g., email, SMS, contracts, leases, photos, audio)
• 2 out of 3 organizations say that “documents create problems for most RPA tools.”
• 70% say “unstructured information is the Achilles' Heel for many RPA implementations.”

The demands for machine understandable data created by intelligent automation will exert a massive “pull” on technologies that can turn unstructured information into structured data. Content intelligence turns unstructured content into structured data. The combination creates true Systems of intelligent automation and takes organizations to the next level—true digital transformation. These new “systems of intelligent automation” are the next step on the stairway to digital transformation.

^[1] AIIM, Getting Ahead of the Digital Transformation Curve, 2018

^[2] AIIM, Automating Governance and Compliance, 2018

^[3] M-Files, The 2019 Intelligent Information Management Benchmark Report, 2019

^[4] M-Files, The 2019 Intelligent Information Management Benchmark Report, 2019

^[5] AIIM, Automating Governance and Compliance, 2018

^[6] AIIM, Enhancing Your RPA Implementation with Intelligent Information, 2018

^[7] AIIM, Automating Governance and Compliance, 2018

^[8] Geoffrey Moore, for AIIM, A Sea Change in Enterprise IT, 2010

^[9] Geoffrey Moore, for AIIM, A Sea Change in Enterprise IT, 2010

^[10] AIIM, Getting Ahead of the Digital Transformation Curve, 2018

^[11] McKinsey, 2017, Intelligent process automation: The engine at the core of the next-generation operating model

^[12] AIIM, Getting Ahead of the Digital Transformation Curve, 2018

^[13] Everest Global, Inc., Robotic Process Automation (RPA) –Technology Vendor Profile Compendium 2018

^[14] Gartner, 2017, Market Guide for Robotic Process Automation Software and Everest Global, Inc., Robotic Process Automation (RPA) –Technology Vendor Profile Compendium 2018

^[15] Forrester, 2017, The RPA Market Will Reach $2.9 Billion By 2021

^[16] Gartner, 2018, Competitive Landscape: Consulting and System Integration Service Providers for Robotic Process Automation

^[17] Forrester, 2017, The RPA Market Will Reach $2.9 Billion By 2021

^[18] AIIM, 2018, Enhancing Your RPA Implementation with Intelligent Information