Robotic process automation is one of the more promising and exciting tools in an organization’s digital transformation tool bag. Early uses have delivered a strategic impact on meaningful business processes innovation, and as a result, it’s getting C-suite level interest.

Forrester Research has made several public statements about the dramatic increase in inquiries on RPA in just the last six-months. C-suite interest was also clearly evident by the audience attendance at various RPA and AI conferences I attended throughout 2017 and was heavily influenced by larger digital transformation initiatives within their organizations.

As a result, RPA vendors are growing incredibly fast with many being VC funded. In 2016, there was a record $5B venture capital funds flowing into AI companies worldwide, an increase of 60 percent compared to 2015. This has some important consequences on how information management companies can collaborate with RPA providers. But first, a few challenges must be overcome before the opportunities are realized.

Not Invented Here Syndrome

It’s important to understand that while most RPA vendors are focused on their own IP, they need additional capabilities to enable robust information, document and data capture and seamless access and integration into enterprises’ applications. However, many cannot increase their resources fast enough to deal with growing demand, and consequently, are unable to allocate resources to learn about complimentary technologies.

Compounding this challenge is the common “not invented here” syndrome where, for a variety of reasons, they prefer technologies organically grown rather than best-of-breed, proven technologies.

When partnering with an RPA vendor, it’s important to fully comprehend the various levels of RPA capabilities to ensure the best resources are utilized.

Currently, RPA solutions are moving rapidly past the screen scraping roots of the industry into highly complex value-added solutions that drive real business benefits. Document classification and automated data extraction of structured and semi-structured documents (accounts payable automation for example) are seen as high value RPA use cases.

Also, more advanced organizations have begun to look for entity extraction capabilities so they can build robots that can process unstructured content. This helps companies be more responsive and automated related to customer inquiries and process decisions. Many vendors and customers are associating the term cognitive with these advanced capabilities.

There is also a lot at stake with investing in RPA. According to the 2017 McKinsey Global Institute report, “A Future that Works: Automation, Employment and Productivity,” labor associated with technically automatable activities consists of 1.1 billion FTEs worldwide, and the wages associated with technically automatable activities is $15.8 trillion worldwide. With this in mind, think of RPA use cases as a series of three steps on a stairway.

Basic RPA

The first step being the orchestration of basic repetitive tasks. In many instances, corporate business users are creating robots without any prior training and having a positive impact on productivity and efficiency. Customers don’t need vendor support or professional services… heck, they don’t even need IT. RPA has established a self-service mindset relative to orchestration and automation.

Basic RPA includes macro-based applets, screen scraping data collection, workflow automation, Visio-type building blocks, process mapping and business process management. They eliminate the swivel chair processes for data entry commonly seen in logistics and transportation and invoice processing.

Enhanced RPA

Use cases in the second step begin to include the extraction of metadata from forms, followed by addressing the inclusion of completely unstructured content within RPA use cases such as contracts.

Enhanced RPA addresses automation of processes that are less structured and often more specialized. Tools and platforms supporting enhanced process automation offer some capabilities such as out-of-the-box built-in knowledge, an understanding of natural language, ability to consume and leverage unstructured data, automated learning capability, pattern recognition and e-bonding capabilities to other well-established software platforms.

A consumer-friendly vision of enhanced RPA can be exemplified in virtual personal assistants like Amazon Alexa. In the enterprise world it is best used when targeting a specific functional area such as IT, finance and accounting, and in industries where poor documentation is not acceptable such as healthcare and legal.

Enhanced process automation does require a stronger feedback mechanism to leverage the learning capabilities built around machine learning to continually increase savings, so having the right skills on hand is essential to continuous improvement.

Cognitive RPA

The third step in the RPA stairway includes the learning of human interaction with RPA robots and the eventual recommendation and automation of action, then finally gaining better understanding of the impact of automation within an organization. This evolution of use case sophistication clearly shows the convergence of RPA and traditional capture use cases.

Cognitive RPA combines advanced technologies such as natural language processing, artificial intelligence, machine learning and data analytics to mimic human activities such as perceiving, inferring, gathering evidence, hypothesizing, reasoning and interacting with human counterparts. Envision the capabilities in self-driving vehicles where systems are taught rather than programmed, a process that can take months to years depending on the complexity of the problem domain.

Cognitive RPA requires the largest investment in time and dollars but simultaneously has the greatest potential to transform. Therefore, it will require new hires or “rented” expertise as this expertise is rarely found internally. Industries that have a high quantity of available data, are highly regulated and strive for continuous improvement benefit most from cognitive automation.

Examples include the oil and gas industry where systems review machine manuals, company policies and maintenance records; and the financial services industry where a sample of a bank’s loan portfolio is audited to determine if it meets certain standards, values, scores, points and ratings.

Opportunities with RPA Vendors

The smart RPA vendors will realize they can’t be everything to everyone. Their respective roadmaps are considerable so the likelihood of them wanting to recreate complimentary technologies is minimal. The vendors that have higher ‘not invented here’ mentalities will likely experience a higher failure rate when trying to integrate on their own.

Demand for the more advanced cognitive uses cases will grow dramatically in 2018 and will start with structured and semi-structured documents but will quickly move to include completely unstructured documents. It is essential that you are prepared to illustrate your expertise to compliment and meet RPA demand on multiple levels.

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