Why Intelligent Process Automation Looks Like the Next Wave

eWEEK DATA POINTS RESOURCE PAGE: IPA involves more than RPA with a dash of artificial intelligence thrown in. It involves several key requirements as spelled out in this latest Data Points article.


Robotic process automation, acronymed RPA, has been one of the hottest areas of tech in the last two years, based on venture investment and enterprise adoption. In 2018 alone, three companies raised more than $1 billion: Automation Anywhere ($550 million), UiPath ($378 million) and BluePrism ($130 million). Additionally, it was recently reported that UiPath plans to raise another $300 million to $400 million by the end of this year.

RPA’s simple, easy-to-understand value prop–process automation and cost efficiency–is hard to ignore for any company looking for productivity gains. As a result, it has quickly replaced Business Process Management (BPM/BPA) as the new engine for enterprise efficiency. 

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As enterprises look to expand their use of RPA to other areas of the organization though, they are starting to discover that RPA has limits. As a result, there is a new wave of automation emerging called intelligent process automation–IPA for short. 

IPA involves more than RPA with a dash of artificial intelligence thrown in. It involves several key requirements as spelled out in this latest eWEEK Data Points article. Tom Wilde, CEO of Indico, shares industry information in six data points on what IPA is and why he believes enterprises are ready to embrace a new wave of automation.

Data Point 1: RPA = Deterministic Business Processes and Structured Content

RPA is great with repetitive, rule-based business processes involving structured data in which no judgment is involved. Tell it exactly what you need it to do and it can do it better, faster and cheaper than a human. If your business process is flawed, however, it simply automates and accelerates that flaw. It cannot make judgments about information or learn and improve with experience. Because of this, enterprise users are finding that RPA is ineffective with workflows involving unstructured content–those that require some level of cognitive ability. It’s simply not possible to write enough rules to automate workflows like this. This type of data makes up over 80% of the data in most enterprises today.

Data Point 2: IPA = Unstructured and Semi-Structured Content

Intelligent process automation is fundamentally different than RPA in a few important ways. For one, it is purpose-built for the document-based workflows that drive so many enterprise business processes today, such as contract analytics, audit planning and reporting, RFP analysis and composition, sales opportunity workflow automation, customer support analysis and automation, appraisal and claims analysis, etc. IPA has the ability to understand the text, images, documents and other unstructured data that are fundamental to all these types of business processes.

Data Point 3: IPA Is Cognitive and Probabilistic

IPA uses the cognitive (intelligent) abilities of algorithmic deep learning models without the requirement for huge training data sets that are out of reach of 95% of enterprises. It can make accurate judgments based on the information and context available. This has been one of the huge stumbling blocks of trying to automate document-based workflows that involve a lot of unstructured content. IPA provides a generalized knowledgebase or "meaning engine" that can be leveraged to train machine learning models more quickly and easily. 

Data Point 4: IPA Is Collaborative

IPA facilitates collaboration between the data science teams and the line of business professionals that have the necessary subject matter expertise about the business processes being automated. This is especially important when the underlying technology is so highly complex. Business users need appropriate technical context to deliver the necessary inputs, and technologists need appropriate business context to drive implementation decisions.

In practice, this means IPA solutions must enable SMEs to be more involved in the definition of use cases, what constitutes good training data and the training of machine learning models.

It enables data scientists and line of business professionals to set realistic expectations for their initiatives. It also requires solutions and models that provide “Explainable AI” to overcome business skepticism; see the next Data Point.

Data Point 5: IPA Is Explainable

In highly regulated industries such as financial services, regulators are increasingly requiring full explainability and auditability into the actions of AI-based processes. There is an increasing need for transparency in how it works and makes decisions on our behalf. Explainability will increasingly be defined, not only in terms of formulas and algorithms but in real-world, plain English examples. This makes it easier for data science and line of business teams to collaborate on improving IPA’s contribution to the business.

Data Point 6: Summary

IPA does not replace or compete with RPA. It complements it, handling those workflows that can’t be automated using RPA. IPA translates the unstructured content into structured data so it can be plugged back into business process flows. IPA is already being applied to a number of common back-office use cases related to legal and compliance, sales and support, and finance and operations. The benefits are real and can include up to 85% faster cycle times, up to a 4X increase in organizational capacity and throughput, and the ability to redeploy valuable resources to higher-value activities for the business.

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