The rise of RPA bots and the role of C-level decision makers Automation Anywhere's Microsoft pact indicative of hot RPA market

What's the difference between RPA and IPA?

RPA and IPA can work in tandem to expand the scope of your process automation strategy. But first, it's important to understand their key differences.

Intelligent process automation is increasingly seen as a complement to robotic process automation, extending the scope of RPA with artificial intelligence technologies. CIOs and other IT leaders should understand the important distinctions between the two automation technologies before incorporating them into their technology roadmaps.

What is RPA?

RPA is a type of automation that mimics the way humans interact with computer programs by typing and scrolling with a mouse. It is often used for automating simple processes, as well as integrating legacy applications to each other or to modern applications. The applications created on top of RPA are called Software robots, or bots for short.

Technically speaking, RPA is only used to automate tasks rather than processes. This limits the complexity and longevity of the kinds of automation that can be built using RPA tools. At the same time, all the leading RPA vendors, including UiPath, Automation Anywhere and Blue Prism, are extending the capabilities of their tools. New capabilities aim to better support management, scalability and integration with other tools, including AI, digital process automation, process mining and business rules engines.

What is IPA?

Intelligent process automation (IPA) describes software that combines process automation capabilities -- including RPA, digital process automation (DPA) and process mining -- with AI and machine learning.

IPA uses AI technologies to help structure unstructured data; optical character recognition (OCR) is used to read text in an invoice, for example, and natural language processing is used to interpret the fields so that the data can be copied into an ERP system. These expanded capabilities add context to unstructured data, enabling companies to automate repetitive tasks, such as customer onboarding, that were previously done manually.

IPA infrastructure makes it easy to package various AI capabilities with common business or industry workflows, such as processing invoices, procurement and contract lifecycle management.

RPA and DPA vendors are extending their products with a complete IPA stack of capabilities that will support the development, management and governance of more complex automations.

IPA adds AI capability to process automation tools.

What are the differences between RPA and IPA?

Scope. IPA covers a larger scope of work than RPA -- it can handle more types of data formats and promises to enable new types of more intelligent decision-making.

Collaboration. To capitalize on IPA, however, IT and data science teams will need to collaborate more extensively with each other and with business users than they typically do on RPA deployments

"RPA is purely robotic in nature and doesn't require intelligence to operate," said Banwari Agarwal, global head of cloud, infrastructure and cybersecurity business at Larsen & Toubro Infotech (LTI). Consequently, it is a good technology for well-defined, rules-based processes that can be implemented by IT and even by business users.

AI capabilities. In contrast, IPA is used for more complex processes that benefit from AI capabilities. This involves combining RPA with smart data intake, natural language processing, machine learning and operational analytics -- domains requiring the expertise of data scientists.

Software development. IPA platforms can also take advantage of low-code and digital process automation platforms to build out more complex and scalable automations than possible by mimicking a user's behavior with traditional RPA development tools. The value and outcomes of RPA projects was traditionally limited given RPA's focus on tasks versus end-to-end processes. This distinction is starting to disappear with the rise of hybrid RPA development tools that generate code that runs using application APIs rather than strictly emulating keystrokes and clicks.

Many RPA technologies can also be implemented with low-code or no-code. However, the value and outcomes of RPA projects are limited given RPA's focus on tasks versus end-to-end processes.

Resiliency. IPA applications can also be more resilient because they operate at the level of managed application APIs. In contrast, RPA operates at the level of the user interface, which is more likely to change without notice in ways that break the automation.

Ease of use. Agarwal believes the main attraction of RPA platforms is that they are easy to use and don't require deep technology skills.

IPA development and implementations are significantly more complex. The technology requires data extraction and classification, machine learning and AI to foster decision-making. Businesses using IPA will need experts on hand who have an in-depth understanding of an ever-growing set of tools and capabilities in the space.

Agarwal said technical skill requirements for users are key distinctions IT executives should be aware of upfront. The technical skill required for RPA ranges from basic to mature, whereas the technical skill required for IPA ranges from mature to advanced.

RPA, not surprisingly, has considerably more traction as a result of this ease of use. "There are more processes being automated with RPA than IPA," Agarwal said.

Efficiency gains. Process efficiencies associated with RPA, however, are not as high as the potential efficiencies realized by IPA. In RPA deployments, humans continue to play a role in data extraction and decision-making alongside the rules-based processing handled by RPA tools, Agarwal said. IPA, in contrast, promises greater value in reducing manual labor costs, because it automates much of the human decision-making.

According to Agarwal, the technologies that enable IPA can also help companies migrate from RPA to IPA deployments. Among them: smart intake tools, machine learning, AI and an operational analytics platform.

The RPA and IPA continuum

Indeed, Deven Samant, director of enterprise solutions at Infostretch, a digital engineering solutions company, sees the move to IPA as a continuum, with RPA serving as the foundation for the AI, machine learning and analytics that IPA brings to automating business processes. "You can't have IPA without the foundation of RPA," he said.

Samant said this continuum is unfolding in three phases:

  • Enterprises create digital "bot" workforces and automate business processes that are highly defined.
  • Machine learning helps these automated systems understand and operationalize decisions.
  • AI begins making decisions typically made by humans.

The first two phases are more process driven -- they are about automating very defined and deterministic processes. In the third phase, machine learning and AI enable the bots to handle more nondeterministic in behavior. It's about moving from getting a machine to think about a task to getting a machine to think about the process, Samant said.

Bringing the bots to life

Angelo Poulikakos, managing director of Protiviti's internal audit and financial advisory practice, also sees RPA and IPA as a continuum. IPA is all about combining RPA with complementary technologies, such as OCR, natural language processing, data analytics and chat interactions, to bring the robot to life, he said. These capabilities extend the robot's work, allowing it to read unstructured data, interpret human speech, pay attention to trends and predict outcomes.

Poulikakos agreed that most organizations typically start with RPA before embarking on IPA-oriented use cases. For example, Protiviti has helped several clients build RPA robots that automatically provision or deprovision access to systems based on a well-defined access request form and approval workflow. These workflows are commonly specified using things like checkboxes and drop-down menus to identify the user, level of access and current status.

After an RPA robot has stabilized in an environment, it can then be extended via IPA, such that a chatbot can facilitate the provisioning or deprovisioning of access. The chatbots can interpret a user's intent to drive actions that may not have been spelled out. For example, if someone said, "Mary left the organization. Please remove her access," the bot would gather the input and subsequently trigger the RPA robot that would initiate an approval workflow and perform a defined action. At the same time, it would save the conversation history to serve as an audit trail.

RPA rules orientation vs. IPA's capacity to learn and iterate

Eldon Richards, CTO of Recondo Technology, a healthcare revenue cycle automation platform, said that one of the key differences between RPA and IPA is IPA's ability to learn from experience. This skill matters most when there is a high degree of variability in a process or in the data used to support the process. With RPA, the implementor must handle the variability in programmed algorithms or rules ahead of time. With IPA, handling the variability can sometimes be learned automatically from experience.

There are two key ways these differences play out in practice. First, IPA can be used to automate certain processes that are too labor-intensive for RPA tools. When there are large numbers of edge cases -- for example, when unexpected circumstances occur such as missing or inaccurate information or where numbers exceed a typical threshold -- implementing RPA requires developing logic to handle each one. IPA may be useful in such situations if it is possible to learn from an experienced human actor performing the processes -- as long as the IPA tool can observe enough of those edge cases.

Second, IPA can be used when higher-level cognition is required to make a decision in a process. For example, RPA can be effective at filing emails if the filing is based on attributes such as sender, key words found in the subject line, or whether the email has an attachment. In contrast, IPA would watch which emails a human put into their spam folder and which ones get immediate replies. This would allow it to make a more sophisticated decision, Richards said.

Collaboration requirements of RPA vs. IPA

As noted above, IPA requires more collaboration between teams than RPA does. Data science and IT teams must consult with the line-of-business professionals that have the necessary subject matter expertise about the document-based business processes being automated. This results in better implementations and can lead to the identification of additional high-value use cases, said Tom Wilde, CEO of Indico, an IPA platform for unstructured content.

Adding a layer of intelligence to RPA can have a transformative effect on processes, as they do when teams collaborate on finding better feedback loops for training the AI models. "Suddenly the bots can cope with high-value, decision-making tasks, as well as repetitive ones," said Arvind Jagannath, senior director of product management at Sagent, a loan servicing platform company.

In his business, the AI models driving RPA decisions are improved when business users and data scientists identify which sets of data to use for ongoing training. This collaboration might include evaluating the performance of models over different time scales. A short time scale model might look at which loans human experts approve or deny -- a longer time scale could consider which loans human experts approved, but subsequently defaulted, to further refine the model. "With more data, the models for making decisions can become more accurate and reliable," Jagannath said.

How do RPA and IPA complement one another?

RPA, as noted, is an ideal technology to start an automation project. The tools allow business users in many roles to create a simple bot by essentially recording how they interact with various applications in a process. This makes the bots easy and intuitive to use to automate simple repetitive tasks.

Once an automation initiative is underway, teams can start to evaluate ways to extend these capabilities using tools for reading documents such as invoices and contracts, structuring the data in them and connecting this data to other applications such as ERP and CRM systems.

In summary, there are many ways that RPA and IPA can complement each other.

  • RPA can help pave the road to more sophisticated automation initiatives involving IPA. For example, business teams might start out by developing simple bots that make it easier to see how processes are executed in the business.

    These simple bots can make it easier to identify when exceptions to normal processes occur and to inform the development of the more complex processes and automations built on IPA platforms.
  • Once a company has adopted an IPA platform, RPA can in turn provide a simple way of connecting more sophisticated automations into various applications through the user interface. This means that business users can extend these applications rather than relying on developers who have a deeper understanding of API integration techniques.

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