Today’s knowledge workers are like the office workers of yesterday. They spend their time in email, on the phone, in various desktop and online apps and websites dealing with customers, suppliers, employees, partners, and internal stakeholders. Much of the time is spent dealing with various systems to shuffle information from one place to another, or enter/manipulate data from one system to another. If you’ve ever dealt with a bureaucratic organization, such as your Department of Motor Vehicles, you’re experiencing the joys of dealing with a knowledge-based service economy. But it doesn’t need to be this way.
Much of the reason why organizations seem to see limited productivity from their office and knowledge workers is because information resides in multiple, different systems, in different formats, and with various processes that determine how information can flow from one place to another. One might have thought that the move to Application Programming Interfaces (APIs) and other computer-based technology systems might have solved this problem. Yet while APIs have simplified the technical aspect of moving information from one place to another (sometimes), it has not solved the problem of dealing with differences in information. These various differences require a human to understand when information is needed, how it has to be manipulated, and how to utilize it for whatever particular task is needed by the organization.
Why Robotic Process Automation is Not Enough
Into this space of aggregating, managing, and manipulating data from a wide variety of sources is emerging a new class of automated “machine”: Robotic Process Automation (RPA) tools. These robots act on behalf of, or in place of, their human counterparts to interact with existing, legacy systems in the enterprise or anywhere online. They mimic the behavior of humans so that the human can focus on more important tasks for the company, rather than say, copying information from a website into a spreadsheet.
Yet, while RPA is making significant improvements into company’s operations by replacing rote human activity with automated tasks, Artificial Intelligence (AI) is poised to give this new engine of productivity a gigantic boost. RPA tools get stuck when judgement is needed on what, how, and when to use certain information in certain contexts. What if systems can learn from its human supervisors about how to utilize that information? Systems that leverage machine learning (ML) to dynamically adapt to new information and data will shift these systems from mere robots that automate processes to Intelligent Process Automation (IPA) tools that can significantly impact the face of the knowledge worker economy. Or as McKinsey Consulting puts it, “In essence, IPA takes the robot out of the human.”
Intelligent Process Automation: The Next Step
Even traditional RPA tools tend to get tripped up when things deviate substantially from what has been recorded. In particular, there are times when the context of the page needs to be understood, and different actions taken depending on understanding the circumstances. For example, if transcribing medical information from one system to another, the use of one laboratory system over another depends on the sort of diagnosis or treatment. Machine learning and other AI approaches can help deal with these situations by using natural language processing on text or spoken words, use different determinations on next steps based on learned interactions and thus provide a certain level of reasoning and insight on the different paths that the automated system can take.
In addition, there are many times when information is incomplete, requires additional enhancement, or combination with multiple sources to complete a particular task. For example, patient data might have incomplete history that is not required in one system but required in another. Another example is customer information that needs augmentation from other systems to provide greater value. Intelligent systems can work to build and maintain a more complete profile of a customer, patient, employee, partner, consumer, or other individual and company and use this knowledge to help fill gaps in information received by different sources. In this way, intelligent process automation systems can help eliminate many of the exceptions that require human handling of RPA systems.
|Level 0: Enhanced RPA (not AI)||Level 1: Language & Context Aware||Level 2: Intelligent Process Awareness||Level 3: Autonomous Process Optimization|
|Source: Cognilytica – Intelligent Process Automation Report (http://www.cognilytica.com)|
These levels represent increasing AI-enablement of processes. Currently, there are no vendor at the Level 3 of AI-Enablement as listed here, just as there are no commercial autonomous vehicles at Level 5 of enablement there either. The goal of calling out these AI capabilities in this article and in the report is to push vendors and the market to demand more intelligence from their process technology vendors. RPA is yesterday. IPA is today and tomorrow.