In 2017, the Robotic Process Automation / RPA market has matured.
Learning from the evolution of RPA, in this post, we explore the wider implications for Enterprise AI i.e. the deployment of Artificial Intelligence to the Enterprise
BACKGROUND
The post is based on my course on Implementing Enterprise Artificial Intelligence (AI) course where we explore these ideas in detail.
For this article, we consider AI to be based on Deep Learning technologies. In contrast to Machine Learning, Deep Learning implies the automatic detection of features. Features could be either a large number of possible impacting characteristics or a hierarchical set of features. As an application, RPA uses various Deep Learning technologies (such as Natural Language Processing).
For many of us, AI implies machines taking over jobs.
Dilbert explains this best!
In the case of RPA, the current technology is designed to take over specific tasks (as opposed to jobs). Initially, these are mundane tasks. But increasingly, with the deployment of AI – we can also automate complex tasks – potentially full job functions.
Thus, leaving aside the moral arguments, we can consider RPA as a foundation set of technologies to understand the wider deployment of AI to the Enterprise.
The current definition of ‘RPA’ as per the Institute of Robotics Processing Automation emphasizes the status quo: “the application of technology that allows employees in a company to configure computer software or a ‘robot’ to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems.”
However, if we consider AI with RPA we get a more complex picture because we can interject decision making into existing processes. This allows us to automate higher order tasks which previously needed perception and judgement from humans.
WHAT CAN ENTERPRISE AI LEARN FROM THE EVOLUTION OF ROBOTIC PROCESS AUTOMATON?
Here are three things which can infer from the evolution of RPA to the wider deployment of AI in the Enterprise
1) What can AI emulate by observation?
If a process can be observed or simulated, it can be duplicated.
So, the logical question follows: how can processes in an Enterprise be observed?
Here are some of the ways:
- Natural Language processing and automation of forms – NLP can automate many forms in an Enterprise. This allows us to learn a process based on automation of forms
- Identity: KYC++ and Blockchain to understand workflow – Both KYC and Blockchain allow for better Identity – hence richer data and a greater understanding of the process flow interlinking multiple Identities
- Testing as learning: automated software testing products like selenium can help to identify process flow
- Observing processes and workflows through APIs: APIs and Integration software like mulesoft could provide us a greater understanding of workflows
- Observing processes and workflows in the front office through unstructured data and chatbots: In communication intensive industries like Financial services, Healthcare and Insurance – the deployment of chatbots can be the first step to understanding workflow.
- Observing systems as they are being built: Finally, it could be possible to observe systems as they are being built – leading to cheaper support costs
2) How could deeply coupled AI systems impact workflow?
The current success of RPA is due to loosely coupled systems – leading to quicker deployment. The ‘robot’ essentially logs on as a traditional user and mimics the behavior of the user. This means existing systems do not have to change. As these systems start to incorporate AI, RPA could be more deeply integrated. In theory, it is possible for an RPA system to learn different sub systems and then mimic a higher-level process through deeper integration
3) How could we use anomaly detection to drive more inductive processing?
To create complex use cases based on reasoning – we need to incorporate inductive processes. Anomaly detection is a driver to greater inductive processes in the Enterprise.
To recap, Deductive reasoning starts with a hypothesis/theory. Based on a theory, we then try to predict observations. If the observations validate the theory – then the theory is valid. In contrast, inductive reasoning starts with specific observations from which we aim to build the broad generalizations. Thus, there is an interplay between inductive and deductive reasoning. Inductive reasoning allows scientists to form hypotheses. In turn, Deductive reasoning allows scientists to apply the theories to specific situations.
Anomaly detection techniques (both in Machine learning and deep learning anomaly detection) provide opportunities to interject more reasoning into processes
CONCLUSION
In a report (“Where machines could replace humans—and where they can’t (yet)”), McKinsey Consulting states that almost 33% of the time spent across all US occupations is related to Data processing. The report also indicates that we can automate more than 60% of this work with currently available technologies. They categorize work into the following classes: Managing others, Applying expertise, Stakeholder interactions, Unpredictable physical work, Data collection, Data processing and Predictable physical work
As RPA becomes more integrated and can observe more of the Enterprise processes, it can potentially mimic and emulate complex processes which require reasoning covering many of the categories of work listed above.
Once RPA ROI has been demonstrated at one level, it will be scaled and integrated deeper into the Enterprise. This will lead to continuous improvement and self-healing processes. It would also radically change the nature of work itself. The first group of workers to feel the impact of RPA and AI as described above will be offshore workers (including offshore developers). Ironically, these same companies are the largest adopters of RPA today
Ultimately, we could undertake a radical form of process engineering. We cover these ideas in the Implementing Enterprise Artificial Intelligence (AI) course
