In this article, we explore the journey from robotic process automation (RPA) to intelligent automation (IA). We'll look at what is different, what technological changes we're seeing, how the nature of processes change, how people's expectations change and why enterprises have an existential imperative to embrace intelligent automation. Finally, we'll conclude with what business leaders need to do to help them succeed on this journey.
Let's get into it!
We all know RPA, well at least I hope we do! RPA is a business process automation technology that can mimic humans in interacting with software systems through graphical user interfaces or APIs. Intelligent automation is the next chapter in this journey, Intelligent Automation combines RPA with advanced technologies such as artificial intelligence, prescriptive analytics, intelligent document processing and process & task mining to deliver end-to-end business processes that think, learn and collaborate in real-time with humans as co-workers.
What is the journey from RPA to intelligent automation ?
Traditional RPA focuses on rule-based processes with structured data. For example, moving data from one system to another. Intelligent Automation, however, unlocks data-driven processes with unstructured data, for example, evaluating a car accident insurance claim by analyzing and classifying images and making decisions or recommendations on the claim outcome.
This change means automations shift from a focus on information worker tasks - where workers would traditionally access, transfer and carry out calculations on perfect-data-sets to a limited set of outcomes, to automation or augmentation of knowledge worker tasks - where decision making on imperfect information leads to highly variable outcomes.
From deterministic modes of operation using decision trees that expand exponentially with every new variable, to probabilistic modes of operation, powered by deep neural networks that can balance many different variables for an optimum outcome.
Intelligent automation enables use-cases that involve machine learning to bring together interactional, transactional, financial and operational data sources to not only provide insights but to segment, profile and make decisions.
Whereas in RPA, digital workers are usually separate from human workers, intelligent automation transforms digital workers into co-workers.
What are the technological changes powering intelligent automation?
Undoubtedly, the underlying technologies continue to become faster, easier to use, democratized, more powerful & cheaper. The center stage is machine learning, in particular deep learning. Deep learning involving neurons and synapses is similar to how biological brains operate. These enable digital robots to see using computer vision, understand language through natural language processes, and talk back through natural language generation. Figuring out the underlying processes through process synthesis and understanding their human counterparts through affective computing.
We're also seeing a wholesale shift from on-prem siloed software that is difficult to scale, to public/private cloud-enabled microservices that scale effortlessly.
Even the internet-of-things, which can be seen as dumb sensors that send data to servers, is giving way to internet-of-behaviors that combine behavioral sciences, edge analytics and sensors to understand the how and the why, far beyond just reporting on the what.
How do processes change as we embrace intelligent automation?
We're on a journey toward autonomous systems - from manually discovering and assessing processes to automated discovery and assessment, through process and task mining.
From manually finding and integrating with systems and data to automated discovery and integration through semantic analysis. From manually building and maintaining processes to automated generation of processes that can self-heal and self-manage.
From manually managing and scaling the portfolio of automations to fly-by-wire management and auto-scaling of an elastic workforce that grow and expand to meet demand.
And from the separation of human and automated tasks to intertwined processes that seamlessly handoff between humans and their digital co-workers.
How do people's expectations change as we move towards intelligent automation?
We're changing all the time. However, one of the fundamental changes unlocked by intelligent automation is the transformation of employees from information workers, who extract data and shift it from one system to another, to employees as knowledge workers augmented by automation, where the human workforce can focus their time on high-value interactions with customers, leveraging their creativity to solve problems and create new and better services and products.
As a result, our expectation for greater flexibility, less presenteeism and more significant consideration for our mental and emotional state is growing. We also expect greater transparency and fairness from the IA systems that identify and address discrimination rationally and without bias.
Why should enterprises care about intelligent automation?
Happier customers and happier employees, as IA can free up people to focus on high-value interactions.
Faster time to market for new products and services made possible through the agility that quickly reconfigurable system of IA afford.
Far greater opportunities for innovation of business models that extract value from parts of the business that are either too rigid or difficult to evolve today because of silos.
All of these lead to increased revenue and top-line growth further enhanced by flexing a limitless workforce that can expand instantly to meet demand in real-time.
What should executives and business leaders do to reap the benefits of intelligent automation?
Without a doubt, executives must position intelligent automation as a strategic imperative, not a tactical fix – they need to articulate the vision and elevate priority to take action now before their competitors overtake them.
Leaders need to bring everyone along, fostering greater trust and accountability about direction and impact on the workforce, and in so doing, they must invest in employees' skillset. This is especially important for skills that incorporate; compassion, emotional intelligence, customer interaction, managing complexity across multiple knowledge domains, improvisation and decision making in ambiguity.
Enterprises need to grow or buy the talent pipeline to scale automations, especially in process optimization and intelligent business management.
The strategy must incorporate engaging employees in re-imagining the processes that improve quality, manage complexity, and build stronger connections with customers.
And finally, executives need to be aware of automation silos and rationalize the organization's growing automation portfolio, selecting the right tool for the right job, but focusing on oversight, governance, and employee experience.