Using enterprise intelligent automation for cognitive tasks
RPA is no longer comprised of simple chatbots or repetitive programmed tasks. Enterprises are looking at RPA to move up the ladder of cognitive automation.
For enterprises to achieve increasing levels of operational efficiency at higher levels of scale, organizations have to rely on automation. Organizations adding enterprise intelligent automation are putting the power of cognitive technology to work addressing the more complicated challenges in the corporate environment.
The past few decades of enterprise automation have seen great efficiency automating repetitive functions that require integration or interaction across a range of systems. Businesses are having success when it comes to automating simple and repetitive tasks that might be considered busywork for human employees. Just about every industry is currently seeing efficiency gains, with various automation tasks helping businesses to cut costs on human capital and free up employees to focus on more relevant or higher-value tasks.
However, simply automating rote tasks is not sufficient to deal with the continuous changes those enterprises face. In order to provide greater value, these automation tools need to step up the ladder of cognitive automation, incorporating AI and cognitive technologies to see increased value. Evolving from roots in business process management and workflow automation, the latest evolution of robotic process automation (RPA) is focused on helping companies automate their routine operations across user interfaces and disparate systems, while also expanding access to data on which those processes are dependent.
Moving up the ladder of cognitive automation
In its most basic form, machine learning encompasses the ability of machines to learn from data and apply that learning to solve new problems it hasn't seen yet. Supervised learning is a particular approach of machine learning that learns from well-labeled examples. Companies are using supervised machine learning approaches to teach machines how processes operate in a way that lets intelligent bots learn complete human tasks instead of just being programmed to follow a series of steps. This has resulted in more tasks being available for automation and major business efficiency gains.
When it comes to automation, tasks performed by simple workflow automation bots are fastest when those tasks can be carried out in a repetitive format. Processes that follow a simple flow and set of rules are most effective for yielding immediately effective results with nonintelligent bots. For example, employees who spend hours every day moving files or copying and pasting data from one source to another will find significant value from task automation.
However, there are times when information is incomplete, requires additional enhancement or combines with multiple sources to complete a particular task. For example, customer data might have incomplete history that is not required in one system, but it's required in another. In these instances, enterprises need tools with more intelligence. The ability to capture greater insight from unstructured data is currently at the forefront of any intelligent automation task.
Moving up the ladder of enterprise intelligent automation can help companies performing increasingly more complex tasks that don't always follow the same pattern or flow. Dealing with unstructured data and inputs, fixing and validating data as necessary for context or virtual assistants to help with process development all require more cognitive ability from automation systems. Companies want systems to automatically perform reviews on items like contracts to identify favorable terms, consistency in word choice and set up templates quickly to avoid unnecessary exceptions.
With disconnected processes and customer data in multiple systems, resolving a single customer service issue could mean accessing dozens of different systems and sources of data. To bridge the disconnect, intelligent automation ties together disparate systems on premises and/or in cloud, provides automatic handling of customer data requirements, ensures compliance and reduces errors.
Using more cognitive automation, companies can experience a significant boost in performance-related business outcomes, consolidate dozens of systems into just a handful of coordinated processes and accelerate customer service response times tenfold. In addition to simple process bots, companies implementing conversational agents such as chatbots further automate processes, including appointments, reminders, inquiries and calls from customers, suppliers, employees and other parties.
What should be automated?
When determining what tasks to automate, enterprises should start by looking at whether the process workflows, tasks and processes can be improved or even eliminated prior to automation. There are some obvious things to automate within an enterprise that provide short-term ROI -- repetitive, boring, low-value busywork, like reporting tasks or data management or cleanup, that can easily be passed on to a robot for process automation.
Automated process bots are great for handling the kind of reporting tasks that tend to fall between departments. If one department is responsible for reviewing a spreadsheet for mismatched data and then passing on the incorrect fields to another department for action, a software agent could easily manage every step for which the department was responsible.
However, once we look past rote tasks, enterprise intelligent automation become more complex. Certain tasks are currently best suited for humans, such as those that require reading or understanding text, making complex decisions, or aspects of recognition or pattern matching. In addition, interactive tasks that require collaboration with other humans and rely on communication skills and empathy are difficult to automate with unintelligent tools. This is where cognitive automation tools come into play.
Cognitive automation incorporates machine learning to learn decision-making, computer vision for image recognition, natural language processing for text and voice understanding, and advanced pattern matching and predictive analytics to handle more complex decision-making and process exception handling.
Autonomous process optimization
Still, the enterprise requires humans to choose and apply automation techniques to specific tasks -- for now. One area currently under development is the ability for machines to autonomously discover and optimize processes within the enterprise. Some automation tools have started to combine automation and cognitive technologies to figure out how processes are configured or actually operating. And they are automatically able to suggest and modify processes to improve overall flow, learn from itself to figure out better ways to handle process flow and conduct automatic orchestration of multiple bots to optimize processes.
A large part of determining what is effective for process automation is identifying what kinds of tasks require true cognitive abilities. While machine learning has come a long way, enterprise automation tools are not capable of experience, intuition-based judgment or extensive analysis that might draw from existing knowledge in other areas. Because cognitive automation bots are still only trained based on data, these aspects of process automation are more difficult for machines.
While some worry about bots taking over administrative and operational jobs in the enterprise due to actively learning complex processes in very little time and with low cost to the organization, it is easy to see that humans still provide value in the enterprise. As enterprises continue to invest and rely on technologies, intelligent automation services will continue to prove powerful additions to the enterprise technology landscape.
