With increased focus on robotic process automation (RPA) to automate mundane and repetitive tasks, the next evolution in cognitive automation is now underway. Chief Information Officers (CIO) are evaluating and deploying second generation Cognitive RPA robots with artificial intelligence (AI) capabilities such as sentiment analysis, natural language and machine learning to enhance the decision-making process and to more cognitively orient tasks traditionally reserved for humans.
While traditional RPA has been around for several years, it gained extensive popularity around 2015. RPA addresses the need to provide a comprehensive business solution in order to address operational questions in a holistic manner. It utilizes existing disparate and isolated legacy systems by emulating the same daily manual activities that a human operator takes to link functionality within systems together. The smart RPA takes the concept one step further leveraging AI and machine learning to resolve and address exceptions that require cognitive intervention in an identical manner as a human.
CIOs are now partnering with functional units in order to create an enterprise-level AI-enhanced RPA platform to drive cost savings. Additional benefits are: minimized institutional risks and increased efficiencies through a digitally augmented workforce. The combination of RPA and applied AI techniques allows organizations of all sizes to automate processes where prior automation was impossible with speed and reliability at scale. By using cognitive RPA, employees are freed from repetitive, low value activities and can concentrate on value-added higher level analytic tasks. This change will lead to better job satisfaction which is very important to millennials and the next generation, Generation ‘Z’, who view work as an extension of their identity and a social environment ripe with challenges.
"RPA with AI will accelerate exponentially due to its exceptional ROI model that can be easily articulated and remunerated at the highest executive board level. "
The traditional RPA without AI or machine learning is a rule-based/heuristic system that can only be deployed in structured environments to automate routine tasks. The first generation dumb RPA robots are often prone to excessive manual exception handling and oversight that require human operator intervention resulting in poor return on investment (ROI). On the other hand, AI-enhanced RPA automation (i.e. Cognitive-RPA) can be deployed in a more complex first line of defense environment with enhanced ROI. Machine learning and unsupervised classification techniques leveraged by RPA can be utilized to provide a better experience for the user.
CIOs can introduce RPA to organizations leveraging the Center of Excellence model in which all policy, education, governance, ROI models, best practices and templates are centralized. The actual adoption is enhanced by placing Smart RPA-Robots with no code requirement in the hand of business users to enhance their own job and automate their day-to-day activities. The new augmented workforce must be supported with continuous micro-courses and education to move the adoption curve forward, while adoption starts from the business side with simpler RPAs and attainable tasks that are ‘quick wins’. The need for more cognitively complex RPA and data engineering to support it as an enterprise-governed platform would eventually land the RPA as an Information Technology platform in the CIO space.
A seamless data platform with a rich data lake (i.e. structured and unstructured) with real-time capability will be the key to realizing the full potential of AI-based RPA at scale. The machine learning algorithms are different from procedural programming due to their innate need for data to be able to learn and program themselves. Industries such as financial, core-clearing, insurance, industrial, and healthcare, to just name a few that generate and consume tremendous amount of data, are ripe for the Cognitive-RPA technology revolution. The automation can start from simple repetitive tasks (i.e. do) to more intelligent decision-making activities and messaging under uncertainty (i.e. think) with increased rate of returns on their investments.
The CIOs of the tier-one financial institutions are now focused on organic innovation with technologies such as Cognitive-RPA, machine learning and big data to extrapolate customer risky behavior based on digital signatures of managing liquidity at scale. The complex regulatory landscape provides a shield around the business, providing opportunity for existing CIO’s to concentrate on core-business value creation and speed of execution using innovative technologies such as AI and RPA. This implementation of AI and RPA by these institutions would leave new market entrants at a disadvantage unless they are able to compete with the new technical landscape in an extensively complex regulated environment.
The RPA with Artificial Intelligence is just getting started and will accelerate exponentially in the next three to five years due to its exceptional ROI model that can be easily articulated and remunerated at the highest executive board level. Cognitive-RPA is more about talent augmentation than full-time equivalent replacement. The new work environment and digital workforce using RPA and AI augmentation will free organizations from deploying scarce capital to low value repetitive activities, while addressing the new generation workforce’s desire to be a part of the value creation pipeline without excessive need to participate in repetitive tasks.
RPA can be used to augment standardized processes that span cross functional systems. It can be used as a part of a hybrid solution or as an additional automated solution in tandem with existing technologies.
In conclusion, the recommendation is that organizations introduce simple RPA as a starting point in order to assess the compatibility of this innovation compared to existing business process management methods with a predictable ROI model. CIO’s can additionally use the Center of Excellence methodology to ensure systematic adoption ROI and graduate to cognitive RPA for a higher rate of return. This approach would likely demonstrate increasing rate of return on investment ultimately resulting in a low cost, competitive workflow setting.