Cognitive Automation — Going beyond Rule-based RPA

People And Robots Modern Human And Artificial Intelligence Futuristic Mechanism Technology Vector Illustration
In recent times, organizations across the world from various industry sectors are pushing themselves to become “Digitally Native” by adopting “Digital Transformation” as a foundational pillar for the future starting with Robotic Process Automation (RPA). And, the primary objectives for the most of them are to achieve speed, accuracy, and reduction in headcounts. The convergence of RPAartificial intelligence (AI)machine learning (ML)natural language processing (NLP), and cognitive platforms is potentially so disruptive that Klaus Schwab, founder of the World Economic Forum, calls it the“Fourth Industrial Revolution.” At the same time, there is a good share of apocalyptic warnings from various quarters that the advancement of automation (cognitive form) in the workplace will create a “dystopian society”. To nullify such warnings, Karen Lachtanski aptly wrote, -“If an argument is to be made against digital transformation, it is that the divide between high-level skills and low-level skills will become wider, with little or no middle ground.” In short, she clearly states that there is no time to dwell on it. What going to set the future workforce apart is what we as a part of that workforce are willing to do about it i.e, learn and evolve (or simply perish).

What is Robotic Process Automation (RPA)?

RPA is a basically a software tool that can automate routine tasks/sub-tasks in structured mannered by eliminating human activities such as “copying and pasting data between multiple applications” so that functional/cross-functional teams can focus on more value-adding activities.

In December 2017 survey by Deloitte“53% of the respondents have already embarked on the RPA journey and a further 19% of respondents plan to adopt RPA in the next two years”. If adoption continues at its current level, RPA will achieve near-universal adoption within the next five years. One reason for such prediction is that RPA has become advanced enough to take over the mundane tasks; prior to that, the technology wasn’t quite there.

Let us accept, broader the automation spectrum, more the elimination of manual processes. For organizations to become digitally native it’s very much important that an RPA technology should be designed and deployed as an ideal tool to connect multiple legacy systems rapidly and seamlessly such as Big Data, Internet of Things (IoT), cloud, etc. Eventually, it should become a critical part of their value proposition just not for the internal operations but also for the front and back-office functions.

The Addition of “Cognitive Intelligence” to create Cognitive Automation

While RPA is expected to act as a first step in the adoption of automation, the rise of new cognitive technologies (which can mimic human intelligence and judgment) is expected to increasingly drive automation by matching the current wave of “Digital Transformation” with the application of AI. In fact, cognitive technologies can be considered as a subset of AI, further grouped into capabilities such as ML, NLP with semantic analysis, machine vision, speech recognition, emotion recognition with sentiment analysis, and optical character recognition.

On August 15, 2018, Deloitte and NICE launched a white paper – “The Future of Operations — Moving Beyond Process Automation” which meticulously covered a  futuristic self-service banking scenario that utilizes a myriad of new generation cognitive tools to stay ahead. The paper duly explained the concept called Robotic and Cognitive Automation (R&CA) with a holistic and rich perspective on “how to practically assess and tackle the next technological revolution in artificial intelligence and cognitive automation”. Unlike the RPA, given their probabilistic nature, cognitive technologies need to continuously learn from their past actions and evolve more accurate algorithms.

One of the biggest constraints of RPA is that it needs structured data in the form of a spreadsheet, a web form or a database for the robots to work flawlessly. Hence the need for cognitive intelligence (driven by ML/NLP) arises to deal with the unstructured, or semi-structured data and transform it into a structured form, which can then be later processed by the robots.

WorkFusion’s Smart Process Automation (SPA) is one of the classic examples, which is, in turn, paired with RPA to learn from the humans it supports. Using ML-driven data capturing tools, inbuilt quality control, and algorithmic training capabilities, bots shadow human actions and judgment calls to learn routine decision-making processes.

Kindly do note, many advertised AI-powered RPA solutions often turn out to be a basic extension towards ML (Not purely driven by ML as such). Basically, such extensions are quite useful, but they are based on recognition patterns, i.e., having a rule-based dependency. Before selecting any such solution, a due diligence is recommended.

No matter how lucrative cognitive automation seems to be, the first-mover‘s pursuit in this space may invite risk. The best possible strategy is to test run a set of pilot programs and then evaluate for a smoother downstream implementation. A set of proven pilot results can easily help an organization to formulate a long-term strategy.

The Road Ahead

In a newly published working paper by Lukas Schlogl and Andy Sumner from the think tank, the Center for Global Development (CGD) explained the potential effects of robotics and AI on global labor markets. When automation is used to augment human management, traditional organizational orthodoxies, such as about spans of control, can be challenged. The paper says it’s impossible to know exactly how many jobs will be destroyed or disrupted by new technology. But, authors add, “it’s fairly certain there are going to be significant effects — especially in developing economies, where the labor market is skewed toward work that requires the sort of routine, manual labor that’s so susceptible to automation”.

As in the past, technology will not be a purely destructive force like the introduction of Automated Teller Machines (ATMs) pushed down the branch-wise headcounts but at the same time banks got an opportunity to open more branches at the distant corners. In this particular case, new jobs will be created; existing roles will be redefined, and workers will have the opportunity to switch careers. But, the challenge to this generation will be in managing the transition as the individuals who need to retrain for new careers won’t be the young, but middle-aged professionals.

And from the government’s end, policy-makers should embrace the opportunity for their economies to benefit from the implementation of cognitive automation and boosting productivity across. To achieve that, they should put in place well-defined policies (flexible, not rigid) to encourage investment and offer market incentives to encourage continued progress and innovation. At the same time, they must evolve and innovate policies (keeping pace with time and evolving technologies) that help current and future workforces adapt to the impact on their respective employment demographics. The dawn of “automation age” has already arrived and it needs an extensive level of social re-engineering which must include revamping the education and training systems, creating substantial income support and pre-defined safety nets, as well as a necessary transitional support for those dislocated or about to be dislocated.

About the Author:

Rahul Guhathakurta (ORCID: 0000-0002-6400-6423)

Cite this Article:

Guhathakurta, R., “Cognitive Automation — Going beyond Rule-based RPA” IndraStra Global, Vol. 04, Issue No: 9 (2018), 0006, http://www.indrastra.com/2018/09/Cognitive-Automation-004-09-2018-0006.html | ISSN 2381-3652

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