Leveraging Human Intelligence & Automation To Drive Business Outcomes Of Choice

Leveraging Human Intelligence & Automation To Drive Business Outcomes Of Choice

The human brain is known to rely on heterogeneous data associations to enable computational functions. Looking at a granular level, the brain is seen as a control center, and with every lobe comprising billions of cells work together to ensure what is perceived is conceptualized into thoughts and translated into actions. While the left lobe is analytical and responsible for linear thinking, the right lobe is more into creativity and intuition. The memory is a data bank that is leveraged for references, context and tasks that needs instructions. The peripheral nervous system generates a degree of forces through sensory signals for the required external stimuli. 

Human brain categorizes tasks based on the degree of imagination and cognitive activity, as represented below.

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The only way humans can liberate themselves from routine tasks and free up time for strategic decision making and innovation is through technology intervention. Automation is a way of applying technology on a task to make it more efficiently executed, enable faster processing of voluminous tasks, reducing turnaround time, and improving the ROI. It is an ideal way to improve operational performance  and can also be a strategic lever to sustain a competitive edge. Automating redundant activities by following standard operating procedures can save cost, time, improve workflow efficiencies, thereby reducing human error and increasing accuracy. It also allows employees to take up more challenging activities that can expand their intellect and imagination, leading to better competency and deeper job satisfaction levels. 

To Automate or Hyper automate?

The benefits of automation are undeniable and do have a broader business impact. Robotic Process Automation (RPA) is the most commonly used approach to automate less critical, repetitive tasks using rule-based processes. Humanizing automation was the need of the hour, and hyper automation got perceived as the next big thing in the automation space. However, in reality, hyper automation utilizes AI and ML concepts integrated within automation framework, not only to automate processes but also to augment human abilities to create a more agile and informed workplace. It implies more than just automation of tasks and instead focuses on collaborating with humans to interpret data cognitively and establish smart insights to make better decisions.  

Each business has different sets of drivers or objectives when it comes to automation. The design elements and approach for automation is based on factors such as the domain, process, technology, interdepencies, compliance to name a few. The ‘5-4-4 rule’ framework helps teams to understand and arrive at solving an automation problem. Though there are numerous ways within the solution elements, the decision to get into the right path is completely contextual and driven by business needs. 

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Gains through automation disruption

Here are few practical cases across industries where different scales of automation have been implemented using components of computer vision principles, ML, NLP along with automation framework: 

  • Ecommerce Website UX Analysis: Visual validation plays a significant role in websites, especially e-commerce portals. Usually, end-users face typical issues related to overlapping texts, channel orientation, network-specific or region-specific response, cropping of content, colour contrast issues within the page, to name a few. Most of the sites are specific to regions and hence manual verification is costly and time-consuming. To tackle these challenges, automated solutions leveraging computer vision principles and ML models are built to detect visual defects. The solution helps in improving the coverage, reducing testing time and quality of testing, thereby improving user experience.
  • Geoinformatics: Manual identification and reporting of the Point of Interest (PoI) can take up to 5 minutes per image, and manual processes lead to errors, delay detection of faulty images, and increase the cost of handling projects. Wheres, automation solutions help in solving these issues and reduce the processing time by more than 80%. These solutions leverage machine learning models to detect quality images, improve the image quality by increasing the pixel density, extract text and information using OCR, leverage NLP to determine rules, and use map information to cross-check the correctness. Thus, improving the quality of output and processing time significantly.

Putting it all together

The need for automation continues to grow across industries. Within businesses, everything needs some degree of automation to improve efficiencies, reduce cost, minimize issues and improve end-user experience. The scale of automation is growing and this can be addressed by investing more into intelligent automation. Hyper automation has the ability to augment the automation framework with the right AI-ML techniques, thereby providing increased end to end automation capabilities. With each passing day, IT services are witnessing an increased collaboration of tools, robots, and humans which will further result in maximizing the potential of an automated digital enterprise and thereby reimagining end-user experience.

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Kiran Marri
Dr Kiran Marri is a testing leader, industry consultant, educator, and avid researcher with over 18 years of software and systems experience. He is currently the Vice President at CSS Corp. His role is to develop innovative solutions, create sustainable business models for growth, and launch new services in the Digital, Engineering and Testing practices. He received his B.E. degree from the University of Madras and M.S. by research and PhD from IIT Madras. Dr Kiran has published over 70 papers at various international and national conferences in biomedical engineering, software testing, signal processing, and machine learning areas. He has won 4 best paper awards in 2004, 2011, 2014 and 2015 at international conferences.

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