Remember the RMS Titanic? The ‘unsinkable’ ship that went down on its very first voyage after striking an iceberg would certainly have survived the day if not for a combination of factors, both human and natural. The absence of an experienced crew, the lack of proper equipment (the lookout’s binoculars had been left at Southampton), and an overdependence on manual labor all contributed to the tragedy, relegating the iceberg to the role of the necessary, but perhaps secondary, villain.
Replace the above mentioned conditions with a smart, robot-driven vessel, equipped with radar to look ahead for bumps on the road and we might as well have sailed on the glorious ship even today! And what a treat would that have been.
Lessons for Businesses
Traditional business operations mirror the RMS Titanic. Cumbersome and convoluted, they require high-intensity manual intervention, are affected by the diversity in skill and experience levels, and suffer from a lack of the proper tools and systems. This often results in higher operational risks, a greater number of errors, and a resultant increased time to market, along with the chance of significant financial losses.
The adoption of a business process management system (BPMS) can help address these challenges by facilitating lean and smart practices. However, while a robust BPMS is effective in eliminating unnecessary steps, and can optimize activities, it may fall short when dealing with repetitive rule-based tasks.
Implementing AI-driven robotic process automation (RPA-AI) can be particularly effective in this direction. Augmented by AI and ML capabilities that help convert masses of unstructured data into structured and easily usable output, RPA-AI technology, when combined with BPMS, unlocks new synergies through technology cross-pollination.
A combination of BPMS and RPA can lead to a scenario of reduced human effort and an average handle time (AHT) that is about 60-70% lower than earlier. The benefits from such an implementation are obvious, with near universal adoption being predicted by 2025.
Unlocking the Value Chain
The CAGR for RPA-AI adoption is currently at over 100% and expected to strengthen as a trend. This exponential growth is becoming rapid with the support of BPMS, which acts as an orchestration layer for a smooth transition from legacy systems to ERPs. RPA-AI helps streamline the process even further, and the enhanced interest in such a solution is evident from the joint offerings being provided by such giants like Appian, BluePrism, IBM, and Automation Anywhere.
Combining BPMS with RPA-AI offers an intelligent approach that is vital for a fully digitized business operations value chain. BPMS can be used to develop and implement new solutions for improving processes, enhancing decision logic, and automating vital yet human-effort intensive processes like order management. RPA-AI comes in useful for automating mundane tasks based on procedural logic, affect alterations without causing significant disruptions in the workflow, and automate activities like ship orders.
Our approach, combining a digital workforce of intelligence process automation levers and a 3-lever BPM approach that helps qualify processes for automation needs, helps achieve this synergy and prevents an uncontrolled growth of swivel chair activities.
The Technology in Motion: BPMS-RPA in action
Combining BPMS and RPA unifies process reengineering initiatives along with analytics, leading to a faster turnaround time, reduced time to market, and contained operational costs. The RPA architecture results in the creation of a centralized data repository, leading to structured data flow and a streamlined interdepartmental communication channel that helps transform workflows. Furthermore, it is possible to have a real-time visibility on SLAs through insightful dashboards that can help ascertain the business value being created on the floor.
At HCLTech BServ, we help realize this scenario through the implementation of our proprietary product, EXACTO, on top of the BPM tool TOSCANA, and drive cognitive learning for bots and AI-ML based unassisted learning. To find out more, read our whitepaper here.