Are Bots On Their Own Efficient Enough to Address Real Life Business Problems?
Today, there is a lot of hype around next generation robots, artificial intelligence and robotic automation and how they are poised to transform the way organizations function and interact with customers. Pundits even say that it’s inevitable that the robots of tomorrow will replace humans via automation. Low end jobs will eventually become redundant and humans will only focus on high end and value added work.
Therefore, for obvious reasons, this has created a lot of flutter in the market and some serious concerns have been raised with respect to jobs, human resource capital and the overarching economic repercussions.
Bots today are at a very early stage of their development cycle are being employed in performing mundane tasks like data entry into forms, picking data from one system and pushing it to another system, checking a predefined item on the internet and populating it into predefined locations. All these are essentially motor skills and do not require high intensive brain activity.
But there is a human angle. Bots will perform repetitive, mundane and non-value added tasks allowing knowledge workers to elevate their performance and becoming more efficient. Humans will perform intelligent tasks such as credit approvals, decisions related to business problems, resolution of complex customer problems and the likes. This means that for the bots and the humans to work in tandem, an orchestration layer will be required that will help maintain a fine balance between who performs mundane tasks and who brings the intelligence to the business problem resolution. So in complex business scenarios, humans will still be indispensable. Hence, Business Process Management (BPM) and Robotic Process Automation (RPA) will need to work together to resolve real life business situations. Leveraging RPAs capability, organizations will be able to enhance their digitization strategy by automating repetitive, mundane tasks and enabling continuous process improvement.
RPA will also contribute in driving higher employee self-esteem and motivation levels, because of a greater sense of self-worth given the quality of their tasks. Considering this, it becomes even more pertinent to have a BPM solution that allows such work to happen in a structured and seamless manner. With the proper support via BPM for managing unstructured processes, knowledge workers will be able to make smarter decisions to handle exceptional scenarios.
Be prepared for RPM “Robotics Process Management”. RPM for Speed and Efficiency. An amalgamation of Robots and Process Management. Let’s put this into context. For example – In an Accounts Payable process where the data needs to be extracted from invoices. Here a bot can be deployed for extraction of data from invoices and validating it against the ERP for matching it against the GRN and PO. This will throw up some exceptions, as parts not supplied in full or vendor does not exist in the ERP. In such cases, an intelligent human will be required to decide on the next course of action to resolve the exceptions. Moreover, with integrations becoming difficult to achieve over time, bots can come in handy for populating data from the ERP to this orchestrating BPM layer and vice-versa. This makes the time to go live much quicker.
Taking a similar example to the Banking process, say Retail Lending. A Bot can be deployed to check the Credit scores from Credit Bureaus but whether the approval for credit has to be given or not has to be handled by an intelligent human. Let’s say that the Credit Score is not up to the mark by a few points, but you observe that this is an HNI (High Net Income) customer of your bank and hence the credit officer takes an exception and approves it. All this is recorded in the underlying BPM layer for audit and tracking at a later point in time.
What does the future hold then? Will Cognitive RPA, which brings together cognitive technology with RPA, bring more disruption? It is already being pronounced by many industry pundits as the next big step towards perfect automation. But even then it will have to be based on analytics and past history, and has to be combined with human judgement and intelligence and an orchestration layer to make it a comprehensive real life business problems support infrastructure.