RPA (Robotic Process Automation): Strategic or Impulsive Decision?

• Cost Benefit: A software robot license might be costing lesser. Some surveys say that it costs 30 percent lesser. Also, once configured it does not require constant monitoring and thus free employees to do other tasks. The software can work 24*7*365. Therefore, cost benefits can be realized within a short time span. As per Accenture, processing costs can be reduced by as much as 80 percent.

● Higher Efficiency: RPA can operate 24/7 and takes no vacation when compared to humans, who work from 8 am - 5 pm and take a fixed number of annual leaves. Thus, higher efficiency can be achieved. An insurer can enjoy up to a 150 percent cost advantage over competitors by optimizing efficiency across marketing, sales, back-office operations, IT, and other support functions.

● Reduced Risk: In 2016, Capgemini Consulting and Capgemini Business Services surveyed 150 executives from companies of different sizes and revealed that 86 percent participants feel that RPA can reduce risk and increase compliance. You might be wondering why. It is due to human error or skipping of steps in the process. Now with RPA, all these risks will be performed 24*7*365 with no exceptions and save millions of dollars in fines.

● Scalability or Flexibility: RPA tools have the flexibility to change or add the process to address the changing realities of financial institutions business needs (applications, process, procedures, regulatory requirements). Additionally, we can add tens or hundreds of robots at the same time. Thus, it creates a harmonious framework for financial institutions, to scale up or scale down instantly as well as train and deploy thousands of robots while maintaining consistency.

● Accuracy or Quality: The majority of the errors in a process occur either because of human error or skipping of predefined step or rule. Thus, it compromises accuracy and quality of the process. RPA, on the other hand, follows all the rules and do not whine at the time of the work. This results in 100 percent accuracy or quality every time it is used.

● Predictable: Transactions are no more prone to delays, due to illness or vacations of the employees and are always a backup.

The most significant advantage is that it helps in freeing resources for other higher value analysis and decision-making aspects like product innovations, customer engagement, etc. What do we need for a ‘successful automation’ project? Is there really a need to be strategic? Is just knowing basic definition or terminology of automation enough? Let us find out more about this.

What are the challenges of automation?

In 2012, Redwood Software surveyed the corporate decisions of 300 companies in the US and UK and found out that the main challenges for automation are:

● Possessing inadequate knowledge (52 percent)

● Managing complex process (63 percent)

● Inability to integrate legacy applications with new applications (64 percent)

It is clear from the above survey, that if we need to achieve automation then we need to have a good understanding of all the processes and interfaces around it. In short, we need to know the broader spectrum of organization process and ‘key stakeholders’ who knows them so that they can explain the process or issues during system integration. It means we need to have ‘organization’ level agreement. Let me take an example: mid-size bank decide to have RPA and thus deploys thousands of bots to automate ‘manual data entry’ which generates tons of data. Moreover, nothing more is done to deploy this data for analytical part of it. I am sure it is an excellent idea of doing automation of ‘manual data’. However, it will also bring CIO and its peers in an unfortunate scenario as it is not giving any business benefit.

Key messages from the above-mentioned survey analysis are that automation (RPA scenarios) touches a broader spectrum in an organization, right from the generation of insurance advice to responding to emails. As per the CIO magazine, RPA requires proper design, planning, and governance if it is to bolster the business. Thus, it needs to a be long-term journey or project (a strategic decision) for the enterprise rather than a piecemeal project that evolves into something unwieldy. For example: before we even consider to implement RPA we need to have an operational model design ready otherwise small things like ‘handshakes’ between 2 different RPA process can break the entire business process.

Hence it is apparent with above findings that automation had to be ‘strategic’ not ‘standalone’; we also need to have a clear view of the benefits it will bring to the organization.

Why RPA needs to be ‘strategic decision’?

RPA is a different breed of technology which needs to be deployed efficiently to deliver its full benefit to business operations. Thus, work needs to be done at enterprise level - ‘Strategic decision,’ and multiple stakeholders are responsible to realize its implementation benefits. As per Forrester estimates by 2021, we will have around 4 million robots doing office, administration, sales and other related tasks. Thus, few other essential aspects which are unnoticed during the ‘implementation’ of bots (RPA) -management, ‘interdependence,’ ‘inter-connections’ and governance of bots and we can use automation tactically for cost savings. However, if we want a streamlined process, leverage efficiency, etc then we need to use it beyond a single department or a single process in a department. Thus, the success of any RPA initiative (‘Strategic Decision’) depends on the depth, detail, and feasibility of its implementation plan and the commitment of the organization towards its timely execution.

Future of RPA

According to IDC Worldwide, spending on digital transformation will jump to the US $1.2 trillion. RPA technologies have already traveled so far from simple screen capturing to the streaming of the entire business process. I believe that the industry is going to be more innovative regarding building emerging RPA solutions. Currently what is missing in the solution provided by RPA, is human touch while responding to the scenarios. It can be filled by using machine learning (ML) and artificial intelligence (AI) in future. In short, robots will be able to learn and think while handling tasks. For example, robots are required to receive unstructured data from multiple sources. Using ML & AI, future RPA will be able to interpret the incoming data and automate tasks. Thus, minimal human intervention will be required.

Scary! I also think so. Do not worry, tasks which require ‘emotions’ like customer advice cannot be automated.