Robotic Process Automation is your next competitive advantage
The fight for dominance in a market is fierce today. The fast adopters of emerging best practices are usually the ones that rise to the top and crush their competitors. Consider this, one telecom company replaced 45 offshore employees, costing a total of $1.35m a year, with ten software robots, costing $100,000. This telecom company then spent its savings of $1.25m on hiring 12 new people to do more innovative work locally at its headquarters. Every competitor is now falling behind.
Success in today's business world requires organizations to navigate toward a fundamental shift in how they apply key operational innovations. I have spent most of my career helping clients continually adapt in this changing technological environment and address the challenges that it presents. Those working alongside me have seen that almost all functional areas are burdened with repetitive and time-consuming processes. Clients want to reduce costs, improve delivery and ensure consistent quality.
Robotic process automation or RPA is more than a trend. It is a different way to think about how business processes are solutions delivered and managed. Robotic process automation – or RPA in short – involves the production of automation with the help of software. Robotic process automation differs from artificial intelligence in the sense that software robots must always be provided with instructions, because they themselves are not intelligent — at least, not yet.
RPA further empowers business supervisors, knowledge workers staff in a judgement-based role by removing the mundane and allowing them to spend their time on the parts of the business process that are action oriented or customer-centric.
Humans and machines, each on their own, won't be enough to drive businesses in the coming decades. Tomorrows leading enterprises will be those that know how to blend the two effectively, taking steps toward a virtual workforce. To that end, automation delivers proven results.
When it comes to quality, RPA enhances an organization's ability to create a full audit trail, resulting in better compliance and reduced risk. With RPA in place, human error is eliminated while there is a greater than 40 percent increase in FTE available time, who are now able to focus on insights and actions.
From a customers' perspective, RPA can lead to a 48 percent reduction in average response time and enhance the customer experience and making your business more resilient and operational 24/7.
Below are a use cases that I have leverage RPA over the last few years to highlight what is possible.
Every business needs to sell to survive. issues in the operations side of selling can result in customer complaints or selling at reduced prices due to clerical errors. Over the last few years I have seen an increased amount of companies where one or two individuals are responsible for all the pricing within the organization.
Automating complete sales operations process eliminates the clerical errors and the risk of knowledge residing in a few individuals, while providing fast service to your customers. Remember, customer experience begins at your first contact.
Data migration and entry
Legacy systems still perform critical functions at companies. For example, legacy billing systems need to interface with other systems and such systems may not have the capability to pull relevant data from APIs. In such cases, employees manually migrate data using formats like CSV. RPA can prevent such manual labor and potential clerical errors it brings.
Furthermore, such systems that keep data up to data enable improved analysis and decision making. We are living in a day when even marketing has 5000 applications to choose from. RPA can help integrate applications and allow for more holistic analyses.
SIEMs: designed to alert on “bad” events based on rules that human analysts create and maintain. Each rule represents a single snapshot of a negative event pattern out of a potential universe of billions. Most enterprises, even after creating only a few dozen rules, are overwhelmed by alerts, most of which are false positives. The system doesn’t learn from its experience.
Incident response automation: constructed to automate the steps taken in response to incidents that are deemed to be of high severity, so human analysts don’t have to do the same thing over and over, and over. For example, automation of a new firewall rule after an incident.
Alert triage automation: intended to help humans evaluate whether the torrent of alerts from SIEM systems are real threats. While some of the tasks are routine and require robotic automation (e.g., checking against threat intelligence systems and blacklists), the task of determining if an alert is a true positive requires some cognitive automation. Without the capabilities of machine learning-driven automation, the system must resort to manual steps of analysts examining the data to judge an alert’s severity. However, with cognitive automation, we can automate more of the manual process, thereby truly automating the alert triage process.
Forecast and Pipeline Management
Most companies believe their sales cycle is too long, deals stall in their current stage and often slip backwards while the sales team spends too much time on administration. What's worse is that most customers are treated the same and executives question the data integrity. Much of this can be fixed with an RPA solution. For a recent client, a prototype was built, and the organization chose not to adopt the solution. According to McKinsey, automating business processes through RPA can lead to a Return on Investment of between 30 and 200 percent in the first year alone.
None of these examples were had a 'learning' component, so Machine learning or Artificial Intelligence were not leveraged in these projects. Threat hunting was not a part of cybersecurity examples either, but again, adoption of a neural network would be needed to advance these projects to the next stage.
If you are looking to step into the arena of RPA, open source tools are a great place to start. On the commercial side, there are quite a few. Between Blue Prism, UiPath and Automation Anywhere, Blue Prism is a good one to start with as they work with IBM Watson. The next step would be to start thinking about adoption of Artificial Intelligence and Natural Language Processing for your Robots. If you have questions, you can always reach out to me.