Companies looking to maximize the benefits of Robotic Process Automation (RPA) are turning to machine learning to optimize operational and business efficiency. At present, a majority of organizations are implementing machine learning to single-task processes. However, they still rely on human intelligence to connect the disparate systems which greatly hampers productivity.
A combination of process automation and intelligence through Intelligent Process Automation (IPA) holds a lot more potential to maximize the productivity of automated systems.
What is Intelligent Process Automation?
Intelligent Process Automation seeks to combine Robotic Process Automation and machine learning so as to implement a fundamental process redesign. It mimics the activities that humans carry out, but thanks to machine learning, learns to do them better over time.
Essentially, IPA is an advanced form of Robotic Process Automation as it incorporates intelligence, comprehension and precision.
Difference between Intelligent Process Automation and Robotic Process Automation
Admittedly, Robotic Process Automation has greatly assisted companies to reduce human errors. In turn, this has led to higher productivity as well as profitability. But RPA in itself is insufficient as it lacks some features.
For instance, RPA tools tend to get stuck whenever a user needs judgment on how, what and when to use specific types of information in specific contexts. In such instances, it requires the intervention of human supervisors to aid in decision making.
The use of machine learning in such a context facilitates the shift from RPA to IPA, introducing the intelligence aspect which is requisite for decision-making. RPA is a rule-based system whose tools operate on pre-determined codes to achieve specific tasks. On the other hand, IPA is designed to analyze previous decisions, learn and get smarter over time so as to make its own intelligent decisions.
Here are some key areas in which IPA can augment RPA to optimize efficiency:
- Moving Beyond Basic Automation
Thanks to machine learning, IPA is capable of moving beyond the basic automation that RPA provides. It can learn from historical data and detect patterns to handle input better and offer accurate predictions.
- End-to-end Workflow Management
Using smart IPA tools allows for efficient management of end-to-end processes, enhancing visibility into the workflow.
- Natural Language Generation
A combination of machine learning and RPA is able to create a narrative from the data at hand. As such, it has what it takes both to replace manual jobs and present a narrative based on which analysts can make decisions.
- Analysis of Unstructured Data
For a rule-based system to work, it has to be fed on understandable input in the form of structured data. However, most of the data that businesses and other organizations collect are unstructured. Making use of such data requires a smart model that uses IPA.
Practical Applications of Intelligent Process Automation
Intelligent Process Automation through machine learning holds some obvious merits for businesses today. The business environment is hyper-competitive and to succeed, one has to enhance customer relationship, cut down expenses and consistently innovate. How can IPA help an organization to achieve these objectives?
- Automated Document Generation
Using Natural Language Processing (NLP) capabilities, an IPA system can analyze incoming documents such as emails. Once it comprehends the context, it can carry out automated follow up, e.g. by drafting “thank you” emails.
Similarly, such a system can create contracts and proposals or even pre-populate forms to replace tedious, manual processes. To illustrate this, consider an insurance firm setting where a client gets unique documentation to match their circumstances and needs.
In such cases, it can analyze customer requirements, their gender, age and financial status among other things to generate a specific document suited to their needs.
- Promoting Events
An IPA system can track client location, analyze their probability of attending an upcoming event and send them invitations accordingly.
- Automated Recruitment
The role of HR these days extends beyond finding and recruiting top talent. They too need to create strategies for business growth, track competitors and ensure high employee job satisfaction.
IPA takes the challenge out of their recruitment role by automating the initial steps. An IPA system analyzes applicants’ emails and scans applications to recommend candidates who match the required profile.
Business Value of Machine Learning-Enabled IPA
- Higher Productivity, Better Performance
Just like its predecessor RPA, IPA takes off a load of manual work from various employees and frees them up for more productive endeavors. Everyone wins since the organization gains more from having a fully engaged team and employees enjoy higher productivity and performance.
- End-to-End Process Automation
RPA enhances the individual process and requires human intervention to complete processes. But IPA goes a step further to facilitate complete implementation, enterprise-wide.
- Better Decision-Making
The intelligence aspect of IPA is what endows the concept with decision-making capabilities. IPA models analyze available data, reach conclusions and propose a course of action.
Implementing an IPA Transformation
Clearly, IPA is highly valuable to the modern-day business model and offers the potential to transform numerous aspects of the operation to drive efficiency. Once IPA takes over routine roles then human employees can train their focus on enhancing customer satisfaction and reaching business goals.
Notably, an IPA transformation does not necessarily require a massive investment. To illustrate, RPA, which is an aspect of IPA, operates on existing systems and does not require any major changes. Here are a number of steps that are essential to a successful implementation of Intelligent Process Automation through machine learning:
- Clarify the Business Objective and IPA’s Role in Achieving it
Just like it is with any other business strategy, the success of RPA implementation is pegged on defining business objectives and figuring out the role of IPA in the process. Having a clear outlook on this will allow a business leader to see how it can fit in with other aspects of an operating model.
- Optimize the Complete Portfolio of IPA Solutions
Applying individual aspects of IPA independently will not allow a business leader to make the most of the technology. Rather, full benefits will come from a holistic implementation of the entire portfolio that makes it up. Capturing value from a project implemented in silos is far more challenging than from a holistic implementation.
Implement a fundamental process redesign to transform the way a group of processes functions so as to achieve the full capacity of the model.
- Create a Minimum Viable Product
Instead of trying to begin working at everything once, choose an end-to-end process that you want to enhance and launch an MVP based on this. By doing so, you will be able to assess what works and what does not and make alterations before the full implementation.
Trying to build a complex all-inclusive system at the onset may take decades and go way over-budget before yielding any tangible benefits. But an MVP can start working in weeks yielding immediate benefits.
- Capturing Immediate Value and Creating Long-Term Momentum
After the creation of an MVP, try to create a balance between immediate wins and long-term developments. Have a detailed roadmap in place to lay out the fundamental process transformation outlining both immediate and long-term plans.
Winning the Digitization Race through ML-Based IPA
As is often the case with any large scale transformation, the implementation of Intelligent Process Automation takes time and requires robust communication. Only through such communication can people in the organization adapt to the change and make a success of it.
At present, organizations are merely scratching the surface of what can be achieved through IPA. The ultimate winners in the long run will be businesses that fully embrace the capabilities of the change and take strides to derive value from them.
Latest posts by Janica San Juan (see all)
- How Machine Learning With the Help of SharePoint Can Revolutionize the Manufacturing Industry - October 22, 2019
- Bridging the Gap between Business Processes and Metrics Using Machine Learning-Based Anomaly Detection - September 30, 2019
- SharePoint and Machine Learning – Harnessing the Potential of Corporate Big Data with Intelligence and Automation - September 20, 2019