Striving for freeing employees from excessive labor, organizations have found out that robots can do routine work more efficiently and with fewer errors than humans. Robotic process automation is the one operating on rule-based tasks. For example, RPA bot can extract data from a particular source and get it into a correct format in a spreadsheet.
However, while RPA has become widely and successfully adopted by many corporations, its applications are somewhat limited. An RPA bot’s actions are scripted, and in case data is slightly different or the software’s UI changes, the bot gets stuck, begging for human assistance. Nowadays, organizations want intelligent tools that can operate on their own, make decisions, and adapt to new data and conditions.
The goal of reaching ultimate efficiency has led to the emergence of intelligent process automation (IPA). IPA combines RPA with ML and AI, sufficiently reducing the need for training and human involvement. Now, with thought-out IPA implementation, many business workflows can be intelligently automated, allowing for better decision-making and allocation of resources. Let’s examine four industry use cases for such an intelligent automation.
Customer Service
For any company operating in the B2C realm, customer service improvement is a continuous goal. Responding to customer service requests and resolving them in a timely manner is a critical challenge.
The majority of these requests are handled by call center agents, who need to quickly access one of the many different systems and data repositories. IPA allows us to consolidate a plethora of systems into one convenient process, which significantly speeds up response times. Natural language processing software embedded into IPA tools allows for quick identification of a request’s root cause, allowing agents to access the needed information faster and with more accuracy.
Insurance Claim Management
Insurance is one of the most promising industries for IPA implementation. Given that insurance companies often have different areas of competence, sorting out requests that touch many different issues is rarely an easy task. That’s why fast claim processing is one of the critical factors of insurance companies’ success.
On top of optimized initial claims handling, IPA can also have a significant impact on claim assessment and settlement, which frees up human resources and streamlines essential insurance processes overall.
Employee On boarding
Unfortunately, new hires that can get straight to work in the first few days without friction have become an exception. Oftentimes, they can’t get immediate access to the required tools and their training is delayed because of the miscommunication between departments.
IPA can solve this problem by guiding the new employee and taking care of many required tasks. For example, IPA tools can enroll the new employee into a relevant training program, schedule meetings with managers, generate secure credentials for accessing corporate systems, make sure that digital forms are correctly filled, and order business cards. This will eliminate friction, significantly speed up onboarding, and minimize HR staff’s involvement.
Financial Workflow Automation
Most forward-thinking banking institutions are generally familiar with various AI concepts that are poised to enable more efficient workflows. Capgemini predicted that the finance industry will add around $512bn in global revenues by adopting IPA by 2020. This shouldn’t come as a surprise as the banking industry is notorious for excessive amounts of manual work.
IPA can be used for extracting data from loan applications and then automatically entering this data into relevant accounting systems. Processing payments, removing false positives in transaction screening systems, and customer onboarding in banking can also be automated by IPA. Investment companies can more efficiently assess risks by automating data extraction from financial records. Being a very data-intensive industry by nature, financial services can see a dramatic improvement in operational speed and the amount of errors.
Do You Need IPA?
Regardless of the promising opportunities offered by IPA, the implementation is challenging and organizations often don’t get the expected ROI. However, the root causes of all the adoption roadblocks typically stem from poor planning and a lack of understanding of this technology.
Outlining a clear business case, establishing a definitive vision, and understanding how exactly you want to capture value from IPA implementation are the most fundamental requirements. While this might appear too obvious, most organizations often don’t dig deep enough into tasks they want to automate.
For example, at many large enterprises, IT incidents are often handled by support desks. Given that most such incidents concern very basic tasks like password resets, IPA is not needed here and RPA would most likely be sufficient. This is an important decision, as RPA implementation is much less challenging and resource-intensive. However, when it comes to more advanced tasks, ML involvement might be required. While this is clearly a primitive example, ambitious IT leaders still tend to jump on the AI hype train, adopting the technology where it provides little value.
It’s also crucial to understand that IPA implementation is a continuous process. Establishing a clear path to scalability is what often separates prosperous companies from the rest. Ensure that your workforce has the needed skills to fully reap the benefits of IPA and establish training programs ahead of the implementation. In fact, Deloitte claims that organizations that have the support of a dedicated Centre of Excellence are capturing the most value from intelligent automation.
IPA is taking the leading role in making organizations more efficient. RPA has proved its worth by automating simple processes, but intelligent systems that use AI and ML can provide far more critical impact. If implemented correctly, IPA is a reliable way of reducing costs, enhancing accuracy, and improving employee satisfaction, among other payoffs.
About the Author
Yaroslav Kuflinski is AI/ML Observer at Iflexion. He has profound experience in IT and keeps up to date on the latest AI/ML research. Yaroslav focuses on AI and ML as tools to solve complex business problems and maximize operations.
