Business process automation (BPA) has been around for more than two decades and has mainly been associated with digitizing paper processes. While some organizations are still playing catch up with automating paper processes, the current buzzword around automation is intelligent process automation. So what is intelligent process automation (IPA) and how is it different from the workflow automation of the early 2000s?
In this article we explore IPA, its opportunities and challenges as well as its applications in the workplace.
What Is Intelligent Process Automation?
The advent of machine learning (ML), artificial intelligence (AI) and cognitive technologies have changed the face of business process automation. What started with simple workflow automation, which digitized repetitive manual processes to improve productivity and reduce paperwork, has matured to a state where RPA, artificial intelligence and machine learning tools provide key insights that help increase the efficiency of processes and provide faster response to customers. IPA’s use cases range from intelligent document processing (IDP), streamlining workflows, supply-chain management, claims processing, case management and more.
Benefits of Intelligent Automation
The promise of IPA is rooted in its ability to elevate workflow processing to another degree of efficacy, creating even more productivity, operational cost reduction, agility and better experiences all around. Some of its highest benefits lie in:
- Process Efficiency: The biggest promise of IPA is faster decision making, leading to increased efficiency. Technologies to automate content ingestion, such as signature verification tools, optical character recognition, document ingestion automate the digitalization and sorting paper records. Processes that took days can now be completed in seconds as a result of the combination of these technologies.
- Cost Reduction: Gartner estimates organizations will lower operational costs by 30% by combining hyperautomation technologies with redesigned operational processes by 2024. “The shift towards hyperautomation will be a key factor enabling enterprises to achieve operational excellence, and subsequently cost savings, in a digital-first world.”
- Reimagine business workflows and roles: IPA doesn’t just automate existing processes. It can also help business leaders understand the bottlenecks within existing processes so new processes can be designed to provide better service, leading to new roles and organizational changes as a result.
- Reduced human intervention leading to increased accuracy: Intelligent automation is based on data and largely relies on data sets for decision making, thus reducing ambiguity and increasing agility by reducing manual errors.
Related Article: BPA vs. RPA: How Are They Similar, How Are They Different?
Challenges and Risks of Workflow Automation
Any new technology comes with its own sets of challenges and required dedicated effort across organizations to implement. Some of the major challenges of implementing IPA include:
- Data, Data and Data: AI and ML technologies are highly data dependent. The larger and cleaner the data provided to IPA, the more accurate and efficient the decision making is going to be. Organizations wanting to extract the maximum benefits from IPA need to first establish robust data policies across the enterprise.
- Change Management … Again: Technology insertions often fail due to the lack of adequate change management processes. Change management is a key aspect of delivery and acceptance of any automation effort. Change management includes looking at current business processes and re-engineering them based on the updated business model.
- Training and Skills Gap Assessment: Training programs that cover mission objectives new product features as well as the newly created business processes are imperative to create collective support and awareness for the mission and as well as to increase usage efficiency.
- Lack of Standardization: This situation comes up often when automating long-standing processes where legacy procedures are manual and one off with little or no standardization. In most of these implementations, it is critical to spend time in business process redesign. Automation technologies can only be applied to repetitive processes and require dedicated effort across business and technology to rethink business processes and streamline bottlenecks. Thoughtfully designing a new solution from the beginning can set the foundation for positive change.
- Picking the Right Toolset: The already substantial number of technologies for automation are growing by leaps and bounds. Toolsets range from technologies to automate content ingestion, optical character recognition, document ingestion, conversational AI and natural language technology (NLT). To pick the right combination of toolsets for their needs, organizations will need a clear plan of what they’re looking to accomplish and analysis of the available technologies to understand what they accomplish.
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The Next Generation of Process Automation Is on the Horizon
Do you envision a future where an insurance claim application is able to locate accident pictures, collect incident reports, look up claimant history, even set up calls for human interviews and make decisions based on statistical analysis of similar incidents while combing through millions of records? Well … that future is here.
The future of process automation is far and wide. AI, ML, natural language processing (NLP), robotic process automation (RPA) and optical character recognition (OCR) are among the technologies that will fuel the next generation of process automation. The next step in this journey is when of all these technologies are combined to create integrated ecosystems rather than loosely coupled workflows, leading to what Gartner calls hyperautomation. As hyperautomation becomes more prevalent, get ready to see a seamless blend of robots, human employees and existing systems, which will all work collaboratively to create an intelligent ecosystem.
Geetika Tandon is Managing Director with Deloitte consulting LLP with over 20 years of industry experience with technology consulting. She started her career in IBM as a developer working on voice and RFID solutions, moving to middleware implementation and then acquired deep expertise in IT modernization, helping multiple government agencies move to a cloud and DevOps environment.