Intelligent Process Automation (IPA) vs. RPA: What are the Differences?
According to HFS Research, automation is not just a strategy but a necessary discipline to ensure processes run smoothly. However, the next frontier in automation is intelligent process automation, also known as hyper-automation or intelligent automation, which goes further than RPA. IPA, or IA, as it is commonly known, uses cognitive technologies such as artificial intelligence, machine learning, analytics, and process mining to enable RPA bots to execute a range of tasks, enabling the workforce to take up higher-level tasks. While RPA-centric automation remains relevant, its lifespan is limited in the shifting market dynamics. It won’t be long before we see the rise of a more holistic automation approach led by IPA.
So, how does robotic process automation differ from intelligent process automation? Let’s explore the four key factors below.
Learn More: What is Intelligent Process Automation?
RPA can’t learn and adapt to data in real-time
While RPA is viewed as an enabler for boosting cost efficiencies, improving employee engagement, and adding value by automating manual, repetitive tasks, it is a rules-based technology best suited for repetitive tasks. Additionally, RPA bots can’t adapt to variations or exceptions in real time. Meanwhile, intelligent process automation is a more mature technology leveraging artificial intelligence (AI) technologies such as machine learning and natural language processing and continuously learning from data. In contrast, IPA or IA has a built-in AI engine that can work unattended. It can operate using unstructured inputs, make sense of variances, and adapt. Additionally, intelligent process automation tools often have a guided implementation process with connectors, data tools, KPIs, and dashboards, ensuring a seamless deployment. As a result, RPA is more complex to design and implement than IA.
Cost of automation
According to industry research, 78% of companies implementing RPA expect to increase their investment over the next three years, but only 3% of organizations have successfully scaled their digital workforce. While RPA is the ideal solution for automating high-volume business, delivering on the expected ROI is only possible by identifying a credible automation pipeline. In order to maximize the impact of RPA, enterprises must have granular visibility into the last mile of work and understand how processes work to best meet end-user’s needs. Intelligent automation or intelligent process automation provides the opportunity to reclaim granular visibility across the enterprise and reconfigure processes holistically. Thus, intelligent automation is a key enabler for fast-tracking business value.
While RPA does promise greater value and business efficiencies, many enterprises find it hard to realize value from RPA investments. One of the prime reasons is scaling RPA deployments. According to a Deloitte Global RPA survey, scaling RPA is clearly proving more difficult than anticipated and only 3% of organizations have scaled their digital workforce. Thus, efficiency gains with RPA are lower compared to IPA. Additionally, in RPA deployments, SMEs need to play a huge role in decision-making to guide rules-based deployments. On the other hand, IPA coupled with AI and machine learning, can deliver greater value by automating human decision-making.
Intelligent process automation is more business-ready, as it is designed and implemented with strategic needs in mind. RPA, on the other hand, tackles only high-volume and repetitive tasks that occur the same way each time. This doesn’t really align with the operational reality of a modern, agile business.
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Today, over 60% of the teams’ workday is spent on unstructured interactions in documents, emails, communications, custom applications, and websites – outside of ERP, CRM, and other systems of record. This massive unstructured and undocumented interaction dataset between people and software is untapped and contains a goldmine of insights that could give a significant competitive edge to enterprises. Traditional solutions for identifying and building automation pipelines have significant privacy, accuracy, and scalability issues and fail to capture work done outside the systems of record, such as EPRs and CRMs.
Through Soroco ScoutTM, powered by the world’s first work graph platform, organizations can unlock this data source to discover processes where inefficiencies occur and are prime candidates for intelligent process automation.
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