RPA vs. IPA: Cutting Through the Fog
Robotic process automation (RPA) and intelligent process automation (IPA) facilitate business processes, such as finding and verifying information, filling out forms, and reordering inventory.
RPA and IPA systems take the complexity out of select processes and workflows. And, as their name implies, these technologies can also automate repetitive tasks, freeing up your employees’ time for creative, high-value work. That’s a sharp contrast with traditional business process automation (BPA) solutions, which rely on the back-end integration of enterprise apps and “if-then” programming.
This article will help you navigate the RPA vs. IPA dilemma and choose the right automation solution for your business.
So here we go.
RPA vs. IPA: Where Does the Line Fall?
Robotic process automation is a technology that uses software bots to enhance or automate tedious tasks by mimicking human activity on the user interface (UI) level. In simple terms, RPA bots interact with apps like human workers instead of just parsing information from other apps.
Although RPA tools streamline processes involving a lot of information and copy-pasting, they only work with structured data stored in tables. In a world where 90% of all information generated by businesses is unstructured, this might be considered a serious disadvantage.
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However, RPA is a quick and non-invasive form of automation that does not call for major changes to a company’s business processes or IT infrastructure. And if you align your business and IT teams and choose the right tasks for bot-assisted automation, your company will face virtually no obstacles to a full-blown RPA implementation.
Intelligent process automation solutions, on the other hand, combine RPA bots with various types of artificial intelligence, such as natural language processing (NLP) and computer vision. Using intelligent process automation services is good at data analytics and intelligent recommendations, improving decision-making in standalone business units or company-wide. Furthermore, this technology is based on transfer learning, which allows companies to reuse algorithms trained on specific tasks for other related jobs.
IPA solutions are good at data analytics and intelligent recommendations, improving decision-making in standalone business units or company-wide. And while human employees may need to monitor the performance of IPA systems during the initial deployment, the AI algorithms’ accuracy and autonomy increase over time.
Overall, IPA is a viable option for organizations that produce large amounts of unstructured data, have knowledge-intensive workflows, or frequently review data for inconsistencies and potential fraud.
IPA vs. RPA: Key Differences Summarized
There are several key differences between RPA and IPA systems:
- While RPA tools can only work with structured data, IPA systems can also analyze unstructured data, such as sensor readings, audio files, images, videos, and texts.
- RPA systems handle individual tasks, while IPA systems can coordinate entire processes.
- RPA bots have limited cognitive capabilities that are pre-defined by software engineers. On the other hand, fully-trained AI systems can consume new data and improve their performance without significant involvement from IT teams or consultants.
IPA vs. RPA: What Should Your Company Choose?
In 2022, the global market for intelligent process automation solutions has reached $13.6 billion. Moreover, experts reckon it will grow at a CAGR of 13.8% in the coming years since many companies are already using artificial intelligence in various business functions. However, the path to IPA implementation is rarely straightforward, and nearly 53% of AI projects need expert help to make it from prototypes to production.
RPA service providers are currently leading the enterprise automation race. Still, RPA bots must handle longer, more complex processes, acquire high-level AI capabilities, and work seamlessly with other business process automation technologies to stay ahead.
One possible solution could be infusing RPA tools with intelligent software agents that learn to perform tasks through observation rather than explicit programming. The leading RPA vendors like WorkFusion, Blue Prism, and Automation Anywhere are gradually enhancing their platforms with such features.
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Here’s what you could do to solve the RPA vs. IPA riddle and choose the right automation technology for your business:
- Assess the quantity, quality, and value of data your company generates
- Conduct an IT infrastructure audit to evaluate your current technology stack
- Pinpoint processes that need improvement and gather input from stakeholders and external consultants
- Set specific objectives for your automation initiative using the SMART framework
- Choose appropriate automation technologies with the help of your IT department or external consultants — including professionals who specialize in AI services
- Create a high-level implementation plan that covers a proof of concept, initial deployment, ongoing user feedback analysis, and gradual rollouts across your business units and the entire company
- Start applying automation in select use cases — and scale your efforts after your initial project proves successful
- Establish a center of excellence and a governance, security, and quality assurance framework regulating your automation initiative
IPA vs. RPA: Case Closed
Automation technologies can help companies prevent employee turnover, reduce operating costs, and outperform competitors.
Several factors drive the increasing adoption of business process automation technologies. These include employee shortages, data deluge, the growing number of apps used by enterprises, and, subsequently, the need to toggle between multiple enterprise applications and systems to perform work-related tasks.
Forward-thinking companies often solve the RPA vs. IPA puzzle by combining the two technologies rather than opting for a single automation solution. Given that many RPA tools now support AI, this is not at all surprising. However, you can only achieve the feat if your enterprise applications work in sync and allow automation tools to extract relevant information. That’s why your automation journey should start with synchronizing data across your IT infrastructure, no matter what technology you opt for.