Intelligent Process Automation vs. RPA: The Key Differences

4 min read

In an era where business operations are increasingly digitized, two buzzwords often heard in the corporate corridors are Intelligent Process Automation (IPA) and Robotic Process Automation (RPA). Despite their apparent similarity, they have various functions and offer unique benefits. To assist you in choosing the technology that best meets your needs, this blog article seeks to explain these concepts.

What is Robotic Process Automation

RPA, short for robotic process automation, is a software technology that automates repetitive and rule-based processes that were previously handled by people. It’s like giving a digital robot a set of instructions to follow, and it will execute those tasks without deviation or error.

For instance, in the banking industry, RPA can be used to automate tasks such as loan processing or customer account updates. In the healthcare sector, RPA can automate patient data entry and claims processing.

However, RPA is best suited for structured data entry-type tasks that follow consistent steps. If processes become more variable or complex, RPA faces limitations since it works within a strictly defined rule set without the ability to understand broader contexts.

What is Intelligent Process Automation

Intelligent Process Automation, or IPA, on the other hand, is a more advanced kind of automation that integrates components of artificial intelligence (AI) and machine learning (ML). It does not just obey a set of established rules; instead, it learns from experience and can handle unstructured data as well as complicated circumstances.

For example, in customer service, an IPA solution can interpret a customer’s email using natural language processing (NLP), determine the issue, and generate an appropriate response. In the supply chain business, IPA can forecast problems and provide preventive steps based on past data.

Unlike RPA, IPA can handle exceptions and improve its performance over time through continuous learning. IPA shows its strong points of flexibility and adaptability, better decision-making, and the capacity to handle and analyze unstructured data.


While both RPA and IPA attempt to increase productivity by automating normal corporate operations, there are significant differences between the two methodologies. Choosing the best match necessitates an examination of each technology’s key strengths and limits.

RPA functions as a digital worker, allowing software bots to replicate human movements when doing routine tasks. Bots operate by stringent playbooks, following predetermined rules to execute repetitive tasks like data entry, form filling, and transaction processing. This benefits standardized activities with organized inputs that occur regularly.

However, RPA’s rigidity also presents disadvantages. Without the ability to learn or make judgments, bots cannot easily adapt to exceptions or unforeseen changes that fall outside scripted steps. They are constrained to executing the same predefined tasks without improving over time. For this reason, RPA works best in stable, low-variance environments.

IPA, on the other hand, integrates modern technologies that enable it to comprehend and improve operations intelligently. It can process both structured and unstructured data with its artificial intelligence (AI) capabilities, such as machine learning, natural language processing, computer vision, and cognitive computing.

This intelligence enables IPA to adjust procedures dynamically in response to new information and inputs. It can detect abnormalities, learn from interactions, and optimize operations continually on its own. IPA solutions are also more adaptable than RPA in dealing with unforeseen events, complicated exceptions, and tasks requiring human judgment.

Furthermore, because IPA employs AI, it is capable of indefinitely improving performance through machine learning. Interactions add to IPA’s knowledge base, improving future judgments for greater accuracy, productivity, and long-term consequences. RPA remains static in comparison.

In light of these distinguishing attributes, IPA generally manages non-routine activities and exceptions better than RPA. Its intelligence also makes IPA more appropriate for strategic undertakings that need strong analytical skills and continuous improvement. Meanwhile, RPA continues to be extremely successful for standardized, high-volume activities with predictable, structured inputs. A judicious examination of process characteristics and goals indicates the optimal technology in each unique situation.

When to Use RPA or IPA

When selecting an automation solution, you can consider your working process and goals carefully. While both RPA and IPA deliver value, their ideal use cases differ.

Standardized vs. Complex Tasks

RPA works well for repetitive activities involving unambiguous, structured inputs that occur consistently without variation. For example, insurance companies use RPA bots to efficiently enter claims data from standardized forms into backend systems.

However, tasks requiring judgment or that can’t anticipate every contingency are better suited for IPA. Loan underwriting involves weighing multifaceted applicant profiles – an impractical task for rigid RPA bots but well-handled by IPA’s cognitive abilities.

Static vs. Dynamic Data

RPA functions best with stable workflows and predictable inputs that stay largely unchanged. Processing invoices according to set payment terms month after month is a prime RPA candidate.

But customer needs often evolve unexpectedly. IPA’s self-learning ensured one retailer successfully customized each shopper’s website experience in real time based on unpredictable browsing activity.

Short-Term vs. Long-Term Perspective

RPA delivers rapid results for homogeneous, recurrent operations. It helped streamline patient check-ins at a major hospital system within 6 months, freeing $2M in annual staff costs.

However, IPA ensures enduring value through continuous enhancement. An insurance provider augmented underwriters with IPA, halving processing times while improving accuracy rates even years later as IPA evolves.

Combined Approaches Also Succeed

Sometimes blending RPA for baseline automation with IPA superseding it later proves effective. One bank automated 60% of loans this way before expanding IPA capacities to incorporate exception handling and analytics too.


Although automation may be the shared aim of both RPA and IPA, each has unique advantages. While IPA offers flexibility and intelligence for complicated activities involving unstructured data, RPA excels in accuracy and efficiency for routine, rule-based processes. The requirements and objectives of your company will determine the most intelligent automation solution. Now, that you have a clear idea about the differences between them, you can choose a strategy that can advance your business.

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