Key Differences between BPA, RPA, and AI

Have you heard of terms like ‘Pinsetter’, ‘Slubber Doffer’, ‘Linotype Operator’ or even ‘Human Computer’? No, those are not terms associated with automation in any way but rather jobs displaced by it.

You can see how automation has made some of the jobs like ‘Telegraphist’ or ‘Switchboard Operator’, which at least Millennials like me have heard of, obsolete, but have replaced them with more efficient ones like ‘Customer Care Executive’.

So automation is not always about machines taking over human capabilities but in most cases than few, it’s about enhancing or augmenting human capabilities to deliver more efficient results.

In our present age when you talk about enterprises adopting automation, the most often discussed topic is ‘BPA vs RPA vs AI’, and how to choose between these different automation solutions?

Well, the choice depends on what process of your business you intend to automate. But to know that you must first ask the question — Are they the same or do they have differentiating factors, and what each of them represent.

Let’s take a dive into the subject.

What Is the Difference Between BPA, RPA, and AI?

Though BPA, RPA, and AI, are children of the same mother – Automation, are they technology triplets or clones exhibiting same characters but with different names, or are they siblings or sibling rivals?

Here’s a table to decode this:

	BPA	RPA	AI
Definition	Business Process Automation	Robotic Process Automation	Artificial Intelligence
	Business-led	Technology-led	Enhances Technology
Functionality	Streamlines existing processes or creates new ones.	Best for structured data and repetitive tasks.	Simulates human intelligence in machines.
Focus	Focuses on end-to-end processes.	Focuses on individual tasks.	Can handle both individual and end-to-end processes.
Main Feature	Rule-based. Doesn’t learn on its own, unless integrated with AI.	Rule-based. Doesn’t learn on its own.	Have cognitive capabilities and it can learn on its own.
USP	Not stand-alone, but may make use of RPA and AI among other automation tools to deliver complete process solutions.	Stand-alone solution that can work with pre-existing user interfaces to perform repetitive tasks.	Enhances capabilities of RPA, ERP, CRM, BPA and BPM solutions.
Use Case	Automating the full employee onboarding process — document collection, account setup, training assignments and approvals.	Extracting invoice data from email messages and entering it into an accounting system.	Analyzing customer emails and classifying and routing them to the right department or generating appropriate replies.
Error Rate	High if not implemented properly as it deals with more complex workflows. Also, needs constant monitoring.	Very low error rate as it deals with specific high-volume, repetitive tasks.	High if not deployed properly and without proper training and constant monitoring.
Scope	Scalable	Not scalable	Scalable
Cost	Relatively Expensive	Cost-effective	More expensive than RPA

As you can see, though not one is exclusive of the other, they all serve different purposes, and one must chose wisely between them or a combination depending on the needs of your business, and the cost involved in implementing them.

While BPA can be called the big brother, RPA and AI are his siblings that can add value albeit depending on the purpose of the process that needs to be automated.

Overview of BPA, RPA and AI

Business Process Automation (BPA)

The primary purpose of BPA is the end-to-end process automation of business workflows. It involves many steps and may incorporate RPA, AI and other tools as per requirements. BPA also requires human involvement to monitor, and make changes at various levels for it to give optimum results. It can also be integrated with existing ERP, CRM or BPM solutions to provide scalable automation solutions for enterprises.

Main characteristics

• Rule-based and focuses on end-to-end workflow optimization.
• Involves business logic and process mapping.
• Includes human-in-the-loop decision making.
• It can be scaled according to your needs.
• Users can create customizable dashboards and reports to monitor process health, measure KPIs and metrics, and drive business strategy.
• Unifies people, information, apps, and systems in order to create seamless, harmonious processes that transcend team or departmental boundaries. Collaboration is enhanced, silos are dissolved.
• Takes time to implement.

Robotic Process Automation (RPA)

To put it in simple words, RPA is like a digital employee assigned with one particular high-volume, repetitive task like collecting data from emails and populating them into spread sheets. You can have a number of these digital employees taking care of different such tasks, the only difference being that they can work around the clock without errors or getting worn out unlike their human counterparts. RPA is rule-based and works best with structured data.

Main characteristics

• RPA is not a form of AI – it’s a general misconception.
• Best suited for high-volume repetitive task involving structured data.
• It is strictly rule-based and doesn’t have learning capability unlike AI.
• Good to work with legacy systems.
• Ideal for quick implementation for repetitive tasks.
• Cost-effective among the other two but with limitations.
• Can be integrated to legacy systems without Application Programming Interfaces (APIs).
• Don’t need heavy coding. Because many RPA platforms use visual, drag-and-drop interfaces and pre-built components to automate processes, RPA can be regarded as low-code or no-code.

Artificial Intelligence (AI)

Can be called the youngest of the technology siblings but the smartest. Unlike BPAs and RPAs, AI tools have cognitive capabilities that can study unstructured data like images, natural languages, speech recognition etc. AI along with NLP, and machine learning (ML) can be used for predictive analysis, problem solving, reasoning, create content and more. A simpler way to understand is to say that if RPA imitates what humans do, AI mimics human thoughts.

• Non-rule based, and can learn from data.
• Enables intelligent chatbots, document comprehension and predictive analytics.
• Can respond to customer queries in dynamic and unscripted manner.
• Used along with BPA, RPA and other automation solutions to enhance its capabilities.
• Makes decisions based on patterns and context.
• Difficult to measure the accuracy of its results as it uses both structured and unstructured data. Hence not ideal for tasks that need to comply with certain compliance and regulations.

Conclusion

Automation has become an inevitable need for any organization to scale and succeed in this modern age. Wyatt M, lead AI consultant at Northwest AI Consulting, has said that AI does not always produce immediate returns. In specific contexts, rule-based automation tools like RPA and BPA offer superior performance, particularly when compliance requirements, structured data or limited internal AI capabilities are factors.

DeepKnit AI has extensive knowhow of AI, RPA and BPA, and if you’re looking for the right solution for your enterprise, there’s no need to look further.