Enterprise AI
June 6, 2024

AI vs RPA: Choosing the Right Automation Technology for Your Business

Unsure if AI or RPA is right for your business? Find out the differences between them and why AI has the advantage over RPA.‍
AI vs RPA: Choosing the Right Automation Technology for Your Business

Insurance companies struggle to process thousands of manual claims, and healthcare providers can't process patient data quickly enough to make accurate diagnoses. These challenges drain resources and hinder growth.

You’ll need to choose the right automation technologies to tackle these tricky challenges. Artificial intelligence (AI) and robotic process automation (RPA) are two main options.

We’ll help you choose the best technology based on your organization’s goals.

Key Takeaways

  • Automation lets employees focus on more important tasks rather than spending too much time on administrative tasks, resulting in higher productivity.
  • RPA is suited for automating simple, repetitive tasks, while AI’s advanced capabilities make it more suitable for tasks like complex decision-making.
  • AI adapts and learns over time, while RPA requires constant updates. 
  • RPA automatically follows new regulations.
  • Although AI is more expensive to implement initially, it saves businesses money over time by increasing revenue by 20% and reducing costs by 80%.
  • AI is a more flexible and practical solution for healthcare, insurance, and banking businesses, as it can adapt to a larger variety of tasks than RPA.

What is AI?

AI has a broader range of capabilities than RPA. Some examples of AI capabilities include:

  • Machine learning
  • Cognitive automation
  • Natural language processing

AI systems learn from data, recognize patterns, and make intelligent decisions. AI improves diagnostic accuracy in healthcare by analyzing patient data and providing actionable insights.

What is RPA?

On the other hand, RPA automates structured and repetitive tasks, making it useful for tasks like:

  • Data entry
  • Invoice processing
  • Routine transactions

In banking, businesses can implement RPA to automate account updates and transaction processing, which helps reduce human errors and speed up service delivery.

AI vs RPA: The Main Differences

Understanding the differences between AI and RPA is crucial when choosing the right option for your business goals. For instance, UiPath's RPA tools excel at automating rule-based tasks with predefined workflows, while our AI solutions learn from data and make intelligent decisions. 

Some key differences include:

  1. Rule-based vs. Learning-based: RPA is rule-based and follows predefined rules and workflows to automate tasks. Conversely, AI uses machine learning algorithms to learn from data and make decisions without being explicitly programmed.
  2. Structured vs. unstructured data: AI can extract insights from unstructured data, such as medical records, insurance claims, and financial documents. Meanwhile, RPA is limited to structured data and predefined rules. This limits its applicability in industries like healthcare, insurance, and banking.
  3. Adaptability: When business processes change, RPA bots need to be reprogrammed or updated. Meanwhile, AI systems can adapt and improve performance over time by learning from new data.  AI provides higher accuracy than RPA, making it a more reliable solution.
  4. Decision making: RPA follows predefined rules and can’t make judgments or decisions independently. AI can use machine learning models to make predictions, recommendations, and decisions based on patterns in data. This makes it useful for making complex decisions like disease diagnosis and risk assessment.
Differences between AI and RPA including rule-based vs learning-based, adaptability, decision-making, structured vs unstructured datag

Both technologies automate administrative tasks, but AI can automate a wider variety of tasks than RPA, thanks to its adaptability. This allows approximately 70% of employees to focus on more important tasks instead, helping businesses drastically boost their productivity.

We'll look at real-world business applications to better understand the differences between the two.

Examples: AI in Business 

We automated document processing for a healthcare company, reducing the time spent manually analyzing and labeling medical notes.

In particular, we used the capabilities of a large language model (LLM) to develop an AI Agent that automatically analyzes and labels medical notes. This minimized the chance of human errors during data analysis and allowed the organization to focus on more important tasks, like delivering high-quality patient care.

Another example was in the insurance industry. Manually extracting information from tax forms was a massive issue for a mortgage company, as other automation solutions weren’t accurate enough to handle this task.

Instead, we developed a custom Document AI model that performed data extraction with almost 100% accuracy, reducing analyst involvement by 90%.

Examples: RPA in Business

One example was during the COVID-19 vaccine rollout. BP3 Global used RPA to automate dose registration, allowing medical staff to focus on patient care and vaccine administration.

Another example of RPA in business is Cyclone Robotics, which created intelligent assistants for the Postal Savings Bank of China. These assistants automated repetitive tasks and significantly reduced employee errors. In particular, they automated financial processes like order and invoice management for Xingcheng Special Steel to boost operational efficiency.

Although RPA has had its fair share of successful business applications, AI is a more versatile solution beyond automating simple tasks. This makes it highly practical in healthcare, insurance, and banking.

Advantages and Disadvantages of AI

Even though AI has more advanced capabilities than traditional automation solutions, it’s still important to weigh its pros and cons before deciding which technology to choose for your business.

AI has some major advantages over RPA, including:

  1. Advanced decision-making capabilities: AI solutions like Decision AI analyze vast amounts of data to make informed decisions quickly and accurately. For example, in the insurance industry, AI systems can assess risk profiles and determine appropriate premiums based on historical data and predictive analytics, leading to more accurate and fair pricing.
  2. Ability to learn and improve over time: AI systems continually improve their performance through machine learning as they process more data. In healthcare, AI-powered diagnostic tools enhance accuracy over time by learning from new medical data and patient outcomes. 
  3. Improved customer service: AI-powered chatbots and virtual assistants can provide 24/7 customer support, handling inquiries and resolving issues efficiently. Conversational AI uses unstructured data to answer customer queries. Many banks use AI chatbots to assist customers with account inquiries and troubleshooting to reduce wait times.
Key advantages that AI has over RPA including advanced decision-making capabilities, ability to learn and improve over time, and improved customer service

Meanwhile, some drawbacks include:

  1. High implementation costs: Developing and deploying AI solutions can be expensive, requiring significant investment in technology infrastructure. Smaller businesses might need help finding affordable means of implementing AI.
  2. Data privacy and security concerns: AI systems often require access to large amounts of data, raising concerns about data privacy and security. In the banking industry, using AI for fraud detection involves processing sensitive financial information. If this data isn't stored securely, it could lead to data breaches and loss of customer trust.
  3. Lack of transparency: AI systems (especially ones using deep learning) are "black boxes" where decision-making processes aren’t easily understood. This issue becomes apparent in insurance when customers or regulators require clear explanations for AI decisions, such as claim denials or premium calculations.
AI disadvantages including high implementation costs, data privacy and security concerns, and lack of transparency

Advantages and Disadvantages of RPA

RPA is a highly practical solution for automating repetitive, rule-based tasks despite limited capabilities in comparison to AI.

A few benefits of RPA include:

  1. Cost-effective for automating repetitive tasks: RPA can significantly reduce operational costs by automating mundane and repetitive tasks. In the banking industry, RPA automates routine transactions such as processing loan applications and updating account information, which reduces the need for extensive human labor and cuts down on payroll expenses.
  2. Easy to implement and integrate: RPA tools are typically easier to implement and integrate with existing systems compared to more advanced technologies like AI. Insurance companies can quickly deploy RPA to handle claims processing by integrating it with their legacy systems without major IT overhauls.
  3. Scalability and flexibility: RPA solutions are highly scalable, so your businesses can increase your automation capacity as needed. In the insurance industry, RPA can be scaled up to handle the increased volume of claims, ensuring timely and efficient processing without additional hiring.
Advantages of RPA over AI, including cost-effective, easy implementation, and scalability

Although, a few cons of RPA are:

  1. Limited to Rule-Based Processes: RPA struggles with business processes that require human judgment or handling unstructured data, making it unsuitable for complex tasks. For example, while RPA can automate the processing of structured invoices, it won’t be able to interpret complex or non-standardized invoices without additional programming or AI integration.
  2. Requires Constant Maintenance and Updates: RPA systems need regular maintenance and updates to adapt to changes in the business environment or software updates in the systems they interact with. If a bank changes its transaction processing system, the RPA bots must be reprogrammed to ensure continued operation - a time-consuming and costly process.
  3. Limited Adaptation: Unlike AI, RPA doesn't learn or adapt on its own. If a new regulatory requirement is introduced in the insurance industry, RPA bots must be manually updated to comply with the new rules - potentially resulting in legal issues.
Disadvantages of RPA, including being limited to rule-based tasks, requiring constant maintenance, and limited adaptation

RPA or AI: Which Is Better?

RPA is useful for simple tasks but struggles with adaptability, making it harder to comply with changing regulations.

On the other hand, AI systems are constantly updating, so your business won’t have to worry about complying with new regulations. They also improve over time and can process large amounts of data to help your business make informed decisions. AI can automate entire workflows, while RPA only automates individual, repetitive tasks.

In industries with more demanding tasks, such as healthcare, insurance, and finance, AI has a clear advantage over RPA due to its adaptability and ability to improve over time.

Even though AI has higher initial costs, we offer an affordable means of implementing it into your workflows within 6-8 weeks. To find out how AI can reduce costs by 80% and increase revenue by 20%, schedule a free 30-minute demo with our experts.


Is RPA considered AI?

RPA isn’t considered AI, even though both technologies automate tasks and improve efficiency. RPA uses software robots to perform predefined, rule-based tasks like data entry and transaction processing. These bots follow explicit instructions and can’t adapt to new situations without human intervention.

Meanwhile, AI involves creating systems that can learn from data, recognize patterns, and make decisions. It can handle more complex processes and tasks than RPA.

Can RPA be combined with AI?

Yes, this combination is called intelligent automation. It fixes many of the issues found when using RPA on its own, such as dealing with situations requiring human intelligence. For example, AI can automate customer interactions to provide insights that RPA bots can use to respond to customer queries. 

In banking, AI-powered RPA can automate the entire loan application process, from data collection and risk assessment to final approval.

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