RPA and AI: Differences and synergies in Intelligent Automation

Both AI and RPA are leading methods for automation, with businesses across the UK and globally adopting these systems to improve workforce efficiency and accuracy.

When it comes to integrating automation into workflows, however, many CTOs and decision-makers look at using these systems as a binary choice – choosing between AI vs RPA. While both of these automation methods have their own unique benefits and use cases, this does not mean that they should be used in isolation. Instead, even greater efficiency and results can be achieved by using AI and RPA in combination with each other.

Let’s explore both automation systems, how they work, their differences, and how and where they can be used together effectively.

What is RPA?

RPA, or Robotic Process Automation, is an automation method that completes tasks through variable and rule-based repetition.

In RPA tasks are performed by “bots” that are integrated into digital systems, with the ability to complete basic actions within the system that would usually be completed by a human user. The bots are given a simple workflow for a basic task, with approaches or elements of this task able to alter or change based on pre-defined rules or variables.

The bots might conduct actions such as entering data fields, copying text, navigating software interfaces or processing transactions, or output large quantities of data into a form or a sheet. RPA is most commonly used for low-complexity operations such as data entry, customer service, or transaction processing. Explore more RPA use cases here.

What is AI automation?

AI-based automation, on the other hand, utilises machine-learning models with natural language processing capabilities to analyse large quantities of training and preparation data, and perform predictive analysis to provide an answer closest to what is weighted as a “correct” or human answer.

Through this system AI automation tools are capable of completing tasks that require judgement, analysis, adaptation and decision-making outside of a rules or variable based system. For example, an AI could be given a large dataset or body of text and independently analyse for patterns or critical pieces of information. It could then provide predictive analyses or recommend changes to improve effectiveness towards different goals. With many of these tools possessing a virtual “memory”, they’re also able to adapt and evolve in their approach over time.

The key differences between RPA and AI

Both automation tools differ in their capabilities, niches, and drawbacks – as well as having a number of further technical differences.

• Robotic Process Automation is excellent at performing repetitive tasks, but unlike AI has no ability to think, analyse or learn from the process it is performing. RPA relies solely on the process or instructions the bots are given, performing pre-defined repetitive tasks at speed. If the task at hand requires an awareness of what is being performed or needs adaptation that cannot be written into a rule or variable system, RPA will not be able to fulfil it effectively.
• While AI has the functionality to interpret, analyse, judge, learn and have a conscious awareness of the task it is performing, the machine-learning and NLP components mean it is typically inefficient when performing repetitive tasks. The base-line complexity with which it must conduct tasks means that it consumes unnecessary resource completing basic tasks and can occasionally from “overthink” basic instructions.
• Once a workflow is established, standardised and successfully validated, scaling out RPA is easy to achieve, and is often a simple matter of adding more bots and occasionally more variables to expand and meet automation needs as required. AI, on the other hand, is more time and resource expensive when scaling to wider systems. When scaling out it requires further training data, regular updates and tweaking, as well as increased monitoring to ensure accuracy.
• Similarly, when implementing into new systems, RPA bots only require access to the tasks they will perform and instructions on how to complete then. Meanwhile, AI needs time and resource to learn the task and the environment it is working within, as well as a significant quantity of training data (which itself can be resource intensive to collect), leading to significantly longer ramp-up times in task efficiency and accuracy than RPA.
• Finally, each method also differs in the type of data it can naturally handle. While AI can extract, analyse and process semi-structured or even complex unstructured datasets, RPA needs clean data that is clearly structured and standardised.

How RPA and AI can be used together through Intelligent Automation

The differences between RPA and AI can be treated as strengths and weaknesses to each methodology – but they can also be viewed as areas they could complement each other. Through intelligent automation, both systems can be combined for more effective and comprehensive automation and overcoming the different automation challenges each tool faces.

There are a number of situations and processes in which they can work collaboratively:

• AI can perform oversight on RPA outputs: While an RPA process handles repetitive tasks at scale and speed, AI can be used to analyse RPA outputs for potential discrepancies and anomalies, adding a vital layer of validation into the process. If clearly wrong outputs are identified, the AI model can even be used to then analyse the RPA process itself and feed in changes that improve accuracy or efficiency.
• End-to-end automation: RPA’s need for structured data can occasionally create additional work which could prevent a workflow from achieving full efficiency. Data organisation and structuring, however, can be handled by an AI tool, enabling end-to-end automation of a certain task. In some instances, AI could even suggest RPA processes or instructions just from analysing a dataset.

There also a number of areas of general synergy between the two:

• While AI is efficient at handling situational context and anomalies for a task, RPA can quickly handle standardised tasks, allowing the two to cover all areas of a workflow.
• AI’s analytical abilities can be used to identify trends and then interpret those trends to adapt or create appropriate RPA workflows.

In general, the weaknesses of RPA can be accounted for by the strengths of AI – and vice versa. By combining these tools workflow optimisation can be achieved more completely and more efficiently.

Best practices for combined automation

Combining both tools together through intelligent automation can be effective but it cannot be achieved in a vacuum. To ensure full efficiency and error prevention a number of best practices are required when using RPA and AI simultaneously.

Data readiness and quality

Both AI and RPA rely on the data they are fed being accessible and conforming to their specific requirements. Apply the following best practices on data when using these tools in tandem:

• Validate that AI is being fed with sufficient quantities of data, both for the purposes of training and the tasks it is conducting.
• Ensure that all data being fed or produced by either automation tool are in formats that they can both read.

Assigning processes

Blanket automation can be more dangerous than it is efficient. When adding integrated automation into workflows be careful to target processes that have a high efficiency yield and where AI and RPA will be able to combine efficiently. Trying to introduce them into every facet of your operations is more likely to hinder than help your business effectiveness.

Compliance and regulation

Outsourcing tasks to automation tools carries compliance and regulation risks, especially for tasks involving sensitive data. Carry out reviews of the workflows you are automating and check that RPA and AI automation processes with comply with the necessary compliance and regulatory rules relevant to their tasks. For complete awareness, maintain audit trails for both tools.

Pilot programs

Phased integrations are safer than introducing automation quickly and without an awareness of how they are likely to perform. Start by preparing AI and RPA pilot programs that allow you to test the effectiveness of automation within workflows. Use these programs to measure key metrics such as efficiency, ROI and error rates, and use such findings to properly tailor full automation implementations.

Tech integrations

When adding AI or RPA into your systems, choose tools that have the functionality to integrate naturally with your existing technology stack. Alternatively, if this isn’t possible, upgrade or switch certain pieces of your stack for tools that will work with your new automation processes.

Combined automation use cases

There are a number of use cases across different industries and sectors where these tools can combine effectively through intelligent automation. Explore some of the different use cases where we’ve integrated the combination of RPA and AI into existing industry-specific operations.

Intelligent automation in the Public Sector – including automation of educational resources, reducing repetitive tasks in central government administration and reducing service costs for online portals.

Intelligent automation in Retail – reducing the resource required for effective customer service, improving inventory management and using AI to discover cost savings.

Intelligent automation in Financial Services – enhancing fraud detection in vast data sets, reducing processing time for customer requests and transactions, and optimising loan and mortgage risk analysis.

How Ten10 enables Intelligent Automation

At Ten10, our automation services focus on providing you with an automaton workflow that delivers the maximum benefits of automation and avoiding the pitfalls of rushed implementation.

We’ve developed a specific approach, called Intelligent Automation, for integrating both AI and RPA systems simultaneously. By effectively and intelligently combining automation methodologies, we have been able to help our clients improve the efficiency and accuracy of their operations and training teams to conduct automation best practices while mitigating deployment risks. Our approach is simple – we understand that AI is not about replacing people, but empowering and complementing existing teams. Learn more about our AI implementation strategies and how our automation strategies deliver measurable business value to clients.

What is agentic AI?

Agentic AI represents the next frontier in automation, embodying a higher degree of intelligence and autonomy that builds directly upon the principles of Intelligent Automation. It moves beyond predefined rules and even beyond optimising existing processes, enabling systems to make autonomous, context-driven decisions and adapt dynamically to new information and changing environments without continuous human intervention. Agentic AI’s hallmark is its capacity for independent action in complex, unpredictable scenarios.

Key characteristics:

• High Autonomy: Operates independently, analysing environments and acting based on its insights without continuous human oversight.
• Deep Context Awareness: Processes vast and diverse data streams to make decisions based on real-time context.
• True Adaptability: Learns and adjusts its behaviour over time, enhancing its ability to handle dynamic situations.

Use case example:

Insurance Claim Processing: Agentic AI has the power to autonomously complete almost the entire workflow of an insurance claim procedure – reviewing submission, validating a claim’s eligibility, proactively contacting the customer, tracking their responses and answering questions they have. Unexpected issues can be reviewed by a ‘human in the loop’, which helps the agent continuously learn how to process new claims and requests moving forward.

Benefits of agentic AI

• Improved Decision-Making: With its ability to process context-aware data, Agentic AI can make decisions that are more accurate and aligned with real-world conditions.
• Reduced Human Intervention: Frees up resources by minimising the need for manual oversight in highly dynamic environments.
• Scalability in Complex Scenarios: Thrives in industries requiring adaptable solutions, such as logistics or healthcare.