In recent years, technology has rapidly developed, resulting in a more complex work environment. Creating efficient work processes and driving innovation has become an urgent issue that organizations must prioritize.
In this context, technologies that have gained attention as drivers of digital transformation in businesses include "RPA (Robotic Process Automation)" and "AI (Artificial Intelligence)," both of which have different strengths and play important roles in enhancing work process efficiency and improving decision-making within organizations.
This article will explain the basic knowledge of RPA and AI, the differences between the two technologies, as well as the advantages of using them together, including real-world usage examples, to make it easy to understand, while also recommending which type of technology is suitable for which types of work.
RPA or Robotic Process Automation
“Automating work processes with robots”
RPA is software designed to assist in performing tasks on computers instead of humans, particularly for tasks that are repetitive and have clear steps, such as data entry, data transfer, or generating various reports. The system "records the way humans work" and then automates the repetition, as if another person is always sitting at the computer doing the work.
By implementing RPA to perform tasks, it significantly reduces work time, alleviates employee burdens, and minimizes errors that often occur from humans, resulting in faster and more accurate work.
However, RPA is a system that operates according to predefined steps, making it suitable for tasks that are routine and repetitive, such as document work, accounting, or data management.
Conversely, tasks that are complex or frequently change, such as those requiring situational decision-making, creative tasks, planning, or communication with customers, may not be suitable for RPA as they require human thought, analysis, and flexibility to manage.
AI (AI) or Artificial Intelligence
“Artificial Intelligence”
AI is a technology that allows machines or computers to "mimic human intelligence," whether it is thinking, analyzing, learning, or even making decisions in various situations.
The underlying technology of AI relies on key technologies such as Machine Learning and Deep Learning, which enable the system to learn from vast amounts of data and continuously improve its capabilities. As more data becomes available, the system becomes more accurate and smarter.
With this capability, AI does not just follow commands like a typical system, but can analyze data, make predictions, and assist in decision-making on its own in various situations.
AI can be divided into 2 main types:
1. Specialized AI (Weak AI / Narrow AI)
This type of AI is designed to be "domain-specific" or to work only within a defined scope, focusing clearly on one specific task, such as
- Speech recognition
- Image recognition
- Language translation or summarization
Although it seems very intelligent, this type of AI "does not understand the world like humans." It simply learns from a large amount of data and uses the patterns it has learned to respond or predict outcomes.
2. General AI (General AI / Strong AI)
is AI that can perform various tasks like humans, is highly flexible, and can understand its own situation, as well as think, analyze, and decide on actions independently (similar to human decision-making).
Comparing the differences between RPA and AI
|
Topics |
RPA |
AI |
|
Objectives |
Processing routine tasks (tasks with fixed patterns) |
Solving complex problems, forecasting, interaction, etc. |
|
Learning ability |
Carrying out specific operations according to programmed or predefined rules |
Making decisions and taking actions using Machine Learning and Deep Learning |
|
Characteristics of suitable tasks |
Simple, repetitive tasks and structured data processing, such as data entry, copying/moving data, data extraction, summarization, and connecting data between systems. |
Learning from data and making decisions independently. Tasks that require perception or complex decision-making, such as image recognition, speech recognition, and language processing. |
|
Preparation before use |
Setting up workflows |
Machine Learning training and model evaluation |
|
Ease of implementation |
If it is Desktop or Cloud RPA, it can be started with relatively low costs and in a short time, making it quite easy to implement. |
In cases where AI is implemented in organizational services, it may take time to gather a large amount of data and build models, and specialized knowledge is required, making implementation more challenging. |
Advantages of integrating RPA and AI
RPA operates by automatically repeating predefined steps or rules, which has the downside of being difficult to manage complex tasks or changing data. For example, tasks that require multiple data formats or flexible decision-making will face limitations if only RPA is used.
Integration with AI can make these possible. AI can understand language and data, as well as adapt to changing information. When connecting the operations between RPA and AI, it will be possible to automate more flexible and complex workflows effectively.
For example, tasks that require reading characters from handwritten paper documents or images are difficult for RPA. Automation can be achieved by using it in conjunction with AI-powered OCR.
AI-OCR can predict and read handwritten characters. In the future, tools with AI will be more widely used. The connection between AI and RPA will help expand the scope of tasks that can be automated, which was previously difficult with RPA alone, and will enable the entire workflow to be automated more efficiently and effectively.
Cases where AI should be used
AI can analyze and predict from data, making it suitable for tasks such as natural language processing, image analysis, and sound. Examples of cases where AI can be applied include
- Analyzing customer data from existing information
- Generating text or images with Generative AI
- Answering customer questions with AI Chatbot
AI can learn and make highly complex decisions through Deep Learning.
Cases where RPA should be used
RPA has clearly suitable task characteristics, and in business, tasks suitable for RPA are routine and repetitive tasks, such as
- Tasks with defined rules or steps, such as data extraction, data import, data entry
- Tasks that need to be done across multiple applications
- Processing large amounts of data
- Summarizing and analyzing data
- Email sending
RPA is faster, more accurate, and more precise than human work. For this reason, many organizations implement RPA across various business types.
Summary of the differences between RPA and AI
RPA and AI are technologies that enhance organizational work but have distinctly different roles. RPA focuses on executing predefined steps, suitable for tasks with clear and repetitive patterns, such as document work or fixed-step processes, while AI has the ability to learn from data, adapt to complex situations, and assist in analysis or decision-making at a level closer to human capability. It can be said that RPA automates the "work processes," while AI enhances "thinking and decision-making," making the system smarter.
When both technologies are used together, organizations can create more comprehensive automation systems, from data collection and analysis to process execution, resulting in a clear improvement in overall efficiency.
In the early stages, many organizations viewed RPA and AI with concerns about costs, uncertainty of results, and fear of replacing human labor. However, when implemented, it was found that it could significantly reduce working time, decrease human errors, and allow employees to focus on higher-value tasks such as analysis, planning, or business development. It also helps reduce the burden of overtime work, improving the quality of work life.
With these tangible results, RPA and AI have become increasingly recognized technologies and are becoming an important part of driving organizations in various industries today.
for organizations looking to start simple changes Michiru RPA is another option that makes it easy to start Automation, as it can be used for Routine tasks and can be integrated with AI.
You can see more details about Michiru RPA at
👉https://thai.fakiki.com/pages/michiru_rpa
Or if you want to see a real usage example, you can schedule an online presentation or meet in person at no cost (the team supports presentations in Thai, English, and Japanese). For inquiries, call 080-010-8801
