My Point of View & Executive Summary :
Robotic Process Automation (RPA), Artificial Intelligence (AI), and Machine Learning (ML) are related technologies, but they serve different purposes and have distinct characteristics.
Understanding the “place & usage” of these technologies
- Artificial Intelligence is Futuristic (self-learning, evolving), closest to Human Intelligence and real-time Interactions. Widely used in Front-end systems enabling Virtual Reality, CRM enablement. A Definitive Suggested Exploration Area in the road-map of Digital Transformation Projects.
- Machine Learning (ML) is Predictive Forecast and is based on Historical Data. Wide usage along with Statistical Analysis (after removal of outliers) in Financial Markets, SCM, Finance Predictive Accounting. A Suggested Exploration Area in the road-map of Digital Transformation Projects. Expert Comment : It might be slightly difficult to “test” or see immediate results in New Digital Transformation Journey. My suggestion would be use the existing landscape (ECC & BW) and identify the Business use cases and embed them in the road-map of Digital Transformation Projects. The Global Initial DESIGN must accommodate the considerations and requirements of incorporating processes those will be enhanced and improvised by Machine Learning
- Robotic Process Automation : A good option to automate the process where processes are already established & stable. My suggestion would be, when thinking of starting your Digital Transformation Journey, identify the “Business Use cases” and “spots” for RPA in the existing process (Legacy ECC). These low-hanging-fruits can be immediately implemented in existing process. For the process which would be impacted significantly during Business Process Re-Engineering, such processes, in the NEW-world (after transformation) should be targeted after the new-processes stabilizes in the NEW-world.
The Supporting Details
| Artificial Intelligence (AI) | Machine Learning (ML) | Robotic Process automation (RPA) | |
| Purpose | AI aims to simulate human intelligence. It involves creating algorithms that enable machines to perform tasks that typically require human-like understanding. AI systems can learn from data, recognize patterns, and make decisions. | Machine Learning is a subset of AI that focuses on the development of algorithms that can learn from and make predictions or decisions based on data. ML models are trained using historical data and use this knowledge to make predictions on new, unseen data. | RPA is designed to automate repetitive and rule-based tasks that were previously performed by humans. It mimics human interactions with software applications and systems to execute tasks such as data entry, form filling, and data extraction. |
| Learning and Adaptation | AI systems can learn from experience and improve their performance over time. Machine learning, a subset of AI, involves algorithms that can learn from data and make predictions or decisions without being explicitly programmed. | ML models learn patterns from data and can adapt their behavior based on new information. They improve their accuracy and performance as they are exposed to more data. | Rule Based : RPA operates based on predefined rules and structured data. It follows explicit instructions provided by humans and does not have the ability to learn or adapt without explicit programming. Human Mimicry : RPA bots interact with user interfaces just like humans do. They work in the presentation layer of applications and do not require changes to underlying systems. |
| Use Cases | AI has a wide range of applications, including natural language processing (language understanding and generation), image and speech recognition, recommendation systems, and autonomous vehicles. | ML is used in various applications such as fraud detection, predictive analytics, customer churn prediction, and medical diagnosis. It’s also fundamental to many AI applications. | RPA is commonly used in business processes where tasks are repetitive, rule-based, and involve structured data. Examples include data entry, invoice processing, report generation, and form submissions. |
In summary, RPA is about automating repetitive, rule-based tasks in a deterministic manner without learning from data. AI encompasses a broader concept of creating machines that can perform tasks requiring human-like intelligence, including learning from data and making decisions. Machine Learning is a subset of AI that focuses specifically on developing algorithms that can learn and make predictions based on data patterns. While these technologies can complement each other in certain applications, they serve different purposes and are applied in different contexts.
Hope the learning shared in this blog helps as input to your Digital Transformation Journey.
MP's S/4HANA 