Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.
Artificial intelligence, a daily jargon, is often confused with automation. While it’s not entirely wrong to find both connected somehow, it’s still untrue.
Take Amazon’s Alexa for example. It talks to you, does what you say, and performs tasks on your behalf. Intuitively, it is “automating” some of what you did earlier, like manually playing a song or switching the lights off.
But technically, it’s an AI that comprehends your inputs and responses, and then based on its understanding, it acts. This is not true for automated systems at the core. Automation is generally a rule-based program that performs actions based on the rules. So, if you give a command that hasn’t been configured, the system won’t work. This is the primary difference between AI and automation.
Etymologically, AI refers to the intelligence of a computer-controlled system that performs tasks commonly associated with humans.
The term is widely used in reference to systems that possess human-like cognitive abilities, including the capacity for reasoning, problem-solving, generalization, and experience-based learning.
Machine learning, natural language processing (NLP), computer vision, etc., are commonly used subsets of artificial intelligence that form the basics of all AI course syllabus. At their core, systems based on them crunch massive amounts of data, look for patterns, find anomalies, and then offer insights based on the findings.
You must have spoken to Amazon’s Alexa or Apple’s Siri. These are two of the most unique and far-fetched examples of AI talking and performing tasks like humans. More than 97% of mobile users reportedly utilize AI voice assistants.
Automation is the application of technologies and machines to redundant tasks previously performed by humans or to those otherwise beyond human scope. This can range from straightforward, instruction-based software to more extensive programs that automate repetitive tasks.
Take Ford, a giant in the automotive industry, for example. Ford was one of the first to augment and replace human-driven assembly lines. People working in the mechanical and electrical domains were replaced by preprogrammed rules that controlled and executed specific tasks.
You must have also heard about or seen robots perform specific tasks. Commonly known as robotic process automation (RPA), it is one of the most typical examples of automation. Finance, human resources, and customer support are just a few departments that can automate their procedures with RPA.
|Definition||AI is a collection of technologies that collectively allow machines to act like humans by mimicking their intelligence.||Automation uses technologies and machines to carry out rule-based, redundant, and repetitive tasks with zero to little human involvement.|
|Learning||AI systems learn from the data fed into them. Consequently, their performance and accuracy are incumbent on the input data. However, these systems quickly adapt to changing environments.||Automated systems typically do not possess learning capabilities. They are based on rules, and they follow these instructions without fail.|
|Intelligence||AI systems can have varying degrees and dimensions of intelligence. For example, machine learning, natural language processing, etc., all are differently adept in performing tasks.||Automated systems are as intelligent as their guiding rules or software. This is why it is burdensome to maintain them, especially in evolving workflows.|
|Decision-making||AI can make complex, content-aware decision calls on its own. These decisions are based on the input data. Hence, they come with a certain degree of uncertainty.||Automation systems are static. They cannot ‘think’ and call for action if the environment changes.|
|Complexity||AI systems are more efficient and accurate in dealing with complex tasks.||Automated systems are preferred for less complex, repetitive tasks.|
|Human Interaction||AI can easily interact with humans via technologies like natural language processing (NLP), natural language generation (NLG), etc.||Automation typically lacks direct interaction with humans but can interface with other machines or systems.|
|Industry Applications||AI systems are more prominent in the healthcare, finance, autonomous vehicles, and customer service industries.||Automation is more prevalent in the manufacturing, administrative, logistics, and optimization industries.|
|Examples||Self-driving cars, chatbots, smart roads, image and voice recognition, etc.||Robotic process automation (RPA), data entry, manufacturing, etc.|
Now that you have seen how AI and automation are different, here is a detailed comparison between the two.
AI and automation differ in how the technologies learn and train. This difference primarily accrues to how they observe and apply knowledge.
AI systems are capable of learning from input data as well as experiences. These systems learn through machine learning (ML), one of its subsets. ML enables AI systems to acquire knowledge and improve by analyzing this knowledge. This is done in the following sequence:
Coming to the training process; it’s done in three stages: feeding processed data into an algorithm, creating predictive models, and evaluating their results.
Automating systems, unlike AI, lack inherent learning capabilities. Without explicit programming or reconfigurations, they cannot learn or adapt to new information or changing environments.
Moreover, as they are rule-based, they do not require extensive training like AI systems.
Differences in how intelligent AI and automated systems pertain to the extent of decision-making proficiency and cognitive capabilities.
AI systems are adept at mimicking human intelligence, be it problem-solving, understanding complex data, reasoning, or learning. They can also work with unstructured data (like emails, feedback, webpages, images, videos, etc.) and recognize patterns. The list doesn’t end here. AI systems can also solve complex math problems, play chess, and make split-second driving decisions.
To experience AI’s intelligence, you can utilize AI programs in your data science projects. You must know that typing errors, missing values, and data inconsistencies are common. But AI is intelligent enough to deal with these discrepancies. AI can detect errors automatically and provide evidence-based solutions and explanations while generating results.
Still trying to figure it out? Try out some certification courses to see how it works. You can check one of the best certifications in Data Science for the same.
Automation systems, in contrast, lack cognitive abilities. They are rule-based and do not possess the capacity to learn, reason, or understand data beyond predefined instructions.
Whether it’s a hands-free search engine, an automated texting app, or robotic gas pumps, automated systems work on preprogrammed software or a set of rules. If the environment or requirements change, you must reconfigure them to maintain productivity and efficiency.
AI systems differ from automated ones in their decision-making capacities.
AI systems can make autonomous decisions based on the analysis of data. They can interpret complex, unstructured information and use it to make decisions. These systems also learn from historical data and continuously improve themselves.
For instance, almost all airlines accumulate and work with massive volumes of customer data, including demand, competitor prices, flight frequencies, etc., to optimize their strategy. Based on this information, the companies find the most competitive and profitable ticket prices for their customers.
On the other hand, automation systems cannot learn or adapt. They make decisions based on predefined rules and instructions. Often, these rules do not consider changing environments or complex real-world situations. As a result, the decision-making ability of these systems compared to AI is only deterministic.
Both technologies are adept at reducing human involvement in performing certain tasks. However, the complexity of the ooperations they work on may vary. Let’s see how.
AI is designed to handle tasks of varying complexity. It can be used for simple tasks like automation, but it is competent in dealing with complex, non-routine tasks as well, tasks that require decision-making, problem-solving, and adaptation. For instance, ChatGPT and other similar AIs can precisely understand your prompts using NLP and answer your problems.
On the other hand, tasks performed with automation systems are often of low to moderate complexity as these systems work on rule-based programming. These tasks include data entry, straightforward calculations, or routine manufacturing processes. To configure them for higher complexity problems, you’ll need better programming.
The difference between AI and automation also lies in how they interact with humans. While AI systems can, automated ones cannot.
AI interacts with humans for multiple reasons. Primarily, it learns from the interactions and compares them in different scenarios to work accordingly. Further, AI systems, especially those that make complex decisions, may require humans to set goals, define ethical guidelines, and address limiting cases where AI may get stuck.
Another essential AI-human interaction is to ensure legal compliance. Humans must inevitably address ethical concerns with AI applications and ensure the systems comply with all legal and regulatory requirements.
Automation systems are designed to operate with zero to minimal human intervention. They are typically programmed to perform specific tasks, and once set up, they can work independently. Unlike AI, they cannot converse with and understand human language. However, human intervention is required for troubleshooting and reconfiguring in cases of errors.
AI and automation are utilized in different fields depending on the requirements. While they can be used together, here is an idea of how different their application is in different industries.
Industries that require quick decision-making, dynamic support, and complex analysis prefer AI over automated systems because of its adeptness and inherent abilities. Some of these industries are
Industries that require repetitive assistance for rule-based tasks, efficiency, and precision prefer utilizing automated systems rather than AI. These include
Below are some real-world AI and automation examples in live action.
|Smart Vacuum: Roomba, an AI-based vacuum cleaner developed by iRobot, is an excellent example of a real-world AI system. It scans the room, identifies obstacles, and notes the most efficient pathways for future cleaning sessions. Sounds enticing, doesn’t it?!||FATags: If you drive, you must know how FASTags work. These taxes automatically connect the payer to the receiver, whether it's a tax or a parking fee. Before their introduction, people had to wait in long queues and physically hand over the charges, then pick up a paper receipt before continuing their journey. However, with FASTags, the need for a manual barricade operator and supervisor deprecates and reduces long queues.|
|Intelligent Assistants: Besides Apple’s Siri and Amazon’s Alexa, Samsung’s Bixby is a renowned virtual assistant with Galaxy S8 and S8+ models. Bixby can turn off lights and locate misplaced earbuds, which can be accessed through mobile applications.||Life Support Systems: people on life support systems require uninterrupted care and monitoring. These systems often employ automatic devices to observe signals. For example, ventilators can detect oxygen levels in a patient’s body and automatically turn on the artificial oxygen supply.|
As businesses transition and become more tech-savvy, they realize that the actual expense of automation cannot be realized with a single technology. Moreover, to achieve higher levels of productivity, accuracy, and efficiency, they need better and more able-bodied systems.
This can be realized by adapting AI-powered automation. Automated systems infused with intelligent technologies like AI or ML can make your workflows more efficient. It sounds unconventional, but it is the core of Industry 4.0, the most recent phase of the industrial revolution today.
Traditional automation automates repetitive operations but cannot be used in situations requiring independent decision-making. On the other hand, automation powered by AI that mimics human cognitive abilities might be helpful when you need the systems to observe and respond to changing environments and requirements.
AI-enabled systems are much better suited than traditional automation systems because of their self-learning capacity and ability to make decisions like humans.
Simply put, traditional automation follows pre-established rules to carry out tasks, but when AI is incorporated, business processes become independent decision-makers.
Despite the ever-evolving business and tech outlook, artificial intelligence and automation are two of the most concrete technologies. While AI brings adaptability, learning, and intelligent decision-making, automation allows you to exceed human potential in repetitive and rule-based operations.
When combined, industries can benefit from the unprecedented potential of AI’s cognitive ability infused with the precision of automation. AI guides the way, working with complex data and handling intricate tasks, while automation ensures consistency and reliability in your workflows. So automation vs AI is no longer a topic of discussion but a powerful synergy that can shape the future.
To enhance your knowledge on the subject, enroll in an AI-related course. You can check out the KnowledgeHut AI course syllabus and see how it helps you understand the technology better.
No, automation is not explicitly a part of AI. They are distinct concepts but can be used together to enhance efficiency. Automation still works on traditional software.
While AI entails using cognitive abilities and mimicking human intelligence to perform tasks, robotic automation typically refers to using machines to perform physical tasks, often guided by AI but not always requiring advanced intelligence.
Both technologies are highly efficient in reducing human involvement; however, the better choice depends on the requirements. AI is preferred for complex decision-making and analysis, while robotics has proven to be better at automating repetitive tasks.
Many computer languages, including Python, Lisp, Java, C++, and others, are often used for AI automation. However, Python might be the most popular and versatile language for AI and automation due to its extensive libraries, ease of use, and community support.
AI significantly enhances automation testing techniques but is far from replacing humans entirely. Human intelligence and intervention are required for strategizing, designing, and thinking critically. Most importantly, humans must ensure that AI remains ethically and legally compliant.