Automation refers to processes that can repeat a task over and over again without human intervention, whereas Artificial Intelligence is concerned with how a machine can use data to understand the world, make decisions, make predictions and learn from experience.
For students thinking about future careers, understanding the difference between artificial intelligence and automation and where they are used is important. Although commonly associated with IT companies, AI and automation today are used across a number of sectors, including finance, healthcare, insurance, retail, education, transport and manufacturing companies.
What Is Automation
Automation means using technology to complete repetitive tasks with limited human intervention.
Automation is typically based on fixed rules. The software goes through a number of steps in a predefined order in order to complete a task. There are many processes that can be automated, such as:
Sending automated emails after a form is completed
Generate invoices after payment transactions
Categorise customer requests
Schedule backups automatically
Assemble objects in a factory setting
Send notifications for meetings and deadlines
Traditional automation is designed to handle repeatable processes of a mostly predictable nature and can be used to streamline tasks that are consistently performed in the same manner in order to save time and reduce manual intervention.
However, traditional automation may not work well for tasks that involve changing or irregular patterns, unclear information or decisions that need a lot of human judgement or subject that is too nuanced.
What Is Artificial Intelligence
Artificial Intelligence (AI) enables computer systems to perform functions that would normally require human intelligence, learning or decision-making.
AI can be utilised for:
Reading and comprehension of text
Detection of patterns in data
Forecasting future events
Identification of images or speech
Anomaly detection
Content or product recommendations
Decision-making assistance
For example, a bank could use AI to detect suspicious transactions, an online shop could use AI to offer customers products that they would be interested in, and a hospital could use AI to scan through a patient’s data to identify any health risks.
Students can also read Is AI a branch of engineering to understand where AI sits within technology education.
How AI and Automation Work Together
Automation answers the question: What task should happen and when?
AI answers the question: What does the data mean and what should happen next?
A customer submits a form and receives a standard automatic reply. The human team then reviews the request.
Together with AI and automation:
AI scans the customer query.
It determines if the matter relates to payments, delivery, refunds or tech support issues.
Automation routes the request to the appropriate department.
AI offers a potential solution.
Human intervention is needed for complex matters.
AI makes automation smarter, and automation makes AI more useful. Automation can support real business processes and increase their value through AI.
Real World Examples of AI and Automation
A variety of industries today are leveraging AI and automation. The core concept, however, remains consistent: AI recognises or predicts something, and then automation follows through on the next step.
Customer Support
Within customer support, for instance, AI within support platforms reads customer messages, determines their intent and even suggests possible replies. Automation then takes over the rest and can, for example, forward the ticket to another group, send customers updates on their case, escalate critical issues to human customer support employees, etc.
For example:
AI identifies the type of complaint.
Automation sends it to the correct department.
AI suggests a response.
A human reviews sensitive or complex cases.
Finance and Insurance
The finance and insurance industry creates a lot of data, paperwork and repetitive tasks for an employee to complete on a daily basis.
AI can help with:
Fraud detection
Document reading
Risk checks
Customer query understanding
Claims processing
Policy servicing workflows
A recent example of the application of this technology is an AI-powered tool, built by 18-year-old Advith Sharma, which was featured in an India Today article. It aims to support insurance agents and brokers in their work.
A lot of the work of an insurance agent involves looking through paperwork, following up with clients, processing their requests and repeating mundane tasks. An AI system can help make sense of this information, and automation can help move the workflow ahead.
Healthcare
AI and automation could support healthcare professionals in reading patient scans, monitoring patients, reviewing reports, sending appointment reminders and managing hospital operations.
So, in this example, AI would pick up unusual activity in a patient’s health information, and automation would then alert the relevant people and even book follow-up appointments as needed. Since AI is analysing patient data, the final decision and medical responsibility should remain with qualified healthcare professionals.
AI should support healthcare professionals, not replace them.
Retail and E-Commerce
Retail companies use AI and automation for product recommendations, inventory planning, customer support and personalised offers.
For example:
AI predicts which products a customer may prefer.
Automation sends a personalised message or offer.
AI forecasts demand.
Automation alerts teams when stock may run low.
This helps businesses respond faster to customer behaviour and demand changes.
Manufacturing and Logistics
In manufacturing, for example, AI can detect production defects and forecast when a machine is likely to fail. Automation can then stop the process, alert the technician and schedule maintenance if required.
Using Artificial Intelligence in logistics, for example, can help predict problems like delivery delays and necessary changes in the delivery plan to prevent delays. Automation then updates all connected systems and applications. After that, automated emails and notifications are sent to the customers. Automated tasks for employees are also created.
However, in highly sensitive fields such as healthcare, finance, insurance and customer service, humans must review the work done by AI and automation systems to account for situations that the systems have not considered and to correct errors made by the systems.
What Students Should Learn
If you are interested in Artificial Intelligence and automation, do not restrict yourself to learning only AI tools and automation platforms. It is imperative that you have a very strong foundation of how data is being generated, how it can be used, how users interact with software and systems, and how systems interact with each other.
Important skills include:
Programming
Data handling
Databases
APIs
Software engineering basics
Machine Learning basics
Cloud basics
Problem-solving
Product thinking
Communication
If you are eager to jump-start into the AI world as soon as you get into undergraduate, then you should check out the CS & AI programme by Scaler School of Technology.
In this programme, core CS foundation is paired with learning AI/ML along with software engineering and more, with a strong emphasis on project-based learning and real-world applications. The learn-by-building approach helps students create real-world projects that can utilise AI for making better software systems.
Students can watch this video on how AI is changing Computer Science Education.
How AI is Changing Computer Science Education?
Will AI and Automation Replace Jobs
AI and automation have the potential to remove tedious work from jobs and, in the process, change how many roles work.
Support teams may spend less time manually categorising and assigning incoming tickets to the best person to handle them, and instead focus on dealing with the complex issues that need to be handled through to a successful solution.
Finance teams may spend less time manually cross-checking a large amount of financial documentation uploaded by multiple different people, and instead focus on manually reviewing any exceptions that have automatically been raised.
And, whilst automation can perform automated testing on parts of systems faster than is humanly possible, this will still need to be designed, tested and validated to work end to end by skilled software developers.
Rather than focusing on whether jobs will disappear or not, students need to consider the set of skills required to remain relevant in a future workplace that increasingly utilises AI and automation.
Students worried about future roles can read this guide on will AI take away engineering jobs.
Automation often changes or redistributes tasks within roles. Thus, it is more productive for students to focus on building a set of core skills that can be mixed and matched in order to create value in many different tasks within a given field.
Conclusion
Automation handles repetitive tasks, and AI adds prediction, pattern recognition, human language understanding and decision support to improve existing processes.
Together, automation and AI can support smarter workflows across industries such as finance, healthcare, retail, insurance, manufacturing, logistics and customer service.
The key point for students to recognise is that automation and AI should not only be seen as job-threat topics. Students should also understand how these systems are created, how humans are involved in them and what skills are needed to work with them.
FAQs
What is the difference between artificial intelligence and automation?
Automation uses technology to repeat predefined tasks with limited human intervention, and Artificial Intelligence (AI) is the ability of a computer system to recognise patterns and make predictions. Additionally, AI is capable of processing natural language, including human speech, and aiding in decision-making for humans.
How are AI and automation used in real life?
AI and automation have been adopted in customer support, finance, insurance, healthcare, retail, manufacturing, logistics and software development. Examples of their applications include fraud detection, document processing, ticket routing, predictive maintenance of devices and machines, and offering personalised recommendations to customers.
Should students learn AI or automation first?
First, you need to learn the basics of programming, data and software development. After that, you can start learning AI and connecting automation to different workflows.
