In today’s business and technology landscape, it is quite common for people to interchange the words AI and automation as if they were synonyms. In reality, they are quite distinct instruments. Automation focuses on expediting the execution of tasks, whereas AI is concerned with enhancing the intelligence of those tasks. If you opt to strictly rely on automation without the benefit of AI, you are likely to end up with systems that operate at high speed but remain rather inflexible.
Distinguishing between AI and automation is crucial before you can progress to create a business capable of staying resilient under the pressure of the modern world.
What is Simple Automation?
Automation is like a system that executes predefined commands. You may liken it to a train running on a track. As long as the track is clear and there are no obstacles, the train will run flawlessly. It is a tireless worker that only executes pre-programmed tasks. This quality makes it a perfect fit for monotonous tasks that remain unchanged.
For instance, suppose you require a system to send an email every time someone completes a form; automation will make that happen without any issues.
The Problem with Rules
Traditional automation methods are still mostly based on rules. Sometimes these are called if-then statements. If A happens, then do B. In a perfect world, this would work fine, but the real world is messy. Data is often incomplete, people make mistakes, and things are changing all the time.
When systems are solely based on rules, they can easily become very problematic. To be brittle means to break quite easily whenever anything unexpected occurs. For example, if a customer sends a document that is a little bit blurry or a different type from what the automated system expects, then it simply stops working. It will throw an error, and a human would have to intervene to fix it. That is exactly the opposite of what automation is supposed to do.
Why AI is Different
Artificial intelligence (AI), unlike traditional automation, is not just a system following the rules. It gets familiar with the patterns. In a way, while automation is like a train on a track, AI can be compared to a driver in a car. The driver sees something on the road, decides to make a turn, and finds a new way to the destination.
AI can not only look at data, but also understand the surrounding factors. For example, it can recognize a face in a photo, figure out whether an email is friendly or angry, or find a mistake in a very long document. When you introduce AI into automated systems, your machine is no longer just a rigid device, but instead, it becomes your smart assistant.
The Gap Between Speed and Intelligence
The chief reason why automation fails without AI is that it misses the point altogether. It might be able to process a thousand documents in a minute, but it will not be able to tell if those documents are really correct.
Say you are into mortgage process automation, then speed will be quite a factor in your case. Buyers of houses want their mortgage loans approved fast. Though the loan application process usually involves an unbelievable amount of paperwork. Apart from bank statements there will also be tax forms, letters of employment, etc.
Handling Unstructured Data
Most of the data a business gets is unstructured. This means that it does not fall into clear categories. Emails, chat messages, and handwritten notes are all unstructured. Basic automation simply cannot deal with such data. It literally has to have everything in a particular format in order for it to be able to function.
It doesn’t just scan text; it actually understands it and is able to identify key items such as name, date, or location. When you mix this with automation, it becomes possible to automate tasks that previously were impossible to automate. Without AI, reading each and every email was up to a human being.
Learning from Mistakes
One of the major disadvantages of simple automation is that it cannot improve itself. If one rule is faulty, the machine will keep making the same mistake over and over until a human fixes the code. It is unable to realize that it is making errors.
AI-powered machines behave differently because they are able to learn. As a result, it becomes more precise. In fact, the more a system is used, the better it becomes. A system without AI remains unchanged indefinitely.
Mortgage Process Automation
Let’s take the mortgage industry as an example. This is a perfect example of where the balance of AI vs automation matters most.
Previously, automating mortgage processes meant turning paper forms into digital ones.
It was a major advancement, but it still didn’t address the main issue. The main issue is that mortgage application scenarios are highly varied. For instance, one person might be self-employed while another person could have multiple sources of income.
A simple automated system could reject an application just because the data does not fit a standard form. This results in unhappy customers and lost business. AI can recognize that although the income is different, the person remains a good credit risk. It can identify small patterns that do not look genuine, thus protecting the bank and the client. Basic automation alone cannot provide such safety and flexibility.
Reducing the Burden on Employees
The purpose of technology should be to assist people, not merely to substitute them. It is the staff who will be spending time correcting “automatic” errors, which should not be the case. This causes frustration and eventually burnout of the workforce.
Utilizing AI support in your automation allows your system to perform not only monotonous and repetitive tasks, but also complex and messy ones. On the other hand, human workers get to focus on their core skills: addressing high-level challenges and engaging with customers. Instead of being “error-checkers, ” they switch to “value-adders.”
Scalability and the Future
Business growth requires your systems to expand accordingly. It is challenging to scale simple automation since each time you introduce a new product or service, hundreds of rules need to be created. The outcome is a gigantic, unfathomable code that is cumbersome to handle.
On the contrary, AI enables scaling more efficiently. Its dependence on the data for learning allows you to repurpose it for new functions without reinventing the wheel. It co-evolves with the organization’s expansion.
Conclusion
Automation by itself is like a powerful engine, but AI is the brains that steer the engine. Although automation can bring you speed, only AI can provide you with the smarts to deal with the real world.
Whether your focus is on mortgage process automation or simple data entry, the principle remains unchanged. Fast work alone won’t do; you have to do it right. Without AI, automation will simply be a quick way to make mistakes. Integrating both automation and AI together will lead to a system that is efficient and intelligent at the same time.
