Artificial Intelligence Vs Automation: The Right Business Technology

There was a time when people used to say that automation is only present in big car manufacturing plants, where robots are constructing cars from the ground up and assembling the whole car in less than an hour. On the other hand, we have technology which many people said to be science fiction, but in 2020 we are seeing the catheterization of AI waning, as our lives and use of technology has become a common ground for Artificial Intelligence implementation. So, as a company which technology should you be rooting for to incorporate into your business? Well, let’s find out, Artificial Intelligence Vs Automation, shall we?

What is Artificial Intelligence?

Don’t worry, we are not going to bore you, AI is a much broader and general term which is used to explain anything that is computer software-based, but tries to act and perform tasks like a human. These tasks could be learning, planning, and solving a problem.

Let’s give you an example, if you say, “I saw a red sports car while returning from college.” Well, yes, you did, and it is technically correct. Still, this statement doesn’t cover any specific car, which could be Ferrari, Audi, Maserati, etc., the same goes for AI; you need to dig deeper to know which AI technology will help you in your business.

What is Automation?

Automation, on the other hand, may or may not be based on artificial intelligence. You acquire industrial level automation using some sensors and make the machines do something on their own. The automation which we see right now in industries, evolved through the first and third industrial revolution, in the production cycle of manufacturing companies is not AI.

Now, we have production lines with automatic testing, control systems, and automated mechanical labours. These machines can do work with precision and efficiency without asking for a break during the shift. What else could you wish for as an industrialist?

The Difference in Artificial Intelligence Vs Automation

AI could be used in automation, but to make it happen, a company needs to power the automated machine with gigabits. This high quantity of data can come in the form of neural networks, graphs, and machine learning software. The complexity of the code decides how well a system can simulate the working of a human.

Automation doesn’t have the appetite for massive data sets, and it can work without it. But, if you are planning to make a successful AI and use it in your business, you need to feed it with tons and tons of data. The data could be a compilation of the work results, stats, market pricing, etc. anything that your company has done before and want AI assistance in the future with.

AI could be a bit uncertain at times, just like humans, but the automation will do the same work in the exact same way as it was intended until human intervention changes its code manually.

Automation is the mimicking of well-defined operations to reduce human errors in mission-critical scenarios. At the same time, AI is the mix of science and engineering that infuses the power of intelligence in machines. AI will not only mimic the working of a human mind and its activities, but AI will learn as it works and attains greater efficiency with the task on which it currently operates.

Spotting the Difference in User Cases: Artificial Intelligence Vs Automation

Data Lake

Data for business is what fuel is to automobiles. 60% of the data generated by companies around the world goes unused and can’t be added in its annual analysis reports.

If we look at the data, we can see that it is the most critical difference between automation and artificial intelligence. Automation techniques like mechanical labour, automatic testing, operational equipment, etc. all have the result set to providing constant output. Sending automated emails and messages to customers, is one such example.

“The opposite of automation is how AI works.” AI needs the data houses to power its complex algorithms to get the results out. AI uses data from model training to value generation, neural networks, graphs, and deep machine learning algorithms all of which require data to make AI work.

Rule-Based Tasks

Automation requires a rules-based or decision making tree approach. AI uses machine learning that computes tons of data to learn and process the outputs.

If you have minimal data to work with, automation can be beneficial to set machines to constant output, as most of the automation tasks can be done by “if, this, and that” linguistics. As a result, it is easier to integrate in the business model.

Likewise, if you look at the AI development cycle, you will see it is established on a machine learning approach. A developer first needs to integrate the software with machine learning before AI can start learning from the data, which the company is providing.

Conclusion

If you are a business that deals in manufacturing products on a large scale, automation is right up in your ally. And, it might take some time as you need to revamp your production line. But, the result will be fruitful for the long term.

Moreover, if your company deals with thousands of terabytes of data monthly and works mainly on software, AI is your go-to assistant to find out the data you need from your repository.

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