AI vs. Automation: Key Differences & Operational Impacts

One of the biggest challenges for companies trying to utilize big data, statistics, and programming capabilities is to use those tools effectively...

One of the biggest challenges for companies trying to utilize big data, statistics, and programming capabilities is to use those tools effectively. In particular, there can be immense misunderstandings about how AI and automation work. The differences aren’t always readily apparent, but there are real operational impacts that come from knowing which jobs are meant for AI and which ones are better handled with automation.

What is Automation?

In the simplest form, there is the question of independence that distinguishes AI from automation. Programmable automated systems have existed for centuries, with the first re-programmable machines coming into operation in the weaving industry in 1801. The Jacquard loom automated processes by way of punch cards defined desirable patterns.

No one would confuse the Jacquard loom with anything approaching AI. Instead, the looms were automated by using a series of pre-defined patterns. A machine would read the holes punched into the card, and this triggered a series of tasks. In other words, automation is very good at doing jobs quickly and repeatedly.

How is AI Different from Automation?

Most forms of AI use statistical models to derive inferences from large data sets. Notably, this work often requires continuous adaptation as circumstances change.

For example, take how a spam filter might use AI to keep up with evolving techniques used by scammers. A spam filter might use some combination of techniques, many that are very time-consuming to execute, such as:

  • Word cloud analysis
  • Bayesian inference
  • Seq2Seq correlations
  • Neural networks
  • Sentiment analysis
  • Scoring

Every day, the filter is going to attain some level of success or failure. As end-users mark different emails in their inbox as spam, the AI powering the filter will run a new analysis to adapt.

It’s worth noting that this form of AI is playing against many intelligent opponents. In fact, nothing prevents spammers from using their own AI systems to assess their success and build more suitable emails. This means the AI has to go back to the lab every day to update its analysis of which emails should be let through and which ones need to be flagged.

They Are Not Mutually Exclusive

You should consider that, in many cases, there isn’t an inherent mutual exclusivity between AI and automation. Many AI functions are automated. In the previous example of a spam filter, most people running email servers will have some sort of cron job set up to trigger the next run of the AI’s analysis.

The flow of information can go the other way, too. A set of IoT sensors in a cornfield, for example, might collect data and send it to a central AI. Upon receipt of the new data, the AI goes to work analyzing it and producing insights.

Additionally, a self-perpetuating loop can also be created. The AI might send a fresh clone of a neural network to an edge device each day. Upon completion of its tasks, the edge device then ships relevant data back to the AI. The AI conducts new analysis, creates another neural network, and ships yet another clone of the NN downstream to the edge device. Rinse and repeat in perpetuity.

What Are the Operational Impacts?

A report from 2018 indicated that companies who achieved 20% or greater growth were functioning at 61% automation across their operations. Those producing less growth had only automated 35%. 

Companies are also achieving significant improvements using AI. For example, 80% of customer support queries can now be handled solely by high-quality AI-based chatbots. This means human operators can focus their energy on the challenging cases that make up the other 20%, leading to greater attention to queries and improved customer satisfaction.

To say AI and automation are transformative for businesses is an understatement. Increasingly, the winners in the business world are those enterprises that can leverage both tools. Operations that haven’t automated need to get started yesterday, and the ones that are already invested need to keep pushing the envelope to stay competitive.

Back to blog homepage


Picture of Scottie Todd

Scottie Todd

Digital Marketing Lead

“Level 4 marketing wizard on a quest for
data insights one blog post at a time.”


Polk County Schools Case Study in Data Analytics

We’ll send it to your inbox immediately!

Polk County Case Study for Data Analytics Inzata Platform in School Districts

Get Your Guide

We’ll send it to your inbox immediately!

Guide to Cleaning Data with Excel & Google Sheets Book Cover by Inzata COO Christopher Rafter