Companies are striving to adopt technology that either makes things easier, faster, and more cost-effective or gives them more insights than existing employees combined. Although these rely on very different technologies and serve different purposes, a common misunderstanding occurs between automation and artificial intelligence.
What is Automation?
We have become so accustomed to automation in our life that we don’t even notice its presence or think about it. It is embedded in automated bank statements, calendar reminders, and many more similar applications.
It is one of the best ways to save time and money, especially when high-volume, repetitive tasks are involved, like sending thousands of emails to a mailing list. What would take a human a few days can be done in seconds with minimal human intervention.
Automation is not new. It has been around since the Industrial Revolution, but it has always been subject to various limitations. Vending machines, coffee makers, the ATM, smoke detectors, and more, all are automated.
It can be further divided into fixed automation, which loops on some instructions, and programmable automation, which can follow various decision branches.
What is Artificial Intelligence?
On the other hand, the bet for artificial intelligence has always been mimicking human behavior. It should understand various data sources, process information to identify existing patterns, and even create new hypotheses from existing data.
Understanding ordinary spoken or written text, making predictions, or creating solutions are all covered by the umbrella term of artificial intelligence and are powered by machine learning.
Artificial Intelligence focuses on detecting and classifying patterns, whether for face detection, fraud prevention, or bug classification in an IT app and other IT operations. A self-driving car might be the first idea you can think about when talking about AI. However, usable AI is already in your maps, calculating the best driving route.
Difference and perspectives for AI and Automation
To put it simply, AI tries to replicate human behavior and decision-making, while automation is only about performing repetitive tasks accurately thousands of times, in a mindless way.
Automation for routine
At the lower end of the spectrum, automation is replacing monotonous, low-skill, repetitive tasks like scanning barcodes and sending reminders. In an IT environment, automation is necessary to speed up and replace manual interaction with systems. By deploying automation, IT professionals have more time for value-added work.
Possible examples of automation can be found in each department, from automating invoicing and scheduling meetings to customer service support and software upgrading. A comprehensive list of tasks that can be automated is available here.
AI for human-grade tasks
AI can replace high-end jobs by combining real-time data streams, which would take experts weeks to analyze by hand.
The power of machine learning
While automation relies on clear instructions, machine learning is based on algorithms that make sense of data by looking for patterns. This is a more intuitive approach, the first similarity with human behavior. There are different ways to deploy machine learning based on how much human intervention is in the process
In unsupervised machine learning, data is not labeled or classified in any way and the algorithm is exploratory, trying to find an underlying structure through inferences.
Conversely, supervised learning applies what the algorithm already knows from past labeled examples to predict outcomes from new data sets. The system analyzes the training dataset and makes inferences about the outcome. When new values are received as input, the machine learning mimics the behavior of the analyzed phenomenon and creates some output data which can then be calibrated by comparing it with real-world outcomes, if possible.
Semi-supervised algorithms combine the two approaches, as they combine labeled and unlabeled data. The algorithm learns from a small set of labeled data points and uses a larger set of unlabeled to explore connections. These systems usually learn faster than unsupervised ones, as they have a better starting point.
AI as Machine Learning and Automation
Since the results are the only thing that matters, there is no boundary in combining all these tools. In fact, artificial intelligence works best when machine learning and automation are used together to learn from data.
Through automated machine learning models are built more easily by creating numerous models from raw data and selecting those which are the best fit. This is the best way to remove the real signals from the noise. By creating a lot of models and testing them against each other there is a better chance of selecting the best one.
AI and automation applications- AIOps
A relevant example for this could be AIOps, an acronym used to describe using AI for IT operations: analytics, predictive analysis, root cause analysis, noise reduction, and event correlation. Such a system offers valuable insights for the project managers by using visual dashboards.
AIOps take the input from your environment (sensors, workstations, servers, databases, data lakes, etc.) to provide intelligent responses in a bifold manner. It is reactive because it acts upon the input it gets from the environment, and it is proactive because it can predict potential outcomes starting from a set of conditions which are satisfied simultaneously. For example, it can detect when a server is already down, but it can also see a rissing influx of server requests which could potentially lead to that, before it happens.
The good news is that these two types of systems work best together. In fact, automation accelerates AI adoption and especially AIOps, because it can automatically process the necessary data. Most companies could use a hybrid approach of both automation and AI – e.g. AIOps Deployment – Siscale.
In fact, AIOps simultaneously require different technologies. First, it needs a big data tool hike Hadoop or similar to gather the data and store it. Next, it uses automation to go through the data and extract the relevant information. Lastly, a machine learning algorithm detects potential patterns and deviations. Some people have compared this solution with RPA, but the difference resides in the fact that in the case of AIOps, we don’t need a clear definition of each step, the algorithm is exploratory.