AI vs. automation: What’s the difference?

Artificial intelligence (AI) acquires knowledge and applies informed reasoning to make dynamic decisions, while automation performs repetitive IT tasks and processes. Although AI and automation both promise to reduce manual labor, they aren’t inherently the same and serve 2 distinct functions.

AI acquires knowledge and processes vast amounts of data–more than most humans reasonably can on their own. From this data, AI can pull key insights and make dynamic decisions. Its capabilities can cover something as basic as making a weather prediction to determine if the next few days will be optimal for surfing or as complex as creating a multitiered remediation protocol to guard against a potential security breach.

Automation performs repetitive IT tasks and processes according to manually set guidelines. It consistently and reliably does what it's told. Even when obstacles come up, automation can be programmed to handle these instances on its own without needing further human assistance. Automation can be trusted to do things like run a daily data backup or send updates to thousands of machines on a schedule and address small problems if they come up.

When it comes to enterprise IT, these roles are distinct and important: Automation is the machine that executes tasks based on predefined rules, while AI is the brain power that learns and adapts to make non-rule-based decisions about complex problems. Understanding the difference between AI and automation can help you position your business to work smarter and achieve more strategic outcomes using a combination of insights and planned guidelines from both. 

Learn about AIOps with automation

AI describes systems capable of acquiring knowledge and applying insights to solve problems. Like human intelligence, AI constantly interprets the environment and makes real-time decisions that improve the output. AI doesn’t follow a script—it writes the script in real time. While traditional automation uses repeating logic to perform the same action given the same input, AI is built on probable inference. It chooses the best possible action based on patterns and context, even when the input isn’t identical.

Primary characteristics of AI include: 

• Nondeterministic: AI generates results based on statistical probability and learned patterns. The output can change based on new data or an evolved model, even if the initial prompt is similar.
• Learning-based systems: Machine learning (ML) and deep learning identify complex patterns within datacenters and improve their performance over time without direct human reprogramming.
• Predictive: AI excels at tasks that require predictive analytics, natural language processing (NLP), and classification.

Learn more about AI

Automation is a set of explicit rules designed to consistently and repeatedly execute tasks, reduce the risk of errors, and help you achieve the same results every time. Once IT operations teams code these preset rules, the technology carries out its instructions to do things like run jobs or update databases without the need for further human intervention. As such, automation is the backbone of a well-oiled enterprise IT.

An automation platform is more than just a library of scripts. It’s the common tool your system administrators use to write playbooks that orchestrate complex tasks like infrastructure provisioning, application deployments, and policy enforcement across diverse environments. It’s the consistent engine that runs your operations the same way every time, with little to no surprises.

Primary characteristics of automation include:

• Deterministic: If the input is the same, the output will likely be the same. There is little room for error, and it’s predictable because the instructions are written in YAML, a human-readable data language.
• Rule-based: Automation follows only the instructions it's been given. It can’t deviate from, learn from, or adapt to unforeseen issues unless those potential issues are explicitly coded into the original workflow.
• Consistency: Automation ensures configuration is uniform across thousands of servers, patching happens on schedule, and new infrastructure is provisioned identically every time.

Learn more about IT automation

You can summarize the distinction between AI and automation in 1 question: Does it follow rules or does it learn and suggest rules?

AI interprets knowledge. Its adaptability produces varying results based on data-driven decision-making. It uses ML and deep learning to acquire the best solution or recommendation, which leads to more proactive workflows and less pressure on your IT team to manually intervene. It can spot new anomalies and create new predictive models without being explicitly reprogrammed. 

AI excels at adaptation tasks such as:

• Forecasting behavior.
• Detecting anomalies.
• Classifying patterns.
• Understanding language.
• Making context-based decisions.

Automation follows the rules. An automation tool executes explicit rules that system administrators or engineers provide. It runs predefined workflows, enforces consistent configuration, and eliminates human variability. Automation produces the same output if the input remains unchanged. 

Automation excels at repetitive tasks such as:

• Provisioning.
• Configuration.
• Compliance.
• Patch management.
• Application deployments.

Overall, AI elevates decision-making by analyzing your business’ data, workflows, and environments before giving a strategic recommendation for change. It uncovers insights administrators may miss and optimizes outcomes dynamically. Automation improves efficiency and reduces human error. It accelerates operations and eliminates manual, error-prone tasks. Automation makes sure everything runs as instructed. AI determines what to do next.

See how automation and AI can work together

In terms of curating an enterprise IT environment, the optimal version comes from combining AI and automation to achieve intelligent automation. When both work in tandem, they connect consistent repeatable processes and adaptable AI.

AI needs a reliable automation engine to put its decisions into action. Take event-driven automation, for example. Generally, event-driven automation is a way for IT teams to manage how and when to trigger specific actions by using an “if-this-then-that” set of guidelines. Instead of automation running on its own within its preset rules, adding AI can assist in more immediate responses to real-time triggers. Using AI for IT operations (AIOps) can lend itself to more helpful decisions across your enterprise, like proactively predicting failures and kicking off remediation workflows before problems grow too big to manage. 

Intelligent automation frees up your employees and resources for more critical projects. It can even help expand your team’s development capacity and ability to scale. Using AI-assisted development can shorten the time needed to generate new YAML playbooks or build applications, while allowing for quick adjustments as your organization’s needs change. Intelligent automation not only makes managing things easier, it also helps your IT team make the most of limited resources. 

Red Hat® Ansible® Automation Platform comprises tools to create, manage, and scale automation across the enterprise. This includes automation coding assistant, which allows developers to generate automation tasks using natural-language prompts and create Ansible Playbooks that meet best practices. 

The best part? You can integrate Ansible Automation Platform with Red Hat OpenShift® AI, which provides a consistent user experience across teams. You can also pair it with Red Hat Enterprise Linux®, which serves as a scalable platform that can run on an open hybrid cloud. This gives you more flexibility without any vendor lock-in. 

Read more about Ansible Automation Platform use cases