Automation vs. AI: What's the difference?

Artificial intelligence (AI) has been powering the tools we use in our everyday lives for decades now. And every time powerful advancements in AI are released, conversations around the good, the bad, and the ugly of AI inevitably dominate the headlines.

Amid all the noise, I've noticed people throwing around buzzwords like "AI" and "automation" and using them interchangeably. Only, they're not the same thing.

Here, I'll break down the key points you need to know about the differences between AI and automation. And, more importantly, how to get the most out of both (hint: it involves human brain power). 

Table of contents: 

• What is automation? 

• What is artificial intelligence? 

• How AI and automation work together

• Where do humans fit into all of this? 

What is automation? 

Automation is simply setting something up to run automatically. The heart of any workflow automation boils down to a simple command: "When this happens, do that." For example, when someone fills out a form on your website, then automatically add that contact to your email list.  

What is automation used for? 

Automation is great for replacing repetitive or mundane tasks, which is why the ability to automate workflows is baked into a lot of the apps you already use. Take a scheduling app like Calendly, for example. Instead of manually sending meeting reminders to attendees prior to every meeting, Calendly does it automatically.

You're not limited to only automating workflows within a given app, either. Some apps have native integrations that let you automate across apps. And Zapier connects with thousands of apps, so you can automate end-to-end workflows across your entire tech stack. For example, when someone books a meeting on Calendly, instead of just sending that reminder email, Zapier can also add the meeting attendee to your CRM, automatically create a new client folder in your cloud storage app, and send all the information to your project management app for follow-up.

When you automate these types of tasks, it ensures consistency, reduces the risk of error, and frees you up for more high-value tasks. 

While automation is really good at following a predetermined path (or set of rules), it falls short when an action along the path requires interpreting data and making a decision before it can proceed. That's where artificial intelligence comes in. 

What is artificial intelligence? 

There are multiple definitions of artificial intelligence, ranging from "a poor choice of words in 1954" to "machines that can learn, reason, and act for themselves." For the purposes of contrasting AI against automation (and, later on, understanding how they work together), I'm using the more nebulous definition of machines that can, to some degree or another, "think." 

AI's ability to "think" comes from machine learning—a subfield of artificial intelligence that enables a system to analyze massive datasets, learn from that data, and then make decisions based on it. (This is a gross oversimplification, but you get the idea. For more details, here's a basic guide to AI.) 

What is AI used for? 

AI is already baked into a lot of services you probably use every day, including: 

• Recommendation algorithms on Amazon, Netflix, and other websites 

• Spam filters in Gmail and other email apps 

• Fraud detection for your credit card, bank, and other financial services

• AI agents—from robotic vacuum cleaners to self-driving cars

But since ChatGPT came on the scene and spawned thousands of new AI-powered tools, AI is also changing the way we work. Here's a not-at-all-comprehensive look at the kinds of AI software folks are using every day:

• AI chatbots

• AI image generators

• AI video generators

• AI presentation makers

• AI meeting assistants

• AI scheduling assistants

• AI social media management apps

• AI project management tools

• AI email assistants

• AI sales assistants

• AI predictive analytics software

• AI website builders

• AI notes apps

• AI voice generators

• AI recruiting tools

These tools are adding intelligence and nuance into our work in a way automation itself can't. The caveat with AI is that it's highly dependent on human prompting and accurate data—the output will only be as good as the input you feed it with.

How AI and automation work together 

Some of your daily workflows are probably straightforward—for example, when you react to a Slack message with a specific emoji, then it automatically gets added to your to-do list app. But what happens if you want to add a more complex step to that workflow, like labeling the task as high or low priority depending on the context? Automation on its own can't handle that step.

To build a truly powerful automated workflow—one that addresses these kinds of gaps—you need to add AI to the mix. That's where AI orchestration comes in. It connects AI tools, agents, and automations across workflows, teams, and systems. Here's an example of an AI-orchestrated workflow I created:

In essence, this is what's happening: whenever an article I've published appears in my RSS feed, Zapier adds it to Airtable as a new record (this is the automation part). But before that record is created, ChatGPT scans the article and decides what category it belongs to based on a detailed prompt I previously fed the chatbot (this is the AI part). 

Now, when I look at my list of published works in Airtable, I can see details like the name of the article, the site it was published on, the URL, and the category it falls under—all without having to lift a finger. 

In this example, it's the blend of AI and Zapier's deterministic workflow engine (a system that executes tasks in a predictable manner) that reliably processes the workflow the same way I would if I had to do it myself. 

And, honestly, that's a pretty simple example. AI automation can power entire business workflows across departments. Here are a few other examples:

• Popl transformed its overwhelmed sales process by integrating Zapier and OpenAI, automating lead routing, email filtering, and data enrichment. This streamlined system saved them $20,000 annually, powered over 100 workflows, and allowed the team to scale without additional overhead.

• ActiveCampaign tackled high early churn by building an AI-powered onboarding system that automatically enrolled new users in language-specific webinars and sent personalized follow-ups based on attendance. This scalable automation boosted webinar attendance by 440%, reduced 90-day churn by 15%, and doubled early product adoption, all without manual intervention.

• Faced with skyrocketing support requests and a lean IT team, Remote built an AI-powered, fully automated help desk using Zapier to handle multi-channel employee requests through Slack, email, and chat. This system now resolves 28% of tickets automatically—saving over 600 hours each month—by combining tools like Okta, Notion, ChatGPT, and Zapier Agents to streamline triage, task assignment, and real-time responses.

Here are a few AI automation templates to give you an idea of what's possible.

Unified lead capture

Easily channel leads from multiple sources into your CRM.

Try it

Increase sales leads from support tickets

Identify whether support tickets contain buying signals so you can easily route new leads to sales.

Try it

IT help desk

Improve your IT support with AI-powered responses, automatic ticket prioritization, and knowledge base updates.

Try it

Where do humans fit into all of this? 

Whenever someone cries "the robots are coming for our jobs," I think about ATMs (my background is in banking, so this isn't completely out of left field). In the 1970s, the increase in ATM use sparked concerns that this technology would eventually replace human bank tellers. But that's not what happened. Instead, ATMs freed up tellers to do higher value work, like answering more complex customer questions. 

Because while AI and automation have the power to take on a lot of tasks—especially the monotonous ones—humans are still needed for work that: 

• Is unique

• Requires a point of view

• Requires critical thinking or reasoning 

• Builds on relationships 

Even so, this doesn't mean you can't leverage AI and automation for these types of tasks. Based on my experience, there are plenty of workflows that would benefit from AI, automation, and good old-fashioned human brain power. 

Take an automated approval workflow, for example. With Zapier, you can use AI to automatically mark a request as approved (or rejected) based on prompts you've created, but then give you (the human) final say over every request. Or you can use the AI to approve or reject straightforward requests and flag outliers for human review. 

AI vs. automation vs. human power: they're not mutually exclusive 

I've barely scratched the surface of what's possible when you apply AI, automation, and human power to your workflows. But what I hope my examples emphasize is this: these exciting technological advancements don't pose an either/or dilemma—it's not AI or automation or human power. You can use them together, and, in fact, it's better if you do.

Related reading: 

• How to automate a manual process without feeling overwhelmed

• When you should automate a task

• What is intelligent automation? And how to apply it

• Ways to leverage automation in the workplace

• What is DeepSeek and why does it matter?

• What is digital transformation? And how to build a digital transformation strategy

This article was originally published in September 2024. The most recent update, with contributions from Jessica Lau, was in August 2025.