The Difference Between AI, Automation, and Machine Learning

Imagine investing $100 per user in a “machine learning” tool because you were told AI is the next best thing. 

Your team onboards the tool, but the features prove complicated. Lots of training, lots of hours spent. Finally, after weeks of headaches, you’ve got the tool into your system…

…only to realize that you purchased a machine learning tool when what you really need is CRM automation. Whoops.

Defining “AI” and “machine learning” and “automation” might not scream “sexy business tip.” But it could determine whether your next investment in a tool feels like an absolute life-saving joy…or just a way to slap an expensive tool on your sales problems.

What is Artificial Intelligence (AI)? 

Except it’s not. Which is a little weird.

“AI” is the macro-term here, so it’s worth diving into some of the specifics of what AI software can actually achieve:

• Reasoning through your inputs. AI can run your data and compare multiple variables to spit out something that feels like an authentic interaction. The most obvious example of this is through language algorithms, where its responses make you wonder if someone’s on the other side of the computer. But it can also process PDFs, spreadsheets, and large datasets using the same principles.
• Learning and improving over time. The more you hone AI and “train” it on your data, the smarter it gets. With the right prompts, AI isn’t quite as “forgetful” as a human can be. Keep feeding it good quality data and it can get “smarter” every week.
• Mimicking human-like perception. Give AI something it can process (like text or images) and it can read signals in the tone like a human can.
• Making decisions with proper context. Once AI’s trained on enough data, it can reason through the options and provide you with recommendations for future decisions. That’s one reason some people are even experimenting with AI-selected stock portfolios.

Now let’s get specific. Where do the types of AI show up in business processes these days?

• Conversational AI (in tools like Claude, ChatGPT, and Google Gemini) interacts with single users like a personal assistant.
• Recommendation engines fuel everything from Netflix algorithms to “customers also purchased…” shopping online.
• Generative AI can draft a wedding speech, turn your text prompt into a video, and handle just about anything you might have done with manual software around the year 2015.

The next question is where this kind of technology pays off in your sales process:

• AI reps can handle inbound questions, and even “triage” your incoming messages to highlight when a potential lead is ready to buy.
• Smart email drafting can help you become more prolific with your outbound sales process, even if you still review each email before it goes out.
• Predictive lead scoring can help you pore over swaths of data to find the patterns in your leads: who’s most likely to convert to a customer, and where your priorities should lie.

So far, so good: AI’s ability to mimic decision-making can take a load off your shoulders. But what specifically can it do to plug into your existing sales ecosystem?

What is Automation?

Automation is the logic-based ability of tech to make decisions based on existing formulas. It can turn once-manual workflows into machine-operated streams of action. It doesn’t have to think here; it can work off your predetermined rules and execute your workflows with mathematical precision.

You can typically distinguish automation from all AI thanks to a few key characteristics:

• It’s rules-based, which means you can set the standards for automations in your workflows before you execute them. It will all go according to your predetermined decisions.
• It’s repeatable, which means it can execute at scale, even while you’re sleeping. That’s probably the #1 appeal of automation, after all.
• It’s reliable because it works within existing parameters, such as fields defined in your software. There’s minimal “decision-making,” which means all you have to do if you want to change the automation is change the rules.
• It’s fast and boring. We mean that in a good way. Problems being solved—with minimal input from you in the future—is boring. You’re free to focus on something else once you’ve automated a sales process. 

Of course, not all automation is built the same. 

There’s task automation, which can refer to sending emails and follow-ups—the kinds of tasks you’d normally take on. 

There’s also process automation in which a Sales AI CRM might handle full sequences or workflows that you establish. 

And finally, there’s the more advanced RPA (robotic process automation) wherein bots—working from programmed rules—mimic human clicks and steps. 

Each degree of automation has its own sophistication. But even the simplest automation can make you far more efficient, especially in sales:

• Automation can auto-create contacts from inbound leads, which lands new leads into your CRM as new contacts without you having to input the fields manually.
• Automation can also run triggered emails and sequences so it feels to a new lead that your company is highly responsive. Maybe they sign up for your email list and receive an asset, like a helpful ebook—all completely handled by email automation sequences.
• Automation can update deal stages within your CRM, which may not make major workflow triggers happen, but will keep your data clean and your leads well-organized.
• Most conveniently, automation can handle reminders, follow-ups, and nudges on your behalf. Automation can handle a follow-up with a lead after a meeting; or it can surface a nudge to remind you when you haven’t followed up about a potential sale.

What is Machine Learning?

Machine learning is a subset of AI that learns from data rather than being programmed from a set of pre-established rules. Machine learning will feel more organic for businesses because it can continue to digest new data, becoming “smarter” the more you feed it.

Machine learning is that part of the overall AI umbrella that learns to recognize patterns. With automation, you need clear, defined rules to establish a new workflow. 

Machine learning is a little more subtle: it needs both quantity and quality of data to generate valuable insights.

One key distinction: machine learning can start predicting outcomes. For example, if you feed it all sorts of leads and sales data, ML can look through your data and isolate certain variables:

• Likelihood of a lead to convert
• Any potential risks in the deal
• Churn probability in the long term
• Recommending a next-best action for the sales team

Machine learning typically works on probabilities. For example, when you interact with an AI chatbot, it’s using language patterns to generate new words based on algorithmic probabilities, which is what makes it sound like it has human intelligence.

One key caveat: it sounds like machine learning is here to steal all the thunder from your key decision makers. Nope. It’s an augmentation to your insights.

Here are some of the most useful cases with ML:

• Spotting patterns humans miss. Admit it: you don’t like scrolling through data in vast spreadsheets hoping to find patterns. Even at its best and most useful, it’s still time-consuming. ML’s advantage is that it can process that data much faster and highlight some patterns for you to review. 
• Improving with more data. ML is unique in that it creates a virtuous cycle. You feed it sales data. That sales data helps you make more sales. You feed it that sales data, and the process continues. Eventually, ML learns your audience, strategies, and patterns so well that it starts to feel like a member of the team.
• Making predictions. This is where the fun of ML starts coming into play. It can predict future actions, maybe even assigning probabilities. How likely is a lead going to convert into a customer? Which lead is the most likely to convert? What’s the next best action you can take? The most likely pain point of each lead? ML can take your data and weigh in on each of these questions.

“Okay, but what if machine learning takes all the wrong lessons from my data?” you might wonder. Valid question. 

You don’t have to metaphorically plop machine learning technology into a room and leave it with all your data. You can train machine learning on specifically labeled examples, essentially “supervising” its learning. You can also assign it to do completely unsupervised learning and ask if it can find hidden patterns you might not have otherwise discovered.

And, perhaps most importantly, ML takes feedback. 

You can give it a thumbs-up or a thumbs-down. Machine learning digests via trial and error, then tweaks its results. This way, you’ll always feel like the quality of the data you’re getting is more in line with your priorities as a business. After all, you’re the one giving it ratings, almost like it’s your Uber Driver.

It learns by itself, yes. But it can also learn what you want it to learn.

This gives your machine learning an almost spooky level of human-like abilities. After all, you’re the one influencing how well it learns. 

Where does this show up in the quality of your sales? 

• Predicting which leads might close
• Improvements to how well it forecasts your sales
• Behavior-based routing, interacting with your CRM to predict probabilities and assign workflows
• Personalized recommendations
• Potential feedback for coaching your sales team

Like the other definitions on this list, machine learning isn’t a single replacement for anyone. It’s a new tool you can use to augment how well you handle sales processes as they already exist. 

Think of it like a smart second brain—one willing to weigh in with feedback, learn from your data, and tell you what needs to happen in your business next.

Where AI vs Automation vs Machine Learning Fit (And Where They Fail)

Differences Between AI, Automation, and Machine Learning 

“AI” and “machine learning” may belong in the same technological categories, but that doesn’t mean they’re the same thing. Let’s break down how each of these three terms is different.

Purpose

• AI is a decision-making technology. It simulates “thinking” with less programming. You can also think of it as the umbrella term for any technology supposed to simulate human intelligence.
• Automation follows strict rules, functioning more like an assembly line with predefined parameters for each station than a creative force.
• Machine learning learns from patterns and starts creating predictive models to help you understand data better. 

Level of Intelligence

AI and machine learning are “smart” in the sense that they will “feel” intelligent to the person using them. Meanwhile, automation can often feel like a soulless machine that’s more akin to a basic computer program working within a set of rules you establish.

Database vs. Rule-based Actions

Machine learning is a data-hungry force. In fact, the quality of your machine learning is dependent on the quality (and quantity) of the data you feed it. Automation, in contrast, is almost entirely rule-driven. It will stop working if you don’t give it solid rules for dealing with everything in front of it.

AI, as the umbrella term here, is a mix. It can absorb swaths of data. And it can automate certain processes by using your prompts to establish ground rules.

When to Use Each

• Use AI for human-simulated intelligence: conversations, scoring, and decision support. AI is your higher-level strategist. It’s there to help blur the boundaries between hard tech and the soft skills of being a human.
• Use automation for specific, rule-driven purposes and tasks. If you have highly predictable workflows—such as the way you qualify a lead—automation will be your star. It can also help organize large sales teams by assigning/tagging them with the appropriate leads. It’s effective for scaling, but not for thinking.
• Use ML for predictions, optimization, and data processing. ML can be the way you survey the territory. And don’t forget its role as a self-reinforcing virtuous cycle: if you keep feeding it better and higher-quality data, it will give you better and higher-quality feedback.

Benefits of Using AI, Automation, and Machine Learning in Sales

It’s not bold to say that using artificial intelligence the right way can help you make sales. 

But even though AI is supposed to take the “intelligence” part of the equation and outsource it to software, you’ll find that an intelligent approach from your end will lead to all sorts of sales benefits:

• Improved efficiency. If you automate even one task in the next week, you’ve immediately gained a win that will pay dividends all year long. No more mind-numbing manual labor or data entry. Simply letting your AI system handle it automatically is an instant win.
• Better lead scoring and prioritization. Handling lead scoring manually is data-intensive. It’s not always pleasant work, unless you love spreadsheets. ML tools are great at this—and can process your data much faster, too.
• More accurate forecasting. Imagine AI helping you make data-driven forecasts that feel less like guesswork and more like actual forecasting. Feeding good data into your systems and having the tools to process them into precise forecasts will feel more like a crystal ball than software.
• Personalized outreach at scale. The better you personalize, the more human your sales feel. The problem is scaling this. With AI, automation, and machine learning all performing their designed functions, you’d be surprised at how easy this is to achieve.
• Higher conversion rates. What happens when the right people don’t get the right follow-ups? They don’t convert. They feel left out. But with AI tools helping “triage” each lead and automating follow-ups, every new lead feels like they’re properly cared for. And that means they convert.
• Streamlined workflows and sales pipelines. Imagine your tool handling handoffs between departments, teams, and sales reps. You can have a well-oiled machine of unbroken workflows keeping the whole operation running smoothly.
• Cost savings. Notice what these tools don’t require? As much input or training as a human being might. This translates to cost savings because you’re doing more with less.

The Power of AI in Your CRM

Maybe you don’t need to know every last definition in the AI world to make sense of it. But you do need to remember an old phrase: 

A place for everything, and everything in its right place.

That’s what having an AI-powered CRM designed to provide the best tools for the best tasks can do for you. So if you want to sell smarter with AI-powered CRM in your toolkit, consider trying Close today.