The Role of AI And Machine Learning in Sales Today

Artificial Intelligence (AI) has been captivating us for decades as we saw computer systems able to do more and more. AI has reached incredible levels of functionality, and with today’s technology, it’s becoming more readily available than ever before as businesses find new uses for it… including in sales and marketing.

While sales is largely a human-to-human specialty, Machine Learning and AI have both offered exceptional new ways to connect with more users, drive more leads, and create stronger and more effective sales experiences for everyone involved. 

But what exactly is the role of AI and Machine Learning in sales today? When and how should it be used to the best effect? 

That’s what we’re here to discuss. 

The Difference Between AI and Machine Learning 

The terms “Machine Learning” and “artificial intelligence” are sometimes used interchangeably, but they’re two completely separate things, and it’s important to differentiate between the two.

AI is the notion that machines can take in information and then respond, react, and/or take some sort of action in a way that mimics human behavior. 

Think of a self-driving car; it has sensors that can recognize that a deer (or at least that something) ran out in front of the car, and it can slam on the breaks. 

Machine Learning, on the other hand, is the ability of machines to continuously learn, improve predictions, and potentially make optimizations based on what they’ve learned from large data sets.  

Much online software (including SaaS tools and PPC platforms) actually use some form of Machine Learning. It’s how Facebook is able to test out different audiences for a single ad and ultimately determine which ad is best suited for each audience in order to get a specific desired action (like a click or video view) to occur. 

How AI & Machine Learning Can Benefit Sales 

AI and Machine Learning are two different things, but both have a role in sales. 

AI is nascent, and it can be tricky when it comes to sales. You’ve got different options for tools that can improve sales. My go-to suggestion would be something like Jasper (formerly Jarvis), where you can use AI-fueled tech to improve on or generate subject or body copy in an email, for example, while reducing the time it takes to create copy at scale. 

The Role Of Ai For Sales: Jasper
Image source: Jasper 

Machine Learning, however, is incredibly powerful when it comes to sales teams. It has the capability to consume enormous datasets, and it does so objectively; there’s no human bias or error. This can help you garner insights that can provide recommendations on everything from who your sales team should reach out to and what the best approach to do so would be. 

Whether it’s AI like Jasper or Machine Learning like Breadcrumbs, thoughtful human inputs are crucial in the application of these tools to business. 

As much as bias-free analysis and data-driven decision-making seem like the ideal approach, this is true contextually. Context is a uniquely human concept that must inform the application of AI or ML for the action or outputs to be meaningful. 

Think of the world of chatbots, if there is some natural disaster or another catastrophe that directly impacts your customers and they are flocking to your site for help or support at the moment, your AI-driven chatbot is most likely oblivious. A human would need to intervene to make sure your chatbots are responding appropriately to the current situation.

You can see this with lead scoring tools like Breadcrumbs. 

How Lead Scoring Has Been Impacted By Machine Learning 

Lead scoring and contact scoring is the process of using qualities like a client’s demographic traits (like job position, industry, company size) and activities (site clicks, email responses, in-app activity) to help your sales team identify new sales opportunities. 

This includes high-value leads who are most likely to convert, and it also covers existing customers who can be sold on new or higher-cost features.  

Lead scoring has definitely been impacted by Machine Learning in significant ways.

Here at Breadcrumbs, we believe that a human-driven but Machine Learning assisted approach is the best way to go. 


That being said, Machine Learning can help you best assess and optimize what’s currently working now. Our algorithm can process and analyze enormous data sets to help you determine which industries, locations, company sizes, job titles, and behaviors impact your propensity to purchase. This makes it easier for sales team members to identify those opportunities right away so they can act the second it’s relevant.

And even beyond lead scoring, Machine Learning can help sales reps determine which action to take. If it recognizes that prospects that fit a certain buyer persona respond well to a specific offer, communication type, or deal, Machine Learning can offer those tips to your sales team. Their chance of success goes up with each deal as a result, giving you better revenue acceleration potential. 

It’s essential to acknowledge, of course, that the human-first aspect we mentioned above is important; there are certain things that Machine Learning algorithms just aren’t prepared to handle in a timely fashion (at least not yet).

This might include shifts in your own brand identity, a change in your target audience, or even an adjustment to the market overall. There are a number of different factors that can cause dramatic changes in what contacts may be high-value or high intent very quickly. 

Recently, our use of Breadcrumbs for our own purposes highlighted interest from Financial Services companies. 

This is a vertical that we hadn’t previously considered as a target, and without Breadcrumbs, it likely would have taken much longer for us to identify the opportunity. However, if Breadcrumbs had simply been allowed to prioritize FinServ leads autonomously, we would have been facing some serious challenges. 

Our sales team would have been ill-prepared to speak to these prospects in a relevant way and would have been unarmed without the necessary content and collateral to support these conversations. 

Instead, humans reacted contextually to make sure all of the elements were in place to take advantage of this information and, when appropriate, decided to change the prioritization of leads with Breadcrumbs.

The Role of AI And Machine Learning in Sales: Final Thoughts

Machine Learning and AI technology absolutely have a place in the sales and RevOps world. That’s not going away any time soon, and the automation, optimization, and analysis that these tools can offer are exceptional.

They are not, however, an end-all-be-all approach to sales. Your sales approach still needs to have a strategy and be action-driven, primarily by your talented sales team. 

It’s essential to remember that nothing can replace a well-trained sales team, their knowledge, and their instincts. They can respond in real-time with judgment calls that no machine can match. These tools are meant to be assistive instead of making up the entirety of your sales strategy. 

You need those talented, well-trained salespeople who love your brand, know your product, and truly want to help your customers find the perfect fit for them. You can use AI tools to streamline the process and Machine Learning to make your processes more effective, as long as you’re keeping a talented team at the center of what you do. 

Want to learn more about how to leverage Breadcrumbs lead scoring and Machine Learning to identify more sales opportunities? Book your free demo here

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