You’ve heard that AI is transforming businesses, providing millions of dollars in growth opportunities while streamlining processes and automating tasks. Jackpot! But you can’t win big if you don’t know how to play. You won’t see any benefits from AI if you don’t know how to use it.

According to research from Gartner (available to clients), small businesses face the challenge of identifying the right AI use cases. Marketing, sales, and customer service are all seeing the benefit of technologies that leverage artificial intelligence to better connect with customers. But who gets first dibs? How do you choose which department should adopt AI before the others?

Maybe you don’t have to.

Artificial intelligence isn’t just blurring the lines between human and machine. It’s softening the edges that keep sales, marketing, and customer service operating in separate boxes.

AI lends itself well to trends toward automation, personalization, and conversation. Coincidentally, all of these trends are permeating customer experience interactions with sales, marketing, and customer service teams.

The benefit of these disciplines overlapping—beyond improving the customer experience—is being able to use one tool across multiple departments.

You’ll see the biggest benefits from AI if you invest in one tool that offers features for personalization, conversation, and automation. Not only will this close the gap between marketing, sales, and customer service departments, but it will reduce the need for unnecessary investment in multiple tools.

Here, we’ll cover AI use cases for marketing, sales, and customer service that can enhance the customer experience using automation, personalization, and conversation.


It’s almost implicit, but automation is one of the biggest benefits of adopting AI for marketing, sales, and customer service. It’s heavily blurring the lines between these disciplines because it makes the process of transitioning from one to the other seamless.

When done properly, AI can automate the entire sales cycle, from the ads that a customer sees to the products they buy to the support requests they make post sale.

Use: AI-driven analytics

The key to any type of successful AI implementation is data—the more you have, the more accurate your machine-learning engine will be. Data helps you see trends and, more importantly, create a path toward the best customer experience.

From predictive to prescriptive analytics, knowing how to act and when to react will be the differentiator for businesses using data-driven insights. Using aggregate data to see, for example, that customers who bought one particular product are more likely to buy another, you can set up triggers so that members of the right departments get notified when it’s time to take action.


AI use cases for automation

AI use cases for Automation


As customers seek a more human experience with brands, personalization plays a key role in how much a company is tapped into its customers. According to Gartner (research available to clients), 56 percent of marketing leaders increased their spend on personalization last year, and sales and customer service can follow suit with the help of AI.

Personalization is more than just sending emails with a customer’s name in the headline: It’s about being able to connect with customers at the exact place and moment that they’re looking for your product or service. If that happens to be their inbox, then email works, but you need to be prepared to reach out on other channels too.

Use: A CRM with AI

As CRMs become the hub of customer data, AI is getting baked into CRM tools as a way to make use of all of that data. By collecting and storing customer details and a history of interactions, businesses are able to create customer profiles, better segment groups, and personalize communication to individual customers.

AI features are being built into these tools so that they require minimal configuration and setup to get up and running. For example, an AI-injected feature could look like predictive lead scoring that takes stock of every interaction that you have with a customer, how much time they spend reading or opening your emails, or if they clicked on any links or attachments. This information is then used to rank leads from those most to least likely to convert.


AI use cases for personalization

AI use cases for Personalization


One of the biggest buzzwords around water coolers in 2018 was “conversation.” The conversational approach to sales, marketing, and customer service is about creating an ongoing dialogue with customers to get to the root of their requests more quickly and provide a better customer experience.

It’s not exactly a novel approach to customer service, but online sales and marketing is seeing the benefits of actually talking to customers throughout the entire sales cycle.

Use: Conversational platforms

Conversational platforms, sometimes called conversational user interfaces (CUIs), use AI and natural language processing to allow customers to have a conversation with websites, devices, or apps.

Using pop-up web chats, messaging apps, or voice communication, AI-driven conversational tools start the conversation by asking a couple of leading questions and then direct the discourse toward a sales, marketing, or customer service rep to cater to the request.

Take a look at the graphic below to see an example of how a conversation can start with the marketing department and end up in the hands of sales or customer service teams.

conversational interaction chart

Conversational marketing tools are a great example of multipurpose tools that can help streamline conversations between customers and departments in the same way as the example above.

In fact, research from Gartner (available to clients) predicts that “conversational marketing will be a recognized channel of B2B and B2C customer engagement and revenue, displacing a combination of marketing, sales and service activities” by 2020.


AI use cases for conversation

AI use cases for Conversation

One size fits (almost) all

The blurring of lines between sales, marketing, and customer service is inevitable. These disciplines are an extension of each other and integral parts of the customer experience.

Being successful means ensuring that these departments are working together, and AI is a way to help bridge that gap.

The mechanisms that make AI useful for these disciplines are also making it more difficult to differentiate between them. And that’s a good thing.

If you want to start using a solution that incorporates AI to help bridge the gap between departments:

  • Talk to other departments about their AI needs: Believe it or not, many departments still operate in silos. Having a conversation with managers from other areas of the business will help you determine what each department needs most so that you can find a tool that serves everyone.
  • Take a look at your data: I don’t mean analyzing your data—first, you need to analyze what type of data you have and if there’s enough of it to be useful. Make sure to go beyond your own departmental data to see if the potential to consolidate data from other areas of the business is there.
  • Start with your CRM: You’ll likely already be using a CRM to manage your customer relationships. Dig a bit deeper to see which AI features are available in the solution that you’re currently using. If you’re in the market for a new CRM, keep AI options in mind as you evaluate potential solutions.

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