crm ai use cases

Top 4 CRM AI Use Cases

Explore the top 4 CRM AI use cases: personalized customer experiences, predictive analytics for sales, automated customer service, and enhanced data management.

91% of companies with more than 11 employees use Customer Relationship Management (CRM). However, only 12% of them actually use an AI-powered CRM tool!

A lot of users believe that AI in CRM is still in the future and the current processes don’t require automation.

But is this the case?

What is an AI-powered CRM, and what are its use cases? Can businesses realize real benefit by using AI in their CRM? If so, what are the processes they can automate?

We discuss all this, and a lot more in this detailed blog post. Read on.

What is AI in CRM?

CRM manages and maintains relationships with customers, prospects, and other business contacts. AI in CRM refers to integrating AI technologies into CRM software and processes to enhance customer interactions and improve business outcomes.

AI in CRM enables businesses to analyze vast amounts of customer data, predict customer behavior, automate tasks, and personalize customer experiences. It empowers companies to deliver more targeted marketing, provide proactive customer support, and make data-driven decisions, ultimately leading to better customer satisfaction and increased sales.

Why is AI required in CRM in 2023?

In the modern business world, organizations require AI due to the following reasons:

1. Increasing unstructured data

As the number of transactions grows, so does the volume of customer data. While this presents an opportunity for businesses to gain deeper insights into their customers, it also poses the challenge of managing and extracting relevant information from the predominantly unstructured data.

However, AI tools offer a solution by converting unstructured data, which accounts for approximately 90% of the total data, into structured data, enabling businesses to leverage valuable information effectively.

2. Complex processes and relationships

In addition to the expanding data volume, the rise in transactions leads to more intricate business processes and relationships. This complexity hinders a clear understanding of company relationships and accurate analysis of customer patterns. According to Xant, sales representatives spend over half of their time within CRM attempting to manage tasks more efficiently. AI technology presents a solution to this challenge by automating many of these tasks and providing valuable insights.

By understanding customer sentiments, businesses can promptly address issues, improve their offerings, and enhance overall customer satisfaction.

Benefits of AI-powered CRM

Let’s have a detailed look at the benefits of AI-powered CRM.

1. Clean CRM data

AI can help you achieve comprehensive contact lists for each account in your CRM by extracting them from your representatives’ email inboxes, calendars, and Zoom meetings.

You can precisely categorize Account contacts and Opportunity Contact Roles (OCR) based on their engagement and relevance to ongoing Opportunities. AI can also automatically enhance each contact with updated job titles and phone numbers that remain up-to-date whenever changes occur.

2. Qualified pipeline

Detect the absence of pre-engaged contacts or leads within the CRM. Conduct campaigns targeting GDPR-compliant contacts to expand the pipeline and expedite sales cycles, and identify contact roles to enhance targeted outreach efforts.

3. Supercharged ABM

You can recover inactive and lost deals and impact ongoing opportunities by employing Account-based Marketing (ABM) campaigns with current first-party buyer contacts directly sourced from sellers’ email inboxes and calendars.

Increase funnel conversions by precisely targeting buyers through deep insights into buyer engagement within high-priority accounts, their buying roles, and the current sales stage of each account.

4. Data analysis and insights

AI can allow you to process and analyze vast amounts of customer data to identify patterns, trends, and customer preferences, allowing businesses to make data-driven decisions.

5. Predictive analytics

AI algorithms can predict customer behavior, such as identifying potential churn or predicting purchase intent, helping businesses proactively address customer needs.

6. Sentiment analysis

AI can analyze customer sentiments from various sources, such as social media and surveys, helping companies understand customer satisfaction levels and identify potential issues.

7. Lead scoring and nurturing

Assess and prioritize leads based on their likelihood to convert, optimizing sales efforts and increasing conversion rates.

8. Personalization

AI Customer Relationship Management (CRM) systems have the capacity to customize customer engagements through the examination of valuable customer information like previous buying patterns, online activities, and demographic details.

Subsequently, this data can be utilized to personalize marketing communications, suggest products, and facilitate customer support interactions, catering to the distinctive requirements and preferences of each individual customer.

9. Chatbots

Chatbots can deliver immediate customer support, thereby enhancing response times. These chatbots are capable of addressing common inquiries, offering product suggestions, and handling uncomplicated transactions.

Consequently, this expedites the overall process and allows human agents to concentrate on more intricate and demanding matters.

10. Omnichannel presence

AI-driven CRMs have the capability to deliver smooth and uninterrupted customer support through various communication channels, including email, social media, chat, and phone. This enables customers to interact with businesses through their preferred means, leading to enhanced engagement and satisfaction.

11. Sales forecasting and performance analysis

CRM can analyze historical sales data, market trends, and external factors to predict future sales performance accurately. This enables businesses to make informed decisions, allocate resources efficiently, and set realistic sales targets for their teams.

12. Churn Prediction and Customer Retention

AI can analyze customer behavior and historical data to predict the likelihood of customer churn. By identifying potential churn risks, businesses can implement targeted retention strategies to reduce customer attrition and improve loyalty.

With so many benefits, it is no wonder that businesses use AI-powered CRM for various business use cases. Let’s have a look at some of them: 

Top AI CRM Use Cases

By analyzing vast amounts of customer data, AI enables businesses to make data-driven decisions, predict customer behavior, and identify opportunities for upselling and cross-selling. AI enhances customer engagement through virtual assistants and chatbots, providing personalized and real-time support.

AI CRM also streamlines sales processes with lead scoring, automates data capture, and enhances customer service through sentiment analysis and proactive issue resolution. 

Let’s have a look at AI CRM use cases for different functions: 

1. AI CRM Use Cases for Sales

a. Increase your chances of success with a broader network of engaged contacts. Foster dynamic conversations among your representatives by providing them with additional contacts who are:

  • Automatically found from your entire sales toolkit
  • Automatically enhanced with buying group designations; and
  • Automatically linked to ongoing opportunities in Salesforce

b. Initiate crucial interactions using buying group insights, empowering your representatives with immediate access to a buying committee map. This equips them with the information needed to identify the right stakeholders, determine the best approach, and strategically influence the advancement of deals.

  • Gain a clear view of your pipeline through real-time activity intelligence
  • Highlight sales representative performance, pipeline advancement, and potential risks requiring mitigation
  • Utilize these valuable insights to create a scalable sales strategy perfectly tailored to your company’s requirements

c. Save your reps from mundane manual data entry

  • The system automatically captures structured information like names, titles, email, and phone numbers, as well as unstructured data from email threads
  • It then updates this data in relation to active opportunities, all without requiring any manual intervention from your reps.

2. AI CRM Use cases for Marketing

  • More first-party, marketable contacts to target:
  • Boost your campaign’s return on investment by reinvigorating past opportunities and expediting ongoing ones.
  • Boost funnel conversions with buying group intelligence
  • Expand your reach with a greater number of contacts, enhanced by buying group insights to ensure comprehensive account coverage. 
  • Stimulate demand by directing relevant messaging and content towards essential buying committee members at the opportune moment, driving faster conversions throughout the sales funnel.
  • Achieve more effective Go-To-Market (GTM) execution

Obtain live insights into the Sales team’s handling of each opportunity, enabling quicker deal closures through targeted marketing initiatives that engage the appropriate buyers at the most opportune moments.

3. AI CRM Use Cases for Customer Success

  • Personalized Customer Engagement: AI-driven recommendations enable customer success managers to provide tailored solutions and personalized support to each customer based on their preferences and behavior.
  • Automated Support and Ticketing: AI-powered chatbots and virtual assistants can handle routine customer inquiries, reducing response times and allowing customer success teams to focus on more complex issues.
  • Churn Prediction: AI algorithms can predict customer churn by analyzing historical data, allowing customer success teams to take proactive measures to retain at-risk customers.
  • Upsell and Cross-sell Opportunities: AI can identify upsell and cross-sell opportunities based on customer behavior, helping customer success managers suggest relevant products or services to existing customers.
  • Customer Onboarding: AI can streamline the onboarding process by automating tasks, sending personalized welcome messages, and guiding customers through the initial setup, ensuring a smooth and efficient onboarding experience.

4. AI CRM use case for Revenue Operations

  • Pipeline visibility: Gain up-to-the-minute insights into the status and details of every deal, including risk assessment, buyer-rep interactions, and confidently optimize sales strategies to increase closures and overall efficiency.
  • Accurate Data and Reporting: Automatically capture contacts, buying roles, meetings, emails, and activities without errors, while ensuring contextual mapping to the appropriate accounts and opportunities.
  • Account-based selling: Accelerate opportunity creation through the utilization of unified and enriched contact data for account-based selling, thereby maximizing revenue potential.
  • CRM data enrichment: An exceptional contact and activity capture solution not only fills in the gaps in your CRM data but also enhances the data accuracy across all integrated tools.

To ensure that AI makes the best use of your CRM, it is critical that the data is of high quality. Automation tools like Nektar remove manual interventions and assure quality control and data enrichment in your CRM.

Learn how Nektar uses AI to automate your CRM data hygiene Puts CRM Data Hygiene on Auto-pilot is the sole purpose-built solution for contact and activity capture, accompanied by an extensive repository of logical inferences incorporated within its framework.

Opportunity Affinity AI takes into account various inputs, including sender and recipient information, frequency of interactions, and the number of completed activities between the involved parties.

These inputs are used to create a graph connecting all individuals associated with the current and past activities across different opportunities and accounts.

Subsequently, contacts and activities receive confidence scores, which determine the final synchronization of contacts and activities into the relevant opportunity. is capable of managing complex combinations/use cases. These include:

  • Leads and Contacts
  • Rep in CC
  • Closed opportunities
  • Combination of open and closed opportunities
  • Activities between a prospect and a rep’s colleague also get captured
  • Email from a prospect to a non-Sales member of the Seller company with the sales rep in CC, also gets captured.
  • Activities that involve Salesforce and non-Salesforce users also get captured by
  • Contacts and activities from multiple child domains within one parent domain are also captured

Nektar streamlines the CRM process by automatically detecting and filling in missing contacts, incorporating historical data, and providing contextual value, all without requiring any intervention from sales representatives. This creates a path towards efficient and consistent data collection, enabling data-driven decision-making.

Request a Demo to learn how Nektar can help you with your CRM data hygiene goals.

Learn More:

– 2 min-Intro video

– Our Founding Story

See Nektar in action.


Stay up to date with our latest blog posts, podcasts and news

See revenue execution in Action

“ has helped us operationalize playbooks for our Go To Market teams. Not only do we have clear visibility into the process gaps, but we can proactively and consistently guide the rep into taking the next best action in line with our GTM playbooks.”
Yash Reddy
Chief Business Officer