AI in CRM:
top use cases, best platforms, and guidelines

AI in CRM: top use cases, best platforms, and guidelines

June 29, 2023

expected CRM market value by 2029, with AI as a major demand driver

Fortune Business Insights

of companies consider improving customer experience the top AI use case


faster response to customers and partners by companies adopting AI


12 use cases of AI in CRM by business area

AI-based technologies сan augment your CRM's functionality in various use cases and workflows.


  • Assess leads and forecast customer lifetime value based on sales, activity, and demographic data to increase potential customers’ conversion rate.
  • Automate routine tasks like scheduling appointments and sending follow-up emails to customers to free your sales managers’ time for tasks requiring a more personalized approach to particular customers.
  • Analyze salesperson performance and provide account-specific recommendations via smart assistants to assess your sales team and adjust their KPIs.

Image title: SAP lead scoring feature interface
Data source: SAP

SAP lead scoring feature interface

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AI-enabled CRM implementation examples

Look at real-life CRM implementation scenarios to assess the potential impact of artificial intelligence on your customer-centric operations.

    A major commercial property management company in the UK partnered with Itransition to implement a Salesforce-based CRM solution. Its AI features include automated reporting and predictive analytics to forecast customer behavior.

    + 15%

    conversion rate

    The system built by Itransition helped us improve decision-making and get a 360-degree view of our customers.

    Sam Wilson

    Solutions Architecture Director, Bruntwood

    Top 5 CRM platforms with AI-based features

    Most major CRM platforms currently include an extensive range of AI-powered features to enhance customer-related operations. Check out the following options on the market:

    Customer 360 relies on Einstein, a powerful AI that integrates easily with Salesforce’s services and provides valuable business insights.

    AI-based features
    • Prediction Builder to forecast customer churn, lifetime value, and other business outcomes
    • Customizable chatbots automating customer support tasks across multiple digital channels
    • Identification of potential leads and opportunities based on past deals and sentiment analysis 
    • New Einstein GPT functionality to deliver AI-generated content, such as personalized emails
    • Four bundles (sales + cloud services)
    • from $25 to $325 per user per month, billed annually

    How to implement AI in CRM

    While the roadmap to build and adopt an AI-powered CRM solution can vary depending on the model selected, these are some common stages you should be ready to go through.



    Start by framing your current process and CRM flaws, business goals, and user requirements to choose the main AI use cases your future solution should target. Then, set up an implementation plan defining an MVP, budgeting, and change management. You should also select suitable internal and external data sources to integrate your CRM with.



    Prepare a functional specification encompassing various scenarios for different user roles and describing the CRM architecture, along with a visual mock-up and wireframes for the user interface. You should also detail the tools, technologies, and AI-based platforms you’ll rely on, along with the deployment model and the software integrations you plan to configure.



    Proceed with the coding phase, covering front-end, back-end, and integrations. As for the AI and ML features developed from scratch for a custom CRM, their operation will rely on ML models built through complex data preparation and algorithm training. Alternatively, you can opt for a platform-based CRM and customize built-in AI functionalities.


    Rollout and support

    After performing accurate functional, performance, and usability tests, you can deploy your AI-powered CRM solution on local servers or in a cloud-based environment. The adoption should be facilitated through ongoing user training and support, followed by regular audits and software maintenance to solve issues or address user feedback.

    Benefits of AI in CRM systems

    Supported by AI technologies like machine learning, a CRM platform will strengthen your customer relationships and improve business outcomes.

    Benefits of AI in CRM systems
    Improved decision-making

    based on data-driven insights and trend forecasts

    Better sales performance

    via segmentation, targeted marketing, and lead scoring

    Enhanced customer engagement

    due to personalization and 24/7 user support

    Faster and more accurate operations

    through robotic process automation

    Improved data reliability

    via automated mapping, cleansing, and integration

    Easier development

    with AI-based platforms enabling low-code CRM customization

    Safer data management

    through ML-based anomaly detection and fraud prevention

    AI-based CRM implementation challenges and solutions

    When implementing AI functionalities in your CRM software, you can face a number of business and technical challenges. Here are some recommendations from our experts.



    The performance of the algorithms powering AI-based CRM systems largely depends on the data they process. Such information can be outdated, inaccurate, or spread across multiple data silos.


    Data quality and availability



    Collecting high-volume, reliable data requires a solid data management strategy. This includes data mapping of suitable sources, data cleansing and transformation, and integration among multiple systems via APIs or ESBs. The top CRM providers generally offer data preparation features (such as Salesforce Data Prep) and cloud data integration tools with built-in APIs (like SAP Data Services).

    Whether you design a custom CRM or select an AI-powered CRM platform, you should define a suitable feature set addressing essential use cases in your business scenario.


    Feature and use case selection



    When implementing AI-based features, prioritize use cases based on feasibility and impact. McKinsey suggests campaign retargeting and brand management. Some platforms have tools to identify suitable AI use cases and CRM functionalities you can fully leverage (if your data availability matches their requirements), including Einstein’s guide to AI use cases and readiness assessor.

    The ML algorithms used to process information and build the data models that fuel most AI-based features should be trained on vast datasets. Also, AI systems require massive computing power.


    Data processing



    If you build your solution on top of an AI-based CRM platform, model training and data processing won't be a major issue, as you'll rely on the selected platform’s native capabilities. As for a custom CRM, you can still integrate your software with cloud-based ML tools, such as Amazon SageMaker, to benefit from their built-in algorithms, pre-trained models, and scalable computing resources, or purchase your own infrastructure with powerful GPUs, depending on your business and technical requirements.

    Bridging AI and customer data

    Bridging AI and customer data

    As customer interactions, driven by the rise of ecommerce and other digitalization trends, progressively shift towards virtual spaces, the volume of generated data constantly expands. The latest advancements in AI technologies provide the right tools to turn these data assets into valuable insights.  AI-enabled CRM represents the perfect embodiment of this data-driven approach to customer management. Equipped with AI functionalities, CRM platforms enhance decision-making, streamline business processes, and improve customer satisfaction and retention. To fully seize the benefits of an AI-driven CRM, rely on an experienced partner like Itransition.

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