Assess how you can leverage generative AI to transform the service department.
- Gartner client? Log in for personalized search results.
Customer service AI use cases hinge on balancing value and feasibility
The hype surrounding generative AI (GenAI) has brought adoption of AI, which enables numerous relevant use cases, back into focus for customer service and support leaders. In fact, 85% of customer service leaders plan to explore or pilot a customer-facing conversational GenAI solution in 2025.
Gartner analyzes the use cases for customer service AI as being along two axes: the value a use case can deliver for the business in terms of cost reduction, revenue growth and service quality; and the feasibility of implementing the use case successfully, mindful of technical skills, readiness and adoption.
3 categories of use cases for customer service AI
Based on their probability of success, you can then divide customer service AI use cases into likely wins, calculated risks and marginal gains.
Likely wins
Likely wins are customer service AI use cases that are both high-value and highly feasible. They include:
Customer personalization. Uses customer details such as behavior, preferences and interaction history to provide a more personalized customer experience.
Case summarization. Provides a short summary of open customer service cases, allowing agents to understand and resolve issues more quickly.
Agent assistant. Helps human support agents source relevant insights and reformat and rewrite content.
Calculated risks
Customer service AI use cases that qualify as calculated risks have high potential value but are less feasible. They include:
Customer correspondence generation. Generates personalized communications to customers based on the customer’s history, profile and preferences.
Real-time translation. Translates a customer’s speech in real time into another language, allowing for interactions with a more diverse customer base.
AI agents. Autonomous or semiautonomous software agents can make decisions and achieve goals within their environment. For example, AI agents can collaborate with other AI agents and human agents as needed to orchestrate the steps to resolve a customer issue.
Marginal gains
Customer service AI use cases with marginal gains have low feasibility and value. Proceed selectively and with caution with these. They include:
Customer segmentation. Divides customers into segments based on demographics, location, behavior and other metrics.
Social media monitoring. Assesses social media posts to understand customer sentiment and resolve questions and issues.
Agent passive voice biometrics. To improve security, analyzes support agents’ voices to ensure that the agent is the person speaking.
Customer service AI FAQs
What is customer service AI and how can it benefit my organization?
Customer service AI enhances interactions, automates tasks and boosts agent productivity, leading to more efficient and personalized customer experiences.
How is AI used in customer service?
AI is used across the customer service life cycle. For example, virtual customer assistants and agent assistants can provide real-time answers to queries; sentiment analysis tools can look at customer interactions to gauge satisfaction; ticketing and routing platforms can automatically categorize and prioritize customer issues; and personalization engines can provide more tailored experiences and better recommendations.
How is AI used in call centers?
Specific use cases for AI in call centers include:
Case summarization to give human agents a quick overview of the customer’s issue
Workforce scheduling to match agents with the right cases based on preference and skills
Postinteraction wrap-up to help agents summarize the call and log its contents
Real-time translation between languages to allow agents to serve a greater pool of customers.
Attend a Conference
Accelerate growth with Gartner conferences
Gain exclusive insight on the latest trends, receive one-on-one guidance from a Gartner expert, network with a community of your peers and leave ready to tackle your mission-critical priorities.
Drive stronger performance on your mission-critical priorities.