Next-best action
Next-best action refers to an AI-driven decisioning approach that recommends the most appropriate step to take for a specific customer in a specific moment. Rather than following a rigid script or generalized workflow, next-best action models evaluate customer context, interaction history, stated needs, and real-time signals to recommend a tailored response or intervention.
In customer service environments, next-best action often operates behind the scenes within chatbots, voicebots, agent-assist dashboards, and case-routing systems. These recommendations are sensitive to technical performance factors such as latency and inference time, which influence how quickly AI can evaluate incoming information and return actionable suggestions.
How next-best action works
Next-best action models rely on probabilistic reasoning and decisioning frameworks to evaluate how likely it is that a particular response will resolve an issue or improve the customer experience. The system typically analyzes:
- Customer profile and past interaction history
- Current intent signals from chat, voice, or messaging
- Issue type and urgency
- Relevant policies, product details, or constraints
- Live contextual data, such as channel behavior or sentiment
Using this information, the model generates a ranked list of recommended actions—for example, offering a refund, escalating to a specialist, providing a troubleshooting step, prompting a knowledge article, or triggering an automated workflow.
Next-best action can also vary by channel. In self-service environments, it may suggest the ideal next prompt or answer. For agents, it may surface a recommended path on their desktop or guide them through step-by-step resolutions. Over time, the system improves by learning from which actions led to successful outcomes.
How AI uses next-best action to personalize customer support
AI-driven next-best action systems help personalize customer support by evaluating intent and interaction history to determine the most effective step in real time. This approach reduces unnecessary back-and-forth and helps agents and customers move more directly toward resolution.
Key ways AI uses next-best action to improve personalization include:
- Guiding customers efficiently: AI recommends the most relevant step—such as a troubleshooting action, policy explanation, or escalation—reducing friction and lowering average handle time (AHT).
- Maintaining consistency across channels: Recommendations align with current policies and past interactions, helping ensure customers receive accurate, compliant guidance whether they use chat, messaging, or voice.
- Adapting to real-time context: AI uses live signals, such as sentiment or channel behavior, to tailor suggestions to the customer’s situation.
- Supporting faster decisions: Quick access to the right action helps agents stay focused on higher-value tasks.
- Depending on response speed: High latency or slow inference time can delay recommendations; when systems respond quickly, interactions feel more seamless and personalized.
These capabilities help AI-driven service environments deliver support that feels more tailored to each customer.
Factors that influence next-best action performance
Several elements determine the quality and usefulness of next-best action systems:
- Training data quality: Poor or outdated examples lead to ineffective recommendations.
- Real-time system responsiveness: High latency or slow inference time reduces the system’s ability to guide interactions effectively.
- Workflow integration: If recommendations are difficult to access or do not align with existing processes, adoption suffers.
- Feedback loops: Continuous evaluation of which actions succeed helps refine and improve the model.
Use of next-best action enhances customer satisfaction while strengthening operational efficiency by guiding both agents and AI systems toward the most efficient resolution paths.
