AI for Business – Artificial intelligence (AI)

How is AI used in the contact center?

AI in the contact center means using artificial intelligence to handle customer conversations, support employees and improve day-to-day operations. Modern tools can understand natural language, predict what is likely to happen next, analyze large volumes of conversations and carry out approved workflows across connected systems.

Key takeaways

• AI supports customer self-service, agent assistance, analytics, workforce planning and workflow automation.
• Different types of AI solve different problems: generative AI handles language, predictive AI supports forecasts and decisions, and agentic AI carries out approved multi-step actions.
• It works best when it shares customer context, company knowledge and operational data with the contact center platform.
• Success should be measured by resolution, customer effort, quality, productivity and workflow outcomes.

Major AI use cases in the contact center

• Virtual agents: handle customer conversations and routine tasks without a person.
• Agent assist: shows relevant knowledge and next steps while an agent is on a live interaction.
• Interaction summaries: writes call or chat notes and drafts the outcome code.
• Conversation analytics: finds common themes, customer sentiment and the reasons people get in touch.
• Quality management: reviews far more interactions than a team could check by hand and gives coaches real examples to work from.
• Workforce optimization: helps forecast demand and build schedules.
• Routing: uses context and predictions to match each customer with the best agent or queue.
• Agentic workflows: coordinates multi-step actions across different systems.

Generative, predictive and agentic AI

1. Generative AI: works well with language. It writes, summarizes and holds natural conversations.
2. Predictive AI: estimates what is likely to happen, such as contact volume, customer churn or the chance a customer will reach out.
3. Agentic AI: plans and carries out several approved steps to reach a set goal, such as updating a record and then notifying the customer.

A good contact center strategy combines all three instead of treating AI as a single feature.

AI for customers

• Natural-language self-service that understands everyday wording
• Voice automation for phone calls
• Proactive service, such as alerting customers before a problem affects them
• Answers drawn from approved knowledge
• Scheduling and status checks without waiting for an agent
• Smooth handover to a human agent when needed, with the conversation history attached

AI for agents and supervisors

• Real-time guidance during live interactions
• Short summaries of the customer’s context
• Automated notes after each contact
• Quality analysis across more interactions
• Coaching insights based on real conversations
• Workforce recommendations for staffing and scheduling
• Root-cause analysis to find why customers keep contacting you

AI governance in the contact center

Customer-facing AI should only use approved knowledge, work within the right permissions and follow clear escalation rules. Before any change goes live, teams need a process to test the models, prompts and workflows.

AI that supports employees needs the same care. Its outputs should be reviewable and regularly checked for accuracy, especially when they influence quality scores or performance decisions.

How to measure contact center AI

Track the following:

• Resolution rate
• Repeat contacts
• Task completion
• Customer effort
• Answer accuracy
• Agent productivity
• Quality scores
• Workflow success

Cost savings matter, but they should not come at the expense of customer needs that go unresolved.