AI agents reason, adjust, and automate tasks across the enterprise.
Customer support teams can deploy AI agents to automate customer queries.
However, that’s only one use case.
AI agents will have a much bigger, broader impact on the contact center over the coming years.
Indeed, AI agents will augment the roles of contact center employees, automating much of their daily work. That includes reps, supervisors, managers, quality analysts, planners, etc.
While contact centers may build their own AI agents, expect CCaaS providers to embed them into their solutions to help service operations take advantage of the technology.
Underlining this, Gartner predicts that 33 percent of enterprise software applications will include agentic AI by 2028.
CCaaS applications will not be an exception. Indeed, some contact center solutions providers already onboarding AI agents.
As these spread, they will mechanize new tasks, and more use cases will come to the fore.
Here are ten potential applications, with massive potential to transform the contact center.
- Proactive Customer Service Agents
AI agents can detect signals in external systems that indicate a customer issue. From there, they can act to resolve that problem before the customer reaches out.
There are many possible examples of this. For instance, a proactive AI agent may communicate updates during a weather emergency, reschedule bookings, and remind customers of safety protocols. That’s not only helpful but demonstrates that the business cares.
Also, consider an industry-specific example. In healthcare, an AI agent may check a patient’s records to send reminders for checkups. It could even auto-suggest appointment times/dates based on availability and recorded patient preferences.
If the customer agrees to the time, the AI agent could then automatically schedule it. If the customer doesn’t agree, it may share alternative options.
Those are two of many examples that could change the nature of many contact center operations.
Notably, a CCaaS provider with CPaaS on the back end will likely be best placed to orchestrate and deliver these proactive AI agents. As such, that may soon become a more significant contact center buying consideration.
- Pre-Emptive Customer Service Agents
Instead of proactively solving a customer issue, there are opportunities for AI agents to stop a problem from happening in the first instance. That’s pre-emptive customer service.
An excellent example is an AI agent that listens to customer interactions and picks up on subtle customer statements regarding their preferences.
For instance, a customer may make statements like: “I don’t start work until noon.” An AI agent may funnel that insight into the customer’s CRM profile, ensuring they don’t receive outbound comms after midday.
Such an AI agent use case may also bolster the CRM with valuable first-party data that helps personalize future customer experiences.
- Reactive Customer Service Agents
Older contact center virtual agents require brands to predefine the conversations the bot will encounter and tell them what tasks to perform. That rigidity drove customers crazy.
Generative AI changed this by powering virtual agents that could understand intent and surface relevant knowledge content to help solve many queries without programming decision trees.
Yet, AI agents can go further. They may trigger actions in third-party systems, unlock new data and knowledge, and reason to its accuracy.
If unsure, it can escalate. If certain, the AI agent may adapt its responses to the individual customer and automate more conversations.
With the emergence of proactive, pre-emptive, and reactive AI agents, Gartner predicts that agentic AI will automate 80 percent of customer service queries by 2028.
- AI Agent Receptionists
Contact centers may not want to automate every customer query. Some offer upsell/cross-sell opportunities, while others benefit from human empathy.
Still, AI agents can act as receptionists to benefit the customer service experience.
For instance, they may route the customer by considering their intent and scouring quality assurance (QA) data to determine which agent typically handles these queries best.
Additionally, they may ask customers questions in the queue and feed that additional context to the live agent to support their troubleshooting process.
