AI Revolutionizes Customer Service: 5 Concrete Uses Transforming Phone Interactions!

For many years, voice communication has taken a backseat in customer relationship strategies. Seen as expensive to operate and challenging to automate, it has lost ground to written channels as customer interactions have become more digital. The rise of conversational AI is changing this balance, bringing the telephone back as a high-yield channel, provided it is properly equipped.

With the help of Aircall, a VoIP solution provider, we present five concrete uses of AI in customer service.

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1. Managing High Call Volumes Without Increasing Staff

Handling spikes in call volume has always been a challenge for support teams: beyond a certain number of simultaneous calls, the available advisors are no longer sufficient, and the quality of service deteriorates just when customer pressure is highest. In such scenarios, sizing teams appropriately can be particularly tricky, as hiring to manage peaks means maintaining oversized staff for the rest of the time.

Vocal AI addresses this issue by autonomously handling requests for which it has the necessary information. It identifies the reason for the call, queries connected business tools, and handles the most common cases without human intervention: order tracking, appointment changes, standard information requests. When a situation exceeds this scope, the call is transferred to an advisor with the context already consolidated. This is what Aircall’s AI Voice Agent offers, operating as an overflow or front-line solution depending on the chosen setup, with continuous availability independent of schedules.

2. Identifying the Reason for Missed Calls Before Returning Them

A missed call without information about its purpose is a double lost opportunity: once at the time of the call and again at the time of the callback, which occurs without context. For sales teams, this problem repeats during periods of high activity or extended absence.

Vocal AI changes this by qualifying incoming missed calls. Instead of leaving a generic message, it initiates a conversation with the caller, collects information about their request, and creates a structured task in the CRM. Salespeople are then presented with a set of actionable reminders, such as the caller’s details, the reason for the call, or the estimated urgency.

3. Providing Advisors with the Right Context During Calls

During a call, time spent searching for information is time taken away from the conversation itself. An advisor who has to navigate between multiple tools to find a history, check a deadline, or identify a procedure loses track of the conversation and extends the handling time.

Real-time conversational analytics anticipate these needs. By detecting keywords mentioned by the client, the AI automatically brings up relevant information on the screen without the advisor needing to request it.

AI Assist Pro by Aircall operates on this principle, assisting the advisor during the call while handling transcription, CRM updates, and generation of post-call actions. For new recruits, this system acts as an informational safety net from the first weeks, without extending the initial training period.

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4. From Profile Data to Conversation Signals

Traditionally, customer satisfaction measurement relies on declarative indicators like NPS or CSAT. These tools provide a synthesized yet static view: they capture what the customer expresses after the fact, not what they feel at the moment of contact. A CRM profile deemed satisfied might correspond to a customer calling under stress, after several unsuccessful attempts on other channels.

Analysis of conversational signals addresses this blind spot. Speech rate, hesitations, and the recurrence of recent contacts on the same issue help assess the level of tension in an exchange without waiting for the customer to explicitly state it. Combined with CRM data, these signals enable advisors to adjust their approach during the call rather than afterward, turning satisfaction from a post-measured indicator into a continuous reading of each interaction.

5. Extracting Value from Conversations Beyond Immediate Handling

Support and sales teams generate a significant volume of telephone conversations, most of which remain unutilized once the call ends. According to data from Aircall, less than 3% of exchanges are currently subject to qualitative analysis. Recordings accumulate, but the signals they contain—purchase intents, recurring objections, reasons for cancellation—never reach the teams that could benefit most.

Automated analysis allows for large-scale processing of this data. Trends that span hundreds of calls become visible: a consistently recurring contact reason, an objection that advisors handle inconsistently, a signal of attrition that regularly precedes a cancellation. These insights, aggregated and relayed to managers, transform customer service into a management tool, beyond its primary function of handling flows.

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