AI call analysis: How to use AI to analyze phone calls

What is AI call analysis?

AI call analysis uses speech recognition and natural language processing to understand the content of phone conversations, not just their metadata. Traditional call analytics tracks how many calls came in and how long they lasted. AI call analysis looks at what was said, how it was said, and what should happen next.

Approach	What it measures	Example question it answers
Traditional call analytics	Call volume, duration, wait time, abandonment, missed calls	How many calls did we miss during Monday's peak?
AI call analysis	Sentiment, topics, intent, quality, satisfaction, and action items in each call	Why are customers calling about billing this week, and how do they feel about it?
Conversation analytics	Patterns across calls, chats, and messages over time	Which product issues are driving complaints across channels?

The three often work together. Contact center analytics dashboards can combine operational metrics with AI-generated insights, and conversation analytics applies the same kind of analysis beyond the phone.

How does AI analyze phone calls?

AI call analysis generally follows five steps, whether it happens live on the call or after it ends:

Transcribe: Automatic speech recognition (ASR) converts the audio into a transcript and separates the speakers.
Understand: Natural language processing identifies intent, topics, details such as products or order numbers, and sentiment.
Score and summarize: Models evaluate the conversation against criteria such as QA questions or predicted satisfaction, and generate a summary with action items.
Surface: Results appear in dashboards, alerts, and call records for agents, supervisors, and other teams.
Act: Teams coach agents, update knowledge sources, fix processes, or hand routine requests to AI agents.

Accuracy at the first step affects everything after it. Call audio includes cross-talk, accents, background noise, filler words, and industry vocabulary, so models trained on real business conversations often handle it better than models trained mostly on written text.

What can AI analyze on a phone call?

Most AI call analysis falls into a few categories, and each answers a different question about your customers or your team.

Customer sentiment

Sentiment analysis evaluates the language in a conversation to estimate how the customer feels as the call unfolds. In Dialpad, supervisors can see live sentiment across their team's calls and open the real-time transcript when a call starts trending negative.

Call purpose, topics, and keywords

AI can identify why customers call and track how often specific topics come up. With Custom Moments in Dialpad, teams choose the words or phrases to track, such as “refund,” “cancel,” or a competitor's name, and can set alerts when they appear. A spike in one topic can point to an issue with a product, a policy, or a recent campaign.

Customer satisfaction without surveys

Post-call surveys capture feedback from a small share of customers, and responses often skew toward the very satisfied or the very unhappy. AI can estimate satisfaction from the conversation itself. Dialpad AI CSAT detects CSAT scores from call transcripts, and AI CSAT explanations identify the reasons behind low scores so supervisors can coach with specifics.

Quality assurance and process adherence

Manual QA usually covers a small sample of calls. AI Scorecards grade contact center and coaching team calls against criteria you define, such as whether an agent verified the caller's identity or confirmed next steps, and supervisors review the AI-assigned grades. AI Playbooks help sales reps follow prescribed behaviors on live calls and give managers post-call analytics.

Summaries and action items

AI can summarize each call with key points, outcomes, and next steps. AI Recaps reduce after-call work for agents and let managers review a conversation without listening to the full recording.

Churn signals and knowledge gaps

Patterns across many calls can reveal risks that single calls hide, such as repeated complaints about the same issue, cancellation language, or questions agents struggle to answer. Tracking these patterns over time can help teams address churn drivers and update help content and training before small problems spread.

How to use AI to analyze phone calls, step by step

1. Keep calls on one platform

AI call analysis works best when calls, transcripts, and customer context live in the same place. Analysis tools added on top of a separate phone system can require integrations to move recordings and data between systems, and insights may arrive after the moment to act has passed. On one platform for calling, contact center, and AI agents, transcription, analysis, and coaching can happen while the call is still in progress.

2. Turn on transcription

Transcripts are the raw material for every other kind of analysis. Make sure AI transcription is enabled for the teams and call types you want to analyze, and add company-specific vocabulary, such as product names and acronyms, where your tools support it.

3. Decide what you want to learn

Customer calls can cover anything from complaints and pricing questions to praise for an agent. Start with the questions that matter most to your business, for example:

Why are customers calling, and is that changing?
Which calls are at risk of escalation or churn?
Are agents following required steps, such as identity verification or disclosures?
How satisfied are customers after phone support, beyond survey responses?
Which questions do agents struggle to answer?

Each question maps to a different kind of analysis, so choosing them up front keeps reports focused on decisions.

4. Use real-time analysis during live calls

Real-time analysis helps teams act while the customer is still on the line. Supervisors can monitor live sentiment across calls and step in by listening in or using call barging when a conversation needs help. Agents can get in-the-moment guidance from AI Live Coach, which surfaces pre-built guidance when specific topics come up, and from Dialpad's AI Assistant, which pulls answers from connected knowledge sources during live calls.

5. Review post-call analysis

After calls end, AI takes on review work that used to mean hours of listening to recordings. Teams can grade calls with AI Scorecards, monitor satisfaction trends with AI CSAT, follow topic trends with Custom Moments, and read summaries instead of replaying full calls. Chicago-based New City Moving, for example, increased the number of calls it scores by 6x with AI Scorecards.

“With Dialpad, we're not just seeing—we're understanding and acting.”

- Clare McDevitt, Director of Sales, New City Moving

6. Share insights beyond the contact center

Call insights are useful well beyond support. Product teams can see which features cause confusion, sales teams can hear which objections come up most, and leadership can track why customers stay or leave. When conversations across sales, support, and other teams are captured on the same platform, they add up to conversation intelligence the whole business can use to understand each customer more completely and make better-informed decisions.

From analysis to action: where AI agents fit

Call analysis shows what customers need, and AI agents can act on it. Dialpad Resolve, AI agents for voice and digital interactions, can handle routine requests such as appointment scheduling, order status checks, and intake, then pass the conversation to a human agent when it needs one.

Call analysis can also show what to automate first. Dialpad's Skill Mining analyzes transcripts of conversations between human agents and customers, recommends skills an AI agent could take on, and estimates the share of calls each skill could handle. Teams can create a skill from a recommendation in one click, with its name, description, and steps pre-filled.

When an AI agent hands off a call, context carries forward, including the reason for the call, the customer's identifier, the escalation reason, troubleshooting steps already attempted, and customer sentiment. The human agent can pick up where the AI agent left off, and that conversation becomes part of the record teams analyze to improve over time.

Benefits of AI call analysis

Broader coverage: AI can analyze far more calls than a team can review manually, so insights reflect more of your customer base than a small sample.
A fuller view of satisfaction: Measuring sentiment and predicting CSAT from conversations gives teams a picture that doesn't depend on who answers a survey.
Faster, more specific coaching: Automated scoring and summaries help supervisors find coaching moments quickly and back feedback with examples from real calls.
More productive teams: An IDC Business Value Study, based on in-depth interviews with Dialpad customers and published in January 2026, found 13% more productive call center agents and 24% more productive call center managers.
Better-informed decisions: Patterns in customer needs, friction points, and churn signals become visible to support, product, sales, and leadership teams.

What to look for in AI call analysis software

When you evaluate AI call analysis software, look for:

Real-time and post-call analysis: Live sentiment and agent guidance during calls, plus scoring, summaries, and trends afterward.
Automated quality assurance: AI-assisted grading against criteria you define, with supervisors able to review and adjust grades.
Satisfaction measurement without surveys: CSAT estimated from conversations, with explanations for the scores.
Custom topic tracking: The ability to track the words and phrases that matter to your business and get alerts on them.
Accuracy on business conversations: Transcription and language models trained on real business calls, such as Dialpad's suite of proprietary models, can handle industry vocabulary and noisy audio more reliably.
Voice and digital coverage: Analysis that spans phone calls, chat, and messaging, so customer context isn't split across tools.
Integrations: Connections to CRM and help desk systems through a partner ecosystem such as the Dialpad Partner Marketplace.
Governance and privacy: Clear controls over who can access call data and how it's protected; the Dialpad Trust Center covers Dialpad's security and compliance details.
A path from insight to action: AI agents that can act on what analysis reveals, with context passed to human agents when needed.

Turn customer conversations into operational insight

Phone calls already hold much of what a business needs to understand its customers. AI call analysis makes that information usable at scale, and connecting it to coaching, workflows, and AI agents turns it into action. Dialpad's AI platform for customer experience brings calling, contact center, conversation intelligence, and AI agents together, so insights from each customer conversation can inform better decisions across the business.