Companies are transforming sales intelligence with speech AI, and it's easy to see why—sales teams that leverage AI are 1.3x more likely to see revenue increases than those that don’t, according to a recent study by Salesforce.
Modern sales teams are using AI to help target the right leads, increase call velocity, analyze lead sentiment, tailor customer messaging, and more. And as AI models only improve, so too will the use and utility of AI-powered sales tools.
This article will provide a comprehensive guide to AI applications in sales calls, including:
- Core speech AI technologies
- Benefits of AI integration
- Top use cases and software
- Common challenges of using AI
Core Speech AI technologies for sales calls
Several core Speech AI technologies are powering the analysis tools behind today’s top sales platforms: asynchronous and real-time transcription, speaker diarization, sentiment analysis, topic detection, and Large Language Models, or LLMs.
Asynchronous and real-time (streaming) transcription
Before any analysis can occur, all conversations must first be transcribed into readable text.
Speech recognition and transcription for sales conversations occur in one of two ways:
- Asynchronously, or after a conversation has been captured via a meeting recording platform
- In real-time as the conversation occurs with the aid of a streaming, or real-time, speech-to-text model
Regardless of the method, the speech-to-text model must be able to transcribe all conversations with high accuracy across noisy environments, speaker accents, and other limiting factors. This is because the accuracy of the analysis depends entirely on the accuracy of this transcription—if words are mistranscribed, this will lead the analysis tool to generate faulty, or simply wrong, conclusions and analysis.
If real-time speech transcription models are used, latency, or the time between when the speaker talks and the transcript is generated, also becomes extremely important.
Highly accurate transcripts enable downstream AI-powered features such as real-time coaching, instant objection analysis, searchable conversation records, and more.
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Speaker diarization
Speaker diarization models help distinguish between different speakers in an audio or video stream, a critical component of multi-speaker conversations like sales calls.
Integrating accurate speaker diarization APIs allows providers to analyze talk-to-listen ratios between a sales agent and lead, analyze question patterns, and determine the conversation dynamics that correlate with successful sales outcomes.
Sentiment analysis
Sentiment analysis models use AI to identify emotional signals and attitudes in conversations by classifying speech segments as positive, negative, or neutral.
For sales tools, sentiment analysis can be used to create tools that track sentiment data that determine prospect engagement in one topic versus another, identify common hesitation points in the selling process, or to better understand emotional patterns in won deals.
Topic detection
Topic detection models automatically (a) identify and (b) categorize discussion themes—like products or locations—within conversations. Topic detection models can be used to build tools that track competitive mentions, identify which topics correlate with deal advancement, or to ensure methodology coverage.
LLM integration
Many sales analysis platforms are also integrating Large Language Models, or LLMs, to analyze conversation data at scale and extract insights based on user prompting, such as asking the LLM to “generate coaching insights in bullet point form from call data ranging from May 2024 to January 2025” or to “recommendation next steps to close this deal based on prior closed won deal conversations.”
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Benefits of using speech AI in sales calls
The wealth of benefits from integrating Speech AI into sales call analysis platforms is nearly boundless.
Top benefits include:
- Increased efficiency and productivity
- Improved lead qualification and conversion rates
- Enhanced customer insights and personalized communication, leading to an improved customer experience
- Data-driven decision-making
Extracting actionable insights from sales conversations also reduces a team’s manual effort, augmenting a smaller sales team’s ability to do more with less concentrated effort.
Transforming sales conversations with AI: Top use cases and software
Enterprise-grade conversation intelligence platforms offer sales analysis tools in five main buckets:
- Sales coaching and feedback
- Deal intelligence and risk analysis
- Performance insights
- Competitive intelligence
- Improving sales agent performance
Sales coaching and feedback
Revenue intelligence platforms like Jiminny use AI to secure higher win rates for its customers. Jiminny’s AI-powered platform offers tools that help managers more easily “listen” to and analyze bulk sales calls for each team member. By automatically identifying coachable moments and areas for improvements, companies can more effectively scale a manager’s impact while improving individual sales rep performance.
Deal intelligence and risk analysis
Sales engagement platforms like Clari use AI to help their users automatically identify and flag at-risk opportunities. This proactive risk mitigation leads to better forecast accuracy, as well as higher win rates as users can combat problems before it is too late and the deal is lost.
Performance insights
Conversation intelligence platforms like Chorus.ai by ZoomInfo leverage AI models like speaker diarization and topic detection to help users identify winning behavior and talk patterns. By surfacing these key performance insights, users can replicate key findings across the entire sales organization, driving success and improving the bottom line.
Competitive intelligence
AI sales engagement and conversation intelligence platforms like Revenue.io help users create a real-time competitive intelligence database by tracking competitor mentions and objection handling. The platform helps teams of all sizes maintain better awareness of the competitive landscape and stay ahead of the market.
Improving sales agent performance
Finally, AI contact center platforms like Observe.ai built AI-powered conversation analysis tools for agent scoring, compliance monitoring, and personalized coaching suggestions to improve overall sales agent performance for its customers, as well as to drive improved customer service and customer loyalty.
Common challenges of using AI in sales calls
As with any newer technology, some common challenges and concerns persist as more companies adopt AI as an integral part of their sales workflows.
Data privacy and security are a top concern, especially in industries that handle sensitive customer data like medical information or identification data like credit card numbers or social security numbers.
Some sales organizations may also be concerned about the initial cost of implementation, as well as the adoption and learning curve for existing sales teams. However, as noted in the beginning of this article, the cost of not implementing is actually greater, as sales teams who utilize AI realize much higher win rates than those who do not.
Final words
Speech AI transforms sales conversations from ephemeral interactions into strategic assets that drive measurable revenue growth and team development.
Organizations that are adopting these AI-powered technologies gain a competitive advantage through data-driven coaching, risk identification, and uncovering strategic insights that would otherwise remain hidden in conversations.
AssemblyAI's industry-leading speech AI models, including Universal with industry-leading accuracy and advanced speaker diarization, provide the essential foundation for all conversation intelligence applications, enabling sales teams to build reliable, scalable solutions without compromising on quality.
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