Many wondered “Is there anything AI can’t do?” But as we began to test the limits of the technology, it became clear that different AI tools are required for certain tasks.
Generative AI on its own may have its limitations, but as artificial intelligence technology advances, we’re unlocking new ways to use AI, particularly for business communications. These developments build on Natural Language Processing (NLP), Large Language Models (LLMs) and speech recognition, enabling greater efficiency across organizations and departments.
If, for instance, an organization wanted to build a solution that uses AI to summarize meetings and calls, ChatGPT alone wouldn’t be able to do that without adding a separate transcription tool since ChatGPT only analyzes text inputs. However, with technology like LeMUR, it becomes possible to create solutions that summarize and analyze meetings from audio and video streams without any additional steps.
Getting Data from Your Meetings
AI summarization models can analyze large bodies of text from conversations, identify the vital takeaways, and provide summaries with the key information. This makes them powerful tools for reviewing sales calls, taking meeting notes, coaching agents, and more.
When you leverage AI to summarize, analyze, and report on a meeting or conversation, using the original source of the meeting data makes a significant difference between high-quality and low-quality results. (Many AI tools like ChatGPT can’t transcribe meetings and summarize them on their own; they need text input or the transcription from somewhere else first, so it’s best to leverage an all-in-one AI system or platform that offers transcription and summarization.)
Voice-to-text technology has grown faster and more accurate in recent years, making it easy to transcribe meetings in real time during a call. However, transcriptions alone only tell half the story. Factors like the speaker’s tone of voice play a role in the overall conversation, and any inaccuracies in the transcript will carry over into the analysis.
The most efficient and effective way to get meeting summaries is directly from the recording. This method is accurate and eliminates the extra step of requiring a separate transcription to run through the Generative AI model, making it a preferable option for anyone building a solution with AI-powered summaries.
Generating AI Summaries and Insights with Generative AI Tools
While many AI solutions already provide call transcripts, users still need to analyze and draw insights from the audio data—and transcripts don’t provide that information. So how can users draw insights from their audio/video data?
A user could record and automatically transcribe a meeting using a virtual meeting solution. Then, if the virtual meeting solution has incorporated LLMs or frameworks for applying LLM capabilities like LeMUR, the user can ask questions about the transcript, such as “What are the main takeaways from this meeting?” The solution would then analyze the meeting directly from the recording and provide insights, such as important notes, action items, and any agreements reached.
Users can also ask LeMUR for general or more specific insights, such as:
- Questions about patterns or trends
- Exact quotes from the conversation
- Generate content for sales workflows
- Simple yes or no questions about the content of the call
- Sentiment analysis when talking with customers
- Summaries of key decisions, events, or points made
This provides detailed, intelligent insights for each call or meeting, adding significant value to an AI-powered summarization tool. Additionally, it’s far more efficient than extracting the transcript, feeding the information to a separate AI-powered solution, and asking it to create a summary from the text, especially when analyzing call and meeting data at scale.
When creating a platform that includes AI-powered summaries and analysis, not any Generative AI will do. It requires streamlined technology designed specifically for calls and meetings that can understand recordings and draw information directly from the audio and video. The efficiency, ease of use, and accuracy of technology like AssemblyAI’s LeMUR will make a significant difference in the quality of the final product and the insights it provides.
