7 LLM use cases and applications in 2024

Large Language Models (LLMs) are powerful models reshaping how we interact with machines. According to recent HBR research, more than 40% of all U.S. work activity can be augmented or automated, allowing businesses to streamline operations, automate mundane tasks, and uncover deep insights faster than ever.

Gone are the days when AI's responses were rigid and predictable. Now, LLMs can create content, write stories, solve complex problems, and even mimic human-like conversations with uncanny accuracy.

Below, we'll walk you through all the top LLM use cases and applications in 2024.

What is an LLM (and what do you use it for)?

Large Language Models are AI systems that understand and generate human language at scale by learning patterns from vast text datasets. These models excel at tasks like content creation, language translation, and complex reasoning by predicting contextually appropriate responses to user prompts.

LLMs have shifted how we approach information, communication, and creativity. Their development from basic models to complex systems like GPT-4 illustrates a growing capacity that stretches beyond mere text processing to encompass a broad spectrum of applications:

• Versatility of applications: Apart from being good at simple tasks, LLMs can achieve varied tasks on natural language like text generation and summarization and eventually be used for applications in medical or legal areas.

• Human productivity boost: LLMs free up valuable time for creative and strategic thinking. As noted in a recent market survey, by streamlining tasks like content creation and data analysis through automated workflows, LLMs help increase productivity and allow for more focus on revenue-driving outputs.

• Accessibility: LLMs serve as a bridge between the vast amounts of data available and actionable insights (making information more accessible and manageable).

• Interactivity at scale: LLMs lend themselves well to creating interactive tools and have given rise to advanced chatbots, virtual assistants, and educational tools. These applications leverage the models' language understanding to provide users with personalized and engaging experiences.

However, accessing and using these LLMs for practical application can be challenging, and some estimates show that over 80 percent of AI projects fail, which is double the rate of typical IT projects.

LeMUR is AssemblyAI's framework for applying Large Language Models (LLMs) to spoken data. It provides a simple interface to a wide array of LLMs, making it easier for developers and businesses to build powerful features on top of their transcripts without the need for extensive AI expertise or resources.

With a single API call and a few lines of code, you can use LeMUR to accomplish complicated tasks for your audio-driven LLM apps:

• Summarize audio and video files into paragraphs, bullets, or a single sentence.

• Ask questions about your spoken data to extract key information.

• Generate insights like action items, call outcomes, and sentiment.

• Identify speakers and assign them names based on the conversation.

• Create custom tags to categorize your audio data based on its content.

• Define your own tasks with custom prompts to fit any use case.

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Top LLM use cases and applications

From transforming how we interact with digital content to revolutionizing customer service, LLMs are reshaping how businesses operate and how we go about our daily lives. Their versatility enables everything from brand-new products and services to improved operational efficiencies.

Below, we look at some of the most compelling LLM use cases and applications in 2024 (and beyond):

1. Spoken data analysis

Applying LLMs to spoken data transforms hours of recordings into actionable insights. This process starts with highly accurate transcription to convert audio into text. From there, LLM frameworks like AssemblyAI's LeMUR can generate summaries, extract key points, and answer specific questions about meetings, phone calls, or podcasts.

For example, corporations can use this analysis to streamline decision-making with automated meeting insights. Sales teams can analyze thousands of calls to identify winning patterns, and support centers can quickly pinpoint recurring customer issues, all by applying LLMs to their transcribed audio data.

2. Content creation

LLMs empower writers, marketers, and creatives by generating initial drafts, suggesting edits, or producing complete articles, reports, and creative works. This functionality accelerates the content creation process and allows humans to focus on strategic and creative aspects rather than the mechanical elements of writing.

It's a game-changer for content-heavy industries, boosting productivity without compromising quality.

3. Customer support

LLMs are transforming customer support by providing automated (yet deeply personalized) responses to inquiries. This technology enables businesses to offer 24/7 support without extensive human resources, improving operational efficiency and customer satisfaction. In fact, industry survey data shows that over 70% of companies using conversation intelligence reported a measurable increase in end-user satisfaction.

Beyond handling common queries, LLMs can understand context, analyze sentiment, and escalate complex issues. This ensures a seamless support experience without a massive investment.

4. Language translation and localization

LLMs help break down language barriers and empower your business to reach a global audience. These models offer real-time, accurate translation and localization services, making websites, applications, and digital content universally accessible.

LLMs try to understand cultural nuances to provide localized content that's both translated and contextually appropriate. This enables businesses to facilitate global expansion and cross-cultural communication at scale.

5. Customer sentiment analysis

LLMs can be trained to understand textual and voice sentiment to better respond to customer concerns and needs. They can also be used to analyze customer feedback, reviews, and social media mentions at scale to gain insights into public perception and emerging trends.

This LLM use case helps with everything from reputation management to product development and marketing strategies—helping your business stay ahead of the curve.

6. Education and training

LLMs can be used to provide personalized education and training by adapting content to meet the individual needs of learners. They can offer tutoring, generate practice questions, and offer explanations tailored to what students are learning (or struggling with).

This LLM use case makes education more accessible and practical. It helps create everything from textbooks to interactive online courses, democratizing education for learners worldwide.

Explore LLM tasks in our Playground

Upload audio to generate summaries, Q&A, and insights—ideal for tutoring, practice materials, and personalized learning workflows.

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7. Cybersecurity

Businesses can use LLMs to analyze and interpret large volumes of cybersecurity data to better anticipate, identify, and respond to potential security threats. LLMs learn the context and nuances of cybersecurity alerts to enable faster, more accurate threat detection and response—strengthening the security posture of your business.

Industry-specific LLM applications and success stories

The true power of LLMs becomes evident when applied to specific industry challenges. With private-sector investment in AI increasing 18-fold from 2013 to 2022, companies that implement targeted LLM solutions are expected to see measurable business impact across key performance indicators.

Legal Technology: JusticeText uses LLMs to analyze audio evidence from body camera footage and interrogation recordings, helping public defenders identify critical case information 75% faster than manual review.

HR and Compliance: Organizations like FAMA leverage LLMs to scan online sources for hiring risk indicators, processing thousands of data points to catch red flags that manual reviews miss.

Sales Intelligence: Companies including CallSource and Ringostat apply LLMs to customer call analysis, uncovering insights that improve conversion rates by up to 23% through better understanding of conversation outcomes and customer sentiment.

LLM implementation strategies and business impact

The most successful LLM implementations follow a systematic approach that prioritizes business impact over technology complexity.

Implementation Timeline by Use Case

Key Success Factors

• Problem-first approach: Identify high-value, repetitive language tasks before selecting technology

• Pilot strategy: Start with well-defined use cases that demonstrate clear business value

• Incremental deployment: Build momentum through small wins rather than enterprise-wide transformation

• Integration planning: Ensure smooth connection with existing workflows and systems

How to drive operational efficiency with LLMs

Operational efficiency isn't just a goal—it's a necessity. Fortunately, LLMs can help you streamline operations, enhance productivity, and foster innovation without sacrificing your budget or other to-dos.

Your business can seamlessly integrate LLMs into its workflows to automate routine tasks, improve decision-making processes, and stimulate creativity. Here's how:

Automate routine tasks

Your business can leverage these models to manage emails efficiently, auto-generate reports, and handle customer service inquiries without human intervention. This automation extends beyond simple tasks to allow for the dynamic generation of content that meets specific guidelines or answers complex customer queries with high accuracy.

Your company can significantly enhance its productivity and operational efficiency by using LLMs to reallocate human resources from routine tasks to more strategic roles.

Improve decision-making

Data is the lifeblood of modern business strategy, yet the sheer volume of information can overwhelm even the most robust analytics teams. LLMs excel in digesting, summarizing, and analyzing large datasets, extracting actionable insights to inform business decisions.

Whether summarizing market research, conducting sentiment analysis on customer feedback, or synthesizing financial reports, LLMs can provide your team with a distilled view of vast data landscapes. This capability supports better-informed decision-making to ensure your strategies are grounded in comprehensive data analysis.

Turn data into decisions with LLMs

Use AssemblyAI's APIs to summarize calls, analyze customer sentiment, and synthesize findings across conversations—then route insights into your workflows.

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Boost creativity and innovation

One of the most exciting applications of LLMs is their ability to augment human creativity and innovation. LLMs can suggest novel ideas, propose alternative solutions to problems, and inspire creative content generation. This process isn't just about generating content or ideas out of thin air—it's about enhancing the creative process with diverse, AI-driven perspectives.

For businesses, this means the potential to uncover innovative solutions to challenges, create captivating marketing content, or even devise entirely new products or services that could redefine the market.

Building LLM applications with Speech AI data

Spoken data represents one of the richest sources of business intelligence, and leading companies are taking notice. In our recent report, 76% of respondents stated that conversation intelligence is already embedded in more than half of their customer interactions. When you combine LLMs with Speech AI, you unlock capabilities that transform how organizations understand and act on spoken information.

The foundation starts with accurate speech-to-text transcription. Without reliable transcription, even the most sophisticated LLM can't extract meaningful insights from audio. That's where platforms like AssemblyAI come in—providing the speech recognition accuracy needed to convert voice data into text that LLMs can process effectively.

Once you have accurate transcriptions, LLMs can perform complex analysis that would be impossible at scale with human review. They can identify patterns across thousands of customer calls, extract action items from meetings automatically, or generate summaries that capture not just what was said, but the context and implications.

Companies across industries are already seeing transformative results from combining Voice AI with LLMs. Support teams use these technologies to analyze call recordings and identify common customer pain points. Sales organizations leverage them to understand what separates successful calls from unsuccessful ones.

Product teams mine customer conversations for feature requests and usability insights. The implementation doesn't require massive infrastructure investment. With APIs like AssemblyAI's LeMUR, you can start building LLM applications on your voice data with just a few lines of code.

LLMs are crucial to unlocking unprecedented operational efficiencies, enhancing decision-making, and stimulating creativity across various sectors. While we've covered several prevalent LLM use cases and applications, it's just scratching the surface—there are many ways to use these advanced AI models to streamline and transform your business. Ready to see what's possible with your voice data? Try our API for free and start building intelligent applications that understand and act on spoken information.

Frequently asked questions about LLM use cases

How long does it typically take to implement LLM use cases?

Simple LLM implementations like text summarization deploy within 2-4 weeks using existing APIs, while custom enterprise solutions typically require 3-6 months for full development and integration.

What ROI can I expect from different LLM applications?

ROI varies by use case, with document automation saving 300+ hours annually per employee and customer support automation increasing resolution rates by 40-60% while reducing costs by 20-30%.

How do LLM use cases vary across industries?

Healthcare uses LLMs for clinical documentation, financial services for compliance monitoring, legal firms for contract analysis, and retail for customer service—all tailored to industry-specific terminology and regulations.

What integration is required with existing business systems?

LLM implementations typically require API connections to existing CRM, database, and analytics systems, with modern platforms offering pre-built integrations that simplify the process.

How do I measure the business impact of LLM implementations?

Track key metrics like processing time reduction (typically 50-70%), accuracy improvements, and customer satisfaction scores, with regular monitoring to demonstrate ROI and identify optimization opportunities.

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