Advanced Large Language Models (LLMs) are powering chatbots, image generators, and software that can handle complicated requests from users and return near-human results.
As businesses integrate this generative AI technology, they also unlock opportunities to enhance operations, improve the customer journey, and drive innovative product development, with one economic analysis estimating it could add up to $4.4 trillion in annual value.
In this article, you'll learn more about building with LLMs and the top business use cases for Generative AI tools and applications.
What is Generative AI?
Generative AI is a broad category of artificial intelligence that creates new content from existing data, enabling businesses to automate content generation, streamline operations, and unlock insights from unstructured data like voice conversations.
Content generation can come in a variety of forms. Its versatility extends across domains, empowering businesses to create in unprecedented ways. Some examples of Generative AI include:
Text generation: Language models are capable of generating coherent and contextually relevant text for content creation, language translation, and code generation.
Image synthesis: Synthetic image models can render human faces, generate artwork, and enhance image editing tools.
Music composition: Generative models can compose melodies, harmonies, and even entire musical compositions.
Data augmentation: A technique using generative models that can create diverse and realistic variations of training data to help improve the robustness and generalization of machine learning models.
What are Large Language Models (LLMs)?
A Large Language Model (LLM) is a type of deep learning neural network trained on massive amounts of data and then fine-tuned for specific applications. OpenAI's GPT-4 model is one example of an LLM that can understand the complex patterns in language and generate contextually appropriate responses.
For example, LLMs can be used to create tools that perform sophisticated audio analysis, enhance customer support interactions, create new creative content, and more.
AssemblyAI's LLM Gateway is a unified API that allows companies to leverage over 15 leading Large Language Models (LLMs) from providers like Anthropic, OpenAI, and Google. This framework simplifies building Generative AI tools on top of spoken data without needing to manage multiple provider integrations. With the LLM Gateway, developers can use a single API to perform tasks like generating custom summaries, extracting action items from meetings, or answering specific questions about audio content through flexible prompting.
Unify Top LLMs with One API
Use AssemblyAI's LLM Gateway to generate summaries, extract action items, and power Q&A over your audio data—without juggling multiple provider integrations.
Generative AI delivers measurable business outcomes by transforming unstructured conversational data into actionable intelligence. This drives value across three key areas:
Boost employee productivity: Automating routine tasks like summarizing meetings, generating reports, and identifying action items frees up your team to focus on high-impact, strategic work. In fact, recent research suggests the technology could automate work activities that currently absorb 60 to 70 percent of employees’ time.
Improve customer experiences: By analyzing customer conversations at scale, businesses can quickly identify sentiment, detect emerging trends, and understand user pain points, leading to higher satisfaction and lower churn; an industry survey found that more than 70% of companies reported a measurable increase in end-user satisfaction after implementing conversation intelligence.
Accelerate process optimization: Generative AI can analyze thousands of hours of operational data to pinpoint inefficiencies, ensure compliance, and provide insights that guide better business decisions.
Business Area
Traditional Approach
With Generative AI
Value Generated
Meeting Documentation
Manual note-taking and summary creation
Automated transcription and intelligent summarization
Time savings, consistent documentation, searchable archives
Ultimately, integrating Generative AI is about creating a competitive advantage. It unlocks new opportunities for product innovation and allows businesses to operate with a level of intelligence that was previously impossible to achieve at scale.
Industry-specific Generative AI use cases
The capability of Generative AI applications to understand and create new content has immense potential in various industries. Whether you want to add customer-facing functions to your platform or develop in-house methods that streamline operations, here are five ways businesses can build powerful Generative AI applications that harness speech data.
Healthcare: transform patient care and clinical workflows
The virtual platforms facilitating these meetings are crucial to delivering quality care, despite barriers that may keep patients from the physical office. Telehealth developers are responsible for creating software that is not only secure and reliable, but capable of supporting relationships between providers and their patients.
Speech data is an asset to telehealth—immediate appointment transcriptions can provide valuable information to both provider and patient, when the tools are integrated seamlessly.
Then, LLMs can be used to build additional patient- and provider-facing enablement tools.
At the individual appointment level, for example, an application using LLM Gateway for summarization could send patients automatically generated follow-up emails with a description of their appointment. Additional features could be built by prompting LLM Gateway to extract any action items mentioned by the provider during the appointment.
Patient-facing applications could also utilize LLM Gateway's Question & Answer capabilities to help patients query the transcript from their own appointment and ask questions such as "What did my doctor advise me regarding side effects?" or "When am I supposed to book my next appointment?"
Provider-facing implementations could also help them manage their practice as a whole. Imagine a Question and Answer integration that helps providers learn more about their patient population as a whole: What were the five most common reasons for an appointment this week?
Customer service: Analyze interactions and improve experiences
Businesses that are constantly taking feedback and requests from customers over the phone are collecting massive amounts of data—but without a system in place, that data is unstructured and impossible to interpret as a whole.
Enter conversation intelligence platforms, which help businesses record and analyze conversations with customers. Tools offered might include Zoom or video call integration, transcription, and different data analysis services. For example, Speech Understanding models such as Sentiment Analysis or Topic Detection can make it easier for businesses to quickly understand thousands of hours of customer phone calls at once.
Software providers in this space can also harness Generative AI to add functionality to these platforms. For example, by prompting LLM Gateway, a feature could be built to generate a description of individual phone calls, or a batch of calls, immediately. Developers could also let users fully choose the type of summary they prefer by providing integrated customization options.
LLM Gateway's Question & Answer capabilities could also be implemented to let end-users query their phone call logs for immediate insights across all available data, or a specified time frame. What are the ten most common issues that customers were calling in this week? How often did customers ask for refunds yesterday? What were the most common solutions offered by the customer care team?
Content creation: Accelerate video and marketing workflows
Video creators and editors, whether they are independent or work on behalf of an organization, are responsible for a long lifecycle of content production and management. From storyboarding and shooting to editing and all the subsequent repurposing and marketing, there are opportunities to develop tools that can help.
With content demands nearly doubling in the past year according to a 2024 report, software that integrates the LLM gateway framework can help automate tasks. For example, video titles and descriptions are crucial to attracting the right audience and marketing content.
Using built-in speech understanding features, creators can summarize the main topics in an audio file. Then, by prompting LLM Gateway, creators can generate a description of the video and optimize it for social media, video platforms, or websites. AI-generated titles and descriptions can also take SEO strategies into account, helping make content more discoverable.
LLM Gateway can also be used to integrate functions that make repurposing video content easier. One such feature could take custom requests from video editors for timestamps of video highlights, making clip generation and editing more efficient.
Event marketing: Generate real-time social media content
In the event marketing space, preparation is paramount. However, there are some aspects that cannot be predetermined. Live social media posts, for example, are a great way to engage audiences beyond the people in the room.
Platforms like TweetDeck and Hootsuite are powerful tools to schedule posts and save time, but pre-written tweets can't capture the unexpected moments during a live event. Developers who are building tools for marketers on-the-go can use the LLM Gateway to turn live events into immediate marketing assets.
With real-time speech-to-text models, live captions and transcripts are generated within a few hundred milliseconds using streaming. An LLM Gateway-powered application could then use this data to generate social posts based on a live interview, presentation, or conference session.
The option to integrate several social platforms and style guides could also make immediate formatting possible, ensuring generated copy adheres to brand guidelines and character limits.
There are Generative AI applications for this data, too. For example, LLM Gateway could be used to summarize live session content immediately following class and deliver the information to students. An LMS could add customization options for these summaries too, so educators can select preferences for how the summary is formatted or focused.
Reduced transcription costs, faster case preparation, comprehensive documentation
These real-world applications show that the right AI foundation enables companies to move faster, innovate more effectively, and deliver a superior experience to their end-users.
Getting started with Generative AI implementation
Integrating Generative AI doesn't have to be a massive, multi-year project. The key is to start with a specific, high-value problem and build from there. Here's a practical framework for getting started:
1. Identify a clear business problem
Instead of asking "How can we use AI?", ask "What is our most pressing business challenge that AI could solve?" This aligns with advice from founders, one of whom recommends, "Don't try to incorporate just because it is the current buzzword. Have a realistic feel for what AI can do to make your product better and help your customer." Focus on problems with clear success metrics that directly impact your bottom line.
Examples of high-impact starting points:
Reducing customer support resolution times
Automating sales call analysis
Making video content accessible
Streamlining meeting documentation
2. Build a proof-of-concept (PoC)
Use a flexible, developer-friendly API to quickly test your hypothesis. A PoC allows you to validate the solution's impact with minimal upfront investment and risk. Focus on a single workflow to prove the value before expanding. This approach helps you demonstrate ROI to stakeholders while learning what works in your specific environment.
3. Measure and scale
Once your PoC demonstrates clear value, you can scale the solution with confidence. Building on a reliable and scalable infrastructure is critical to ensure your application can handle production-level workloads without issues. Track key metrics like processing time, accuracy rates, and user adoption to guide your expansion strategy.
4. Choose the right technology partner
Your AI foundation determines your application's capabilities. Look for partners that offer:
Industry-leading accuracy: Especially for specialized terminology in your domain. A recent survey of tech leaders found that accuracy, quality, and performance are among the top factors when choosing an AI vendor.
Comprehensive documentation: Clear guides and code examples accelerate development
Scalable infrastructure: Ability to handle growth without performance degradation
Flexible pricing: Models that align with your usage patterns and growth trajectory
Responsive support: Expert assistance when you need it most
Transform your business with Voice AI-powered Generative AI
The true, transformative potential of Generative AI is unlocked when it's applied to a company's most valuable and underutilized asset: its voice data. Every customer call, video meeting, and podcast contains a wealth of unstructured information. By combining speech-to-text with Generative AI, you can turn these conversations into actionable intelligence, driving efficiency and innovation across your entire organization.
Successful companies systematically apply Generative AI to solve specific business problems. They start with clear objectives, build incrementally, and scale proven solutions.
Ready to get started? Focus on one high-impact use case:
Automate documentation workflows
Enhance customer experiences
Extract insights from conversational data
The best way to understand the impact is to see it for yourself. Try our API for free and start applying the power of Generative AI to your own audio and video data today.
Frequently asked questions about Generative AI business use cases
How can businesses measure the ROI of Generative AI?
Track metrics tied to specific business goals: reduced operational costs, increased productivity, higher customer satisfaction scores, and new revenue from AI features.
What are the first steps to integrate Generative AI into an existing product?
Start with a single high-impact problem, build a small proof-of-concept using a developer-friendly API, then scale based on validated results.
How is Generative AI different from other types of AI?
While traditional AI is primarily analytical—focused on recognizing patterns and making predictions from data—Generative AI is creative. It goes a step further by producing new, original content such as text, summaries, images, or code based on the data it has been trained on. It generates outputs rather than just classifying inputs, enabling applications that can create meeting summaries, draft responses, or generate entirely new content based on context.
Which industries benefit most from Generative AI implementation?
Industries with high volumes of unstructured data see the greatest benefits from Generative AI. According to McKinsey research, about 75 percent of the value from generative AI use cases is concentrated in customer operations, marketing and sales, software engineering, and R&D. Healthcare organizations use it for clinical documentation and patient communication. Financial services leverage it for compliance monitoring and customer service automation. Media companies apply it for content creation and repurposing. Education institutions use it for accessibility and personalized learning. The common thread is any industry where converting unstructured information into actionable insights drives value.
What are the common challenges in Generative AI adoption?
Key challenges include data privacy concerns, integration complexity, and managing team learning curves. Success comes from starting small and focusing on specific, measurable outcomes.
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