Business use cases for Generative AI

Advanced Large Language Models (LLMs) are powering chatbots, image generators, and software that can handle complicated requests from users and return near-human results. From agentic AI systems that autonomously execute multi-step workflows to voice agents that hold real-time conversations, generative AI use cases are expanding rapidly across every industry.

The investment is real—and so are the results. According to McKinsey's 2025 State of AI survey, 64% of organizations already report measurable cost and revenue benefits from AI adoption. 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 category of artificial intelligence that creates new content—text, images, audio, code—from existing data. Businesses use it to automate content generation, streamline operations, and unlock insights from unstructured data like voice conversations.

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. Models like Anthropic's Claude, OpenAI's GPT, and Google's Gemini are examples of LLMs that can understand 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 gives companies access to 25+ leading LLMs from providers like Anthropic, OpenAI, Google, Alibaba Cloud, and Moonshot AI. It simplifies building Generative AI tools on top of spoken data without managing multiple provider integrations.

With the LLM Gateway, developers can use a single API to generate custom summaries, extract action items from meetings, or answer specific questions about audio content through flexible prompting.

Business value and ROI of Generative AI

With U.S. companies alone spending $37 billion on generative AI in 2025, the pressure to demonstrate returns is real. Generative AI delivers measurable business outcomes by transforming unstructured conversational data into actionable intelligence 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.

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 seven ways businesses can build powerful Generative AI applications that harness speech data.

Healthcare: Transform patient care and clinical workflows

Despite declining rates since their peak in 2020, telehealth visits remain higher than pre-pandemic levels. According to data released in April 2023, about 22% of U.S. adults reported attending a telehealth appointment within the preceding four weeks.

Telehealth developers need software that is secure, reliable, and capable of supporting provider-patient relationships. Speech data is an asset here—immediate appointment transcriptions provide valuable information to both parties when tools are integrated seamlessly. LLMs can then power additional patient- and provider-facing enablement tools.

For example, an application using LLM Gateway for summarization could send patients automatically generated follow-up emails after appointments. Additional features could extract action items mentioned by the provider during the visit.

Patient-facing applications could use LLM Gateway's Question & Answer capabilities to let patients query their own appointment transcripts: "What did my doctor advise me regarding side effects?" or "When am I supposed to book my next appointment?"

Provider-facing implementations could surface population-level insights: 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.

Conversation intelligence platforms help businesses record and analyze customer conversations at scale. Speech Understanding models such as Sentiment Analysis and Topic Detection make it possible to quickly understand thousands of hours of customer phone calls at once.

By prompting LLM Gateway, developers can build features that generate call descriptions instantly—individually or in batch. Users can also customize the type of summary they receive.

LLM Gateway's Question & Answer capabilities let end-users query their phone call logs for immediate insights: What are the ten most common customer issues this week? How often did customers ask for refunds yesterday?

Financial services: Automate compliance and risk analysis

Financial institutions generate massive volumes of voice data every day—advisor-client calls, compliance recordings, trading floor communications, and internal meetings. As AI compels organizations to elevate their compliance practices, generative AI unlocks the ability to systematically analyze this audio at scale—turning conversations that once sat in storage into structured, auditable intelligence.

By combining accurate speech-to-text with speech understanding models, teams can build automated workflows that address some of the industry's most pressing operational challenges:

• Compliance monitoring: Transcribe and analyze advisor-client calls to flag potential regulatory violations, detect unauthorized disclosures, and ensure adherence to scripts and disclosure requirements—without manual review of every recording.
• Audit trail generation: Automatically summarize calls and extract key decisions, commitments, and action items to create searchable audit trails that satisfy regulatory retention requirements.
• Risk detection through sentiment: Apply Sentiment Analysis to customer interactions to identify dissatisfaction patterns, escalation risks, or potential complaints before they become formal disputes.
• Real-time trading floor transcription: Stream live audio from trading desks through real-time transcription to capture verbal orders, confirmations, and communications for immediate documentation and surveillance.

With Speech Understanding models like Topic Detection and Sentiment Analysis handling the structured extraction, and the LLM Gateway enabling custom question-answering and summarization workflows on top of transcripts, financial services teams can move from reactive, sample-based compliance reviews to continuous, automated oversight across every conversation.

Content creation: Accelerate video and marketing workflows

Video creators manage a long lifecycle—from storyboarding and shooting to editing, repurposing, and marketing. With content demands nearly doubling in the past year according to a 2024 report, software that integrates Generative AI can help automate key tasks.

Using built-in speech understanding features, creators can summarize audio topics automatically. By prompting LLM Gateway, they can generate optimized video descriptions for social media, video platforms, or websites—with SEO strategies built in.

LLM Gateway also makes repurposing easier. Editors can request timestamps of video highlights automatically, making clip generation and editing more efficient.

Education: Enhance learning management systems

Learning management systems (LMSs) are online platforms that store, manage, and deliver educational content for schools and businesses. Speech data is integral to these platforms—transcriptions of pre-recorded lectures and live meetings ensure that content is accessible to all students.

LLM Gateway can be used to summarize live session content immediately following class and deliver the information to students, with customization options so educators can select preferences for format and focus. It can also automatically generate study guides based on lecture video recordings.

Legal: Streamline case preparation and documentation

Law firms and legal departments handle vast quantities of spoken testimony—depositions, witness interviews, court proceedings, and client consultations. Manually transcribing and reviewing this audio is one of the most time-consuming and expensive parts of case preparation.

By combining accurate speech-to-text with Speech Understanding models, legal teams can automatically transcribe depositions and hearings, then apply Sentiment Analysis to identify moments of heightened emotion or evasion that may warrant closer review.

LLM Gateway takes this further by enabling attorneys to query transcripts directly: "What did the witness say about the contract date?" or "Summarize all testimony related to the damages claim." This turns hours of manual review into seconds of targeted search, accelerating case preparation and reducing the cost of discovery.

Voice agents: Build real-time conversational AI

Voice agents represent one of the most tangible generative AI use cases shipping today. By combining real-time speech-to-text, LLM reasoning, and text-to-speech into a single conversational loop, voice agents deliver natural, human-like interactions that go far beyond traditional phone trees. The market reflects this momentum—the AI voice agent market was valued at $2.54 billion in 2025 and is projected to reach $35.24 billion by 2033, growing at a 39% CAGR.

Here's where voice agents are making the biggest impact right now:

• Customer support automation. Voice agents handle routine inquiries—order status, account changes, troubleshooting—without a human in the loop. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues, leading to a 30% reduction in operational costs.
• Real-time agent assist. Not every voice agent replaces a human. Some sit alongside them. Real-time agent assist listens to live calls and surfaces relevant knowledge base articles, compliance reminders, or suggested responses as the conversation unfolds—live coaching that improves outcomes without interrupting the flow.
• Appointment scheduling. Voice agents handle the back-and-forth of finding availability, confirming details, and sending reminders. For healthcare clinics, dental offices, and service businesses, this eliminates the phone tag that burns staff time and frustrates patients.
• IVR modernization. Legacy IVR systems force callers through rigid menu trees. Voice agents replace "press 1 for billing" with natural conversation—callers state what they need in their own words, and the agent routes or resolves accordingly.

The challenge with building voice agents has been the infrastructure. You need a speech-to-text provider, an LLM, and a text-to-speech provider—three vendors, three invoices, three sets of logs to debug. AssemblyAI's Voice Agent API collapses that stack into a single WebSocket connection. Stream audio in, get audio back. One API, one bill.

The Voice Agent API is built on Universal-3 Pro Streaming, which means the speech accuracy foundation is best-in-class—names, account numbers, and accented speech are transcribed correctly, so the LLM reasons over the right input. Built-in turn detection and endpointing handle the nuances of real conversation: knowing when a caller is pausing to think versus done speaking. Semantic barge-in ensures back-channels like "uh-huh" don't interrupt the agent, while genuine interruptions like "wait, stop" are handled immediately. Voice focus filters out background noise and other speakers automatically, so agents stay accurate in real-world environments without extra configuration.

The API also supports session resumption—if a connection drops, conversations can be restored within 30 seconds without losing context. Tool calling and MCP integration let agents perform actions mid-conversation, like looking up account details or scheduling appointments. And with 18+ built-in voices spanning English, Spanish, French, German, Japanese, Korean, Mandarin, Hindi, Italian, and Russian, teams can deploy multilingual voice agents from day one.

Pricing is a flat $4.50/hr covering STT, LLM, and TTS—no token math across three invoices. It's invisible infrastructure that lets developers focus entirely on their agent's logic, not voice plumbing.

Customer success stories and implementation results

Leading companies across industries are already seeing measurable results:

• Conversation intelligence: CallSource and Ringostat use Voice AI to analyze customer interactions and provide actionable insights
• Content creation: Veed, Descript, and Podchaser automate editing workflows, reducing production time by up to 70%
• Healthcare: T-Pro and Clinical Notes AI automate clinical documentation, reducing physician administrative burden
• Education: Edthena and EnglishScore make learning content accessible across diverse student populations
• Voice agents: Companies are using the Voice Agent API to build real-time conversational AI that handles customer support, appointment scheduling, and live agent coaching

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
• Building voice agents for real-time customer interactions

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

Build 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
• Build voice agents for real-time interactions

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

What is the best speech-to-text API for building voice agents?

The best speech-to-text API for voice agents needs ultra-low latency, high accuracy on names and domain-specific terms, and intelligent turn detection. AssemblyAI's Voice Agent API combines all three into a single WebSocket connection—built on Universal-3 Pro Streaming for best-in-class accuracy with flat-rate pricing of $4.50/hr covering STT, LLM, and TTS.

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-powered features. Companies that start with a focused proof-of-concept and measure against clear baselines see the fastest path to demonstrable returns.

What speech-to-text API is recommended for healthcare applications?

Healthcare applications require HIPAA-compliant infrastructure, high accuracy on medical terminology, and reliable speaker diarization for provider-patient conversations. AssemblyAI offers all three, with Speech Understanding models that can summarize clinical encounters, extract action items, and enable patients to query their own appointment transcripts.

What is AssemblyAI's LLM Gateway and how does it work?

AssemblyAI's LLM Gateway is a unified API that provides access to 25+ leading LLMs from providers like Anthropic, OpenAI, Google, Alibaba Cloud, and Moonshot AI through a single interface. Developers can use it to generate custom summaries, extract action items from audio, or build question-answering features on top of spoken data—without managing multiple provider integrations or separate billing.

How is Generative AI different from other types of AI?

Traditional AI focuses on recognizing patterns and making predictions. Generative AI goes further by producing new content—text, summaries, images, or code—based on the data it was trained on. When applied to voice data, this means turning raw audio into structured insights, automated documentation, and conversational AI experiences.

Which industries benefit most from Generative AI implementation?

Industries with high volumes of unstructured data see the greatest returns—healthcare, financial services, customer operations, content creation, education, and legal. The common thread is converting voice and text data into actionable insights that drive efficiency and improve outcomes.