6 min read

Improving Your Enterprise with GenAI: Industry Use Cases and 3 Sample Applications

Explore the transformative potential of GenAI in our latest blog, showcasing industry use cases and sample applications that offer practical solutions for businesses ready to innovate.

There’s a lot of excitement about generative AI (GenAI) these days, but what does it really mean for your business?

Have these models reached the level of AGI — the kind of AI that can do anything a human can do? Or is GenAI simply a sophisticated tool for enhancing text?

In this blog, we’ll demonstrate how GenAI can be a powerful asset for your business. We’ll explore practical industry-specific use cases and introduce three sample business applications.

What is Generative AI?

GenAI is a class of artificial intelligence that learns from data to create new content. Through its training, GenAI can generate digital outputs such as text, images, videos, and even code. Essentially, it’s capable of producing new digital artifacts that mimic or extend the patterns it has learned, offering innovative solutions across various formats.

Broad Use Cases of Generative AI

  • Content Generation: From writing articles to producing artistic images, GenAI can automate and enhance creative processes.
  • Summarization: This feature enables the distillation of large documents into concise summaries, saving time and enhancing comprehension.
  • Information Extraction: GenAI can sift through massive datasets to find relevant information, transforming data analysis and decision-making.
  • Questions and Answers: By understanding and processing user queries, GenAI can provide accurate responses and support dynamic learning.
  • Automation: GenAI can automate routine tasks, streamline workflows, and increase productivity in numerous fields.

Data: The Backbone of GenAI Solutions

Every GenAI solution starts with data, the fundamental building block that fuels these systems. This data can take various forms: 

  • Unstructured: Such as PDFs or natural language text.
  • Structured: Including formats like CSVs, SQL databases, or even graph databases.
  • Multimedia: Comprising audio, images, or video content.

Using these diverse data types, GenAI algorithms are trained to recognize patterns, grasp subtle nuances, and generate outputs that are finely tailored to meet specific requirements.

Exploring Industry Use Cases

With a clear understanding of how data empowers GenAI, let’s dive into the practical applications across various industries. Here are some of the key industry use cases that we have identified:

IndustryData TypesUse Cases
CreateSearchAnalyzeSummarize
HealthcareDocuments (patient records, research papers)

Images (X-rays, MRIs)

Videos (surgical procedures)
Generate synthetic patient data for medical research while protecting privacy.Find relevant information for diagnosis or research based on analysis of unstructured medical documents like reports.Analyze medical images and videos to identify potential abnormalities or diseases.Generate concise summaries of lengthy research papers for doctors to stay updated on advancements.
FinanceDocuments (financial reports, contracts)

Audio (conference calls)
Generate realistic financial data for stress testing models and fraud detection.Find potential risks and key metrics based on analysis of financial documents.Use anomaly detection on transaction data to identify potential fraud in real-time.Summarize audio recordings of financial meetings to capture key decisions and action items.
Retail & E-commerceImages (product photos)

Videos (customer reviews)

Text (customer feedback)
Generate variations of product images for personalized marketing campaigns.Find similar products based on user queries and image searches.Extract insights from customer reviews (text & video) to improve product development and marketing strategies.Automatically generate product descriptions based on customer feedback.
Media & EntertainmentDocuments (scripts)

Images (movie stills)

Audio (music)

Video (movies, TV shows)
Generate personalized movie trailers based on user preferences.Use video annotation to automatically tag objects and scenes in movies for easier content discovery.Analyze social media sentiment around a movie release using text analysis.Generate automatic captions for videos or transcripts.
ManufacturingDocuments (engineering plans)

Images (product defects)

Videos (production lines)
Generate synthetic images of potential product defects to train AI models for automated quality control.Search through video footage of production lines to identify defects or maintenance needs.Analyze video feeds for security purposes, automatically detecting suspicious activity or unauthorized access.Summarize engineering reports for easier review and decision-making.
Government & Public SectorDocuments (legal documents, public records)

Audio (speeches, public hearings)
Generate synthetic data for training AI models for fraud detection in social welfare programs. Search through large databases of public records (text & audio recordings) to find specific information for investigations.Analyze public records to identify potential misuse of funds or identify missing information.Summarize reports on public service usage and identify areas for improvement.

To illustrate the potential of these GenAI solutions, we’ve developed three basic sample applications — simple yet effective examples to showcase what can be achieved.

Application 1: Unlocking Inventory Data with Conversational Queries

Imagine having all your inventory data at your fingertips, accessible by using natural language.

Now, let’s dive into our first sample application: a conversational knowledge base custom-built for a fictional auto parts store.

This application features a comprehensive table listing spare parts along with details about suppliers, stock availability, pricing, and the specific cars they fit.

Typically, enterprise data like this is maintained in a database that requires queries to be written in SQL or similar languages.

However, our application allows users to query the database using natural language, simplifying the process.

This application is built on three main components:

  • Chatbot Interface: This provides the user-friendly front end.
  • LangChain: Acts as the orchestrator, managing the flow of queries and data.
  • LLM (Large Language Model): Generates the SQL query based on the user’s question.
  • SQL Database: Stores all the data, including the comprehensive parts table.
Unlocking Inventory Data

Watch the short video below to see the application in action:

The process begins when the system retrieves data from the Parts Table. The LLM then analyzes this data to inform whether parts are in stock, which suppliers have them available, and other relevant details. Responses are delivered in natural language, making the information easily accessible and understandable.

Similar Use Cases for GenAI-Based Structured Data Applications

This model can be adapted for various other applications, demonstrating the versatility of natural language-based enterprise solutions:

  • Natural Language-Based Enterprise Applications
  • Enterprise Knowledge Base
  • Customer Service
  • FAQ Bots
  • Dashboard with Data Insights
  • Health Assistants
  • Synthetic Data Generation

Application 2: Conversational Chatbot for Troubleshooting

Our next sample application is a conversational chatbot that uses a troubleshooting manual document (unstructured data) as its knowledge base.

Mechanics often face the challenge of navigating through extensive manuals to find troubleshooting information, which can be tedious and time-consuming. This application simplifies the process by enabling the chatbot to access and interpret the unstructured data of the manual.

Users can ask questions in natural language, and the chatbot responds with relevant information extracted directly from the manual.

Conversational Chatbot for Troubleshooting

This application features a unique setup designed to efficiently handle unstructured data: 

Chat Interface: Mechanics can upload troubleshooting manuals directly into the system. 

Orchestrator: Upon upload, the system ‘chunks’ the document into manageable parts, creates text embeddings from these chunks, and stores the embeddings for quick retrieval. 

Query Process: Users pose questions via the chat interface. The application interprets these questions, identifies the most relevant embeddings, and retrieves the corresponding information.

Conversational Chatbot for Troubleshooting

To show how this application works, we’ll be using the Employer’s Guide to the Family and Medical Leave Act.

So, I first upload the PDF.

5. PDF-upload

Then, I can ask the chatbot anything I want about FMLA:

6. GenAI-Helper-1

As you can see, it immediately went through the 76-page manual, recognized the relevant answer, and gave me the answer in a natural language.

Similar Use Cases for GenAI-Based Unstructured Data Applications

  • Contract Review and Analysis
  • Citizen Information Systems
  • Compliance Question Answering
  • Scientific Literature Exploration
  • Interactive Learning Materials
  • Technical Support
  • Employee Onboarding and Training

For these use cases, you can upload a variety of documents to serve as the knowledge base.

Application 3: News Article Summarization Chatbot

Our third sample application specializes in summarizing news articles. This tool can be used for quickly disseminating relevant information to decision-makers, conducting sentiment analysis for brand monitoring, and more.

To show you what’s possible without getting into copyright hot water, we’ll use a news article from 1941. However, you can upload any news article you want. You can even upload a collection of articles to get more insights.

7. Article

First, we’ll upload that article into the application. Then, we can ask the chatbot to summarize the relevant text for us, as shown:

8. GenAI-Helper-2
9. GenAI-Answer

As you can see, the chatbot returned a summary specifically focusing on the baseball stories, filtering out any irrelevant information and providing a streamlined view tailored to what I asked for.

The workflow is similar to our previous applications, where you upload a document and interact with the chatbot about that document. However, this tool allows for more tailored summaries by specifying different aspects of the article you want emphasized.

Conclusion

We hope we’ve sparked new ideas for innovative and productive uses of GenAI that you might not have considered before.

From revolutionizing inventory management to enhancing customer service, GenAI holds the key to unlocking new opportunities for your business. We encourage you to explore these solutions further and take the next step towards driving growth and success in your industry.

What Relevantz can do for you?

When used correctly, GenAI unlocks numerous possibilities for your enterprise, from automating routine tasks to crafting innovative solutions for complex problems.

At Relevantz, we excel in crafting custom business solutions powered by GenAI platforms. Our expertise ensures effective adoption and integration of GenAI, propelling your business toward innovation and growth.

Ready to improve your operations with custom GenAI solutions?