Generative AI Use Cases: Driving Innovation Across Industries

Technology   |   Peter Martinez   |   Dec 14, 2023

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These are some of the most common requests people give to generative AI tools like ChatGPT. However, these use cases only scratch the surface of its capabilities. Other uses include:

Improving employee retention and satisfaction
Predicting the best products to sell
Identifying valuable business use cases

So, what are some of the most common generative AI use cases, and how can you apply them to industry-specific problems to drive value and efficiency?

Let’s explore.

Defining Generative AI

While there are many ways to explain what generative AI is and how it works, one of the simplest ways to describe it is that generative AI is an AI model trained on a lot of language data that creates novel outputs.

Key things to know to understand generative AI:

  • Generative AI is powered by a language model (most are large language models, but there are small language models as well.)
  • Generative AI is enabled by a technology called transformers. Transformers allow machine learning to focus on distinct parts of input differently. They are the foundation of advanced AI language systems.
  • A GPT model is a pre-trained transformer. This is the breakthrough that has enabled generative AI.

generative ai use cases

A Generative AI Use Case Development Framework

There are a lot of different use cases for generative AI; listing them all would be impossible. Instead, I have found the following framework helpful when researching use case applications.

Look at the capabilities of generative AI, what it is good at, and where it can excel, and then extrapolate a use case from there.

What generative AI is best at:

  • Summarization – “Read this travel and expense report and summarize corporate spending for the month in one paragraph.”
  • Code generation – “Using these variables, write a Python script that will predict my sales for the next quarter.”
  • Data generation – “Create a dataset that imitates corporate travel and expense data. Include columns for First Name, Last Name, Description, Date, and Amount.”

If you use these three capabilities as building blocks, you can then piece them together and inform the use cases you want to explore.

The Most Common Generative AI Use Cases

Using this framework, let’s break down some of the most common generative AI use cases.

Text Generation

Generative AI can create copy in different voices, tones, and styles based on user input, speeding up processes such as writing emails, outlining blog posts, and summarizing data and analysis.

Insight Generation

Generative AI can work with different data sources and analyze them to provide insights. It can even do this by summarizing the results in an email or creating a PowerPoint presentation to speed up the process.

Data Set Creation

When healthcare facilities, financial organizations, and other heavily regulated industries need to create and test models, it can be costly and risky to use real patient or customer data.

Generative AI can create synthetic data to train models. Then, once the models are ready, they can apply them to their patient data. Along with reducing the risk of violating regulations, this method can also speed up deployment, saving time and reducing costs.

Natural Language Interface

You can use generative AI to speak directly to your data. The technology can use natural language processing (NLP) to interpret what you’re asking, query your data to find the result, and return its findings to you in a way you can understand.

Workflow Summary and Documentation

Documenting workflows is a task that needs to be done but also one that (almost) nobody likes. Generative AI cannot only do this for you automatically, but it can also improve your governance and auditability.

Generating Use Cases

Yes! You read that right. One of the use cases for generative AI is identifying, selecting, and building new analytics use cases for you. It can cut back any indecisiveness by automatically ideating new ways to use generative AI.

This is a screenshot of a web application interface titled "Playbooks." The interface presents a tool for a Product Analyst at XYZ Pharmaceuticals to identify and explore top use cases for business analytics. Four key analytics use cases are highlighted:

Product Performance Analysis - This section suggests that analyzing how different products perform can help optimize resource allocation. It explains that by examining product sales and usage patterns, the company can identify which products are successful and which may require improvement, thereby enabling more efficient use of resources and directing investments towards the most profitable products. There's an option to "See reports" and two highlighted outcomes: "Optimize resource allocation" and "Increase revenue."

Competitor Benchmarking - This module offers insights into benchmarking against competitors to inform strategic decisions. By comparing product performance against competitors, a company can identify areas of strength and weakness to gain a competitive advantage. There is a "See reports" link and a highlighted outcome to "Help to inform strategic decisions and drive competitive advantage."

Sales Trend Analysis - The third section focuses on the analysis of sales trends to forecast future demand. It mentions that by tracking sales data, emerging patterns and trends can be spotted, aiding in production planning and ensuring optimal inventory levels. The outcomes associated with this analysis are "Forecast future demand" and "Optimize inventory levels."

Customer Usage Analysis - The final section addresses how analyzing customer usage can inform product development. By understanding usage data, insights into customer preferences and needs can be gleaned, which is important for product design and ensuring that products meet customer needs and drive customer satisfaction. There's also a "See reports" link available.

In the top right corner, there is an option to "Generate Use Cases," indicating a feature to create custom analytics use cases for the business. The overall design is clean, with a professional color scheme, and uses icons and check marks to visually represent the benefits of each analytics use case.

Tools like Alteryx Playbooks can use generative AI to generate possible use cases based on your data.

Industry-Specific Generative AI Use Cases

Now that you know many of the common ways you can use and expand on generative AI, let’s explore the ways you can apply these use cases in different industries and departments.

Generative AI Use Cases in Finance

  • Financial Trend Analysis: Many factors can affect financial performance, and understanding all of them is a time-consuming task. Generative AI can analyze financial data for trends and outliers and provide explanations that help you understand how it’s all related.
  • Banking Risk Assessment: Risk comes from many different areas in banking, and it requires an exhaustive level of detail to assess it all. Generative AI can apply deep learning mechanisms to supply comprehensive risk assessment reports. It can closely examine data to generate detailed explanations you can easily understand and share while including recommended actions to mitigate risk. This process speeds up the process of identifying potential problems and gives you more time to decide how to proceed.
  • Tax Compliance Analysis: If you work in tax preparation or accounting, you can provide tax compliance data to Generative AI and have it analyze it, plus generate comprehensive tax compliance reports. Information you’ll receive includes:
    • Explanation of tax codes
    • Potential deductions
    • Recommended strategies for minimizing tax liabilities

Generative AI Use Cases in Human Resources

  • HR (Human Resources) Talent Pool Optimization: If you’re in HR, you can use generative AI to help you with employee retaining projects that you may need to work extra hours to complete. AI can provide personalized recommendations on which skills employees should develop for career growth. You can use the time AI saves you to evaluate the recommendations and adjust them as necessary before supplying them to employees.
  • HR Employee Survey Analysis: Because AI is optimized to find patterns in data, it can help you by analyzing employee survey information and creating engagement strategies you can use for your organization. It can identify trends within your company, compare them to any data you have about the significance of those trends (such as employee satisfaction or engagement), and suggest actions you can take.

Generative AI Use Cases in Legal Departments

  • Legal Document Automation: Writing legal documents takes time, and the mental fatigue that comes with the process can lead to one misplaced comma or the use of the wrong word. AI can speed up legal document creation by suggesting relevant clauses for you. While you should always review and edit all legal documents before approving them, AI removes the time-consuming work of generating a first draft, leaving you with time and energy to apply your knowledge and expertise to ensure everything is in order.
  • Legal Data Summarization: Generative AI can help you review legal documents, too. It can extract insights from the text, analyze them, and present key findings as concise summaries. Again, you should always have people reviewing the work to ensure it’s all accurate, but AI can save you a significant amount of time, especially when it takes over repetitive processes for you.

Generative AI Use Cases in Retail

  • Retail Inventory Forecasting: You already know that people share similarities in the ways they shop. Understanding the habits and connections between products and all the factors takes time. Generative AI can aid you by making predictive inventory decisions for you. You would be responsible for setting up the framework to guide it, but it would do the heavy lifting of automatically forecasting inventory needs.
  • Retail Customer Segmentation: When people receive personalized content, they’re more likely to buy products and report higher customer satisfaction. You see more value from your campaigns, and, more importantly, your customers get relevant recommendations. Generative AI can assist with this process, recommending products, creating marketing copy, and suggesting product recommendations based on your data.

Generative AI Use Cases in Consulting

  • Consulting Data Analysis: When organizations spend money on consulting services, they expect their consultants to understand their business and how it operates. Generative AI can help consultants provide beneficial experiences to clients by enhancing the quality of consulting reports and generating customized insights.

Benefits of using Generative AI

There are many potential benefits to using generative AI. In a survey completed by Alteryx, the top benefits reported were increased market competitiveness (52%), improved security (49%), and enhanced performance or functionality of products (45%). Generative AI is also expected to have a large economic impact globally.


Users across multiple research studies report seeing benefits around their productivity, either freeing up users from time-consuming routine work, or allowing them to focus more on strategic priorities.

  • Salesforce’s research indicates that a significant majority of marketers, about 71%, expect that generative AI will streamline routine tasks, enabling them to concentrate more on strategy.
  • 84% of sales employees using generative AI stated that generative AI helped to increase sales by enhancing and speeding up customer interactions.
  • Research by O’Reilly found that 54% of those using generative AI believe that the tools will lead to greater overall productivity.


  • Research conducted by Goldman Sachs suggests that generative AI has the potential to increase the global GDP by as much as 7%.
  • McKinsey research estimates that generative AI might contribute between $2.6 and $4.4 trillion annually to the global economy.

Other benefits

  • McKinsey research found that by equipping developers with generative AI, the developer’s experience was greatly improved, and they reported higher overall happiness.

Limitations to Generative AI

Using generative AI doesn’t come without risk. As you probably know, the technology has a few limitations. Most of the limitations are related to potential risks, such as compliance, privacy, security, and governance. However, there’s also the issue of scalability.

Current generative AI models excel at tasks involving language and pattern recognition, but may struggle with complex logic and reasoning, particularly in domains like math and finance. While these limitations can hinder direct applications in some areas, generative AI can still be valuable for augmenting existing workflows based on traditional machine learning or analytics by providing additional insights, summarizing information, or generating creative content. This combined approach can enhance efficiency and effectiveness while ensuring accuracy in critical domains.

As you explore generative AI use cases, tools, and platforms, you should keep these limitations in mind.

Let’s explore them further.

Regulatory and Compliance

Even if your use of generative AI meets the regulations of your area or country, it may violate regulations around the world. You should consider how the data you use and what you use it for may violate laws if you use information outside your locale.

  • Does your data contain any information that would violate regional, national, or global regulations?
  • Are you using it to mimic voices or likenesses, and do you have the rights to do so?
  • Will your models adhere to best practices and industry standards?


When you use data for training, you need to ensure you’re only using data that’s open source or available for use. Questions you should ask as you deal with privacy include:

  • What data is off limits, and what data is okay to use?
  • Can I use this data to train models?
  • Is it okay to crawl a source and use that information?


Generative AI faces the same issues with security that all technologies face. The world is already rife with bad actors who want to steal and use data for harmful and dangerous purposes. The same will be true for the expanded use of data for generative AI.

  • Where is data stored, and how is it used to inform AI?
  • How is proprietary data stored, and how is it secured?
  • How does your generative AI tool or platform use your submitted data?
  • Will your data be used to train or improve models?


While governance includes ensuring your data is secure and safe, it also entails keeping your results accurate. Generative AI tools, like ChatGPT, can have issues with hallucinations (making things up). You’ll want to ensure you can trust the results you receive from your generative AI tools of choice.

  • What safeguards do you need to ensure accurate and trustworthy results?
  • How does your tool or platform create a trusted environment for using data?
  • How do you ensure results are accurate and reliable as you expand your use of AI?


If you only need the occasional data analysis for one report, a service like ChatGPT will be more than enough. Most organizations need more than that, though. To quickly scale, you’ll need to know that the tool you use can handle your needs.

  • Can you automate processes with your choice of generative AI?
  • If you need a chat-based interface, how well does it scale?
  • Can you use your generative AI solution to serve the needs of hundreds, if not thousands, of users simultaneously?

A Checklist for Using Generative AI

Here’s a checklist you can use to help identify a generative AI solution that works best for your needs.

Give Alteryx a shot. Get a free trial, try it on one of your use cases, and see how it works.