Generative AI: Use Case Scenarios

The buzz around generative AI is undeniable. Arguably the hottest topic in computer science right now, generative AI has been hailed as the solution to repetitive work, a ubiquitous AI assistant, a new way to approach design, and much more. Business leaders everywhere are wondering how to take advantage of this exciting technology and situate themselves ahead of what appears to be a bloom of innovation.

Such forward-thinking leaders might have key questions, such as:

What is generative AI?

Generative AI is a type of artificial intelligence that uses neural networks to learn patterns and relationships in data and then generate new data that is similar to the original. Working this way, generative AI can create a wide range of outputs, including text, images, music, and even video.

The success of such programs depends on the quality and size of the dataset used for training and the complexity of the neural network the AI is based on. With large, high-quality datasets and complex neural networks, generative AI can produce highly realistic and convincing outputs that are virtually indistinguishable from human-generated data.

What can generative AI be used for?

There are many ways generative AI programs can be applied, but broadly speaking, they are useful for any situation which requires the creation of content based on a pattern or pre-existing framework. For example, generative AI can be used to make marketing materials for a known and understood audience, quickly map trends with given data and parameters, and offer novel designs for drugs, tools, and structures within a set of requirements.

However, there are several applications for generative AI beyond content creation. Generative AI programs can also be used to personalize business offerings or services by offering product recommendations or laying the groundwork for more effective chatbots and online assistants. Generative AI can detect fraud by watching patterns and flagging suspicious behavior. In a similar vein, these programs can assess risk and offer forecasts to help businesses plan or make strategic decisions.

Finally, generative AI could be useful in planning, strategy, and decision-making. For example, these programs can offer new ways to optimize supply chain or pipelines. Due to its pattern-recognition ability, generative AI is being applied to medical situations and diagnostics, helping providers predict the likelihood of certain conditions based on patient data and provide better individualized care. Generative AI could help plan business trips, outline work schedules, and improve real-time translation in business dealings or leisure life.

In short, the potential uses for generative AI are varied and diverse, limited only by the human imagination needed to conceptualize them.

Here are a few ideas of how generative AI could be applied to specific industries:

Legal & Finance

  • Document summarization: Generative AI could be used to read through contracts and long legal documents, summarizing the key points or highlighting changes during contract negotiation. Similarly, generative AI could be used to draft basic contracts or legal documents quickly based on predefined templates and input data.
  • Information aggregation: While conducting due diligence, generative AI can condense large volumes of research into easily-digestible materials, allowing lawyers or investors to reach decisions faster.
  • Informed predictions: Calculated guesses—either in legal cases or financial outcomes—are an important aspect of succeeding in business. Because of its pattern-recognition ability, generative AI can be used to analyze trends, historical data, and other relevant factors to make predictions, identify risks, and highlight opportunities.

Travel & Transportation

  • Route optimization: The trucking industry is an important facet of the US economy, one dealing with an increasing shortage of drivers and operators. Generative AI could help by optimizing travel routes and supply chain, helping the existing workforce and vehicle fleets perform more efficiently.
  • Travel planning: One example of the personalization promise of generative AI is in travel planning. Tailored generative AI programs could create itineraries, provide recommendations for flights and accommodations, and monitor environmental conditions to adjust trips depending on weather patterns.
  • Predictive maintenance: Travel and transportation depend on the proper maintenance of whatever vehicle is being used, whether that’s trucks or airplanes. But with worker shortages it can be a challenge to stay on top of upkeep. That’s why generative AI can be applied to predict equipment failures and maintenance needs by analyzing data from sensors and monitoring equipment, thus preventing issues, unplanned downtime, and unexpected maintenance costs.

Oil & Gas

  • Exploration, prospecting & modeling: Drilling is one of the first steps in the production of oil or gas, so it’s important to find good reservoirs of natural resources before beginning the expensive and dangerous process. Generative AI can assist in several ways by modeling subsurface reservoirs to predict properties, analyzing geological data to identify potential gas reserves, and optimizing drilling sites for maximum return and minimal environmental impact.
  • Asset management: Large oil and gas companies have a multitude of assets, including heavy machinery, pipelines, wells, refineries, and personnel. To keep track of all these moving parts, generative AI programs can monitor equipment, optimize supply chain, identify potential improvements in production schedules, and assist management personnel.
  • Safety monitoring: Much of the work done by employees of oil and gas companies is physical, risky, and takes place in remote, hazardous locations. Generative AI can keep staff safe by analyzing environmental data such as weather patterns to predict issues, making recommended improvements to safety regulations, and identifying potential hazards through equipment sensors and observational data.


  • Diagnostics & risk assessment: As previously mentioned, generative AI’s pattern-recognition could be applied in medicine by helping healthcare providers assess a given patient’s risk for certain afflictions and preemptively treat that patient based on their personalized healthcare profile. Generative AI can also be useful in offering diagnostic suggestions based on symptoms, particularly in rare or complex diseases.
  • Summarizing clinical notes: Clinical notes are a notoriously thorny issue in healthcare, both on the writing and the deciphering side. Providers spend a huge percentage of their time making notes, and researchers and administrators often struggle to understand them. Generative AI could be a solution to this problem, both helping the providers quickly summarize visits and aggregating the information available in medical databases into a digestible format.
  • Improved triage: With looming staff shortages in healthcare, the wait time in emergency rooms is sure to get longer, risking lives and leaving vulnerable patients unattended. Generative AI could improve ER triage systems and minimize the load on healthcare personnel by interacting with incoming patients, assessing risk of immediate and life-threatening issues, and monitoring the less immediate but no less important individuals while they wait.


  • Inverse design: Through most of history, a tool or part was created through trial-and-error, with various configurations tested so that the most useful version could be selected. However, generative AI offers a new way of conceptualizing this process called inverse design, where the AI system is given a set of requirements and then designs a tool, part, material, or item specifically for the intended role. This will streamline production and cut down on inefficient, time-consuming, and costly processes.
  • Quality control: There are already vision systems monitoring output in some factories, watching for defective products. But generative AI could provide enormous improvements on such systems, offering smarter, faster, and more nimble ways to ensure quality and also make recommendations on how to prevent quality issues.
  • System optimization: On a related note, generative AI offers many ways to optimize manufacturing factories, plants, or environments by analyzing data on equipment performance, marking inefficiencies, and offering recommendations on how to decrease waste.

Administration & HR

  • Personalized training: Even though there are widespread worker shortages in almost every industry, bringing on new employees can be a challenge because of the time and work-hours necessary to train them. Due to its personalizable nature, generative AI could provide a way to train new hires quickly and efficiently by creating content specific to them based on the parameters of the job and an individual’s background.
  • Augmenting low-level & repetitive tasks: Anyone who works in an office knows how much time is spent on emails, scheduling, and other small tasks that distract from the core mission of a given job. An exciting use case of generative AI is to automate much of the work that employees consider “boring,” allowing workers to focus on the more intellectually stimulating and dynamic tasks they enjoy.
  • SEO & marketing: Administration jobs often come with an aspect of public relations, either to employees internally or to the market/world. Generative AI can be useful to help create materials such as flyers, social media ads, presentations, and blog posts; identify opportunities for SEO optimization; translate campaigns into other languages; and generally help spread important information about the company and their message.


  • Brainstorming assistance: Despite the popularized image of a creative living alone in the woods, producing entertainment is most often a collaborative process. Writers and artists need something to bounce their ideas off or assistance clarifying their vision. Generative AI programs such as ChatGPT can help by creating rough outlines, scripts, and first drafts of ideas for creatives to work with, speeding up the brainstorming process and helping artists manifest their vision quickly and efficiently.
  • Content personalization: Moving beyond the current mechanisms that recommend content by genre, generative AI could bring about a whole new opportunity for fully personalized content, such as images, music, and even shows or movies tweaked for a given consumer’s specific tastes, maximizing their interest and improving ROI.
  • Special effects: Currently, the special effects in movies, TV shows, and games are extremely time-consuming and expensive to create. But entertainment-specific AI programs could rapidly generate CGI characters, animations, or simulate complex physics-based effects in a way that would speed up production, bring more content to the market, and allow designers and CGI artists to do more with limited timeframes.


These are just a few examples of the widespread possibilities offered by generative AI programs such as DALL-E and ChatGPT. As these algorithms get better, smarter, faster, and more reliable, it’s easy to imagine how they might integrate into daily life, assisting employees with easily automated tasks, optimizing large and complex networks, analyzing risk in a wide array of situations, and helping people work, play, and learn.

Learn more about the generative AI work happening at CSAIL by connecting with CSAIL Alliances at or by reaching out to Lori Glover, Managing Director of CSAIL Global Strategic Alliances, at

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