8 Use Cases and Applications of Generative AI
by Anna Morelock56% of businesses are either using or experimenting with the use of artificial intelligence (AI). It’s getting smarter every year, making processes more efficient, generating revenue, and streamlining the way teams operate.
The latest advancement in AI technology is generative AI.
This blog post will:
- Define generative AI and how it differs from traditional AI.
- Explain why companies are investing in it.
- Discuss the top Gen AI use cases.
- Offer tips on how to implement generative AI at your business.
What is Generative AI?
Generative AI, or Gen AI, is a type of artificial intelligence that produces content—like text, images, and videos—in response to prompts or conditions provided by an end user.
That’s the short answer, but in practice, it’s more complicated than that.
Gen AI uses a method of machine learning called deep learning. Deep learning involves the use of deep neural networks, which consist of multiple layers of interconnected nodes that mimic the structure of a human brain. This lets Gen AI produce sophisticated, realistic, and human-like outputs.
Kind of like how our brains use information to generate new thoughts and ideas, generative AI creates content and insights from the data it processes. It can comprehend and synthesize complex information, then create sophisticated outputs based on the data it’s trained on (though generative AI technology cannot fully replicate human creativity and understanding).
Traditional AI vs. Generative AI
Artificial intelligence has been around for a while, so what’s special about generative AI tech? There’s been a huge leap in capabilities and intelligence between traditional AI and generative AI.
Traditional AI operates within predefined rules and algorithms and relies on labeled training data to perform tasks.
In contrast, Gen AI emphasizes autonomous generation, often without explicit programming or predefined rules. Gen AI creates new content, ideas, or solutions beyond what is explicitly taught or trained.
So, traditional AI focuses on specific tasks like classification or prediction, while generative AI focuses on creating brand new content based on patterns it learns.
Both have their use cases, but they’re applied in different ways.
Traditional AI Use Cases
- Spam filtering
- Image recognition
- Personalized recommendation systems
- Customer segmentation for targeted marketing campaigns
Generative AI Use Cases
- Conversational chatbots and virtual assistants
- Code generation and application testing
- Creative content generation
- UI/UX elements for web and mobile applications
Why Are Companies Investing in Gen AI?
Some companies are already testing the waters to see how generative AI can benefit them—and they have high expectations.
A Forbes Advisor survey found that most surveyed business owners anticipate a long list of advantages from AI implementation:
- 64% are optimistic about AI’s potential to enhance customer relationships.
- 64% foresee AI contributing to increased productivity.
- 60% believe AI will play a role in boosting sales.
- 59% expect AI to help save on costs.
These are just a few of the potential benefits, but they won’t materialize without strategic action. Companies are applying Gen AI in new and innovative ways to get tangible outcomes.
Let’s go over the most valuable Gen AI use cases our experts have seen so far.
Top Gen AI Use Cases
1. Software Development
Generative AI coding tools are already showing promising results in terms of developer productivity, accuracy, and speed. Developers use Gen AI for:
- Documentation—Gen AI can generate comprehensive documentation for entire codebases, including descriptions of functions, API functionalities, and technical processes.
- Software testing—Gen AI is useful for testing software. It can augment the workload capacity of QA (quality assurance) engineers by automating tasks like writing and executing unit and regression tests.
- Code translation—Gen AI can translate existing codebases from one language to another. Imagine you have a codebase written in C# that needs to be converted to Python for production deployment. Traditionally, this would be a time-consuming manual process, but generative AI tools can speed up this task by translating the C# code into Python.
- Bug detection and resolution—Gen AI can also help developers identify bugs in their code and suggest fixes. You’ll always need some level of human oversight, but AI-assisted bug detection can improve code quality and reduce debugging time.
Assistance with these tasks helps ease developer burnout, too.
Developers using GitHub Copilot, one of the most widely adopted Gen AI-assisted coding tools on the market, reported feeling less frustrated with their jobs and more satisfied at work.
Burnout is a killer of employee satisfaction. It can lead to decreased engagement, higher turnover rates, and lower quality output. By easing the stressors associated with burnout, Gen AI tools can help contribute to a healthier work environment for developers.
2. Contact Center Support
Another generative AI use case is enhancing the customer experience provided by call center agents and other customer service professionals.
For example, Five9 offers their users an AI Insights tool powered by Gen AI. It clusters customer conversations into categories based on traits like contact intent.
Once customer conversations are clustered, the AI Insights solution can easily identify patterns, then highlight frequent customer inquiries, common pain points, and repetitive tasks to recommend where processes can be improved or automated.
For instance, if the tool identifies a lot of similar customer inquiries about a particular product, the contact center can automate responses to these inquiries in their chatbot and reduce the workload on human agents.
The tool can also help contact centers understand why customers contact them and how quickly and effectively customer issues are being resolved.
Additional ways Gen AI is transforming the customer service industry include:
- Automating Post-Contact Processing—After customer interactions, contact center agents handle tasks like ticket tagging, summarizing the contact, and sending follow-ups. Gen AI models can automate all of that and reduce those handling times.
- Auto-Generating Knowledge Articles—There are Gen AI tools on the market now that can sift through customer conversation transcripts and auto-generate knowledge articles. Agents can then review, tweak, and publish these articles for other agents to learn from.
- Smarter Chatbots and Virtual Assistants—Generative AI is also enhancing customer interactions with more human-like and intelligent conversations.
3. Supply Chain Management
Consider the steps involved in the product lifecycle, from development to its purchase off store shelves:
- Concept and design
- Production
- Inspection and quality control
- Packaging and transportation to distribution centers
- Inventory management and shipment to retail locations
- Purchase by consumers
Each of these steps involves a complex network of processes and stakeholders working together to bring the product to market, and it can take a long time.
Thankfully, advances in technology—like automated manufacturing systems—have accelerated the process, and generative AI can take those processes to the next level of efficiency.
Here are a few use cases where Gen AI in supply chain management is already making a big impact:
- Demand forecasting— Gen AI can be trained on historical sales data, then generate more accurate demand forecasts so businesses know exactly how much stock they need.
- Logistics— Gen AI can design optimized delivery routes that minimize transportation costs, reduce delivery times, and enhance delivery reliability. It considers factors like traffic, fuel prices, and vehicle capacity.
- Quality control—There are Gen AI models that can analyze manufacturing data to detect patterns and predict potential quality issues. Manufacturers can use those predictions to inspect the products and prevent defects before they impact consumers.
- Supplier management—You can take a more proactive approach towards supplier management with generative AI. It can analyze supplier performance data, track metrics like delivery times and quality of goods, and identify patterns. This approach allows companies to address potential issues with suppliers before they become too disruptive.
4. Retail
Gen AI use cases don’t stop at traditional problem-solving tasks. It can be creative, too. In the retail industry, it can generate personalized product recommendations to enhance customer engagement and drive sales.
For example, IKEA noticed a surge in online purchases after the COVID-19 pandemic, so they launched a series of initiatives to offer online customers a better, more personalized shopping experience.
Most recently, they introduced a Gen AI-powered shopping assistant that offers personalized furniture recommendations based on users’ preferences—like price, color scheme, style, sustainability preferences, and room dimensions.
The tool can also generate images of spaces to inspire users on how to curate their rooms with IKEA furniture.
5. Marketing & Content Creation
Another widely adapted generative AI use case is helping marketers and creative employees boost their productivity. It can:
- Generate ideas for blog posts, webinar topics, and other mediums
- Write product descriptions and image captions
- Draft script outlines for videos and webinars
- Check for spelling and grammar errors
Gen AI can also help repurpose content. It can take a speech and turn it into a blog post or turn client testimonials into a case study.
This is how Gen AI tools can help marketers boost their day-to-day productivity. Other AI algorithms can help marketers target specific audiences for personalized marketing campaigns, predict demand trends, and anticipate customer behavior.
More Innovative Applications of Gen AI
Real-world applications of Gen AI are making an impact now, but a 2023 Gartner poll suggests a majority (70%) of businesses are still in ‘exploration mode.’ This tells us generative AI’s true impact is yet to be fully realized.
Here are some more innovative Gen AI use cases that we could see pick up in the future:
- It’s in the beginning stages, but Gen AI in healthcare is helping hospitals, pharmaceutical companies, and medical institutions diagnose and educate patients, discover new drugs, and offer personalized treatment plans.
- One of the biggest use cases of generative AI in manufacturing is in predictive maintenance. Machines undergo routine maintenance, but surprise damage can still happen, and unplanned repairs are costly. Plus, a slowdown in production can impact revenue. Generative AI algorithms can help better predict equipment failures before they happen and offer solutions to resolve the issue.
- In banking, generative AI can analyze transaction data to identify patterns of fraudulent activity. It can also access and analyze a wider range of data points to assess a borrower’s creditworthiness, rather than relying on traditional, limited credit score elements.
Considerations to Make Before Implementing Gen AI
The transformative impact of generative AI is clear, but it has limitations. For example, generative AI models are only as good as the data they’re trained on, and not all models are equipped to handle certain tasks or operate in every industry.
For your use case, will you need to buy a pre-built model, develop your own, or take a hybrid approach?
There are data privacy and security concerns with Gen AI models as well. How will you collect and store user data? How will you protect that data from cyberattacks?
To make sure you’re using this technology responsibly, you need to develop a comprehensive generative AI governance framework.
And of course, implementing generative AI into your business can be expensive, especially if you’re not managing those costs the right way. That’s why you need to think carefully about Gen AI costs and create a strategy to optimize.
Gen AI Governance Considerations
- Ethics and biases
- Transparency (there needs to be a level of transparency around how models arrive at decisions)
- Accountability and responsibility
- Data privacy and security
Gen AI Cost Considerations
- There are standard infrastructure costs
- Costs based on potential data estate modernization
- Costs based on model complexity and size
- Expenses associated with monitoring and optimizing your AI models
- There are talent costs to account for, too. This means hiring and training employees to manage your AI solutions.
Discover Your Gen AI Use Cases
There are several ways to use generative AI in business, but to navigate its limitations, you need a strategy.
An AI services and solutions partner like Evergreen can help you:
- Identify your use cases
- Assess your underlying data estate and modernize
- Create an AI strategy and governance framework
- Build and train Gen AI models
- Manage costs
- And more
Get started today by filling out the form below.