Since the recent advent of large language models, the artificial intelligence (AI) landscape has evolved rapidly. AI technologies offer businesses a number of benefits. Yet many leaders are still unsure which technologies to use and how to use them. One model that many companies explore is generative AI, also known as GenAI.
Generative AI has become a household name thanks to tools like ChatGPT and DALL-E. Still, many businesses wonder: Is generative AI worth the investment? The answer is yes. Investing in generative AI is proving to be highly profitable for businesses in a variety of industries. According to an IDC report sponsored by Microsoft, the estimated return on investment (ROI) for top leaders using GenAI is $10.30.
But how are businesses using GenAI to achieve this ROI? In this article, we're exploring generative AI use cases that prove the value of this incredible new technology.
What is Generative AI?
Generative AI (GenAI) refers to AI systems that create new content based on user prompts. This content includes:
- Text
- Images
- Code
- Audio
- Video
GenAI models are trained on vast amounts of data from various sources. It recognizes patterns in this data, then uses these patterns to generate new content.
Unlike other AI models, GenAI needs human input. It responds to specific requests rather than acting independently. For example, users can ask GenAI to write product descriptions for an e-commerce website or create logo graphics for a new enterprise. It cannot make independent decisions or pursue long-term goals without continuous prompting and refinement from a person.
But despite its limitations, the business value of generative AI is undeniable, and its adoption continues to grow exponentially.
7 Generative AI Use Cases
If you're still unsure how to incorporate GenAI into your business model, consider these seven generative AI use cases:
1. Marketing and Content Creation
Marketing is a leading area for generative AI adoption. Businesses use GenAI for writing blogs, creating social media posts, and drafting email campaigns.
The technology helps marketing teams create content at scale while ensuring the brand voice remains consistent. Tools like Jasper and Copy.ai are popular examples that allow for rapid content production, freeing up human marketers to focus on strategy.
Generative AI is also used for creating ad copy and personalizing marketing materials to target different audiences.
2. Customer Service Automation
Generative AI is also transforming customer service. AI-powered chatbots and virtual agents can answer customer inquiries, provide 24/7 support, and offer context-aware responses.
By processing large amounts of information from a knowledge base, these tools can provide accurate and helpful information to customers. This reduces the need for constant human intervention, leading to significant cost savings and improved customer satisfaction.
Beyond chatbots, generative AI can draft follow-up emails, summarize support tickets, and create knowledge base articles for self-service.
3. Software Development
Today, software development is also one of the most popular generative AI use cases. GenAI tools can assist developers in writing, updating, and maintaining code. AI can handle bug fixes, assist with testing, and generate technical documentation.
By automating repetitive coding tasks, generative AI allows engineers to focus on higher-level tasks like system architecture and design. This makes development more efficient while also empowering less experienced staff to implement code changes.
4. Product Design
In product design, designers are using GenAI to draft new concepts and models based on market trends, user preferences, and demographic data. Generative AI tools can rapidly produce a wide range of ideas, then iterate and fine-tune these initial concepts at a much faster scale than human designers alone.
From fashion to industrial design, genAI is being used to greatly enhance output. According to a report by McKinsey, using generative AI can reduce the product development lifecycle by upwards of 70%. This enables teams to spend more time with consumer testing, product refinement, vetting suppliers and manufacturing operations, and other phases of production.
5. Financial Services and Fraud Detection
In the financial services sector, GenAI analyzes market trends, generates reports, and automates financial forecasting. Algorithmic trading platforms use AI to execute trades and manage risk around the clock. The technology also plays a crucial role in fraud detection and risk management.
By quickly scanning and summarizing vast amounts of data, generative AI can identify patterns and anomalies that might indicate fraudulent activity. Generative AI use cases for the financial sector prove how this technology improves security and ensures compliance with regulations.
6. Healthcare and Pharmaceutical Industry
Additionally, generative AI is revolutionizing healthcare. It assists with medical documentation, diagnostics, and patient engagement. AI-powered tools summarize patient histories and lab results, thereby helping doctors make more informed decisions.
In the pharmaceutical industry, the technology can be used to model molecular structures, predict the effectiveness of new compounds, and generally accelerate the development of new medications. The ability of generative AI to analyze complex medical data and simulate drug behavior can potentially save immense time and resources.
7. Education
Finally, there's education. Although it's important to use GenAI with discernment and make sure it doesn't enable plagiarism among students, education startups, and academic institutions are increasingly leveraging generative AI for personalized learning. AI tutoring is one of the leading generative AI use cases in education.
Generative AI can also assist educators by automating administrative tasks such as grading and lesson plan creation. By offloading these day-to-day tasks, teachers are able to spend more time on one-on-one student interaction. Generative AI additionally enables access to personalized learning paths in rural areas and the developing world.
Growing Your Company with Generative AI
As these generative AI use cases prove, this technology is highly valuable. Companies that don't incorporate GenAI into their processes and operations may find themselves rapidly falling behind.
To take advantage of everything that genAI has to offer, it's critical to hire the right talent. Unfortunately, hiring a generative AI engineer can be a difficult task due to skilled labor shortages and high demand for these positions. While valuable, a full-time, in-house GenAI engineer may also be too costly for many businesses. The annual salary for this position can range from $114,000 to $158,000.
That's where JADA Squad comes in. Rather than the expense and lengthy timeline of traditional hiring, JADA offers access to pre-vetted generative AI experts who can integrate directly into your team.
We offer top-tier AI professionals, including machine learning engineers, AI strategists, and data scientists, on a flexible subscription-based model. This allows you to scale your team with predictable costs and get specialized expertise right away.
Ready to build your generative AI team? Contact JADA today and discover a better way to scale your data and AI initiatives.
Frequently Asked Questions
What are some use cases of generative AI?
Generative AI is highly versatile, with use cases that span content creation (writing, images, code, music), data analysis and synthesis (synthetic data, summaries), customer service (chatbots), and software development (code generation).
What is generative AI most commonly used for?
GenAI is most commonly used for creating content such as text, images, code, and videos. Businesses also rely on generative AI for tasks like chatbots, data summarization, and automating parts of software development.
What is a real life example of generative AI?
One common real-life example of generative AI is using ChatGPT to draft emails, articles, or reports. Other examples including using image tools such as DALL-E or MidJourney to generate logos, infographics, or product mock-ups based on simple text prompts.
