Ling Zhang
- Sep 11
- 3 min read

How to Start Using Generative AI: Key Use Cases for Your Business

Accelerate Your Business with Generative AI, From Basics to Breakthroughs

Generative AI is no longer a distant futuristic concept but a transformative technology that businesses can start leveraging today. As a CEO, understanding how to implement generative AI within your company is key to staying competitive and unlocking new opportunities. Here’s a simple guide on how to begin using generative AI, with use cases that demonstrate its potential across industries.

1. Start Small with Off-the-Shelf Solutions

For companies just beginning their generative AI journey, using off-the-shelf tools can provide immediate value with minimal investment. One great example is implementing generative AI for code completion in software engineering. These tools help developers write and debug code faster by suggesting lines of code in real-time based on natural language descriptions. With such tools, companies have seen up to a 50% increase in developer productivity.

These types of off-the-shelf generative AI solutions are easy to integrate into existing workflows. They typically come with fixed-fee subscriptions, require little technical expertise, and offer fast results. This is an ideal entry point for businesses looking to dip their toes into AI.

2. Enhance Productivity with Custom Models

As you grow more comfortable with generative AI, a logical next step is to build custom AI models using APIs. For example, a bank might build a model to help relationship managers (RMs) keep up with large amounts of public information, like annual reports and earnings calls. The AI can scan and summarize large documents quickly, providing RMs with synthesized insights they can use to better serve their clients.

In this case, the bank customizes a model by building software layers on top of an API, which allows for seamless integration into existing systems. The AI assists RMs by analyzing complex data in real-time, allowing them to focus on higher-level tasks and improving their job satisfaction. Custom solutions like this provide a deeper level of personalization, but require moderate investment in data scientists and engineers to build and maintain the AI.

3. Free Up Resources with Fine-Tuning

For businesses that deal with specialized industries or handle large amounts of data, fine-tuning foundation models is an effective way to tailor AI for specific needs. In customer service, for example, a company could train an AI chatbot on its specific customer data and sector-specific language. The result is a customer service AI that can handle frequent, simple inquiries, freeing up human representatives to focus on more complex issues.

Fine-tuning requires more resources than off-the-shelf models or API-based solutions, but the payoff is significant. The AI chatbot can interact with customers in a personalized manner, drastically reducing response times and improving customer satisfaction. Investing in this level of AI could also enhance customer retention by providing consistently high-quality service.

4. Tackle Complex Problems with Custom-Built Models

In certain industries, like pharmaceuticals or advanced research, companies may need to build a foundation model from scratch. For example, a pharmaceutical company working on drug discovery could create a model that processes millions of microscopy images to identify relevant cell features. This helps accelerate research and development efforts by providing scientists with actionable insights based on vast amounts of data.

This approach requires significant technical investment, including the use of GPUs, large data sets, and expert teams of PhD-level engineers. While the upfront costs are high, custom-built models offer the potential for groundbreaking advancements in highly specialized fields, making the investment worthwhile for companies in industries where precision is critical.

The value of generative AI is clear across industries and use cases, whether you start small with prebuilt tools or aim for more ambitious, custom projects. The key is to begin the journey by identifying a business need and matching it with the right AI solution. By starting now, you’ll not only gain valuable insights but also stay ahead of the competition in this rapidly evolving technological landscape.