Generative AI in business: Benefits, use cases & how to get started

Generative AI is changing how businesses think about digital innovation, efficiency, and growth. 

Rather than just automating repetitive tasks, it brings fresh capabilities that let companies create and solve problems in ways that weren’t possible as little as a year ago. 

Google surveyed 50 technology businesses and found that 64% want to adopt generative AI. This shows strong momentum among technology businesses.

For business leaders, generative AI delivers more than process improvement. It introduces routes to developing new products, personalizing customer interactions, and making decisions with exceptional precision. 

This article will provide an overview of generative AI in business. We’ll explore generative AI in business, why it’s important, enterprise-level benefits, industries, use cases, key considerations, and how leaders can get started. 

What is generative AI in business?

Generative AI in business is an advanced AI system that creates original content, solutions, or ideas based on input data. 

Unlike traditional AI, which focuses on automating tasks and optimizing processes, generative AI can produce entirely new outputs such as text, images, computer code, or product designs.

This ability unlocks transformative opportunities for businesses. Companies can use generative AI to innovate faster by developing new products, personalizing customer experiences, and designing interactive tools. 

For example, it can craft tailored marketing campaigns, generate unique prototypes, or provide real-time insights that support decision-making.

Why is generative AI in business important?

Generative AI is a powerful tool for businesses, as it helps scale operations and automate complex tasks. This is especially important for large companies where efficiency is critical. 

Statista estimates the generative AI market will be worth $44.89 billion in 2023 and $207 billion in 2030. 

Generative AI can streamline supply chains, reducing costs and improving response times. It also provides executives valuable insights to support faster, more informed decision-making. 

But beyond just improving efficiency, generative AI drives innovation. It opens up new ways for companies to design products and create services that better meet customer needs. 

For big enterprises, generative AI pushes the boundaries of what’s possible. It sets the stage for growth, creativity, and a stronger market position. 

How is generative AI beneficial for businesses?

According to Reuters, ChatGPT sets a record for the fastest-growing user base. It reached 100 million monthly active users just two months after launch. This impressive statistic shows how rapidly generative AI tools are adopted, highlighting their staunch value to businesses. 

The global business landscape will continue to face uncertainty in 2025, with geopolitical conflicts, climate risks, and regulatory pressures. This highlights the importance of investing in AI solutions that help navigate challenges and drive growth.

With that in mind, let’s look at how generative AI will benefit businesses:  

Expanding labor productivity 

Generative AI greatly improves the amount of work companies and their employees can do. It can automate boring, repetitive tasks like data entry, freeing workers to focus on more important things like coming up with new ideas and solving problems. Companies can grow and stay ahead of competitors by getting more done with fewer people.

Personalizing customer experience 

Generative AI allows businesses to personalize how they interact with customers. It analyzes tons of data to recommend products, craft tailored marketing messages, and provide customized support. This makes customers feel special, which builds their loyalty and satisfaction. It also helps the company make more sales.

Speeding up product development

Generative AI accelerates research and development by rapidly testing many different product designs. It uses algorithms to find the best designs based on specific goals. This approach is much faster than traditional trial-and-error so that companies can market new and better products faster.

Creating new business models

Generative AI is enabling companies to rethink how they provide products and services. Businesses can now generate personalized content, offer on-demand services, and anticipate customer needs in innovative ways. This leads to new revenue streams, flexible pricing, and more agile operations, all of which help companies stay competitive.

What business industries benefit the most from generative AI? 

Generative AI transforms key industries, improves efficiency, and opens new doors. Its impact lies in its ability to automate complex processes, enabling businesses to innovate faster and make smarter decisions.

Here’s a look at how it benefits various sectors:

Consumer marketing

Generative AI helps businesses create personalized ads, offers, and content by analyzing customer data in real-time. It allows companies to send highly tailored messages and promotions, improving engagement as customers connect with content made specifically for them.

This targeted approach boosts sales, increases customer retention, and strengthens loyalty. By leveraging generative AI, businesses can build meaningful relationships and stand out in a competitive market.

Finance

Generative AI is also making a big impact in the finance industry. It is helping banks and investment firms manage risk, detect fraud, and conduct automated trading more effectively. 

Businesses can also forecast market trends and optimize investment portfolios. It automates many routine finance tasks, such as payroll and accounts.

Biopharma

Generative AI is helping the medicine industry discover new drugs faster. This type of AI can quickly analyze huge amounts of data, allowing researchers to find promising drug ingredients that could become new medicines.

Beyond just speeding up discovery, AI is enabling personalized medicines. These are treatments made specifically for each person based on their unique genetics. This can make the medication work better and have fewer side effects.

Manufacturing

Generative AI in manufacturing helps create better products and manage the supply chain. It can test designs to find cheaper and better ways to make things. AI also predicts when machines might break down so they can be fixed before problems happen. 

This helps reduce delays and keeps everything running smoothly. With AI, manufacturers can help workers focus on more important factory tasks while the AI handles routine ones.

Employment

Generative AI is also impacting HR and workforce management. It supports recruiting by automatically matching candidates’ skills to job requirements. It can also take care of many administrative HR tasks, freeing up HR teams to focus on more strategic work. 

Additionally, generative AI personalizes employee training plans, improving worker productivity and development.

How can business leaders get started with generative AI?

Business leaders may overestimate their ability to use generative AI responsibly. 

Salesforce’s research found that while 83% of C-suite leaders believe they can use AI securely, only 29% share the same confidence. This shows that there is still work to be done to understand and implement secure AI practices. 

With that in mind, let’s take a look at how business leaders can get started with generative AI: 

Finding unused data

Most companies have access to valuable data but often need to use it more effectively. For generative AI to reach its full potential, it requires comprehensive, high-quality data sets that span historical records, customer feedback, and operational metrics. 

Unlike traditional AI, which analyzes data to optimize existing processes, generative AI uses this data to create entirely new solutions, from personalized customer content to predictive business strategies.

By tapping into underused data, companies can unlock actionable insights and enable generative AI to drive innovation across the organization. The key is identifying and integrating diverse data sources, ensuring the AI system has the breadth and depth needed to deliver transformative results.

Customizing AI models

Once you’ve gathered all the relevant data, the next step is to fine-tune the AI models tailored to your business needs. 

This involves training the models using your company’s unique data sets to improve accuracy and relevance. You can keep optimizing the models’ capabilities by continuously feeding the AI the latest, most pertinent information. 

This allows them to generate useful and valuable outputs, whether content generation, trend predictions, or anything else. Customizing the AI in this way aligns it with your goals and priorities.

Preparing data for AI

For generative AI to operate at peak performance, the raw data must be carefully cleaned, structured, and organized beforehand. 

This data transformation process guarantees that AI systems can properly interpret the information and identify critical patterns and relationships. 

Taking the time upfront to prepare the data in a format the AI can easily understand allows the technology to generate the most relevant outputs for your business needs. This step sets the stage for the AI’s success.

Knowing what to expect

Before you deploy AI across your organization, you must have a clear vision of what you want the technology to achieve. 

Are you hoping to automate certain routine tasks? Enhance the customer experience? Drive innovative new solutions? Understanding the exact types of outputs and results you need from AI will guide the entire implementation process. 

Defining these expectations ahead of time ensures the AI’s impact in IT is maximized, the outcomes are valuable, and they align with your business objectives.

What are some key considerations for adopting generative AI in the enterprise? 

As businesses grow and evolve, the technologies adopting generative AI present both opportunities and challenges. 

Key considerations for adopting generative AI on the enterprise level include:

Using good data

Businesses must have high-quality, complete data for generative AI to work well. The AI will only function properly if the data is clean, modern, and organized. It could give inaccurate insights or predictions. 

Companies spend time and money building strong data systems. This includes processes for cleaning the data, storing it all in one place, and keeping it up-to-date. When the data is reliable, AI can run at its best, helping businesses make better decisions and work more efficiently.

Keeping data secure

A big concern with generative AI is keeping all the data it uses secure and protecting people’s privacy. Risks include hackers getting into the systems and companies breaking privacy laws. 

Businesses must design their AI with strong security measures, like encryption and controls on who can access the data. They also need plans to protect customer information and avoid legal problems. Managing AI for cybersecurity is crucial for building trust using AI.

Owning AI’s creations

The complicated thing about generative AI is that it can create new inventions, content, and ideas. This raises questions about who owns the rights to those new things. Is it the business, the AI developers, or the AI itself? 

Companies need clear policies and legal frameworks to decide ownership. This prevents expensive future fights about patents, copyrights, and other intellectual property rights.

Avoiding bias and mistakes

AI can reflect biases if the data used to train it is incomplete or unfair. This could lead to unjust results, like job applications favoring certain groups. Businesses should use diverse data sets and thoroughly test their AI for bias and errors. Regularly updating the AI models is important to ensure they stay accurate, fair, and trustworthy.

Customizing for the industry

Generative AI works best when tailored to a specific industry or field. A general AI may not provide relevant, useful insights without being customized. For example, healthcare AI needs specialized medical knowledge. Companies should use AI models trained on data and information from their own industry to get the most accurate and helpful results.

What are some generative AI in business use cases?

Now that you have a fair amount of knowledge on generative AI in business, it’s time to look at some use cases to help you understand how it can drive results across different areas.

Here are some specific examples:

Advanced Chatbots

Advanced chatbots use AI to provide fast, real-time help to customers. They can answer questions and solve problems on different platforms, learning from every interaction. This helps businesses respond quickly, cut costs, and offer 24/7 support, making customer service more efficient.

Coding assistants

AI coding assistants help developers write, fix, and improve code more quickly. They suggest fixes, catch errors, and even create parts of code based on a description. These assistants handle repetitive coding work, speed up software development, reduce errors, and raise code quality.

Marketing support

AI helps in marketing by studying customer behavior, segmenting audiences, and creating personalized messages. AI tools can generate content, predict what customers want, and improve ad placements. This helps businesses create focused campaigns that drive engagement and sales while saving time on manual tasks.

What are some real-life examples of businesses using generative AI? 

It’s important to look at real-life examples of businesses using generative AI to push the boundaries of product and service innovation. 

In this section, we will explore three real-world examples of businesses that have used generative AI successfully: 

Figma 

Figma has added generative AI tools to make design workflows smoother and more collaborative. With features like “Dev Mode,” Figma assists designers by generating specifications for handoff to developers. 

The AI also suggests design variations and automates repetitive tasks, making it easier to create cohesive designs across teams. These tools let designers focus on creativity instead of routine adjustments. Figma’s AI integration is especially helpful for speeding up the design process while maintaining consistency across projects.

Kensho (S&P Global) 

Kensho uses generative AI to simplify financial data analysis. Through tools like Kensho Scribe, which turns data from audio sources like earnings calls into structured information, Kensho enables analysts to find insights quickly. 

Kensho’s AI extracts important information from large datasets, making complex analysis easier. S&P Global clients use Kensho to discover patterns and make predictions. This helps them make faster, better decisions in financial markets and investments.

Runway AI  

Runway AI is advancing generative AI in media, offering tools that let creators make videos, edit images, and enhance content. Its Gen-1 and Gen-2 models allow users to turn text prompts into video. This makes it possible to create high-quality video production that is accessible to everyone. 

Filmmakers and marketers use Runway to save hours of editing by applying complex effects and transitions automatically. Runway AI has made media production easier, providing small teams with powerful editing tools that once required a full studio.

How can businesses expect a good ROI from generative AI?

According to Gartner, the biggest challenge in adopting generative AI is accurately assessing and demonstrating its business value.

Businesses can achieve a strong return on investment (ROI) from generative AI by reducing costs and improving efficiency. This means pilot projects should have clearly defined success criteria before launching. 

Like any transformative technology, generative AI can drive measurable outcomes in key areas, including enhancing customer experience and lowering unit costs. Focusing on these dimensions means businesses can unlock significant ROI and set the stage for long-term success. 

The future of generative AI in the enterprise

The future of generative AI in business isn’t just about small improvements—it can completely change how companies operate and grow. 

Automating repetitive business tasks can give employees more time to focus on creative and important work. AI helps companies become data-driven, create better customer experiences, and even discover new products. 

It’s not just about saving money but also about finding new ways to grow. As companies start using generative AI, they can stay ahead of competitors and lead in their industries. 

The opportunities are huge; now is the time for business leaders to act and use AI to improve their businesses.