Generative AI Business Use Cases

Let’s talk about four generative AI business use cases. Companies are boosting productivity with AI-powered virtual assistants, intelligent search, and content summarization.

When business leaders use AI to address needs, generative AI can provide them a competitive edge. This groundbreaking technology can understand and speak in natural language, enabling individualized consumer encounters, immersive virtual experiences, and employee enhancement.

Generative AI does more than generate fresh data from patterns. With generative AI, organizations may increase productivity and cut expenses, revolutionizing their operations.

Here’s how four generative AI business use cases are changing the business landscape:

1. Virtual Assistants

Chatbots, copilots, and virtual assistants are helping companies boost efficiency and customer satisfaction. These technologies use generative AI and a company’s data to create customized virtual assistants that can have interactive conversations.

By automating chores and giving insights, these assistants empower staff to focus on strategic work. They increase customer encounters by swiftly understanding and responding to questions with basic conversational prompts.

Kore.ai trained BankAssist for voice, web, mobile, SMS, and social media interactions. This system lets clients transfer money and pay bills. Personalized suggestions from the AI-powered voice assistant cut customer service time by 40%.

2. Intelligent Search

People use intelligent search every day thanks to internet dataset-trained LLMs. These models understand natural language and user requests. Private documents and platforms like Snowflake Data Cloud and Oracle Cloud ERP store lots of sensitive data that businesses need. Until now, truly exploiting this data was nearly difficult.

Generative AI lets companies start with a foundation model, or LLM, trained on public data. This training guarantees the model learns human languages and general knowledge. This methodology can create applications that comprehend business-specific terms and give appropriate, up-to-date search results for employees and customers after being customized with company data. An additional LLM is often used to monitor the first and ensure that exchanges stay within boundaries and prevent inappropriate content.

3. Content Summarization

There has always been a need to manually and tediously translate meeting minutes and papers into easy action items. However, businesses may quickly and easily summarize audio, video, and document files using generative AI models.

Consider healthcare as an example. Now, with the help of generative AI, doctors can speed up the process of reviewing patient records, better understand their needs, and provide better care. An LLM that is trained on ten years of patient records is being developed by researchers at NYU Langone Health. Here, summarizing is only the beginning; we’re also talking about health outcome prediction, including the likelihood of a patient’s readmission within 30 days.

Artificial intelligence algorithms filter through thousands of data points in real time in the financial sector, much like high-speed analysts. Investors and portfolio managers can look forward to more targeted investing methods and, hopefully, higher returns as a result.

4. Document Processing

Machine learning methods, such as natural language processing (NLP) tools, enable generative AI to mimic human language comprehension, interpretation, and manipulation. Businesses may streamline data access and deployment using AI-powered processing solutions that automate content production, translation, proofreading, data extraction and analysis, and audience or individual preference personalization.

The financial and legal industries, which deal with massive amounts of paperwork, will feel the effects of this transformation the most. Organizational information access, management, and utilization are undergoing a sea change due to the incorporation of generative AI, which simplifies document processing while simultaneously improving data currency and accuracy.

Conclusion

It is highly beneficial for business executives to utilize generative AI in order to acquire a competitive advantage. This revolutionary technique improves efficiency, cuts expenses, and creates new data from preexisting patterns. Important uses include intelligent search for accurate data insights, content summarizing for faster data processing, virtual assistants for better interactions with customers, and virtual assistants overall. Businesses can transform their operations and propel strategic progress by customizing LLMs to meet their unique requirements.

Frequently Asked Questions On Four Generative AI Business Use Cases

Q: What is generative AI?

A: Generative AI is a type of artificial intelligence that can create new content, such as text, images, or music.

Q: How can businesses benefit from generative AI?

A: Generative AI can improve efficiency, creativity, and decision-making across various business functions.

Q: What are the risks associated with using generative AI?

A: Risks include the potential for bias, misinformation, and intellectual property issues.

Q: Can generative AI be used for customer service?

A: Yes, generative AI can power chatbots and virtual assistants to provide customer support.

Q: How can generative AI be applied in marketing?

A: Generative AI can create marketing content, personalize campaigns, and analyze customer data.

Q: Can generative AI be used in product development?

A: Yes, generative AI can assist in product design, prototyping, and testing.

Q: What is the role of human input in generative AI applications?

A: Human oversight is essential to guide the AI, ensure quality, and address potential biases.