Real-world generative AI use cases, AWS style

This syndicated post originally appeared at Zeus Kerravala – SiliconANGLE.

It has been about a year since OpenAI LP launched ChatGPT. That thrust generative artificial intelligence into the spotlight, creating more hype around a technology than anything I can remember.

The intense focus on generative AI has made many information technology and business leaders I have talked to ask how generative AI can transform their business. Playing with ChatGPT to write an essay or a Beatles song is easy, but that doesn’t directly apply to a business use case. What’s missing today are some case studies for generative AI, as seeing what it can do in a business context can help “prime the innovation pump.”

Amazon Web Services Inc. recently provided a few examples of how their customers use generative AI to transform business operations. This technology is not just an incremental improvement but a fundamental change, enabling companies to rethink and redesign processes that were previously limited by the constraints of traditional computational methods.

AWS makes it easier for companies to build generative AI applications with enterprise-grade security and access to leading foundation models. Here are a few examples of how AWS is helping its customers in different industries automate complex tasks, generate dynamic content and provide more personalized user experiences.

PGA Tour enhances fan and player experiences

The PGA Tour, a professional golf organization, uses Amazon Bedrock to make it easier for golf fans to interact with the sport and for players to get feedback on their performance. Bedrock is a managed service from Amazon that lets users build AI apps, which, in this case, include tools to analyze golf games and suggest how players can adjust their strategies.

By implementing the Claude large language models available through Bedrock, the PGA Tour can create content that responds to fans’ questions and interests in a more engaging way. This also helps players understand their game by providing personalized data on how they’re doing and what they can change to play better based on the current conditions of the golf course.

As a result, the PGA Tour has been able to offer fans a more interactive experience and give players better insights into their performance. The golf organization wants to develop its AI capabilities further, potentially integrating virtual reality for fans or including more varied data sources to fine-tune player strategies.

Thomson Reuters speeds op AI experimentation

Thomson Reuters Labs developed a tool called Open Arena in collaboration with AWS. This tool is essentially a space where Thomson Reuters employees can experiment with large language models, regardless of whether they can code. Open Arena was developed in less than six weeks using AWS services during a hackathon.

The idea behind Open Arena was that by combining LLMs with the company’s data, Thomson Reuters could come up with innovative ways to improve its products and services. The LLMs could be those the company created or sourced from the open community, thanks to partnerships such as the one between AWS and Hugging Face. The easy-to-use tool quickly reached more than 1,000 monthly users.

The technical side of building Open Arena involved using AWS serverless services, machine learning tools such as SageMaker, Lambda, DynamoDB and prebuilt Hugging Face containers. It also included secure ways to manage data and connect the different parts of the tool. Overall, Open Arena has allowed Thomson Reuters teams to test and refine AI apps in a controlled environment before they become part of the company’s offerings.

Eversana implements generative AI in life sciences

Eversana, a company that provides commercial services to the life sciences industry, partnered with AWS to help pharmaceutical and life sciences companies become more efficient, drive business value and improve patient outcomes. Eversana is integrating Amazon Bedrock generative AI into various customer solutions.

Eversana has used AI for more than 10 years across its various services. However, by working with AWS, Eversana expects to quicken the pace of AI innovation and expand its reach. The goal is to automate some of these tasks to improve medical and regulatory review processes, which are typically manual and time-consuming.

The company is building chatbots and other tools to assist field workers and patients. Additionally, it wants to generate and personalize educational content about diseases and products to help life science brands better engage with healthcare providers and patients.

LexisNexis enhances legal services with Lexis+ AI

LexisNexis built a transformative tool for the legal industry called Lexis+, which generates accurate, reliable legal content at a pace much faster than traditional methods. AWS Bedrock is the driving force behind Lexis+. It provides a secure and reliable cloud platform that supports the deployment of AI apps on a large scale.

Thanks to generative AI, Lexis+ AI has key features like conversational search, smart legal drafting, and quick case summarization. Bedrock isn’t just the infrastructure; it’s also crucial for developing and expanding these AI capabilities. It allows LexisNexis to improve and add new functions continously to Lexis+ AI, which benefits legal professionals.

The partnership is also essential for integrating LexisNexis’ tailored Anthropic Claude 2 model within Lexis+ AI. Using this model, Lexis+ AI can quickly adapt and roll out new features while handling sensitive legal data. The use of Lexis+ AI in the legal sector shows how generative AI can be vital for specialized industries, enhancing not just speed but also the efficiency of work.

These are great case studies, but it’s important to understand the differences between ChatGPT and generative AI. The former uses generative AI and all public data available on the internet to enable consumers to do things quickly. Generative AI is a generic term for AI that can generate content.

When used in a business context, the AI models will be applied to a closed, curated data set, and no information should be shared with publicly available data. Search is similar, where Google search uses all public data but search on a Bloomberg terminal searches only the curated data set owned by Bloomberg. As the four case studies above show, generative AI, when used correctly, can transform organizations.

Business and IT leaders should not shy away from it. In fact, it’s best to get out in front of it to ensure it has been used correctly.

Author: Zeus Kerravala

Zeus Kerravala is the founder and principal analyst with ZK Research. Kerravala provides a mix of tactical advice to help his clients in the current business climate and long term strategic advice.