Welcome to the Generative AI Explorer’s Guide. Check out the introductory video above, then keep scrolling to learn more about where to start your AI journey.
From streamlining logistics to boosting customer service, generative AI promises to take business operations to the next level. Indeed, the technology is expected to increase U.S. productivity growth by up to 1.5 percent annually over the next decade1 as more companies use it to drive innovation.
But for leaders who must navigate this future, the path to success with large language models (LLMs) can be full of twists, turns and forks in the trail. That’s where this Generative AI Explorer’s Guide comes in. A compendium of short videos, snackable insights and quick case studies, it’s filled with actionable information that will help guide you toward generative AI success.
The AI Explorer’s Guide is divided into three sections that mark major waypoints along the path:
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MOVE:Getting started
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BUILD:Piloting solution
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SCALE:Going into production
At the end, you’ll find a summary of key learnings and a look at what’s ahead for generative AI.
1 Goldman Sachs
Lesson 1:
Move
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You can’t afford not to
do anything,
you have to get on this journey.
The time to go from thinking about generative AI to implementing it is now.
Early movers will enjoy sustained advantages over competitors. Conversely, organizations that sit on the sidelines will fall behind. “You can’t afford not to do anything,” says Francessca Vasquez, VP of Professional Services and Generative AI Innovation Center, AWS. “You have to get on this journey.”
Gen AI in action
Mitsubishi manufactures speed and efficiency
Mitsubishi Electric is one of the world’s leading electronics manufacturers. As it navigates the complexities of the manufacturing landscape – marked by shorter release cycles and increasing software demands – the company is dedicated to empowering its engineers to concentrate on high-value tasks. By using generative AI to automate repetitive processes, Mitsubishi Electric aims to significantly enhance the quality and speed of software development.
In collaboration with AWS, Mitsubishi Electric initiated a generative AI discovery workshop to identify and validate impactful use cases that address real business challenges. The workshop identified an opportunity to streamline requests from the manufacturing department to revise code that is embedded in a device’s hardware, known as firmware code.
This solution, powered by Amazon Bedrock, will save software developers time by automating the search of internal documentation. The company estimates that this approach could reduce its internal team’s workload by 20 to 40 percent, as it expands across its 18 production sites and continues to gather user feedback.
The right tools for better banking
Organizations should get started with generative AI by identifying business goals that the technology can support. Let’s explore how this applies to a specific industry like financial services.
Lesson 2:
Build
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You don’t do gen AI unless you’ve got a robust security and governance structure.
It’s time to build a generative AI application and the right decisions will help you stay on the path. Choosing a generative AI tool like Amazon Bedrock, which provides access to the latest LLMs, can help to future-proof your solution as generative AI evolves. “Models are changing rapidly,” says Nandi. “You don’t want to get stuck with an old model.”
What follows are some key considerations for building generative AI applications.
Gen AI in action
GoDaddy lights up customer care
GoDaddy hosts millions of websites, from blogs to sophisticated e-commerce portals. Looking to better understand the factors driving the most common customer care interactions and resolve them more quickly, GoDaddy’s Care & Services team worked with AWS’s GenAI Labs team to build Lighthouse.
Lighthouse is a generative AI-powered system that uses LLMs managed through Amazon Bedrock to generate insights informed by customer support interactions.
GoDaddy product experts draw upon a pre-built prompt library to input natural language queries into Lighthouse to mine information from transcripts of customer interactions. This data is aggregated and visualized in dashboards and other tools. This enables GoDaddy analysts to quickly spot recurring problems and develop repeatable solutions.
GoDaddy customer care experts can also craft one-time prompts that reveal insights for highly specific customer scenarios.
Armed with insights from Lighthouse, GoDaddy can:
- Identify the most common customer care issues
- Develop website and product experience improvements
- Create more efficient customer call routing systems
- Improve and personalize customer experiences
Tailored to perfection
There are several methods to build trusted and differentiated generative AI experiences using your organization’s data. See how each approach works, and the amount of data required.
Lesson 3:
Scale
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Generative AI is actually making workers more productive, more effective and giving them back time to do the work that matters.
Once your project has been built and successfully piloted, it’s time to roll it out at scale. That could mean supporting thousands or even millions of users, so it’s important to get things right. Using a managed service like Amazon Bedrock to ensure data integrity and establish guardrails allows engineers to focus on business outcomes as the solution grows. “It’s a massive accelerant for scaling,” says Nandi.
Gen AI in action
Purina uses AI to help pets find their people
Purina has a long history of helping people adopt pets through Petfinder, a digital marketplace of over 11,000 animal shelters and rescue groups. As the leading pet adoption platform, Petfinder has helped millions of pets find their forever homes. However, the organization recognized a common challenge faced by its shelter partners: volunteers often struggled with the time-consuming task of crafting compelling pet bios to capture the attention of potential adopters.
To address this, Purina looked to the transformative power of generative AI and Amazon Bedrock. By integrating cutting-edge language models into Petfinder, Purina has empowered its shelter partners to easily generate personalized bios for each rescue pet. Using a pet’s personality and background as prompts, the software generates a biography for reviewing and editing by the shelter. Powered by Amazon Bedrock, the application can create a steady stream of unique pet profiles, maximizing the chances of each animal finding its person.
Review
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The path forward
Congratulations, you’ve completed this phase of your generative AI journey. But the adventure is just beginning. Here are some things to keep in mind as you move forward:
- Generative AI is quickly evolving and gaining new capabilities. There will be continuous improvement of models, and new approaches to implementation. One of the most important trends is agents. These are interconnected AI services that work in concert to perform complex tasks, such as end-to-end workflow automation for HR or accounting.
- Multimodal models will become more prevalent as use cases grow.
- Standards and regulations will continue to evolve, so organizations should keep a close eye on AI policy.
Summary of key learnings
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