Generative AI on AWS: Everything You Need To Know

Conversations around artificial intelligence (AI) are all the rage right now, particularly in the tech sector, where rapid developments are creating exciting new opportunities for both organizations and tech professionals.

Amongst the hottest topics is generative AI, which promises to streamline business processes and maximize efficiencies while carving out a wide range of new paths for talent to explore. In fact, hundreds of startups are already gaining momentum with their solutions in this space.

And it’s not going anywhere. According to the World Economic Forum, over three-quarters of organizations plan to adopt AI and Machine Learning (ML) in the next five years.

AWS services and capabilities democratize the use of generative AI by making it more accessible to all industries and customer types. Meaning: now’s the time to get in the know to ensure you can capitalize on everything generative AI has to offer.

As Vice President of Database, Analytics, and Machine Learning at AWS, Swami Sivasubramanian, noted in his AWS NYC Summit 2023 keynote, “generative AI has captured our imaginations [and] this technology has reached its tipping point.”

But what exactly is it all about? How can generative AI benefit organizations across different industries, and what new doors does it open for talent across the AWS community? Let’s explore.

What is generative AI?

Generative AI is a type of artificial intelligence…obviously.  

More specifically, it’s artificial intelligence that’s capable of creating content and ideas—be that text, images and videos, or prototypes and synthetic datasets.

It’s powered by generative machine learning models often referred to as foundation models (FMs), which are pre-trained on the patterns and structure of vast amounts of input data. A class of these FMs—like GPT models, for example—are commonly called large language models (LLMs) and focus on performing language-based tasks like content creation and information extraction.

Because these FMs are exposed to such a large scale of data and parameters, LLMs are supremely capable of learning advanced concepts and applying them to a wide range of different contexts. Best of all, they’re adaptable to perform domain-specific functions by being highly customizable, meaning organizations can build widely varying applications with FMs using just a small sample of data and compute, compared to what’s needed to train a model from the ground up.

What does generative AI mean for businesses?

As we’re sure you’re already aware, generative AI applications have already demanded the attention of business leaders with popular applications such as ChatGPT.

As Peter Kiernan, AWS Competency and Capability lead at Credera explains, “One of the biggest benefits of generative AI tools is that it opens clients’ minds to the broader capabilities of AI and machine learning.”

According to Goldman Sachs, this increased adoption of generative AI could accelerate productivity growth by 1.5 percentage points over the next 10 years, leading to a 7% increase in global GDP worth almost $7 trillion. Yep, that’s $7 trillion.

Yet while this sure sounds mighty impressive, what does it actually mean for organizations and their day-to-day operations?

Business uses for generative AI

Some of the fundamental use-cases for generative AI on AWS include:

• Content generation: create content and assist with writing and ideation tasks across a range of internal and external resources.
• Personalization: personalize customer experiences across the service cycle with highly relevant content, product recommendations, and more.  
• Software development: quickly generate code snippets, comments, and documentation based on natural language inputs.
• Conversational AI: utilize speech-to-tech and language translation, or build natural language-based conversational interfaces like chatbots and virtual assistants that provide human-like interactions.
• Question answering: locate and synthesize information across large bodies of data to answer customer questions using natural language prompts.
• Text summarization: generate concise overviews and receive the main takeaways from long articles, documents, and webpages.
• Search: quickly locate content and information across internal knowledge banks to make data-driven business decisions.

Business benefits of generative AI

With such a huge variety of fundamental uses, generative AI can benefit organizations in a variety of different ways, including:

• Enhanced customer experiences: chatbots and virtual assistants are super-effective at successfully automating customer experiences, streamlining self-service processes, and reducing operational costs—the very definition of a win-win! Elsewhere, insightful call analytics and handy agent assistance improve the assistance offered by your customer service team (remember those living, breathing humans?), enabling them to better personalize your customers’ experiences from start to finish.
• Increased employee efficiency: undoubtedly one of the biggest benefits of generative AI is its positive impact on employee productivity. Code generation tools, for example, enable tech teams to source code quickly and accurately, while low-code/no-code applications simplify development and minimize maintenance. Elsewhere, automated report generation speeds up internal reporting and ensures fewer errors, while conversational search features help employees source information efficiently to minimize hurdles.
• Accelerated process optimization: from document processing and fraud detection to supply chain optimization, generative AI solutions can be applied in a wide range of contexts to improve business operations, reduce costs, and maximize the impact of internal processes.
• Improved creativity and output: generative AI can maximize your organization’s creative output by optimizing content creation and ideation. Whether it’s data-driven sales scripts, design prototypes, or engaging marketing copy, generative AI tools save valuable time and resources, while serving as a handy sidekick to multiple teams across an organization.

But of course, this barely scratches the surface of what organizations can achieve with generative AI on AWS. What’s more, industries such as Financial Services, Healthcare and Life Sciences (HLS), Automotive and Manufacturing, Media and Entertainment, Telecoms, and Energy are well-poised to benefit from higher-level, advanced applications of generative AI sooner than most.

Hiring talent with generative AI skills

While the opportunities presented by generative AI sound super exciting for organizations, its adoption and implementation are unlikely to be quite as straightforward.

As with all emerging and fast-developing technologies, organizations will rapidly be on the hunt for professionals with the skills and expertise they need to implement and maintain AI solutions. Credera’s Peter Kiernan notes:

“Client demand for professionals with a deep understanding of these tools who can incorporate them as part of a well-architected AWS environment is growing. Any solution leveraging AWS’ AI services is still highly reliant on a well-architected platform with strong technical and procedural processes embedded for data governance and more. AI services are most effective when they can access a broad set of data points—these must be well-understood and controlled.”

However, real-world experience with generative AI is extremely limited right now, meaning the talent pool currently lacks qualified professionals. In fact, according to our database, less than 10,000 candidates list generative AI or LLM as something they have experience working on daily, while just 6,800 candidates list MLOps as something they practice day-to-day.

The table below shows just how small the qualified candidate pool is right now.