Navigating the Waters of GenAI with WWT & AWS

The GenAI waters are warmer than you think

When it comes to GenAI, you can think of it like plunging into the ocean. Some will be seasoned swimmers, having navigated the depths of machine learning (ML) and artificial intelligence (AI) for years. For this group, GenAI is just another deep dive — they're comfortable exploring uncharted waters and pushing their limits. Others are waiting on the shoreline contemplating how, when and where to jump in. And then there is the wide spectrum of companies that fall somewhere in between.

Many assume they have to jump all the way into the deep end. But the reality is you can get started with GenAI at the water's edge. Whether you're gearing up for your first plunge or you're a seasoned pro, WWT offers vast resources and expertise in GenAI offerings built on AWS to support, validate, and fine tune your GenAI strategy and operations.

Does the perception of GenAI meet reality?

A lot of people hear "GenAI" and think hefty budgets, massive in-house builds with thousands of new processors to order, robust cooling and power systems to install, plus long waits before you can start using it. But it doesn't have to be that way. You don't need to assemble a legion of technical specialists or pour resources into infrastructure that might not deliver results for months or years. For example, consider a tool like Amazon Q Business, a GenAI-powered assistant that can answer questions, provide summaries, and complete tasks based on the data in your enterprise systems today, not tomorrow. Whether you're just starting or a seasoned veteran, such GenAI tools can be both straightforward and budget-friendly.

Another important insight is that you can start small and scale up. The beauty of modern GenAI applications is their scalability. You can begin by making small, manageable investments that start paying off right away, and gradually invest more as your needs grow. This means GenAI projects can be successful no matter where you are in your AI journey. 

Building a common AI foundation: Aligning goals and language for success

A partner like WWT can provide access to specialized expertise with tremendous knowledge and resources that can streamline the entire GenAI implementation process. The rapid pace of development of GenAI tools and methodologies makes it very difficult for clients to decide when and where to invest. Given the landscape's intrinsic fluctuations, partners such as WWT are imperative to a successful GenAI project.

WWT's approach is collaborative and provides clients with a clear understanding of the evolving landscape of AI technologies and best practices. We can help your organization navigate these complexities and avoid common pitfalls in your AI journey. The journey can begin with a simple conversation about what GenAI means to your organization. Our aim is to establish a foundational understanding, clarifying concepts and aligning on terminology that might be muddled by the diverse industry perspectives that don't necessarily resonate with every organization's unique challenges. This step ensures we're all on the same page, using a unified language.

Once we have the necessary information, we will assess your IT environments to discover what data is available. Data governance and responsibility are important during this discovery process. This assessment provides a baseline understanding we can use to review the value of use cases to your organization. We'll then help you put your strong potential use cases into test groups, review the success criteria, and develop production plans to rapidly achieve ROI.

Following the above process, your can develop and swiftly move your GenAI strategy from theory to practical application that generates tangible business results quickly and efficiently. WWT works with both public and private sector organizations, each handled according to the appropriate standards and governance requirements.

A window into the world of the AWS AI/ML toolkit

AWS has been at the forefront of AI/ML, offering a wide range of services and solutions even before GenAI became popular. Their extensive suite of incredibly useful and effective tools and platforms allow companies to deploy AI solutions across the GenAI journey, which is probably why 96 percent of AI/ML unicorns are running on AWS. AWS released more than 326 AI features in 2023, outpacing all other providers combined. 

And that's how AWS and WWT excel together, by providing a comprehensive spectrum of solutions that meet you where you are in your journey and propel you to where you want to be.

Below are three key AWS offerings that differ in complexity and user engagement. Together, they provide a window into the soul of AWS, if you will, and its commitment to making AI accessible to companies of all levels of experience:

1. Amazon Q Business is the simplest solution, allowing users to quickly implement natural language processing (NLP) capabilities without deep technical knowledge, making it ideal for rapid deployment and ROI.
2. Amazon Bedrock serves as an intermediary, providing a user-friendly platform for testing and integrating various large language models (LLMs), while still requiring some technical understanding for effective use.
3. Amazon SageMaker is the most complex option, offering comprehensive tools for building, training and deploying ML models, catering to data scientists and organizations needing extensive customization and control.

Amazon Q Business: Simplifying AI with fast, tangible results

For businesses looking to start small and achieve ROI quickly, Amazon Q Business provides an easy entry point for experimenting with AI solutions. Amazon Q Business is an AI-powered business assistant that streamlines how knowledge workers interact with organizational data. Whether it's internal support documentation, employee onboarding information or customer queries, Amazon Q Business allows you to implement a natural language search engine that connects your various data sources across your organization for improved productivity. Best of all, it doesn't require a degree in data science — Amazon Q Business' plug-and-play design is built for speed and simplicity.

• Use case: An online brokerage firm used Amazon Q Business to improve both internal operations and customer experiences in just a matter of weeks. By connecting Amazon Q Business' data connectors to their disparate internal data systems, the company reduced customer support response times by 50 percent while also reducing employee onboarding time by 45 percent.

Bedrock: The integration playground for more complex AI solutions

Once organizations have had success with smaller AI projects, they often want to scale their efforts or integrate AI more deeply into existing operations. This is where Amazon Bedrock comes into play. Bedrock is an integrated test ground for various AI components, developed for ease of use with a managed interface. It is fully compliant with regulatory requirements and integrates with Amazon APIs and systems.

Bedrock serves as your "easy button" for combining various AI models and integrating them into your business applications. Bedrock allows you to mix and match models, pull in data from different sources, and test various use cases — all without needing to build the infrastructure from scratch. Bedrock offers a lab environment where you can test LLMs and foundation models. This is ideal for businesses that want to explore AI's full potential without the technical overhead of setting up and managing complex systems.

• Use case: A transportation client used Bedrock to effectively integrate AI into their logistics operation, while meeting strict uptime requirements due to the highly regulated nature of their industry. By running various models in the Bedrock environment, the organization was able to optimize route planning and improve overall efficiency without having to worry about the underlying technical challenges. This allowed the client to verify the feasibility of AI integration while ensuring uninterrupted service.

Amazon SageMaker: The go-to tool for full AI customization

SageMaker is a comprehensive suite for ML model development and workflows. It's the core of machine learning operations (MLOps) and LLM operations (LLMOps). For organizations with more advanced AI capabilities or those looking to build complex AI pipelines, SageMaker is a choice solution.

SageMaker offers the full spectrum of  operations, from developing and deploying models to managing them at scale. With SageMaker, you are the mechanic under the hood. It gives you full control over the entire pipeline, allowing data scientists and technologists to create, train and manage models.

SageMaker is also a great option for organizations that want to build custom AI models tailored to their specific needs. While tools like Amazon Q Business and Bedrock focus on simplicity and ease of integration, SageMaker allows organizations to go deeper, working on a granular level to optimize every aspect of their AI initiatives.

• Use case: An automotive company leveraged SageMaker to build and deploy custom models to optimize their manufacturing processes. The flexibility of SageMaker allowed their data science team to experiment with, refine and deploy models that were finely tuned to the specific challenges of their production lines.

4 key benefits of partnering with WWT and AWS

1. Reduced time to value: With solutions like Amazon Q Business, enterprise organizations can achieve measurable ROI in just weeks.
2. Flexibility in AI integration: From the ease of Bedrock to the power of SageMaker, WWT can help you utilize a range of AWS tools to scale with your AI goals.
3. Expert support: WWT brings deep expertise to the table, from consulting through training, validation, integration and optimization, helping you navigate the complexity of AI in a way that makes sense for your business.
4. Security and compliance: AWS tools are built with privacy and security at their core, helping ensure your data remains compliant with industry regulations.

WWT's collaborative approach

Step 1: Practical AI briefing: Our Practical AI Briefing is a great starting point; it's a two-hour engagement that offers a realistic, outcomes-based exploration of AI tailored to your needs.

Step 2: Assess your data: Next, we'll look at your data landscape via our Data Foundations of AI Briefing. We'll help you identify what data is available and what you'd like to leverage. Together, we'll pinpoint use cases that could bring real value to your business.

Step 3: Build and test a proof of value: In this phase, we'll assist in building a proof of value (POV) and test it. We'll look at the success criteria and determine the best path forward — whether that's moving into production or testing other use cases.

Take action now

WWT's unique AI Proving Ground, which features AI/ML readiness workshops and labs, offers the perfect opportunity to see for yourself how AI can drive value for your business. Don't wait on the shoreline to start harnessing the power of GenAI — get your toes wet today by scheduling this exciting and important conversation with your WWT client representative or our dedicated AI team.

About the authors

Todd Barron, Technical Solutions Architect at WWT

Rashid Sajjad, Sr. Partner, Management Solutions Architect at AWS