How to Start Innovating with AI Using AWS Bedrock

AI has the potential to revolutionize business, yet its implementation remains out of reach for most organizations. This is because proprietary AI engines require a massive amount of data and capital to operate. Additionally, successful AI deployment is far more challenging than hype cycles suggest. As of Q4 2023, eight of 10 enterprise AI projects have already failed.

But don’t let this statistic suggest that AI can be ignored. Quite the contrary: Top Tech investors agree that ignoring the “AI revolution” is dangerous. Therefore, the question isn’t “Should I implement AI,” but rather, “How will I implement AI?”

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With AWS Bedrock, Amazon has provided a compelling answer to this question. The company made waves in September 2023 by expanding its generative AI (GenAI) application builder from beta testing to general availability. Now, organizations leveraging Bedrock have the opportunity to significantly scale their GenAI applications with improved ethics- and privacy-focused guardrails. But how should businesses looking to accelerate their AI offerings get started?

Let’s review how your organization can implement AWS Bedrock today.

What is AWS Bedrock?

Perhaps the biggest competitive differentiator — and struggle — with AI implementation is gaining access to relevant data. No two organizations are the same, so it’s generally insufficient to rely on broad-application large language models (LLMs) and GenAI engines to provide industry-specific suggestions about productivity and efficiency gains.

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However, developing and deploying a proprietary GenAI engine is out of the question for most organizations. For one, it’s cost-prohibitive and requires immense cloud storage. It’s also labor-intensive, demanding the attention of a fully staffed software development team.

Bedrock alleviates these issues by providing access to foundational models (FMs) pre-trained on vast datasets. FM options come from top AI companies like Anthropic, Meta and Amazon. Bedrock clients can toggle between different FMs to decide which foundational data best suits their organizational purpose.

The service is fully managed and serverless, meaning clients don’t need to manage their AI infrastructure after adopting Bedrock. Furthermore, it integrates with existing AWS services, enabling clients to integrate and deploy GenAI capabilities with relatively little coding ability.

Once clients have selected an FM, they can bolster its capabilities with existing organizational data, tailoring it to industry-specific knowledge and use cases. This is accomplished through a process known as retrieval augmented generation (RAG). Amazon fully automates RAG by ingesting, retrieving and fine-tuning proprietary business information. After that, clients maintain access to an operational and private GenAI engine trained on a robust foundational model and augmented with proprietary data.

Bedrock vs. SageMaker JumpStart vs. Amazon Q

AWS offers various AI and machine learning (ML) services that you may already employ. These solutions are not mutually exclusive and can work harmoniously to create a more comprehensive AI strategy.

SageMaker JumpStart is another central component of AWS’s AI and ML suite. It offers a curated collection of pre-built ML models and end-to-end solutions, simplifying the process of getting started with ML. SageMaker, in general, helps data scientists and developers build, train and deploy ML models at scale. The critical difference lies in the workflow stage: While Bedrock is used for quickly integrating advanced generative AI capabilities, SageMaker is introduced earlier in the development process to build and train custom models from scratch.

You may also be familiar with Amazon Q. This tool leverages natural language processing (NLP) to help users find answers to business questions. It’s closer to a broad-purpose LLM and cannot provide the same degree of context as Bedrock-based GenAI engines.

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Implementing GenAI use cases

The use cases for GenAI are endless. This fact is both exciting and potentially intimidating; without an idea of where to start, leaders may ignore GenAI altogether. I urge leaders to fight this notion and consider the following potential applications:

• Accelerated project development: Bedrock can analyze proprietary data to generate product ideas specific to your ideal customer profile. Furthermore, it can go a step further to design prototypes and even suggest improvements to existing products based on industry-specific trends or fluctuations in demand. This accelerates the product ideation and development phases, reducing time-to-market and enabling businesses to stay competitive.

• Enhanced customer experience (CX): Personalization is key to enhancing CX. In fact, over 76% of consumers become frustrated if their customer journey isn’t personalized. Bedrock excels in this area because it relies on existing data about consumer needs and preferences to create personalized recommendations, tailor marketing messages and even develop customer-specific solutions.

• Optimized efficiency: GenAI powered by Bedrock can increase efficiency by automating complex, well-documented tasks. For instance, in supply chain management, AI models can predict demand, optimize inventory levels and suggest efficient logistics strategies. These optimizations lead to cost savings and improved operational efficiency. Critically, by basing these suggestions on deep organizational data, GenAI engines improve the relevancy of their outputs.

• Expedited content generation: LLMs and other GenAI engines have spurred significant interest based on their potential for creative content creation. For marketing teams, this means working hand in hand with human copywriters to generate high-quality campaign ideas and content. By using organizational data, Bedrock-based GenAI engines can create more relevant and on-brand marketing content and creative in significantly less time.

Getting started with Bedrock

With so many organizations clamoring to implement AI solutions as quickly as possible, the implementation details are more important than ever. Bedrock helps AWS users focus on the big picture by handling the fine print, including foundational data and AI training.

However, its implementation may still involve roadblocks. Data leaders seeking to deploy and reap benefits from AI quickly should consider contracting a knowledgeable third-party vendor. Managed service providers (MSPs), for example, can guide you through the AWS Bedrock implementation and maintenance processes. Alternatively, leaders can contact an MSP to audit their data and ascertain if a GenAI model is practical or if core data needs to be reorganized (e.g., moved into the cloud or on-premise).

Regardless of your first step, leaders should start investigating the potential of AWS Bedrock as soon as possible. Harnessing Bedrock-based GenAI can unlock unparalleled levels of creativity and efficiency, enabling leaders to stay ahead in an increasingly data-driven landscape.

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