From Amazon Q to NVIDIA: An AI market overview (AIM382)

Generative AI for Improved Business Operations and Efficiency

Solving Internal Challenges with Generative AI

  • CDW, a leading provider in the cloud market, is leveraging generative AI to address internal challenges and improve business operations.
  • One of the key problems they aim to solve is the complexity and coordination required in scoping and pricing cloud transformation projects for customers.
  • Traditionally, this process involves multiple meetings with subject matter experts, which can be time-consuming and costly for both CDW and their customers.

Developing a Flexible Toolkit

  • CDW has built a flexible toolkit using generative AI to streamline this process.
  • The architecture includes various AWS services, such as Cognito, load balancing, and infrastructure as code, to create a scalable and secure solution.
  • At the core of the toolkit is the use of a large language model (LLM) from Anthropic, combined with a custom knowledge base of PDFs and data sources.
  • This allows the system to provide informed responses and recommendations to customers, reducing the need for extensive human involvement.

Key Features and Capabilities

  • The generative AI-powered system can assist in qualifying opportunities, providing funding information, and generating cost estimates and pricing proposals.
  • For example, the system can analyze a customer's data center inventory and quickly produce a cost estimate for migrating to AWS, a task that previously took 40-80 hours of human labor.
  • The goal is to empower CDW's sales team, allowing them to be more agile and provide better service to customers, while also reducing the workload on subject matter experts.

The Generative AI Market

Spectrum of AI Capabilities

  • Pete Johnson, the head of CDW's AI practice, provides an overview of the generative AI market and the different levels of AI capabilities.
  • On the left side of the spectrum are AI features that can be easily integrated into existing applications, providing general productivity improvements.
  • On the right side are highly specialized, proprietary AI models that require large datasets and significant resources to develop, typically used by Fortune 100 companies.

Affordable Custom Solutions

  • In the middle of the spectrum, Pete highlights the emergence of affordable, customizable solutions that can address a broad range of business problems.
  • This is enabled by the availability of hosted large language models (LLMs) and techniques like retrieval-augmented generation (RAG) and modular reasoning, knowledge, and language (MIRACLE).
  • These solutions allow organizations to leverage the power of LLMs without the need to invest in building their own proprietary models.

The Benefits of Choice

  • Pete emphasizes the importance of choice when it comes to generative AI solutions, as different models and techniques may be better suited for different use cases.
  • AWS Bedrock, a managed service for running large language models, provides this flexibility, allowing organizations to experiment with different models and approaches.

Conclusion

  • CDW has demonstrated how generative AI can be leveraged to improve internal business operations and efficiency, serving as a real-world example of the practical applications of this technology.
  • The generative AI market offers a range of capabilities, from easily integrated productivity features to highly specialized, custom-built solutions, providing organizations with options to address their specific needs.
  • The ability to choose from different models and techniques, as offered by AWS Bedrock, is a key advantage in this rapidly evolving landscape.

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