By Priya Arora, Global Head of Generative AI Center of Excellence – AWS
By Jacob Newton-Gladstein, Generative AI Center of Excellence Field Enablement Lead – AWS
As the field of generative AI continues to evolve, companies are looking for ways to evaluate their generative AI prototypes and determine which ones are driving real business value. This means establishing a concrete link between an application and its total business value.
The latest assets available through the Generative AI Center of Excellence (CoE) aim to help AWS Partners understand this business opportunity by diving deeper into use cases in partnership with generative AI thought leaders and CoE contributors. Please visit the CoE to learn more about: Precision LLM for Biomedical Engineering with Provectus And Generative AI for Retail and Ecommerce with Software one.
Additionally, the AWS Generative AI Center of Excellence leveraged QuantumBlackAI by McKinsey to provide AWS Partners with insights into the unique needs and priorities of different personalities engaging on the topic of generative AI.
Since business value can be derived differently across different pillars of an organization, we will examine potential use cases of generative AI in four distinct organizational business units, which based on McKinsey researchrepresent 75% of the total opportunity for generative AI workloads. Specifically, we take an in-depth look at the needs of operations managers, research and development (R&D) managers, revenue managers, and marketing managers.
Key Considerations When Hiring Revenue Managers
Revenue managers are uniquely positioned for generative AI because they span both customer operations and marketing and sales functions. McKinsey & Company estimates that generative AI could increase sales productivity by approximately 3-5% of current global sales spending.
Since revenue growth leaders are looking for a very clear correlation between generative AI and revenue growth, consider establishing specific growth goals to achieve for your generative AI workloads. Finally, by focusing on reusable foundational application components that can be adapted based on use cases, you can accelerate your delivery time.
Key Considerations When Engaging Marketing Managers
Marketers have a tremendous opportunity to improve their organizations with generative AI. McKinsey & Company estimates that generative AI could increase the productivity of the marketing function by 5% to 15% of total marketing spend.
To capitalize on these opportunities, consider a CMO’s key metrics: conversion rates, customer retention, and advertising spend. To effectively demonstrate the value of generative AI to marketers, consider creating tools and dashboards to A/B test personalized advertising and ad costs, and measure conversion rate alongside your advertising solutions. Generative AI.
Generative AI for research and development leaders
Research and development leaders can evolve their entire value chain with generative AI. McKinsey & Company estimates that generative AI could deliver productivity worth between 10% and 15% of overall R&D costs.
R&D managers are not only focused on business outcomes, but also place great importance on things like data accuracy, explainability, and citations, as well as data privacy. To appeal to these executives, focus on creating tools that clearly link data inputs and outputs and define clear pre- and post-processing rules to improve transparency of AI tool results generative.
Key Considerations When Hiring Operations Managers
Operations managers are well-positioned for generative AI because increased productivity is a major benefit of the technology. McKinsey & Company estimates that generative AI could generate between $280 billion and $530 billion in value for operational functions across all industries.
When designing generative AI applications for operations managers, keep in mind that large-scale operations incorporate large amounts of data and a diverse operator base. Accordingly, a well-thought-out data map and proactive integration plan on how to connect the generative AI application to an organization’s existing work processes would be helpful.
How Generative AI on AWS Enables Partners to Capture Cross-Industry Leaders
There are common needs for all decision-makers when it comes to generative AI:
- Generative AI applications go beyond large language models. You need to understand the full stack of generative AI.
- Different problems require different levels of solutions, from purpose-built applications to custom trained models or even models adapted via Amazon Bedrock.
- It is easier to prototype generative AI applications than it is to generate sustainable business value by putting these applications into production.
By helping your customers build their generative AI solutions on AWS, organizations gain access to a wide range of generative AI tools, including multiple options for generative AI data structures such as Data Lakes and the Amazon OpenSearch Services vector database to prepare your data for generative AI applications.
You can also access the broadest available list of adaptable LLMs through Amazon SageMaker JumpStart and use AWS’s purpose-built generative AI chips, AWS Trainium and AWS Inferentia, to reduce model training costs.
With Amazon Bedrock, getting into production is simple. Customers can quickly leverage the data already available, choose the best FM, establish guardrails, and rest comfortably with the security and reliability of AWS. With Amazon Q, access multiple products tailored to different personas and use cases. Amazon Q can be leveraged to answer employee questions, summarize data, lead a conversation, and help them take action. It can also simplify the work of developers by helping them write, debug, test and transform code.
Finally, the AWS Generative AI Center of Excellence also offers thought leadership articles from AWS Partners, including “LLM Accuracy for Bio-Medical Engineering” from Provectus and “Generative AI for Retail and E-commerce” from SoftwareOne, which can guide you on how to use it. case-specific applications for generative AI across a variety of industries, allowing you to take your applications from POC to production.
Leverage the Generative AI Center of Excellence to obtain resources to develop and execute your generative AI commercialization strategy (login required).