Applying Generative AI for Decision Makers and Architects

Course Duration

1 Day

Audience

Employees of federal, state and local governments; and businesses working with the government.

Prerequisites

No prerequisites required.

Course Description

Generative AI is reshaping how organizations innovate, operate, and compete. This one-day executive-level course gives leaders and architects the clarity and tools they need to make smart, strategic decisions about GenAI adoption. Through real-world use cases, interactive demos, and practical frameworks, you’ll learn how to identify high-value opportunities, manage risks like data privacy and bias, and build the right teams and processes to drive successful AI initiatives. Leave with a clear action plan to evaluate, pilot, and scale Generative AI solutions that deliver measurable business impact.

Learning Objectives

• Understand what Generative AI is and how it differs from traditional AI.
• Identify and prioritize high-value GenAI use cases for their organization.
• Evaluate risks including data privacy, bias, intellectual property, and compliance.
• Build a framework for piloting and scaling GenAI initiatives.
• Make informed decisions about tools, platforms, and team composition.

Course Outline

1. Introduction: Generative AI in Context

• Evolution from predictive AI to generative AI
• Key differences and why decision makers must care now
• Market forces driving adoption across industries
• Introduction to ChatGPT

2. Core Concepts for Leaders

• How large language models (LLMs) work in plain terms
• Key capabilities: text, image, audio, code generation
• Enterprise-ready platforms (OpenAI, Anthropic, Google Gemini, Azure OpenAI, Hugging Face)

3. Business Use Cases and Value Creation

• Framework to identify where GenAI adds value
• Industry-specific case studies (finance, healthcare, manufacturing, software, education)
• Evaluating ROI and setting realistic expectations
• Live demo: GenAI applied to document summarization and knowledge management

4. Securing Your Generative AI

• Data privacy, compliance, and IP considerations
• Bias and hallucination risks and how to mitigate them
• Secure deployment models (APIs, private LLMs, on-prem vs. cloud)
• Regulatory landscape: current and emerging rules for GenAI

5. Managing the Lifecycle of a Generative AI Project

• Building a proof of concept (from idea to pilot)
• Decision criteria for tools and platforms
• Integrating GenAI with existing enterprise systems
• Testing and validating your AI system
• Deploying, monitoring, and maintaining your Generative AI solution
• Skills required for success (prompt engineers, ML engineers, AI product managers)

6. Building Your Generative AI Team: Who You Need

• Roles in a GenAI initiative (business sponsor, architect, compliance lead, data specialist)
• Upskilling vs. hiring
• Vendor partnerships and when to outsource
• Fostering collaboration between technical and business stakeholders

7. Future Trends in Generative AI

• Multimodal AI: text, vision, and beyond
• Retrieval-augmented generation (RAG) and domain adaptation
• The road toward autonomous agents in business workflows
• Preparing your organization for continuous disruption

8. Action Planning and Wrap-Up

• Checklist for starting a GenAI initiative