Applying Generative AI for Decision Makers and Architects

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.

1. Introduction: Generative AI in Context

Explore how AI has evolved from rules-based systems and predictive analytics to today s generative capabilities. This lesson highlights what makes Generative AI fundamentally different: its ability to create new content, ideas, and solutions. Examine market

forces such as competitive pressure, cost savings, and customer expectations that are driving rapid adoption across industries.

Objectives:

• • 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

Breaks down the inner workings of large language models in clear, non-technical terms and understand what is happening under the hood without diving into code. The focus is on the core capabilities, text generation, image creation, audio synthesis, and code assistance. Also get an overview of today s enterprise-ready platforms (OpenAI, Anthropic, Google Gemini, Azure OpenAI, Hugging Face), with insights into their strengths, limitations, and typical adoption patterns.

Objectives:

• • 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

Learn how to recognize the best places to use Generative AI to solve real business problems and improve efficiency. We ll look at practical examples from different industries, including fraud detection in finance, clinical documentation in healthcare, supply chain improvements in manufacturing, boosting developer productivity in software, and personalized learning in education. You ll also get simple methods for estimating return on investment so expectations stay realistic. To bring it all together, we ll show a live demo of how Generative AI can summarize documents and streamline knowledge management.

Objectives:

• • 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

Explore the key challenges organizations face when adopting Generative AI. We ll cover practical risks like data privacy, intellectual property, and compliance, along with technical concerns such as bias, inaccurate outputs, and models that degrade over time. You ll compare different ways to deploy GenAI, whether through public APIs, private setups, or hybrid options in the cloud or on-premises. We ll also walk through the changing regulatory landscape, including the EU AI Act and industry-specific rules, so you know what s coming and how to prepare.

Objectives:

• • 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

Walk through the full process of bringing Generative AI into your organization. We ll start with how to design a proof of concept, test the results, and then move toward scaling a solution into production. You ll get clear guidance on how to choose the right tools and

platforms, with practical factors like cost, scalability, and integration in mind. We ll also look at how GenAI can connect with the systems you already use, such as CRM, ERP, or knowledge bases. To wrap up, we ll cover the key skills your team will need, from prompt engineering to AI product management, and where to find or build that expertise.

Objectives:

• • 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

Introduce the people and teams needed to make Generative AI work in an organization. You ll learn about the key roles involved, from business sponsors and architects to compliance leads and data specialists. We ll look at when it makes sense to train your current staff, when to bring in new talent, and how outside partners can fit into the picture. You ll also pick up strategies for helping business and technical teams work well together, plus tips for managing remote or distributed teams on AI projects.

Objectives:

• • 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

Look ahead to what s coming next in Generative AI. You ll explore how multimodal AI is expanding beyond text to include vision, audio, and other capabilities, and how techniques like retrieval-augmented generation (RAG) are helping organizations connect AI with their own trusted knowledge sources. We ll also look at the rise of autonomous agents and how they could reshape everyday workflows in areas like customer service, operations, and knowledge work. By the end, you ll have practical insights on how to prepare your organization for constant change and stay resilient as the AI landscape evolves.

Objectives:

• • 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

Additional Topic: Time Permitting

These topics will be included in your course materials but may or may not be presented during the live class depending on the pace of the course and attendee skill level and participation.

8. Action Planning and Wrap-Up

Provide a practical roadmap you can take back to your organization. You ll get a checklist that covers the technical, strategic, and organizational steps needed to start a Generative AI project. Working in groups, you ll design a use case tailored to your own business, putting the day s lessons into practice. We ll finish with an open Q&A, giving you the chance to address specific questions and leave with clear next steps for moving forward.

No programming or data science background is required. Participants should have a solid understanding of business operations, decision-making processes, and basic familiarity with digital tools or enterprise systems. Prior experience managing technology projects is helpful but not mandatory.

This introductory course on Generative AI is designed for business and technology leaders who are evaluating, planning, or guiding AI adoption in their organizations. It is ideal for executives, IT leaders, solution architects, project managers, business strategists, and analysts who are responsible for making informed decisions about technology investments and team direction.

No programming or data science background is required. Participants should have a solid understanding of business operations, decision-making processes, and basic familiarity with digital tools or enterprise systems. Prior experience managing technology projects is helpful but not mandatory.