FA-0566AI for Leaders & BusinessAgentic & Generative AI

AI for Sales Professionals

GenAI, agents, RAG and infrastructure conversations

Build clear, credible AI conversations with enterprise customers by connecting use cases to infrastructure requirements, risks and evaluation questions.

Introduction

Why this course

This course helps sales professionals explain generative AI, tool-using agents and retrieval-augmented generation in accessible business language. It connects customer objectives to high-level compute, storage, networking and deployment considerations.

Participants practise discovery questions and value discussions without promising universal hardware requirements, guaranteed accuracy or business results. The course is conceptual, not AI-system implementation or product-specific certification training.

Learning outcomes

The course teaches participants to:

Explain generative AI, agentic AI and RAG in business language.
Connect proposed AI use cases to workload and deployment questions.
Recognise high-level compute, storage, network and data requirements.
Identify limitations, oversight and responsible-use issues customers may raise.
Demonstrate clear prompt/context concepts.
Discuss tools and infrastructure credibly without overpromising.

Prerequisites

Basic familiarity with the hardware products relevant to the participant's role.

General familiarity with typical enterprise IT needs.

Comfort with virtual instructor-led learning.

No prior AI technical background required.

Training outline

5 modules

011 — AI in business and core concepts1 topics

Business context

Business adoption drivers and the evidence behind selected examples.
Why organizations invest in AI capabilities
Relationship between AI initiatives and IT infrastructure
Questions customers may ask about data handling and compute capabilities.

Foundations

Overview of traditional artificial intelligence
Language models alongside other AI approaches; match the approach to the task.
Key terminology used in contemporary AI discussions

022 — Generative AI, RAG and agents1 topics

Generative AI

Definition and core principles
Types of generative models
General categories of business applications
Infrastructure considerations at a high level

Retrieval-augmented generation

Concept and purpose
Role of organizational data in RAG
High level architecture components
Potential relevance and grounding benefits, with retrieval-quality, permission and correctness limitations.

Agentic AI

Agents as goal-oriented model/tool workflows with configured boundaries.
How agents differ from simple model queries
Relevance for workflow automation
Enterprise implications: integration, evaluation, monitoring and human responsibility.

033 — Customer infrastructure needs4 topics

Workload-dependent compute, storage and networking needs; distinguish training from inference and hosted from local execution.
Differences between on premises, hybrid, and cloud environments
Assess when server infrastructure supports the proposed workload; cloud-hosted applications may have different local requirements.
Align options with customer objectives and validated workload assumptions, not generic AI-readiness claims.

044 — Risks, prompting and tool choices1 topics

Responsible use

Data quality issues
Bias and fairness concerns
Security and access control topics
Reliability and oversight requirements
Common areas of customer hesitation

Prompt and context foundations

Structure of input prompts
Context relevance, quality and limits; extra context does not automatically improve results.
Importance of precise instructions
How business users typically interact with AI tools

Tool/platform categories

General categories of widely used AI tools
Characteristics that customers evaluate when selecting tools
Interaction between tools and underlying hardware environments

055 — Sales conversations and future planning1 topics

Customer discovery and communication

Approaching customer discussions about AI objectives
Identifying potential business drivers
Linking customer goals to infrastructure implications
Communicating value without technical overload
Handling common customer questions

Longer-term considerations

Evolving directions in enterprise AI
How workload choices, efficiency and scale may affect compute demand; avoid universal growth claims.
Longer-term capacity and operational planning using documented assumptions and revisited requirements.

A programme built around your team.

Share your training goals and requirements.