The Complete PreSales Course: RFP to Win, AI & Partnerships

An introduction to the role of pre-sales as the link between a client’s needs and a delivered solution. Learn why it’s critical to winning and delivering successful projects.

A breakdown of the stages in a pre-sales cycle, from qualification to closure, and the unique objectives at each step.

An overview of the key players in pre-sales, their responsibilities, and how they collaborate to turn opportunities into signed contracts.

The differences between these three formal request types, their purposes, and how to approach each strategically.

How to navigate informal opportunities—where there’s no formal RFP—and still move toward a structured deal.

Techniques for reading between the lines in client documents and crafting compelling, relevant responses.

Real examples of RFx responses—what works, what fails, and common mistakes to avoid.

A short but essential lesson explaining why discovery is the foundation of every successful pre-sales engagement. You’ll learn how to uncover pain points, confirm priorities, and build trust before you ever present a solution.

A structured, repeatable framework for running professional discovery calls in pre-sales. Learn the four-step approach used by top enterprise sellers to uncover the client’s true needs and shape winning solutions.

What NDAs protect, when they’re needed, and how they enable open discussions with clients.

The backbone of the client relationship—key terms, responsibilities, and approvals that shape the work ahead.

Why the Statement of Work is high-risk, what it must include, and how to make it a shield against misunderstandings.

The tool for managing changes mid-project while keeping scope, budget, and trust intact.

How to organize a proposal so it tells a compelling, logical story—covering scope, deliverables, pricing, and value.

How to define what’s included, set expectations, and identify potential challenges before they become problems.

Methods for estimating effort and cost, from high-level analogies to detailed work breakdowns, and how to apply buffers.

The most common commercial models, when to use them, and how to balance flexibility with predictability.

Step-by-step on preparing, customizing, delivering, and closing enterprise demos that move deals forward.

Strategies for working smoothly with account teams, engineers, and delivery to keep deals moving forward.

How to keep a clear record of changes, get approvals, and avoid “he said, she said” disputes later.

The essential resources—templates, checklists, and tools—that make pre-sales work faster and more consistent.

How to write SOWs that are precise yet flexible, avoiding wording that locks you into unrealistic commitments.

Practical ways to handle demanding, unclear, or slow-moving stakeholders while keeping momentum.

How to work within partner-led opportunities and leverage programs like PSF and PSO.

Spotting opportunities beyond the initial request to expand scope and deliver greater strategic value

How AI can assist in research, proposal drafting, estimation, and opportunity management.

Learn how to identify, shape, and position high-value AI opportunities during pre-sales engagements. We explore Predictive, Generative, and Agentic AI, with practical frameworks and real-world examples to help you align AI solutions with client business needs.

In this lecture, we explore the modern LLM landscape, including frontier models from OpenAI, Anthropic, and Google, as well as leading open-source alternatives such as Llama, DeepSeek, Qwen, and Mistral. We will compare their strengths, capabilities, deployment options, and business trade-offs. By the end of this session, you will understand how to evaluate and choose the right AI model based on use case, budget, security, and operational requirements.

In this lecture, we introduce a practical framework for evaluating AI initiatives based on business value, costs, and risks. You will learn how to assess opportunities across automation, augmentation, and differentiation, identify technical, operational, and strategic risks, and prioritize AI investments using a structured ROI-driven approach.