Lead Generation with Artificial Intelligence Course

Lesson 1 • What AI Means for Sales

Defines AI, machine learning, and automation in a sales context. Establishes shared vocabulary used throughout the course.

Lesson 2 • Ethical and Responsible AI Use

Covers bias, transparency, and consent principles in AI-driven outreach. Sets the ethical baseline required before deploying any AI lead gen system.

Lesson 3 • Data as the Foundation of AI

Explains why data quality determines AI output quality. Introduces data types, sources, and hygiene practices essential for reliable models.

Lesson 4 • How AI Fits the Lead Gen Funnel

Identifies where AI creates leverage at each funnel stage. Connects AI capabilities to specific lead generation pain points.

Lesson 1 • Enriching ICP with External Data

Integrates third-party enrichment sources to fill gaps in internal data. Produces a richer, more actionable ICP profile.

Lesson 2 • Scoring and Prioritizing ICP Segments

Builds a weighted scoring model to rank ICP segments by revenue potential. Outputs a prioritized target list for prospecting campaigns.

Lesson 3 • Mining Existing Customer Data

Applies AI clustering and pattern recognition to CRM and transaction data. Surfaces hidden traits shared by best-fit customers.

Lesson 1 • Trigger-Based Prospecting

Monitors buying signals and business events to time outreach precisely. Increases response rates by reaching prospects at moments of high intent.

Lesson 2 • Manual vs. AI-Assisted Research

Contrasts traditional research methods with AI-assisted approaches on speed and accuracy. Motivates the shift to automated research workflows.

Lesson 3 • Using AI to Find Target Accounts

Applies AI-driven prospecting platforms to identify accounts matching the ICP. Builds a qualified account list ready for outreach.

Lesson 4 • Building a Scalable Research Workflow

Combines tools and processes into a repeatable, documented research system. Enables consistent lead quality across team members and campaigns.

Lesson 1 • Lead Scoring Principles and Models

Explains rule-based, predictive, and behavioral scoring approaches. Provides the conceptual foundation for building a scoring system.

Lesson 2 • Building a Predictive Scoring Model

Walks through data preparation, feature selection, and model training for lead scoring. Produces a working predictive model tied to conversion outcomes.

Lesson 3 • Intent Data in Lead Qualification

Integrates third-party and first-party intent signals into the scoring model. Improves qualification accuracy by capturing in-market behavior.

Lesson 4 • Routing and Prioritizing Scored Leads

Designs automated routing rules that send scored leads to the right rep or sequence. Ensures fast follow-up on high-priority opportunities.

Lesson 1 • Multi-Channel Outreach Sequences

Designs AI-assisted sequences across email, phone, and social channels. Maximizes touchpoint coverage while avoiding prospect fatigue.

Lesson 2 • AI Copywriting for Cold Outreach

Uses large language models to generate personalized email and message copy. Produces high-converting first-touch messages at scale.

Lesson 3 • Testing and Optimizing Outreach Performance

Runs structured experiments to identify the highest-performing messages and sequences. Builds a continuous improvement loop for outreach campaigns.

Lesson 4 • Dynamic Content and Hyper-Personalization

Applies real-time data to generate context-specific message elements dynamically. Elevates personalization beyond name and company insertion.

Lesson 1 • Conversational AI Landscape

Surveys chatbot types, from rule-based to generative AI, and their lead gen use cases. Provides the selection criteria for choosing the right solution.

Lesson 2 • Designing Lead Qualification Conversations

Builds conversation flows that qualify visitors using BANT or similar frameworks. Produces a chatbot script that mirrors skilled human qualification.

Lesson 3 • Measuring Conversational AI Performance

Defines KPIs for chatbot lead capture and qualification effectiveness. Drives ongoing optimization of conversation flows and deployment strategy.

Lesson 4 • Integrating Chat Data with CRM

Connects chatbot outputs to CRM and marketing automation systems. Ensures captured leads flow into scoring and routing workflows seamlessly.

Lesson 1 • Lead Re-Engagement with AI

Identifies dormant leads and deploys AI-crafted re-engagement campaigns. Recovers pipeline value from leads previously considered lost.

Lesson 2 • Predictive Send-Time Optimization

Applies ML models to determine the optimal send time for each individual lead. Improves open and click rates without increasing send volume.

Lesson 3 • Nurture Strategy and Segmentation

Defines nurture goals and segments leads by stage, persona, and behavior. Creates the strategic foundation for automated nurture programs.

Lesson 4 • Measuring Nurture Program Effectiveness

Tracks velocity, engagement, and conversion metrics across nurture programs. Enables data-driven optimization of content, timing, and segmentation.

Lesson 1 • Measuring Revenue Impact of AI

Connects AI lead gen activities to pipeline and revenue outcomes using attribution models. Justifies AI investment and guides budget allocation decisions.

Lesson 2 • Scaling AI Programs Across Teams

Designs enablement, documentation, and governance structures for team-wide AI adoption. Ensures consistent execution as programs grow in scope and headcount.

Lesson 3 • Building the AI Lead Gen Tech Stack

Evaluates and assembles the tools required for a full AI lead gen operation. Produces a documented, integrated tech stack aligned to business needs.

Lesson 4 • Future-Proofing Your AI Strategy

Identifies emerging AI capabilities and builds an adaptive strategy to stay ahead. Produces a rolling roadmap for continuous AI-driven lead gen improvement.