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Free Learn With Nathan Academy book

AI for Sales

Apply AI across prospecting, preparation, conversations, follow-up, and revenue operations.

The learner lacks enough time and relevant insight to research, personalize, qualify, and follow up consistently. From generic sales activity to timely, researched, human-sounding engagement that supports revenue.

The complete book is free. No fee or credit card required.

Author
Nathan Thiyagarajah
Chapters
15
Reading time
About 7 hours
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Book contents

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  1. 01
    How AI Can Help You Generate More LeadsFind fewer, better lead hypotheses by combining fit, observable business signals, and a lawful route to contact.
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  2. 02
    Build Your Ideal Customer Profile With AIBuild an ideal customer profile from customer success, delivery fit, economics, and disqualifying evidence—not founder preference.
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  3. 03
    Research a Prospect Before Calling ThemPrepare a concise prospect brief that connects verified business context to useful questions without pretending to know the buyer.
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  4. 04
    Write Personalized Cold Emails With AIWrite concise cold email that is relevant, truthful, permission-aware, and easy for the recipient to accept or decline.
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  5. 05
    AI Follow-Up That Doesn't Sound Like AIFollow up from the actual conversation state with one useful next action and a clear stop rule.
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  6. 06
    Build an AI Sales AssistantBuild a sales assistant around bounded jobs, approved sources, clear authority, and measurable seller value.
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  7. 07
    AI Cold Calling PreparationPrepare a respectful cold call with a relevant hypothesis, compliant contact handling, and questions that let the prospect correct you.
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  8. 08
    AI Sales Call SummariesTurn authorized call evidence into a precise record of needs, decisions, commitments, objections, and uncertainty.
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  9. 09
    AI Objection HandlingRespond to buyer concerns with diagnosis, evidence, limits, and a respectful next step instead of scripted pressure.
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  10. 10
    Writing Proposals With AIDraft proposals that trace buyer requirements to evidence, delivery scope, price authority, risks, and acceptance.
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  11. 11
    AI Lead QualificationQualify opportunities using observable fit, need, process, timing, and success evidence while protecting buyer dignity.
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  12. 12
    AI CRM Data EntryPrepare accurate CRM updates from sales evidence while preserving field definitions, record ownership, duplicates, and audit history.
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  13. 13
    AI Lead NurturingBuild nurture that matches buyer stage, provides useful information, and stops or changes when the relationship changes.
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  14. 14
    AI Pipeline AnalysisAnalyze pipeline with consistent stage evidence, transparent scenarios, data-quality checks, and operational actions.
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  15. 15
    Building an AI SDR AgentDesign an SDR agent as a supervised set of jobs with strict data, messaging, tool, consent, and escalation controls.
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