10. AI for Sales Professionals

Course Positioning

This course shows sales teams how to use AI to improve prospect research, personalization, outreach, discovery calls, objection handling, proposals, CRM hygiene, and follow-up. The focus is better preparation and consistency, not spam automation.

Learning outcomes

Use AI to research accounts and build useful prospect context.
Create personalized outreach without sounding generic or deceptive.
Prepare discovery questions, call plans, objection responses, and follow-up notes.
Improve CRM updates, proposals, and sales collateral.
Build a sales AI playbook that respects privacy, accuracy, and brand trust.

Expanded Topic-by-Topic Coverage

Module 1. AI across the sales cycle

Module focus: Prospecting, qualification, discovery, demos, proposals, follow-ups, renewals. Primary live activity or lab: Map the current sales workflow. Expected take-home output: Sales AI opportunity map.

Topics and coverage

Prospecting

What it means: define Prospecting clearly and connect it to the module focus: Prospecting, qualification, discovery, demos, proposals, follow-ups, renewals.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

qualification

What it means: define qualification clearly and connect it to the module focus: Prospecting, qualification, discovery, demos, proposals, follow-ups, renewals.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

discovery

What it means: define discovery clearly and connect it to the module focus: Prospecting, qualification, discovery, demos, proposals, follow-ups, renewals.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

demos

What it means: define demos clearly and connect it to the module focus: Prospecting, qualification, discovery, demos, proposals, follow-ups, renewals.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

proposals

What it means: define proposals clearly and connect it to the module focus: Prospecting, qualification, discovery, demos, proposals, follow-ups, renewals.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

follow-ups

What it means: define follow-ups clearly and connect it to the module focus: Prospecting, qualification, discovery, demos, proposals, follow-ups, renewals.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

renewals

What it means: define renewals clearly and connect it to the module focus: Prospecting, qualification, discovery, demos, proposals, follow-ups, renewals.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Map the current sales workflow.
Learners produce: Sales AI opportunity map.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 2. Account and prospect research

Module focus: Company research, trigger events, buying committee, pain hypotheses, source checking. Primary live activity or lab: Build a prospect brief from public information. Expected take-home output: Account brief.

Topics and coverage

Company research

What it means: show where Company research appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

trigger events

What it means: define trigger events clearly and connect it to the module focus: Company research, trigger events, buying committee, pain hypotheses, source checking.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

buying committee

What it means: define buying committee clearly and connect it to the module focus: Company research, trigger events, buying committee, pain hypotheses, source checking.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

pain hypotheses

What it means: define pain hypotheses clearly and connect it to the module focus: Company research, trigger events, buying committee, pain hypotheses, source checking.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

source checking

What it means: define source checking clearly and connect it to the module focus: Company research, trigger events, buying committee, pain hypotheses, source checking.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Build a prospect brief from public information.
Learners produce: Account brief.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 3. Personalized outreach

Module focus: Email/LinkedIn structure, relevance, concise writing, value proposition, tone. Primary live activity or lab: Create outreach variants for three personas. Expected take-home output: Outreach sequence.

Topics and coverage

Email/LinkedIn structure

What it means: define Email/LinkedIn structure clearly and connect it to the module focus: Email/LinkedIn structure, relevance, concise writing, value proposition, tone.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

relevance

What it means: define relevance clearly and connect it to the module focus: Email/LinkedIn structure, relevance, concise writing, value proposition, tone.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

concise writing

What it means: show where concise writing appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

value proposition

What it means: define value proposition clearly and connect it to the module focus: Email/LinkedIn structure, relevance, concise writing, value proposition, tone.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

tone

What it means: define tone clearly and connect it to the module focus: Email/LinkedIn structure, relevance, concise writing, value proposition, tone.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Create outreach variants for three personas.
Learners produce: Outreach sequence.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 4. Discovery and call preparation

Module focus: Call objectives, question ladders, problem diagnosis, qualification frameworks. Primary live activity or lab: Generate and role-play discovery questions. Expected take-home output: Call plan.

Topics and coverage

Call objectives

What it means: define Call objectives clearly and connect it to the module focus: Call objectives, question ladders, problem diagnosis, qualification frameworks.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

question ladders

What it means: define question ladders clearly and connect it to the module focus: Call objectives, question ladders, problem diagnosis, qualification frameworks.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

problem diagnosis

What it means: define problem diagnosis clearly and connect it to the module focus: Call objectives, question ladders, problem diagnosis, qualification frameworks.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

qualification frameworks

What it means: define qualification frameworks clearly and connect it to the module focus: Call objectives, question ladders, problem diagnosis, qualification frameworks.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Generate and role-play discovery questions.
Learners produce: Call plan.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 5. Objection handling

Module focus: Budget, timing, authority, trust, competitor, status quo, risk. Primary live activity or lab: Run an AI-assisted objection role-play. Expected take-home output: Objection library.

Topics and coverage

Budget

What it means: define Budget clearly and connect it to the module focus: Budget, timing, authority, trust, competitor, status quo, risk.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

timing

What it means: define timing clearly and connect it to the module focus: Budget, timing, authority, trust, competitor, status quo, risk.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

authority

What it means: define authority clearly and connect it to the module focus: Budget, timing, authority, trust, competitor, status quo, risk.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

trust

What it means: define trust clearly and connect it to the module focus: Budget, timing, authority, trust, competitor, status quo, risk.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

competitor

What it means: define competitor clearly and connect it to the module focus: Budget, timing, authority, trust, competitor, status quo, risk.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

status quo

What it means: define status quo clearly and connect it to the module focus: Budget, timing, authority, trust, competitor, status quo, risk.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

risk

What it means in this course: define risk in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what SDRs, account executives, sales managers, founders, business development teams must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

Practice and evidence of learning

Learners complete or discuss: Run an AI-assisted objection role-play.
Learners produce: Objection library.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 6. Proposals and follow-ups

Module focus: Meeting summaries, tailored proposals, mutual action plans, next steps. Primary live activity or lab: Turn call notes into a follow-up and proposal outline. Expected take-home output: Follow-up package.

Topics and coverage

Meeting summaries

What it means: show where Meeting summaries appears in the learner's real workflow and which parts are judgment-heavy versus draftable.
What to cover: current workflow, pain points, AI-assisted steps, human review checkpoints, quality standard, and ownership of the final decision.
Demonstration: convert one messy real-world input into a structured brief, draft, analysis, checklist, or next action.
Evidence of learning: learners produce a reusable template or playbook entry that can be used after the course.

tailored proposals

What it means: define tailored proposals clearly and connect it to the module focus: Meeting summaries, tailored proposals, mutual action plans, next steps.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

mutual action plans

What it means: define mutual action plans clearly and connect it to the module focus: Meeting summaries, tailored proposals, mutual action plans, next steps.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

next steps

What it means: define next steps clearly and connect it to the module focus: Meeting summaries, tailored proposals, mutual action plans, next steps.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Turn call notes into a follow-up and proposal outline.
Learners produce: Follow-up package.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 7. CRM and pipeline discipline

Module focus: Call notes, next actions, deal risks, forecast notes, handoff quality. Primary live activity or lab: Convert messy notes into CRM-ready fields. Expected take-home output: CRM update template.

Topics and coverage

Call notes

What it means: define Call notes clearly and connect it to the module focus: Call notes, next actions, deal risks, forecast notes, handoff quality.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

next actions

What it means: define next actions clearly and connect it to the module focus: Call notes, next actions, deal risks, forecast notes, handoff quality.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

deal risks

What it means in this course: define deal risks in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what SDRs, account executives, sales managers, founders, business development teams must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

forecast notes

What it means: define forecast notes clearly and connect it to the module focus: Call notes, next actions, deal risks, forecast notes, handoff quality.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

handoff quality

What it means: define handoff quality clearly and connect it to the module focus: Call notes, next actions, deal risks, forecast notes, handoff quality.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Convert messy notes into CRM-ready fields.
Learners produce: CRM update template.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Module 8. Trust and anti-spam principles

Module focus: Consent, accuracy, privacy, truthful personalization, avoiding fake familiarity. Primary live activity or lab: Audit outreach for trust violations. Expected take-home output: Sales AI ethics checklist.

Topics and coverage

What it means: define Consent clearly and connect it to the module focus: Consent, accuracy, privacy, truthful personalization, avoiding fake familiarity.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

accuracy

What it means: define accuracy clearly and connect it to the module focus: Consent, accuracy, privacy, truthful personalization, avoiding fake familiarity.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

privacy

What it means in this course: define privacy in operational terms, not as an abstract principle.
What to cover: sensitive data boundaries, affected stakeholders, approval paths, documentation, and what SDRs, account executives, sales managers, founders, business development teams must never delegate blindly to AI.
Use case: present one acceptable use, one borderline use, and one prohibited use, then ask learners to justify the classification.
Evidence of learning: learners add a risk control, review step, or escalation rule to their course project.

truthful personalization

What it means: define truthful personalization clearly and connect it to the module focus: Consent, accuracy, privacy, truthful personalization, avoiding fake familiarity.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

avoiding fake familiarity

What it means: define avoiding fake familiarity clearly and connect it to the module focus: Consent, accuracy, privacy, truthful personalization, avoiding fake familiarity.
What to cover: the core concept, why it matters, what good usage looks like, and where learners are likely to misunderstand it.
Demonstration: give one simple example, one realistic example, and one failure or limitation example.
Evidence of learning: learners explain the topic in their own words and apply it to a small artifact or decision.

Practice and evidence of learning

Learners complete or discuss: Audit outreach for trust violations.
Learners produce: Sales AI ethics checklist.
Instructor checks for accuracy, practical usefulness, clear assumptions, appropriate human review, and fit with the course audience.
Learners revise once after feedback so the module contributes to the final project, portfolio, or playbook.

Minimum coverage before moving on

Learners can explain the module vocabulary without relying on tool-generated text.
Learners have seen one worked example, one hands-on application, and one limitation or failure case.
Learners know what must be verified, what data must be protected, and who remains accountable for the output.

Labs, projects, and assessments

Lab 1: Create a verified account brief and persona-specific pain hypothesis.
Lab 2: Build a three-touch outreach sequence and critique it for specificity.
Lab 3: Role-play discovery and objections with AI as buyer.
Capstone: Sales playbook for one offer including ICP, research template, outreach, call plan, objection library, and CRM workflow.

Evaluation approach

25% account research brief.
25% outreach sequence quality.
20% discovery and objection handling.
30% final sales AI playbook.

Recommended tools and materials

AI assistant, CRM sandbox or spreadsheet, public company websites, LinkedIn-style profiles, email tools, meeting notes templates.
Optional: call transcription tool, proposal generator, sales intelligence tools if licensed.

Safety, ethics, and governance emphasis

Avoid sending unverified claims or pretending to know someone personally when AI inferred it.
Respect privacy and anti-spam laws relevant to the geography.
Human review is required for all outbound messages and commercial commitments.

Delivery notes

This course works well as role-play-heavy training.
Use actual company positioning and objection data when delivering inside an organization.

Instructor Build Checklist

Prepare one short demo for each module and one learner activity that creates a saved artifact.
Prepare examples that match the audience, local context, and likely tools learners can access.
Add a verification step to every AI-generated output: factual check, source check, data sensitivity check, and quality review.
Keep a running portfolio folder so each module contributes to the final project or learner playbook.
Reserve time for reflection on what the learner did, what AI did, what was checked, and what remains uncertain.