Mastering Salesforce Einstein: The Complete AI-Powered Sales Automation Blueprint
You're under pressure. Quotas are climbing, deals are stalling, and your team is buried under manual processes that AI should have solved by now. You're not just expected to sell smarter - you're expected to lead the AI transformation, even if no one's given you the roadmap. Most Salesforce users treat Einstein like a magic button they click and hope. But the top performers? They’re designing intelligent workflows that predict objections, prioritise high-intent leads, and automate follow-ups with surgical precision - all before the customer even responds. Mastering Salesforce Einstein: The Complete AI-Powered Sales Automation Blueprint is that roadmap. This is not theory. It's the field-tested system that turns Salesforce admins, sales ops leaders, and CRM architects into AI-driven revenue strategists - with a documented path from setup to board-level impact in under 30 days. One recent learner, Priya M., Sales Operations Manager at a global SaaS firm, applied the framework to retrain her Einstein lead-scoring model. Within two weeks, her team saw a 39% improvement in conversion from MQL to SQL - and presented a data-backed proposal to executive leadership that fast-tracked her promotion. This course isn’t about learning AI. It’s about controlling it - to shape predictions, enforce ethical data hygiene, and deploy automation that scales with precision. You’ll go from reactive problem-solver to proactive architect of intelligent revenue engines. No more guessing which features matter. No more sifting through Salesforce’s ever-changing interface. You get a direct line to what works, what’s stable, and what drives real pipeline acceleration. Here’s how this course is structured to help you get there.Course Format & Delivery Details Your Investment, Protected and Transparent
This is a self-paced, on-demand learning experience with immediate online access upon enrollment. There are no fixed dates, no live sessions, and no time constraints - you progress at your own speed, on your own schedule. Most learners complete the core curriculum in 12 to 18 hours and begin implementing high-impact automations within the first week. The fastest see measurable improvements in lead routing accuracy and forecast confidence in under five days. You receive lifetime access to all course materials, including every update released in the future at no additional cost. As Salesforce evolves its Einstein capabilities, you’ll get refined workflows, updated configuration guides, and advanced use cases - automatically. Designed for Professionals, Not Students
Access is available 24/7 from any device, fully optimised for mobile. Review critical configuration checklists on your phone during downtime, or download detailed implementation templates to your laptop for in-depth work. You are supported throughout by direct instructor guidance via a private channel. Questions are answered within 24 business hours with precise, role-specific feedback - whether you’re a Salesforce administrator troubleshooting prediction thresholds or a sales leader building a rollout plan for your region. Upon successful completion, you’ll earn a Certificate of Completion issued by The Art of Service - a globally recognised credential cited by professionals in over 140 countries. This certification is directly verifiable, LinkedIn-compatible, and designed to signal mastery, not just participation. Zero Risk. Maximum Certainty.
Pricing is straightforward with no hidden fees. The total cost covers full access, all supplemental resources, updates, and your certification - one payment, zero surprises. We accept Visa, Mastercard, and PayPal for secure, frictionless enrollment. If this course doesn’t meet your expectations, you’re covered by our 30-day “satisfied or refunded” guarantee. If you complete the first three modules and don’t feel confident in building predictive scoring models, simply request a full refund - no forms, no debates. After enrollment, you’ll receive a confirmation email. Your access details and portal login will be sent separately once your course materials are fully provisioned - ensuring a clean, error-free start. This Works Even If...
- You’ve never configured an Einstein Prediction before
- Your org has low data cleanliness or inconsistent field usage
- You’re not a developer or data scientist
- Your Salesforce edition has limited Einstein features enabled
- You work in a regulated industry with strict data governance
This system works even if you're starting from scratch because it’s built on modular, permission-safe implementation patterns used by enterprise sales ops teams. Real examples are drawn from financial services, healthcare SaaS, and manufacturing - industries where data sensitivity and compliance demands are highest. Global learners from Salesforce consulting firms, Fortune 500 ops teams, and high-growth startups have used this blueprint to pass certification exams, win internal AI initiatives, and get promoted into strategic roles.
Extensive and Detailed Course Curriculum
Module 1: Foundations of AI-Driven Sales - Understanding the shift from manual CRM to intelligent automation
- Where Einstein fits in Salesforce’s AI ecosystem
- Core terminology: Prediction, scoring, intent, inference, confidence threshold
- Differentiating Einstein Lead Scoring, Opportunity Scoring, and Account Insights
- Identifying high-impact use cases by role and industry
- Common misconceptions about AI in CRM
- The five pillars of a successful Einstein deployment
- Evaluating your org’s AI-readiness: data, governance, and adoption
- Aligning AI goals with revenue leadership objectives
- Developing a cross-functional launch team charter
Module 2: Data Integrity and Governance for AI - Why 80% of Einstein failures stem from data quality
- Identifying critical fields required for lead and opportunity scoring
- Standardising picklists and eliminating free-text chaos
- Validating email, phone, and company data at point of entry
- Mapping behavioural data sources: website visits, email engagement, call logs
- Using validation rules to enforce data completeness
- Creating custom fields to capture missing predictors
- Managing data decay over time with hygiene workflows
- Setting data ownership and stewardship policies
- Configuring GDPR and CCPA-safe field exclusions for AI training
Module 3: Einstein Lead Scoring Setup & Calibration - Enabling Einstein Lead Scoring in your Salesforce org
- Selecting training data windows: 6 vs. 12 vs. 18 months
- Choosing conversion definitions: closed-won, revenue-qualified, deal size thresholds
- Configuring lead source weighting and channel maturity factors
- Understanding feature importance reports and model transparency
- Tuning prediction thresholds to balance sensitivity and false positives
- Setting up high, medium, and low intent score bands
- Integrating lead score into assignment rules and routing logic
- Setting up automated alerts for high-score lead spikes
- Validating model accuracy with test datasets and holdouts
Module 4: Einstein Opportunity Scoring Implementation - Determining which opportunities to include in training
- Defining the “won” outcome: stage, age, and probability rules
- Selecting predictor fields: owner activity, stage duration, deal size
- Incorporating communication frequency and response time
- Configuring confidence intervals and risk flags
- Displaying opportunity scores in Lightning pages and Kanban views
- Using scores to trigger coaching nudges and manager reviews
- Building reports to track forecast accuracy improvements
- Creating dynamic alerts for deals at risk of slipping
- Linking scores to approval workflows for discount overrides
Module 5: Advanced Einstein Analytics and Forecasting - Extending Einstein with custom predictive fields
- Building dynamic dashboards with score trend lines
- Analysing prediction accuracy by sales rep and region
- Using time-series forecasting to project pipeline growth
- Identifying outlier reps with consistently high win rates
- Correlating score changes with specific engagement actions
- Generating weekly AI performance summaries for leadership
- Building cohort analysis for lead-to-customer lifecycle
- Measuring lead velocity before and after Einstein deployment
- Calculating reduction in sales cycle length using score data
Module 6: Automating Sales Workflows with Einstein - Designing lead routing rules based on score and territory
- Automatically assigning top-scoring leads to SDRs with highest conversion rates
- Setting up follow-up tasks for medium-intent leads
- Triggering nurture campaigns for re-engagement
- Using flows to escalate high-value opportunities to senior AMs
- Automating reminder emails for stalled opportunities with low scores
- Generating one-click email templates with score context
- Integrating score updates into Slack or Teams notifications
- Creating automatic team huddles for clustered opportunity risks
- Embedding score thresholds in approval processes
Module 7: Customising User Experience and Adoption - Designing intuitive Lightning page layouts for score visibility
- Adding score badges to lead and opportunity records
- Creating hover tooltips explaining score drivers
- Training reps to act on scores without over-reliance
- Developing role-specific playbooks based on score bands
- Building manager dashboards for coaching intervention
- Using gamification to reward high-scorers and accurate predictors
- Creating “AI adoption” KPIs for team leads
- Hosting internal workshops using your deployment as a case study
- Generating user feedback loops to improve model relevance
Module 8: AI Ethics, Bias Detection, and Model Transparency - Identifying potential sources of bias in training data
- Using fairness reports to audit lead score distribution by industry
- Excluding protected characteristics from model inputs
- Conducting regular model validation audits
- Explaining predictions to stakeholders without technical jargon
- Setting up a model review calendar every 90 days
- Documenting model decisions for compliance teams
- Handling rep objections to AI-based scoring
- Ensuring algorithmic consistency across geographies
- Preparing for external AI governance audits
Module 9: Einstein Discovery for Sales Leaders - Accessing Einstein Discovery through Salesforce Analytics
- Importing custom datasets from external CRMs or marketing platforms
- Building no-code predictive models for custom outcomes
- Identifying hidden drivers of sales success using AI insights
- Running scenario simulation: “What if we double email response time?”
- Exporting model recommendations as actionable strategies
- Deploying discovery models as Salesforce formula fields
- Setting up automated reports based on discovery outputs
- Validating discovery model against real-world results
- Presenting discovery findings to executive stakeholders
Module 10: Einstein Activity Capture and Intent Signals - Configuring Einstein Activity Capture for Gmail and Outlook
- Mapping email and calendar data to Salesforce objects
- Enabling automated logging of customer interactions
- Identifying intent spikes based on communication frequency
- Building alerts for sudden increases in stakeholder engagement
- Linking activity data to opportunity scoring models
- Filtering out noise from internal and non-relevant emails
- Setting up activity trend dashboards by account
- Using AI to prioritise follow-ups based on recency and intensity
- Integrating activity signals into playbooks for next-best actions
Module 11: Cross-Cloud AI Integration - Leveraging Marketing Cloud signals in Salesforce AI models
- Passing lead engagement scores from Pardot to Einstein
- Using Commerce Cloud purchase history for upsell predictions
- Integrating Service Cloud case resolution times with account health
- Building unified customer health scores using multiple clouds
- Setting up data sharing between clouds with zero-code connectors
- Ensuring consistent identity resolution across systems
- Using Mulesoft to synchronise AI outputs between platforms
- Creating shared KPIs for AI-driven customer journeys
- Aligning AI initiatives with broader digital transformation programs
Module 12: Building Board-Ready AI Proposals - Structuring a compelling AI business case for leadership
- Calculating projected ROI from improved conversion rates
- Estimating time saved from automation at scale
- Mapping Einstein adoption to revenue efficiency metrics
- Creating before-and-after dashboards for executive review
- Drafting risk mitigation plans for AI rollout
- Designing phased implementation timelines
- Identifying key stakeholders and their success criteria
- Preparing slide decks using real Einstein performance data
- Practising Q&A responses for technical and strategic challenges
Module 13: Certification and Future-Proofing Your Skills - Preparing for Salesforce certification exams with Einstein focus
- Mapping course topics to Salesforce official certification guides
- Building a personal portfolio of AI implementation projects
- Documenting your Certificate of Completion for LinkedIn
- Joining verified alumni networks and expert forums
- Tracking new Einstein features through release notes
- Setting up automatic update alerts from Salesforce Trust
- Participating in beta programs for upcoming AI features
- Advancing into roles: Sales Ops Architect, CRM AI Strategist, Revenue Scientist
- Designing your personal 12-month upskilling roadmap
Module 14: Final Project - Build Your AI-Powered Automation Blueprint - Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification
Module 1: Foundations of AI-Driven Sales - Understanding the shift from manual CRM to intelligent automation
- Where Einstein fits in Salesforce’s AI ecosystem
- Core terminology: Prediction, scoring, intent, inference, confidence threshold
- Differentiating Einstein Lead Scoring, Opportunity Scoring, and Account Insights
- Identifying high-impact use cases by role and industry
- Common misconceptions about AI in CRM
- The five pillars of a successful Einstein deployment
- Evaluating your org’s AI-readiness: data, governance, and adoption
- Aligning AI goals with revenue leadership objectives
- Developing a cross-functional launch team charter
Module 2: Data Integrity and Governance for AI - Why 80% of Einstein failures stem from data quality
- Identifying critical fields required for lead and opportunity scoring
- Standardising picklists and eliminating free-text chaos
- Validating email, phone, and company data at point of entry
- Mapping behavioural data sources: website visits, email engagement, call logs
- Using validation rules to enforce data completeness
- Creating custom fields to capture missing predictors
- Managing data decay over time with hygiene workflows
- Setting data ownership and stewardship policies
- Configuring GDPR and CCPA-safe field exclusions for AI training
Module 3: Einstein Lead Scoring Setup & Calibration - Enabling Einstein Lead Scoring in your Salesforce org
- Selecting training data windows: 6 vs. 12 vs. 18 months
- Choosing conversion definitions: closed-won, revenue-qualified, deal size thresholds
- Configuring lead source weighting and channel maturity factors
- Understanding feature importance reports and model transparency
- Tuning prediction thresholds to balance sensitivity and false positives
- Setting up high, medium, and low intent score bands
- Integrating lead score into assignment rules and routing logic
- Setting up automated alerts for high-score lead spikes
- Validating model accuracy with test datasets and holdouts
Module 4: Einstein Opportunity Scoring Implementation - Determining which opportunities to include in training
- Defining the “won” outcome: stage, age, and probability rules
- Selecting predictor fields: owner activity, stage duration, deal size
- Incorporating communication frequency and response time
- Configuring confidence intervals and risk flags
- Displaying opportunity scores in Lightning pages and Kanban views
- Using scores to trigger coaching nudges and manager reviews
- Building reports to track forecast accuracy improvements
- Creating dynamic alerts for deals at risk of slipping
- Linking scores to approval workflows for discount overrides
Module 5: Advanced Einstein Analytics and Forecasting - Extending Einstein with custom predictive fields
- Building dynamic dashboards with score trend lines
- Analysing prediction accuracy by sales rep and region
- Using time-series forecasting to project pipeline growth
- Identifying outlier reps with consistently high win rates
- Correlating score changes with specific engagement actions
- Generating weekly AI performance summaries for leadership
- Building cohort analysis for lead-to-customer lifecycle
- Measuring lead velocity before and after Einstein deployment
- Calculating reduction in sales cycle length using score data
Module 6: Automating Sales Workflows with Einstein - Designing lead routing rules based on score and territory
- Automatically assigning top-scoring leads to SDRs with highest conversion rates
- Setting up follow-up tasks for medium-intent leads
- Triggering nurture campaigns for re-engagement
- Using flows to escalate high-value opportunities to senior AMs
- Automating reminder emails for stalled opportunities with low scores
- Generating one-click email templates with score context
- Integrating score updates into Slack or Teams notifications
- Creating automatic team huddles for clustered opportunity risks
- Embedding score thresholds in approval processes
Module 7: Customising User Experience and Adoption - Designing intuitive Lightning page layouts for score visibility
- Adding score badges to lead and opportunity records
- Creating hover tooltips explaining score drivers
- Training reps to act on scores without over-reliance
- Developing role-specific playbooks based on score bands
- Building manager dashboards for coaching intervention
- Using gamification to reward high-scorers and accurate predictors
- Creating “AI adoption” KPIs for team leads
- Hosting internal workshops using your deployment as a case study
- Generating user feedback loops to improve model relevance
Module 8: AI Ethics, Bias Detection, and Model Transparency - Identifying potential sources of bias in training data
- Using fairness reports to audit lead score distribution by industry
- Excluding protected characteristics from model inputs
- Conducting regular model validation audits
- Explaining predictions to stakeholders without technical jargon
- Setting up a model review calendar every 90 days
- Documenting model decisions for compliance teams
- Handling rep objections to AI-based scoring
- Ensuring algorithmic consistency across geographies
- Preparing for external AI governance audits
Module 9: Einstein Discovery for Sales Leaders - Accessing Einstein Discovery through Salesforce Analytics
- Importing custom datasets from external CRMs or marketing platforms
- Building no-code predictive models for custom outcomes
- Identifying hidden drivers of sales success using AI insights
- Running scenario simulation: “What if we double email response time?”
- Exporting model recommendations as actionable strategies
- Deploying discovery models as Salesforce formula fields
- Setting up automated reports based on discovery outputs
- Validating discovery model against real-world results
- Presenting discovery findings to executive stakeholders
Module 10: Einstein Activity Capture and Intent Signals - Configuring Einstein Activity Capture for Gmail and Outlook
- Mapping email and calendar data to Salesforce objects
- Enabling automated logging of customer interactions
- Identifying intent spikes based on communication frequency
- Building alerts for sudden increases in stakeholder engagement
- Linking activity data to opportunity scoring models
- Filtering out noise from internal and non-relevant emails
- Setting up activity trend dashboards by account
- Using AI to prioritise follow-ups based on recency and intensity
- Integrating activity signals into playbooks for next-best actions
Module 11: Cross-Cloud AI Integration - Leveraging Marketing Cloud signals in Salesforce AI models
- Passing lead engagement scores from Pardot to Einstein
- Using Commerce Cloud purchase history for upsell predictions
- Integrating Service Cloud case resolution times with account health
- Building unified customer health scores using multiple clouds
- Setting up data sharing between clouds with zero-code connectors
- Ensuring consistent identity resolution across systems
- Using Mulesoft to synchronise AI outputs between platforms
- Creating shared KPIs for AI-driven customer journeys
- Aligning AI initiatives with broader digital transformation programs
Module 12: Building Board-Ready AI Proposals - Structuring a compelling AI business case for leadership
- Calculating projected ROI from improved conversion rates
- Estimating time saved from automation at scale
- Mapping Einstein adoption to revenue efficiency metrics
- Creating before-and-after dashboards for executive review
- Drafting risk mitigation plans for AI rollout
- Designing phased implementation timelines
- Identifying key stakeholders and their success criteria
- Preparing slide decks using real Einstein performance data
- Practising Q&A responses for technical and strategic challenges
Module 13: Certification and Future-Proofing Your Skills - Preparing for Salesforce certification exams with Einstein focus
- Mapping course topics to Salesforce official certification guides
- Building a personal portfolio of AI implementation projects
- Documenting your Certificate of Completion for LinkedIn
- Joining verified alumni networks and expert forums
- Tracking new Einstein features through release notes
- Setting up automatic update alerts from Salesforce Trust
- Participating in beta programs for upcoming AI features
- Advancing into roles: Sales Ops Architect, CRM AI Strategist, Revenue Scientist
- Designing your personal 12-month upskilling roadmap
Module 14: Final Project - Build Your AI-Powered Automation Blueprint - Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification
- Why 80% of Einstein failures stem from data quality
- Identifying critical fields required for lead and opportunity scoring
- Standardising picklists and eliminating free-text chaos
- Validating email, phone, and company data at point of entry
- Mapping behavioural data sources: website visits, email engagement, call logs
- Using validation rules to enforce data completeness
- Creating custom fields to capture missing predictors
- Managing data decay over time with hygiene workflows
- Setting data ownership and stewardship policies
- Configuring GDPR and CCPA-safe field exclusions for AI training
Module 3: Einstein Lead Scoring Setup & Calibration - Enabling Einstein Lead Scoring in your Salesforce org
- Selecting training data windows: 6 vs. 12 vs. 18 months
- Choosing conversion definitions: closed-won, revenue-qualified, deal size thresholds
- Configuring lead source weighting and channel maturity factors
- Understanding feature importance reports and model transparency
- Tuning prediction thresholds to balance sensitivity and false positives
- Setting up high, medium, and low intent score bands
- Integrating lead score into assignment rules and routing logic
- Setting up automated alerts for high-score lead spikes
- Validating model accuracy with test datasets and holdouts
Module 4: Einstein Opportunity Scoring Implementation - Determining which opportunities to include in training
- Defining the “won” outcome: stage, age, and probability rules
- Selecting predictor fields: owner activity, stage duration, deal size
- Incorporating communication frequency and response time
- Configuring confidence intervals and risk flags
- Displaying opportunity scores in Lightning pages and Kanban views
- Using scores to trigger coaching nudges and manager reviews
- Building reports to track forecast accuracy improvements
- Creating dynamic alerts for deals at risk of slipping
- Linking scores to approval workflows for discount overrides
Module 5: Advanced Einstein Analytics and Forecasting - Extending Einstein with custom predictive fields
- Building dynamic dashboards with score trend lines
- Analysing prediction accuracy by sales rep and region
- Using time-series forecasting to project pipeline growth
- Identifying outlier reps with consistently high win rates
- Correlating score changes with specific engagement actions
- Generating weekly AI performance summaries for leadership
- Building cohort analysis for lead-to-customer lifecycle
- Measuring lead velocity before and after Einstein deployment
- Calculating reduction in sales cycle length using score data
Module 6: Automating Sales Workflows with Einstein - Designing lead routing rules based on score and territory
- Automatically assigning top-scoring leads to SDRs with highest conversion rates
- Setting up follow-up tasks for medium-intent leads
- Triggering nurture campaigns for re-engagement
- Using flows to escalate high-value opportunities to senior AMs
- Automating reminder emails for stalled opportunities with low scores
- Generating one-click email templates with score context
- Integrating score updates into Slack or Teams notifications
- Creating automatic team huddles for clustered opportunity risks
- Embedding score thresholds in approval processes
Module 7: Customising User Experience and Adoption - Designing intuitive Lightning page layouts for score visibility
- Adding score badges to lead and opportunity records
- Creating hover tooltips explaining score drivers
- Training reps to act on scores without over-reliance
- Developing role-specific playbooks based on score bands
- Building manager dashboards for coaching intervention
- Using gamification to reward high-scorers and accurate predictors
- Creating “AI adoption” KPIs for team leads
- Hosting internal workshops using your deployment as a case study
- Generating user feedback loops to improve model relevance
Module 8: AI Ethics, Bias Detection, and Model Transparency - Identifying potential sources of bias in training data
- Using fairness reports to audit lead score distribution by industry
- Excluding protected characteristics from model inputs
- Conducting regular model validation audits
- Explaining predictions to stakeholders without technical jargon
- Setting up a model review calendar every 90 days
- Documenting model decisions for compliance teams
- Handling rep objections to AI-based scoring
- Ensuring algorithmic consistency across geographies
- Preparing for external AI governance audits
Module 9: Einstein Discovery for Sales Leaders - Accessing Einstein Discovery through Salesforce Analytics
- Importing custom datasets from external CRMs or marketing platforms
- Building no-code predictive models for custom outcomes
- Identifying hidden drivers of sales success using AI insights
- Running scenario simulation: “What if we double email response time?”
- Exporting model recommendations as actionable strategies
- Deploying discovery models as Salesforce formula fields
- Setting up automated reports based on discovery outputs
- Validating discovery model against real-world results
- Presenting discovery findings to executive stakeholders
Module 10: Einstein Activity Capture and Intent Signals - Configuring Einstein Activity Capture for Gmail and Outlook
- Mapping email and calendar data to Salesforce objects
- Enabling automated logging of customer interactions
- Identifying intent spikes based on communication frequency
- Building alerts for sudden increases in stakeholder engagement
- Linking activity data to opportunity scoring models
- Filtering out noise from internal and non-relevant emails
- Setting up activity trend dashboards by account
- Using AI to prioritise follow-ups based on recency and intensity
- Integrating activity signals into playbooks for next-best actions
Module 11: Cross-Cloud AI Integration - Leveraging Marketing Cloud signals in Salesforce AI models
- Passing lead engagement scores from Pardot to Einstein
- Using Commerce Cloud purchase history for upsell predictions
- Integrating Service Cloud case resolution times with account health
- Building unified customer health scores using multiple clouds
- Setting up data sharing between clouds with zero-code connectors
- Ensuring consistent identity resolution across systems
- Using Mulesoft to synchronise AI outputs between platforms
- Creating shared KPIs for AI-driven customer journeys
- Aligning AI initiatives with broader digital transformation programs
Module 12: Building Board-Ready AI Proposals - Structuring a compelling AI business case for leadership
- Calculating projected ROI from improved conversion rates
- Estimating time saved from automation at scale
- Mapping Einstein adoption to revenue efficiency metrics
- Creating before-and-after dashboards for executive review
- Drafting risk mitigation plans for AI rollout
- Designing phased implementation timelines
- Identifying key stakeholders and their success criteria
- Preparing slide decks using real Einstein performance data
- Practising Q&A responses for technical and strategic challenges
Module 13: Certification and Future-Proofing Your Skills - Preparing for Salesforce certification exams with Einstein focus
- Mapping course topics to Salesforce official certification guides
- Building a personal portfolio of AI implementation projects
- Documenting your Certificate of Completion for LinkedIn
- Joining verified alumni networks and expert forums
- Tracking new Einstein features through release notes
- Setting up automatic update alerts from Salesforce Trust
- Participating in beta programs for upcoming AI features
- Advancing into roles: Sales Ops Architect, CRM AI Strategist, Revenue Scientist
- Designing your personal 12-month upskilling roadmap
Module 14: Final Project - Build Your AI-Powered Automation Blueprint - Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification
- Determining which opportunities to include in training
- Defining the “won” outcome: stage, age, and probability rules
- Selecting predictor fields: owner activity, stage duration, deal size
- Incorporating communication frequency and response time
- Configuring confidence intervals and risk flags
- Displaying opportunity scores in Lightning pages and Kanban views
- Using scores to trigger coaching nudges and manager reviews
- Building reports to track forecast accuracy improvements
- Creating dynamic alerts for deals at risk of slipping
- Linking scores to approval workflows for discount overrides
Module 5: Advanced Einstein Analytics and Forecasting - Extending Einstein with custom predictive fields
- Building dynamic dashboards with score trend lines
- Analysing prediction accuracy by sales rep and region
- Using time-series forecasting to project pipeline growth
- Identifying outlier reps with consistently high win rates
- Correlating score changes with specific engagement actions
- Generating weekly AI performance summaries for leadership
- Building cohort analysis for lead-to-customer lifecycle
- Measuring lead velocity before and after Einstein deployment
- Calculating reduction in sales cycle length using score data
Module 6: Automating Sales Workflows with Einstein - Designing lead routing rules based on score and territory
- Automatically assigning top-scoring leads to SDRs with highest conversion rates
- Setting up follow-up tasks for medium-intent leads
- Triggering nurture campaigns for re-engagement
- Using flows to escalate high-value opportunities to senior AMs
- Automating reminder emails for stalled opportunities with low scores
- Generating one-click email templates with score context
- Integrating score updates into Slack or Teams notifications
- Creating automatic team huddles for clustered opportunity risks
- Embedding score thresholds in approval processes
Module 7: Customising User Experience and Adoption - Designing intuitive Lightning page layouts for score visibility
- Adding score badges to lead and opportunity records
- Creating hover tooltips explaining score drivers
- Training reps to act on scores without over-reliance
- Developing role-specific playbooks based on score bands
- Building manager dashboards for coaching intervention
- Using gamification to reward high-scorers and accurate predictors
- Creating “AI adoption” KPIs for team leads
- Hosting internal workshops using your deployment as a case study
- Generating user feedback loops to improve model relevance
Module 8: AI Ethics, Bias Detection, and Model Transparency - Identifying potential sources of bias in training data
- Using fairness reports to audit lead score distribution by industry
- Excluding protected characteristics from model inputs
- Conducting regular model validation audits
- Explaining predictions to stakeholders without technical jargon
- Setting up a model review calendar every 90 days
- Documenting model decisions for compliance teams
- Handling rep objections to AI-based scoring
- Ensuring algorithmic consistency across geographies
- Preparing for external AI governance audits
Module 9: Einstein Discovery for Sales Leaders - Accessing Einstein Discovery through Salesforce Analytics
- Importing custom datasets from external CRMs or marketing platforms
- Building no-code predictive models for custom outcomes
- Identifying hidden drivers of sales success using AI insights
- Running scenario simulation: “What if we double email response time?”
- Exporting model recommendations as actionable strategies
- Deploying discovery models as Salesforce formula fields
- Setting up automated reports based on discovery outputs
- Validating discovery model against real-world results
- Presenting discovery findings to executive stakeholders
Module 10: Einstein Activity Capture and Intent Signals - Configuring Einstein Activity Capture for Gmail and Outlook
- Mapping email and calendar data to Salesforce objects
- Enabling automated logging of customer interactions
- Identifying intent spikes based on communication frequency
- Building alerts for sudden increases in stakeholder engagement
- Linking activity data to opportunity scoring models
- Filtering out noise from internal and non-relevant emails
- Setting up activity trend dashboards by account
- Using AI to prioritise follow-ups based on recency and intensity
- Integrating activity signals into playbooks for next-best actions
Module 11: Cross-Cloud AI Integration - Leveraging Marketing Cloud signals in Salesforce AI models
- Passing lead engagement scores from Pardot to Einstein
- Using Commerce Cloud purchase history for upsell predictions
- Integrating Service Cloud case resolution times with account health
- Building unified customer health scores using multiple clouds
- Setting up data sharing between clouds with zero-code connectors
- Ensuring consistent identity resolution across systems
- Using Mulesoft to synchronise AI outputs between platforms
- Creating shared KPIs for AI-driven customer journeys
- Aligning AI initiatives with broader digital transformation programs
Module 12: Building Board-Ready AI Proposals - Structuring a compelling AI business case for leadership
- Calculating projected ROI from improved conversion rates
- Estimating time saved from automation at scale
- Mapping Einstein adoption to revenue efficiency metrics
- Creating before-and-after dashboards for executive review
- Drafting risk mitigation plans for AI rollout
- Designing phased implementation timelines
- Identifying key stakeholders and their success criteria
- Preparing slide decks using real Einstein performance data
- Practising Q&A responses for technical and strategic challenges
Module 13: Certification and Future-Proofing Your Skills - Preparing for Salesforce certification exams with Einstein focus
- Mapping course topics to Salesforce official certification guides
- Building a personal portfolio of AI implementation projects
- Documenting your Certificate of Completion for LinkedIn
- Joining verified alumni networks and expert forums
- Tracking new Einstein features through release notes
- Setting up automatic update alerts from Salesforce Trust
- Participating in beta programs for upcoming AI features
- Advancing into roles: Sales Ops Architect, CRM AI Strategist, Revenue Scientist
- Designing your personal 12-month upskilling roadmap
Module 14: Final Project - Build Your AI-Powered Automation Blueprint - Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification
- Designing lead routing rules based on score and territory
- Automatically assigning top-scoring leads to SDRs with highest conversion rates
- Setting up follow-up tasks for medium-intent leads
- Triggering nurture campaigns for re-engagement
- Using flows to escalate high-value opportunities to senior AMs
- Automating reminder emails for stalled opportunities with low scores
- Generating one-click email templates with score context
- Integrating score updates into Slack or Teams notifications
- Creating automatic team huddles for clustered opportunity risks
- Embedding score thresholds in approval processes
Module 7: Customising User Experience and Adoption - Designing intuitive Lightning page layouts for score visibility
- Adding score badges to lead and opportunity records
- Creating hover tooltips explaining score drivers
- Training reps to act on scores without over-reliance
- Developing role-specific playbooks based on score bands
- Building manager dashboards for coaching intervention
- Using gamification to reward high-scorers and accurate predictors
- Creating “AI adoption” KPIs for team leads
- Hosting internal workshops using your deployment as a case study
- Generating user feedback loops to improve model relevance
Module 8: AI Ethics, Bias Detection, and Model Transparency - Identifying potential sources of bias in training data
- Using fairness reports to audit lead score distribution by industry
- Excluding protected characteristics from model inputs
- Conducting regular model validation audits
- Explaining predictions to stakeholders without technical jargon
- Setting up a model review calendar every 90 days
- Documenting model decisions for compliance teams
- Handling rep objections to AI-based scoring
- Ensuring algorithmic consistency across geographies
- Preparing for external AI governance audits
Module 9: Einstein Discovery for Sales Leaders - Accessing Einstein Discovery through Salesforce Analytics
- Importing custom datasets from external CRMs or marketing platforms
- Building no-code predictive models for custom outcomes
- Identifying hidden drivers of sales success using AI insights
- Running scenario simulation: “What if we double email response time?”
- Exporting model recommendations as actionable strategies
- Deploying discovery models as Salesforce formula fields
- Setting up automated reports based on discovery outputs
- Validating discovery model against real-world results
- Presenting discovery findings to executive stakeholders
Module 10: Einstein Activity Capture and Intent Signals - Configuring Einstein Activity Capture for Gmail and Outlook
- Mapping email and calendar data to Salesforce objects
- Enabling automated logging of customer interactions
- Identifying intent spikes based on communication frequency
- Building alerts for sudden increases in stakeholder engagement
- Linking activity data to opportunity scoring models
- Filtering out noise from internal and non-relevant emails
- Setting up activity trend dashboards by account
- Using AI to prioritise follow-ups based on recency and intensity
- Integrating activity signals into playbooks for next-best actions
Module 11: Cross-Cloud AI Integration - Leveraging Marketing Cloud signals in Salesforce AI models
- Passing lead engagement scores from Pardot to Einstein
- Using Commerce Cloud purchase history for upsell predictions
- Integrating Service Cloud case resolution times with account health
- Building unified customer health scores using multiple clouds
- Setting up data sharing between clouds with zero-code connectors
- Ensuring consistent identity resolution across systems
- Using Mulesoft to synchronise AI outputs between platforms
- Creating shared KPIs for AI-driven customer journeys
- Aligning AI initiatives with broader digital transformation programs
Module 12: Building Board-Ready AI Proposals - Structuring a compelling AI business case for leadership
- Calculating projected ROI from improved conversion rates
- Estimating time saved from automation at scale
- Mapping Einstein adoption to revenue efficiency metrics
- Creating before-and-after dashboards for executive review
- Drafting risk mitigation plans for AI rollout
- Designing phased implementation timelines
- Identifying key stakeholders and their success criteria
- Preparing slide decks using real Einstein performance data
- Practising Q&A responses for technical and strategic challenges
Module 13: Certification and Future-Proofing Your Skills - Preparing for Salesforce certification exams with Einstein focus
- Mapping course topics to Salesforce official certification guides
- Building a personal portfolio of AI implementation projects
- Documenting your Certificate of Completion for LinkedIn
- Joining verified alumni networks and expert forums
- Tracking new Einstein features through release notes
- Setting up automatic update alerts from Salesforce Trust
- Participating in beta programs for upcoming AI features
- Advancing into roles: Sales Ops Architect, CRM AI Strategist, Revenue Scientist
- Designing your personal 12-month upskilling roadmap
Module 14: Final Project - Build Your AI-Powered Automation Blueprint - Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification
- Identifying potential sources of bias in training data
- Using fairness reports to audit lead score distribution by industry
- Excluding protected characteristics from model inputs
- Conducting regular model validation audits
- Explaining predictions to stakeholders without technical jargon
- Setting up a model review calendar every 90 days
- Documenting model decisions for compliance teams
- Handling rep objections to AI-based scoring
- Ensuring algorithmic consistency across geographies
- Preparing for external AI governance audits
Module 9: Einstein Discovery for Sales Leaders - Accessing Einstein Discovery through Salesforce Analytics
- Importing custom datasets from external CRMs or marketing platforms
- Building no-code predictive models for custom outcomes
- Identifying hidden drivers of sales success using AI insights
- Running scenario simulation: “What if we double email response time?”
- Exporting model recommendations as actionable strategies
- Deploying discovery models as Salesforce formula fields
- Setting up automated reports based on discovery outputs
- Validating discovery model against real-world results
- Presenting discovery findings to executive stakeholders
Module 10: Einstein Activity Capture and Intent Signals - Configuring Einstein Activity Capture for Gmail and Outlook
- Mapping email and calendar data to Salesforce objects
- Enabling automated logging of customer interactions
- Identifying intent spikes based on communication frequency
- Building alerts for sudden increases in stakeholder engagement
- Linking activity data to opportunity scoring models
- Filtering out noise from internal and non-relevant emails
- Setting up activity trend dashboards by account
- Using AI to prioritise follow-ups based on recency and intensity
- Integrating activity signals into playbooks for next-best actions
Module 11: Cross-Cloud AI Integration - Leveraging Marketing Cloud signals in Salesforce AI models
- Passing lead engagement scores from Pardot to Einstein
- Using Commerce Cloud purchase history for upsell predictions
- Integrating Service Cloud case resolution times with account health
- Building unified customer health scores using multiple clouds
- Setting up data sharing between clouds with zero-code connectors
- Ensuring consistent identity resolution across systems
- Using Mulesoft to synchronise AI outputs between platforms
- Creating shared KPIs for AI-driven customer journeys
- Aligning AI initiatives with broader digital transformation programs
Module 12: Building Board-Ready AI Proposals - Structuring a compelling AI business case for leadership
- Calculating projected ROI from improved conversion rates
- Estimating time saved from automation at scale
- Mapping Einstein adoption to revenue efficiency metrics
- Creating before-and-after dashboards for executive review
- Drafting risk mitigation plans for AI rollout
- Designing phased implementation timelines
- Identifying key stakeholders and their success criteria
- Preparing slide decks using real Einstein performance data
- Practising Q&A responses for technical and strategic challenges
Module 13: Certification and Future-Proofing Your Skills - Preparing for Salesforce certification exams with Einstein focus
- Mapping course topics to Salesforce official certification guides
- Building a personal portfolio of AI implementation projects
- Documenting your Certificate of Completion for LinkedIn
- Joining verified alumni networks and expert forums
- Tracking new Einstein features through release notes
- Setting up automatic update alerts from Salesforce Trust
- Participating in beta programs for upcoming AI features
- Advancing into roles: Sales Ops Architect, CRM AI Strategist, Revenue Scientist
- Designing your personal 12-month upskilling roadmap
Module 14: Final Project - Build Your AI-Powered Automation Blueprint - Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification
- Configuring Einstein Activity Capture for Gmail and Outlook
- Mapping email and calendar data to Salesforce objects
- Enabling automated logging of customer interactions
- Identifying intent spikes based on communication frequency
- Building alerts for sudden increases in stakeholder engagement
- Linking activity data to opportunity scoring models
- Filtering out noise from internal and non-relevant emails
- Setting up activity trend dashboards by account
- Using AI to prioritise follow-ups based on recency and intensity
- Integrating activity signals into playbooks for next-best actions
Module 11: Cross-Cloud AI Integration - Leveraging Marketing Cloud signals in Salesforce AI models
- Passing lead engagement scores from Pardot to Einstein
- Using Commerce Cloud purchase history for upsell predictions
- Integrating Service Cloud case resolution times with account health
- Building unified customer health scores using multiple clouds
- Setting up data sharing between clouds with zero-code connectors
- Ensuring consistent identity resolution across systems
- Using Mulesoft to synchronise AI outputs between platforms
- Creating shared KPIs for AI-driven customer journeys
- Aligning AI initiatives with broader digital transformation programs
Module 12: Building Board-Ready AI Proposals - Structuring a compelling AI business case for leadership
- Calculating projected ROI from improved conversion rates
- Estimating time saved from automation at scale
- Mapping Einstein adoption to revenue efficiency metrics
- Creating before-and-after dashboards for executive review
- Drafting risk mitigation plans for AI rollout
- Designing phased implementation timelines
- Identifying key stakeholders and their success criteria
- Preparing slide decks using real Einstein performance data
- Practising Q&A responses for technical and strategic challenges
Module 13: Certification and Future-Proofing Your Skills - Preparing for Salesforce certification exams with Einstein focus
- Mapping course topics to Salesforce official certification guides
- Building a personal portfolio of AI implementation projects
- Documenting your Certificate of Completion for LinkedIn
- Joining verified alumni networks and expert forums
- Tracking new Einstein features through release notes
- Setting up automatic update alerts from Salesforce Trust
- Participating in beta programs for upcoming AI features
- Advancing into roles: Sales Ops Architect, CRM AI Strategist, Revenue Scientist
- Designing your personal 12-month upskilling roadmap
Module 14: Final Project - Build Your AI-Powered Automation Blueprint - Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification
- Structuring a compelling AI business case for leadership
- Calculating projected ROI from improved conversion rates
- Estimating time saved from automation at scale
- Mapping Einstein adoption to revenue efficiency metrics
- Creating before-and-after dashboards for executive review
- Drafting risk mitigation plans for AI rollout
- Designing phased implementation timelines
- Identifying key stakeholders and their success criteria
- Preparing slide decks using real Einstein performance data
- Practising Q&A responses for technical and strategic challenges
Module 13: Certification and Future-Proofing Your Skills - Preparing for Salesforce certification exams with Einstein focus
- Mapping course topics to Salesforce official certification guides
- Building a personal portfolio of AI implementation projects
- Documenting your Certificate of Completion for LinkedIn
- Joining verified alumni networks and expert forums
- Tracking new Einstein features through release notes
- Setting up automatic update alerts from Salesforce Trust
- Participating in beta programs for upcoming AI features
- Advancing into roles: Sales Ops Architect, CRM AI Strategist, Revenue Scientist
- Designing your personal 12-month upskilling roadmap
Module 14: Final Project - Build Your AI-Powered Automation Blueprint - Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification
- Selecting a real-world use case from your current role
- Conducting a data readiness audit
- Choosing between lead, opportunity, or account-level automation
- Designing the AI model configuration
- Creating a data governance checklist
- Mapping automated workflows and handoffs
- Designing user adoption playbooks
- Building executive summaries with projected impact
- Developing a 90-day rollout timeline
- Submitting your blueprint for guided feedback
- Incorporating instructor revisions into final version
- Receiving personalised assessment and completion certification