Salesforce AI Fundamentals

Examine the difference between predictive and generative ai, showing how predictive models forecast sales outcomes from data, while generative ai creates new copy from input.

Explore generative AI and how it creates new content from input, with applications in Salesforce for marketing copy, emails, and social posts.

Understand models as trained programs that predict outcomes like churn using parameters and data sets. Learn how machine learning and deep learning enable Netflix and Spotify to tailor recommendations.

Walk through practice questions to familiarize yourself with AI fundamentals, identifying generative AI for marketing copy and language processing (NLP) for analyzing tone and meaning.

AgentForce is Salesforce's AI agent suite that uses sales and service agents to assist customers, connect to data libraries and APIs, and reference knowledge articles and cases.

Explore the Einstein trust layer, Salesforce's security framework for AI tools, showing how dynamic grounding, data masking, zero data retention, and toxicity checks protect data across Copilot and CRM apps.

Explore key AI capabilities in CRM, including agents that communicate with customers, call summaries, and predictive AI lead scoring that uses historical data to assess lead viability.

Explore how Salesforce ai capabilities enhance crm workflows, using Einstein for leads, opportunities, and cases, and apply general ai tools across core sales objects.

Explore Einstein for multiple objects, including discovery for cross-object insights, prediction for lead conversion, next best action via flows, language processing, and call summaries to streamline sales and service.

Explore Einstein for leads, using machine learning lead scoring from 0 to 100 with positives like industry and state to guide outreach, plus prediction builder insights and lead call summaries.

Explore Einstein forecasting for opportunities, using a predictive model to project revenue, rank opportunities by likelihood, and guide next actions with the prediction builder.

Learn Salesforce's trusted AI principles—be responsible, accountable, and transparent; combine human and AI collaboration to boost productivity and customer satisfaction, while advancing inclusive, equitable AI.

Identify biases in AI and Salesforce tools and their impact. Mitigate them by scrubbing data, red teaming, and avoiding parameters such as race, age, income, or ZIP codes.

practice questions illuminate ethical ai concepts within the maturity model, from ad hoc beginnings to transparency, including making customers aware that we use ai and data scraping for outlier data.

Discover how data drives AI, how to create good data, and practical strategies to keep AI models running and delivering the best outcomes for your Salesforce instances.

Data drives model building and shows which lead sources, follow-up frequency, and communication modes matter. Quality data yields better insights and smarter models over time, revealing patterns to boost revenue.

Learn to create clean, reliable Salesforce data by preventing duplicates, updating records, filling missing fields, and applying validation and duplicate rules, with third-party tools for data quality.

Learn to set up Microsoft Copilot to invoke a return flow in Salesforce, activate Copilot, pass a record ID, and view status output to speed returns and boost agent productivity.

Set up and test duplicate rules for leads to detect exact phone number matches, enable or bypass sharing rules, and merge duplicates to keep clean data for AI tools.

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