In this lesson, I teach the high level concepts and history of predictive AI in Salesforce. I also share an old Einstein Discovery course with you so you can learn more on how Salesforce first addressed predictive AI through their earlier solutions.

Here we begin our journey through the first knowledge area of the AI Associate Exam.

In this lesson I provide a high level overview and introduction to generative AI. We use ChatGPT for examples of generative AI responses for both text and images.

We get familiar with Salesforce's Einstein platform and what the capabilities are with AI for Marketing, Sales, Service and Commerce. I also provide you with an Einstein Cheat Sheet resource.

The Einstein Trust Layer is a secure AI architecture, natively built into the Salesforce Platform. Built on Hyperforce for data residency and compliance, the Einstein Trust Layer is equipped with best-in-class security guardrails from the product to our policies. Designed for enterprise security standards, the Einstein Trust Layer allows teams to benefit from generative AI without compromising their customer data.

Here we get familiar with Retrieval Augmented Generation (RAG) on the Salesforce Platform.

In this lesson we become familiar with the Customer360 - Salesforce's entire suite of products.

In this lesson we learn the AI features available in the Marketing Cloud.

In this lesson we cover the various AI features to be found in the Sales Cloud.

In this lesson we cover the various AI features to be found in the Service Cloud.

In this lesson, we look at the AI features in the Commerce Cloud from both the merchant and customer perspective.

In this lesson, we round out the first knowledge area of the exam by looking at other Salesforce products which make an appearance on the exam. We briefly touch on the Data Cloud, Slack, Mulesoft and more to explain what these various tools specialize in, to make you aware of their use cases.

At the start of the second knowledge area, AI Capabilities in CRM, we cover a high level overview of what will be covered in this section of the course.

In this lesson, we walk through the process of gaining your login credentials for your Trailhead playground as well as how to set a password.

In this lesson, we get logged into your Salesforce instance from the main login URL.

In this lesson, we get introduced to the App Launcher and the process of launching and changing active applications in our Salesforce instance. We get more familiar with what constitutes an app in Salesforce, as well as the varying series of tabs in different applications.

In this lesson, we learn how to access all items from the App Launcher.

In this lesson, we cover the core CRM objects that are used in Salesforce.

In this lesson, we get more familiar with a record detail page.

We get more familiar with the various field types that are available by inspecting the fields of the Lead object.

In this lesson, we create a couple of next fields with data types of text and text area.

In this lesson, we visit Session Settings to clear persistent browser caching so that our new text fields will display on our page layout.

In this lesson we explore the differences between Text and Text Area fields, although they both allow for a maximum character length of 255.

In this lesson, we learn how Einstein Lead Scoring works. Since Einstein Lead Scoring is not available to us in our free accounts, we leverage a generative AI tool to generate a lead score formula for us that we will next create manually in our own org.

In this lesson we create a custom scoring algorithm for a Lead Score using ChatGPT, which generates our formula to take into account the entries of several fields on Lead and then provide a 0-100 Lead Score result.

In this lesson, we start to learn about other AI features in Sales Cloud, such as Opportunity Scoring.

We return back to the Salesforce website to go more depth on more of the AI features available in the Sales Cloud from an end user perspective.

In this lesson, we use Google to find various free Salesforce orgs with different features enabled. One of those orgs we are looking for is one that has Einstein Prediction Builder enabled, since it isn't enabled or available in the default Trailhead playgrounds.

In this lesson, we get introduced to AI infused chatbots available for Service reps on the Salesforce platform - Einstein Bots for Service.

Save agent time with Einstein Article Recommendations. In a few clicks, build a model that recommends relevant knowledge articles to solve customer cases.

Use Einstein AI to draft responses from a defined data source with grounding. Grounding indexes your objects and fields so that Einstein knows which information to base recommendations on. When you use grounding, your unique knowledge articles and case history add context and personalization to customer communications.

In this lesson we use the Service Setup Assistant to set up Salesforce chat functionality on a website.

In this introductory lesson to the next knowledge area of the exam, we prepare for our learning journey related to ethical considerations of AI.

In this lesson, we discuss a few important principles to ethical use of AI, such as mitigating human bias, human in the loop (HITL) and more.

In this lesson, we learn about Model Cards, which Salesforce has developed to help provide greater transparency and information related to the training of their large language models.

In this lesson, we get introduced to Salesforce's Trusted AI Principles.

Here we address the first of the five trusted AI Principles of Salesforce - Responsible.

Salesforce strives to safeguard human rights, protect the data we are trusted with, observe scientific standards and enforce policies against abuse. They expect their customers to use Salesforce AI responsibly and in compliance with their agreements, including their Acceptable Use Policy.

Here we address the second of the five trusted AI Principles of Salesforce - Accountable.

Salesforce believes in holding themselves accountable to their customers, partners, and society. They seek independent feedback for continuous improvement of their practice and policies and work to mitigate harm to customers and consumers.

Here we address the third of the five trusted AI Principles of Salesforce - Transparent. Salesforce states, "We strive to ensure our customers understand the “why” behind each AI-driven recommendation and prediction so they can make informed decisions, identify unintended outcomes and mitigate harm."

Here we address the fourth of the five trusted AI Principles of Salesforce - Empowering. Salesforce states, "We believe AI is best utilised when paired with human ability, augmenting people, and enabling them to make better decisions. We aspire to create technology that empowers everyone to be more productive and drive greater impact within their organizations."

Here we address the final trusted AI Principles of Salesforce - Inclusive.

Salesforce says that "AI should improve the human condition and represent the values of all those impacted, not just the creators. We will advance diversity, promote equality, and foster equity through AI."

Einstein Discovery helps you practice ethical use of AI by detecting bias in your data so that you can remove its distorting effects on your analysis and predictions. Bias indicates that variables are being treated unequally in your model.

In this lesson, I share a free eBook I have written to help you to learn and identify the various types of biases that may arise in AI, as well as how to leverage the Salesforce Trusted AI Principles to help mitigate those biases.

In this introductory lesson of the final knowledge area, I provide an overview of the learning journey in which you are about to embark related to Data for AI.

In this lesson, we discuss how data is the fuel for AI. We explore the importance of data quality along with its characteristics.

In this lesson, we walk through the various privacy concerns and best practice recommendations when it comes to data collection in AI.

In this lesson we revisit the Einstein Trust Layer to discuss the Audit Trail portion.

In this lesson, we view a csv file in a text editor as well as Microsoft Excel. I show you how easily data can become corrupted and we view the impact of data skew.

In this lesson, we import our Fortune 500 CSV file, which contains 500 accounts and contact records, using the Data Import Wizard.

In this lesson, we look at different data loading tools which are available for data management in Salesforce.

In this lesson we get familiar with the Salesforce Workbench.

In this lesson, we cover 10 key principles for data security in AI

1. Encryption

2. Access Control

3. Auditing and Monitoring

4. Data Anonymization

5. Data Minimization

6. Regular Updates

7. Training and Awareness

8. Robust Backup

9. Ethical Considerations

10. Regulatory Compliance