Salesforce AI Associate: AI Fundamentals

Salesforce Associated AI

• The Salesforce Certified AI Associate Exam Guide covers AI Fundamentals, AI Capabilities in CRM, Ethical Considerations of AI, and Data for AI

AI Fundamentals

• 17% of this exam is on AI Fundamentals
• AI Fundamentals explains the basic principles and applications of AI within Salesforce
• AI Fundamentals differentiates between the types of AI and their capabilities

Definition of Artificial Intelligence (AI)

• AI is the development of computer systems and programs that can perform tasks that typically require human intelligence
• These tasks include learning, reasoning, problem-solving, understanding natural language, speech recognition, and visual perception
• AI aims to create machines capable of simulating human cognitive functions, enabling them to adapt, learn from experience, and perform tasks in a way that resembles human intelligence
• The goal of AI is to build intelligent systems that can analyze data, make decisions, and solve problems across various domains
• The main ingredients that are part of any solid AI platform are: yes-and-no predictions and answers, numeric predictions, classifications, recommendations, and summarization
• AI is the broad concept of having machines think and act like humans

AI Categories

• Narrow AI refers to AI systems designed and trained for a specific task or narrow set of tasks
• Narrow AI systems excel in performing predefined functions but lack the broad cognitive abilities associated with human intelligence
• Narrow AI is specialized, task-specific, and limited to the scope of their designed function
• General AI represents a form of AI that possesses the ability to understand, learn, and apply intelligence across a wide range of intellectual tasks at a level comparable to human intelligence
• General AI is capable of generalizing knowledge, learning from diverse experiences, and performing various intellectual tasks
• Super AI, or Artificial General Superintelligence (AGI), is a hypothetical level of artificial intelligence that surpasses human intelligence in virtually every aspect, including problem-solving, creativity, and social skills
• Super AI implies intelligence that exceeds the highest human capabilities and has advanced cognitive abilities surpassing human intelligence, self-awareness, and the capacity to outperform humans in all cognitive tasks
• Narrow AI is prevalent today, achieving General or Super AI remains a complex and evolving challenge in the field of artificial intelligence
• The term "Super AI" is often used to denote a level of intelligence beyond human capabilities

Types of AI

• Descriptive AI focuses on describing or summarizing past and present data to provide insights and understand patterns
• Prescriptive AI analyzes data and prescribes specific actions or strategies to optimize outcomes based on the analysis
• Predictive AI utilizes historical data and statistical algorithms to make predictions about future events or trends
• Generative AI involves machines creating new content, data, or outputs, often using neural networks to generate novel and creative results

Salesforce Einstein

• Salesforce Einstein pioneered AI for CRM since 2014
• As of 2023, Salesforce Einstein has 210 AI patents and 227 AI research papers
• As of 2023, Salesforce Einstein has 1 trillion+ predictions a week

Predictive VS Generative AI

• Predictive AI utilizes machine learning techniques to classify or forecast outputs based on input data
• Predictive AI can be applied to tasks such as anticipating customer behavior, optimizing inventory levels, or predicting equipment maintenance needs
• Generative AI focuses on creating new content based on existing data.
• Generative Al can be used to create personalized marketing content or synthetic data for testing purposes

How Generative AI Works using LLMs

• Generative AI models use pre-trained, large-language models (LLMs) to create novel content from text-based prompts
• Generative Al is already helping people create everything from resumes and business plans to lines of code and digital art.
• The potential extends to enterprise businesses to generate code, and or even new proteins
• It is important to include a human in the loop approach when developing and using generative AI technologies
• Businesses can validate and test automated workflows with human oversight and intervention before unleashing fully autonomous systems
• A human in the loop can help build trust and confidence in the technology among stakeholders and customers

Einstein GPT Trust Layer

• Einstein GPT Trust Layer works by using the company's secure data retrieval to prompt Einstein GPT Trust Layer, from CRM apps
• Einstein GPT Trust Layer then sends that data to either hosted models in Salesforce Trust Boundary, or external models with Shared Trust Boundary for data generation
• The data follows Zero Retention rules

Types of AI (techniques)

• Machine Learning (ML:) uses Algorithms and statistical models that enable systems to learn and improve from experience
• Deep Learning uses neural networks with multiple layers to learn from a large amount of data and simulate human-like decision-making
• Natural Language Processing (NLP:) enables machines to understand, interpret, and generate human language
• Computer Vision enables machines to interpret and make decisions based on visual data, similar to human vision
• Predictive Analytics utilizes statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data
• Neural Networks are a computational model inspired by the structure and function of the human brain, often used in machine learning. Web of connections, guided by weights and biases
• Rules-Based Systems are computer systems designed to emulate the decision-making ability of a human expert in a specific domain

Machine Learning (ML) & Relation to AI

• Machine Learning is a subset of artificial intelligence (AI) that involves the development of algorithms and models that enable computer systems to learn patterns, make predictions, and improve their performance based on experiences and data without being explicitly programmed
• Machine Learning focuses explicitly on enabling computers to learn from data
• Machine Learning is AI that can grow its intelligence

Deep Learning & Relation to Al

• Deep learning is an advanced form of Al that helps computers become really good at recognizing complex patterns in data
• Deep learning uses neural networks with multiple layers to learn from a large amount of data

Natural Language Processing (NLP) & Relation to AI

• Natural Language Processing (NLP) focuses on how computers can understand, interpret, and generate human language
• NLP helps computers perform useful tasks like understanding the meaning of sentences, recognizing essential details in text, translating languages, answering questions, summarizing text, and generating responses that resemble human responses
• NLP matured with its two subfields, natural language understanding (NLU) and natural language generation (NLG)
• Data processed from unstructured to structured is called natural language understanding (NLU)
• Data processed the reverse way-from structured to unstructured-is called natural language generation (NLG)

Computer vision & relation to AI

• Computer vision enables machines to interpret, understand, and extract information from visual data, such as images and videos
• Computer vision has applications in various industries, including image and video analysis, object recognition, facial recognition, autonomous vehicles, medical image analysis, and more

Predictive Analytics & Relation to AI

• Predictive analytics identifies the likelihood of future outcomes based on historical data and involves analyzing patterns, trends, and relationships within datasets to make predictions about future events or behaviors
• Predictive analytics is used across various industries to forecast trends, anticipate customer behavior, optimize business processes, and make informed decisions
• Predictive analytics uses data and machine learning to predict future outcomes based on historical patterns

Neural networks & Relation to AI

• Neural networks are interconnected layers of algorithms, inspired by the human brain's structure, that process information and learn patterns to make predictions or decisions
• Neural networks are a crucial component of machine learning and deep learning systems
• Neural networks enable Al models to learn from data, recognize patterns, and make predictions or decisions, mimicking aspects of human intelligence
• Neural networks are a web of connections, guided by weights and biases

The Core Components of AI Systems

• Algorithms are sets of rules and procedures for solving problems or performing tasks.
• Data is information used to train and feed Al models.
• Models are trained representations of Al systems that can make predictions or decisions.
• Training Data is labeled datasets used to train machine learning models.
• The Inference Engine is the component that applies learned knowledge to new, unseen data.
• The Feedback Loop is the mechanism for continuous learning and improvement.
• Memory is where past experiences and information are stored.
• Sensors are input devices providing information from the environment.
• Actuators are output devices enabling Al systems to interact with the environment.
• Computation is the system's ability to perform calculations, process information, and execute algorithms.

AI Capabilities in CRM

• 8% of the exam is on AI Capabilities in CRM
• Identify CRM AI capabilities and describe the benefits of AI as they apply to CRM

Benefits of Implementing AI in CRM

• Enhanced Customer Insights
• Personalized Customer Interactions
• Delivers personalized experiences
• Automated Tasks and Processes
• Improved Lead Management
• Efficient Customer Support
• Sales Forecasting and Performance Analysis
• Reduced Workload and Human Errors
• Adaptive Marketing Strategies
• Customer Retention
• Continuous Learning and Improvement

AI Capabilities in Salesforce

• Predictive
• Generative
• Analytic

Sales Cloud Einstein

• Sales Cloud Einstein enhances sales efficiency and effectiveness
• Einstein Activity Capture is a productivity-boosting tool that helps keep data between Salesforce and email/calendar applications by focusing on emails, events, and contacts
• Einstein Automated Contacts finds new contacts and opportunity contact roles to add to Salesforce using email and event activity
• Einstein Lead Scoring scores leads based on customer data, using data science and machine learning to discover business' lead conversion patterns and provides a simpler, faster, and more accurate solution
• Einstein Opportunity Scoring predicts customer behavior based on data letting AI help teams focus on closing deals with the likelihood is measured from a score of 1 to 99
• Einstein Opportunity Insights provide relevant updates about opportunities so sales can win more deals, with details shown, tying it to relevant metrics
• Einstein Account Insights let AI help maintain relationships with customers providing insights about whether an account is expanding or cutting costs, changing its company leadership, or is involved in merger and acquisition talks

Service Cloud Einstein

• Service Cloud Einstein personalizes service interaction
• Einstein Article Recommendations helps support agents resolve customer cases efficiently by recommending knowledge articles that were attached to similar cases in the past and Agents don't have to waste time searching or scrolling through lists of articles
• Einstein Bots help customers resolve their issues quicker in a guided self-serve application and automate customer support channels by AI-powered chatbots seamlessly integrated into the Salesforce CRM platform to automate common tasks, enhance team productivity, and facilitate faster issue resolutions
• Einstein Classification Apps apply labels based on underlying data and data patterns maximizing efficiency by making the most out of data and learnings from resolved cases using Einstein to analyze cases from previous months and automate the data entry for new cases
• Einstein Conversation Meaning Reports show your data grouped into these contact reasons including the average conversation frequency and the average number of conversation turns to completion for each contact reason to find the best opportunities for automation
• Einstein Reply Recommendations empower agents with a library of approved responses to common questions and build a model that recommendations with replies being based on Salesforce org's closed chat transcripts

Marketing Cloud Einstein

• Marketing Cloud Einstein automates intelligent journeys
• Uses built-in predictive AI to create, test, and optimize personalized campaign variations by automating and customizing all aspects of customer engagement, like channel, content, timing, and send frequency

Generative AI

Einstein GPT

• The world's first generative Al for CRM
• Generative Al and CRM comes together

Einstein GPT Sales Cloud

• Einstein GPT Sales Email creates personalized emails on email campaigns to attract new customers, help sellers introduce themselves, schedule a meeting, or nudge for a follow-up within seconds, and automate personalized communications, enriched with Salesforce and external data
• Einstein GPT Call Summaries logs calls quicker and more accurately, help sales representatives better understand previous customer interactions, quickly generate concise, actionable summaries and Identify important takeaways, customer sentiment, and next steps to help sales team move deals forward

Einstein GPT Service Cloud

• AI in CRM improves employee knowledge of a customer-centric vision by providing insights into customer behavior, preferences, and needs
• Einstein GPT Service Replies on SMS, Whatsapp, and more to analyze content from customer conversations in real time so that Agents can share these replies with customers with one click, or edit them before sending
• Einstein GPT Work Summaries Drive efficiency and boost agent productivity with Al-generated summaries for any work, order, or interaction and at the end of a conversation between an agent and customer, Einstein predicts and fills a summary, issue, and resolution
• Einstein GPT Knowledge Articles Supports customers and save agents time by making useful information easily accessible, Summarizes support interactions and creates helpful knowledge articles
• Einstein GPT Search for Knowledge helps agents and customers find answers faster with Al-powered Search for Knowledge and Surface generated answers to agents' and customers' questions in your search page or agent console

Ethical Considerations of AI

• 39% of the exam assesses the ethical challenges of AI (e.g., human bias in machine learning, lack of transparency, etc.) and the understanding of Salesforce's Trusted Al Principles to given scenarios

Humans and AI

• Effective interaction between humans and Al systems leads to more informed and balanced decision-making.
• A key challenge of human-Al collaboration in decision making is that it creates a reliance on Al, potentially leading to less critical thinking and oversight
• The role of humans in Al-driven CRM processes is crucial, as they oversee the operations, offer context, and make final decisions
• A consultant addressing the role of humans in Al-driven CRM processes should highlight the challenge of interpreting Al decisions as one potential obstacle in human-Al collaboration in decision-making.

Trusted Salesforce AI Principles

• Responsible
• Accountable
• Transparent
• Empowering
• Inclusive

Being a Responsible AI Developer

• Safeguarding human rights, protects the data that AI users are trusted with, observes scientific standards and enforces policies against abuse
• Responsibility focuses on ensuring the ethical use of Al, customer data consent preferences are tracked, and work is done with human rights experts

Being an Accountable AI Developer

• Holding to our customer, society, and partners ethical standards
• Seeking independent feedback from external ethics experts, customers, and advisory boards to improve Al Practice

Being a Transparent AI Developer

• Striving to ensure customers understand the “why” behind each Al-driven recommendation and prediction so they can make informed decisions, identify unintended outcomes and mitigate harm
• A clear and understandable explanation of Al decisions and actions
• Explainability describes how Al models make decisions

Being an Empowering AI Developer

• Leveraging Al to better our people and their decision-making with technology
• AI should empower more productivity and better our organizations

Being an Inclusive AI Developer

• AI should represent and improve the conditions of humanity that affect all stakeholders
• Emphasizing that inclusivity is linked to ensuring Al models are designed to minimize bias for all who may be impacted
• Testing with diverse models + datasets, build inclusive + diverse teams

Ethical and responsible use of AI

• Implement Salesforce Trusted Al Principales to minimize potential Al bias
• Create guardrails that mitigate toxicity and protect PII (personal information
• Ensure appropriate consent and transparency when using Al-generated responses
• Focus on privacy, bias, security, and compliance
• Some of the ethical challenges associated with Al development include the potential for human bias in machine learning algorithms and the lack of transparency in Al decision-making processes.

Five Data Ethics Best Practices

• Appropriately uses and collects individual information by capturing customer preferences and adhering to digital privacy laws to capture only essential personal data
• Gives clear exchange of value for data with offers through recommendations
• Treat sensitive data carefully + handle appropriately + not cause harm
• Collect and use only what's needed to reduce age related bias
• Choose third party partners carefully in data activation partners to understand the custody, privacy, and clarity post activation

Real time Personalization Considerations

• Acknowledge trust is important to address data breaches concerns
• Real time Data includes, security concerns, data being collected and used unexpectedly, and personalizing interactions that feel invasive
• Ethical is essential since it is important that customers can makes decisions based on values and ethics
• Ensure you are transparent

Important Steps to take for Ethical use

• Prioritize the customer and focus on their needs
• Remember valuable and relevant content is essential
• Ensure Salesforce offers transparency

Association Bias, data that are labeled according to stereotypes.

• Examples include results on Google for toys for girls showing, "cooking toys, dolls, princesses, and pink
• Example search results for boys includes superhero action figures and construction

Confirmation Bias

• Reinforcing ideas that are already there
• Superheros for girls don't appear because of marketing biases

Types of Bias

Automation Bias

• Automations bias imposes the systems values on others
• Biases on training data can cause real world outcomes such as the AI in charge of choosing the beauty contest winners in 2016.

Societal bias

• Societal Bias reflects historical bias of marginalized communities

Survival bias.

• Only algorithms focuses on algorithms that have succeeded, at the expense of recognizing the one's that didn't work

Interaction Bias

• Creating biased bots due to humans intentionally creating and sharing biased language and intent

Key Components that enter the system. The How?

• There can be bias in the creators, how, when, and from who the AI is being deployed for
• Assumptions need to be made on what you built and who you built for
• Training data needs to correlate
• Human intervention is crucial

Can AI Magnify bias?

• AI can magnify bias
• Data set had 68% more biases when AI was created than when a data source was created by humans

Data for AI

• 36% of the exam is dedicated to the importance of data quality and elements of data quality

“Data revolution”

• Artificial intelligence strategy is as strong as data strategy + must be done with the core of trust

Data Strategy

• Includes and establishes governance frameworks
• Requires data prepping + cleaning
• Ensures ethical data governance, ethical handling, and compliance with all regulations

Key Elements of Data quality.

• Missing records
• Duplicate records
• Data standards that are needed for analysis
• Must not include incomplete records, and stale data

Key Components of data Quality

• Age, the recency of data to show how the information is up to date
• Encompass the presence of all data fields for effective data which is essential to see up sell opportunities
• Accuracy + precision to align for insights and potential improvements
• Consistency + standardized with the use of spelling, formmating to see variations for specified fields

Additional key Components

• Focus on eliminating dupes + optimizing data utilization
• Utilizining Salesforce's dupe and app exchange to monitor data

Datasets

• They have a critical role as it utilized in both training and testing purposes
• With a great prep, quality, governance, implementation for performance
• It is important to have accurate AI metrics

Best models to create

• Ensuring transparency helps identify biases and equity in AI
• High quality data = accuracy and relevance
• Organizations must have excellent standards to trust relationships from clients