Generative artificial intelligence (AI) exploded on the scene in late 2022, sending people and businesses into a frenzy of curiosity and questions over its potential. In fact, customers across industries are harnessing the power of generative AI in Salesforce to boost productivity, deliver exceptional customer experiences, and streamline business processes. However, in addition to generative AI, Salesforce also offers predictive AI to automate various sales and marketing tasks, accelerate decisions, and guide sellers through data-driven decision making.
| Generative AI | Predictive AI |
| For content creation, task automation | For actionable insights, accurate forecasts |
| Improves efficiency and customer interaction | Aids strategic decision-making |
| Combined Power: Drives innovation and operational excellence | |
In this Salesforce tutorial, we will take a closer look at both generative and predictive AI to get a better understanding of how this makes Salesforce easier to use and what it means for your data quality.
What is Generative AI and How Does It Work?
Generative AI is what it sounds like: it generates new content be it texts, images, videos or music. The reason it is capable of creating this new content is because it was trained with generative pre-transformer models to apply deep learning algorithms to large language models to assimilate knowledge about a subject matter to interact with confidence and accuracy. In essence, it aims to replicate the human thought process in a way that computers were unable to in the past. Generative AI can be thought of as a machine-learning model that is trained to create new data, rather than making a prediction about a specific dataset. A generative AI system is one that learns to generate more objects that look like the data it was trained on.
The reason generative AI has become so important is because of all the opportunities it unlocks for businesses. It can eliminate some of the time-consuming tasks sales and marketing teams need to perform such as writing emails, website content and also with content personalization for a better user experience. Salesforce takes advantage of the capabilities offered by generative AI with Einstein Analytics to generate sales summaries, AI-generated forecasts and content, generate text with Einstein GPT and many other functionalities to streamline everyday processes.
It is worth pointing out that there are various types of generative AI models including:
- Transformer models – Transformers are neural networks that learn context by identifying and tracking relationships in sequential data, such as words in a sentence. They’re commonly used for natural language processing (NLP) tasks. Transformer architectures now underpin most foundation models.
- Generative Adversarial Networks (GANs) – Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator learns to create images that look real, while a discriminator learns to tell real images apart from fakes.
How Does Generative AI Work?
Various methods exist for creating generative AI models, but a particularly popular one involves employing pre-trained, large-language models (LLMs) to generate original content based on text prompts. Another approach is to use adversarial networks (GANs) and transformer-based architectures to produce text, images, music, and other forms of media by learning from large datasets and identifying patterns within them. As generative AI technology advances, it will help us in many parts of our lives, taking on the role of superpowered assistants.
Within the realm of Salesforce, generative AI is used for things like tailored email campaigns and Salesforce automation of routine tasks, like drafting responses for customer service inquiries, thereby improving efficiency and allowing human agents to focus on more complex issues. This technology also assists in generating predictive insights and recommendations for sales strategies, enhancing decision-making, and driving innovation. Overall, Salesforce’s use of generative AI fosters deeper customer engagement and operational excellence.
How Can Generative AI Improve Your Data Quality?
Generative models can be developed to spot irregularities or outliers in data, which is especially useful for finding and fixing errors in Salesforce databases. These algorithms aid in standardizing data formats, units, and naming conventions, ensuring consistency throughout different datasets. By examining patterns and relationships in the data, generative AI can identify and remove duplicate records in Salesforce, thus enhancing data integrity and consistency.
This can be accomplished by using the generative adversarial networks (GAN) mentioned earlier. A GAN model comprises two neural networks: one generates new samples resembling the training data, known as the generator, while the other, the discriminator, learns to differentiate between real and generated samples. These two forces, the generator and the discrimator, are engaged in a constant battle where the generator tries to generate samples that fool the discriminator into classifying them as real, while the discriminator tries to correctly classify real and generated samples.
The data created by GAN models can then be used to train AI data quality applications by providing higher quality data to learn the underlying distribution of normal data points. Later on, when presented with new data, the model will be able to flag instances that deviate significantly from the learned distribution as potential anomalies.
What is Predictive AI and Why is It Important?
Predictive AI uses Machine Learning (ML) and Deep Learning (DL) to analyze past and current data to predict future outcomes. Predictive modeling starts with data gathering, after which the data is analyzed, and a predictive model is used to generate likely outcomes. Companies can use predictive AI to identify future trends. This allows businesses to accurately identify and capitalize on critical characteristics that improve their bottom line. Such hidden trends include things like which customers will be most likely to be upsold.
The benefits predictive AI offer your business include:
- Augmented decision-making: Get AI-driven insight to better inform your decisions and reduce guesswork, leading ultimately with strategic planning.
- Efficiency: AI algorithms can interpret larger datasets in less than a second, this contributes efficiency to operations.
- Risk prediction: The biggest benefit is AI predictive analytics that can help you predict future risks to take action before they happen. This is especially beneficial in sectors such as finance or healthcare.
How Does Predictive AI Work?
Like most types of AI, predictive AI requires the ability to query databases quickly and efficiently and to find relationships between similar items of data. Salesforce and other tools leverage predictive AI to answer the question, “What will happen next?” Broadly speaking, predictive analytics models can be categorized into two main types: classification and regression models.
The goal of classification models is to organize data objects into their own, unique groups of datasets. For example, businesses in the retail segment can leverage this Salesforce functionality to forecast how well a specific group of customers will react to an email drip campaign. The regression models will focus more on continuous data points like determining the lifetime value of a particular customer.
There are three main types of techniques for working with predictive AI:
Regression analysis – This statistical technique is is employed to examine several aspects that could impact a goal, like the accomplishment of a new product launch, company expansion, or advertising campaign.
Decision trees -This flowchart technique begins with a central concept and branches out in response to the outcomes of your choices. These trees are employed in the process of decision tree analysis, which entails putting a complex decision’s possible outcomes, costs, and implications in graphic form.
Neural networks – The main goal of a neural network is to try and replicate the way the human mind works to process information to make a decision. The network is made up of basic processing components coupled by weights.
How Does Salesforce Use Predictive AI?
Salesforce looks at all of the data in your records and analyzes it to give you actionable data you can use to get out in front of future trends. For example, the Einstein Analytics feature uses machine learning algorithms to generate powerful insights and useful recommendations to strategize data-driven decision-making. There are also things like Salesforce Inbox AI which sorts out many manual tasks like email categorization and response drafting, giving sales reps more space to focus on more important tasks. We can see from these types of solutions how Salesforce is making the lives of sales professionals easier by augmenting their analytical capabilities and eliminating manual processes.
How Can Predictive AI Improve Your Data Quality?
Predictive quality analytics looks at different parts of data to make sure it’s good and useful. In particular, predictive analytics rely on:
- Data accuracy – This metric simply indicates how well your data correlates to reality i.e. is the customer information correct. The old adage of “garbage in, garbage out” certainly applies here. If your data is not accurate, you cannot expect your predictive analytics to give an accurate forecast.
- Data completeness – Simply put, this is all about whether or not you have all of the needed information about a customer. The more data you have the higher the quality of the predictive system.
- Data consistency – Data consistency is a combination of the previous metrics above, but also whether or not you have the same data across all of the relevant systems.
- Data integrity – Data integrity is a broad term that includes accuracy, completeness and constancy, but can also refer to the way your data is being used across databases.
- Data relevance – When is the last time you updated your data or checked whether or not it’s up to date? The data you may have gotten a year ago or even last month may no longer be relevant. Consider implementing data validation rules to help you keep data relevant and make sure your employees trust the data that’s in Salesforce.
| Generative AI | Predictive AI |
| Identifies data anomalies and inconsistencies | Validates data accuracy and completeness |
| Standardizes data formats and removes duplicates | Ensures data relevance and integrity |
Leverage Salesforce’s Native AI Capabilities as Well as Other AI Apps on AppExchange
While Salesforce certainly provides robust AI capabilities, there are many ways to augment these capabilities. There are lots of apps on AppExchange that can help you with things like data cleansing, anomaly detection, uncovering data patterns, and a lot more. In today’s data-driven world, organizations face challenges ensuring the accuracy, consistency, and reliability of their data. AI can be used to help you overcome data quality issues and get the most value out of your data.
Actually, it is quite common that generative AI receives most of the buzz because its allure and novelty are undeniable. No human had ever seen computers behave so much like humans before. But this emphasis on generative AI should not distract from how our focus is also shifting toward predictive AI. This generative AI is really impressive! Any machine that can draft up a text or some code truly has value but let us not forget this is still (and will be) just initial drafts which would need to get reviewed and polished by humans. Even so, AI is unlikely to completely supplant human labor for many years. It is still an instrument, not yet a replacement for labor made by humans.
Generative and predictive AI share foundation machine learning principles of – which is highly necessary but mostly taken for granted. Neither is quantitatively better than the other; but they are wholly different – like ski resorts and water parks. Therefore, ideally, organizations should tackle each operational challenge with the technology that provides the most value.
| Aspect | Generative AI | Predictive AI |
| Core Function | Creates new content or data based on patterns and learned information. | Analyzes existing data to forecast future outcomes and trends. |
| How It Works | Utilizes pre-trained large-language models (LLMs) and generative adversarial networks (GANs). | Employs regression analysis, decision trees, and neural networks to interpret data and make predictions. |
| Salesforce Applications | Generates tailored email campaigns. Automates routine customer service tasks. Provides predictive insights and recommendations. | Forecasts customer behavior and identifies potential leads. Recommends personalized actions. Enhances sales and marketing strategies. |
| Data Quality Improvements | Identifies and corrects anomalies in data. Standardizes data formats and removes duplicates. | Ensures data accuracy, completeness, and consistency. Validates data integrity and relevance. |
| Integration Synergy | Complements Predictive AI by creating data and content that can be further analyzed and optimized. | Enhances Generative AI outcomes by predicting the best content or data to generate for specific needs. |
