A Quick Guide to AI, Generative AI, and Salesforce AI Cloud
Author: Latika Parmar
AI and generative AI have turned into buzzwords, but with good reason. Recent advancements in artificial intelligence (AI), namely generative AI, have made it one of the most important technologies and defining innovations of this era.
Generative AI has been bringing about change at a pace that’s so rapid it’s caused legislators to take action — and legislators aren’t the only ones with concerns about the risks it can pose. Business leaders recognize the immense potential benefits generative AI offers, but have concerns as well, mainly around security, privacy, and data bias.
So, how do organizations take advantage of the technological milestone that is generative AI, while mitigating risks? By developing a better understanding of how it functions and investing in generative AI solutions that work to keep risks in check — which this blog will help with.
The information below will explain the difference between traditional and generative AI, and provide details about Salesforce AI Cloud — which is an example of generative AI that provides benefits alongside risk mitigation — for anyone looking to add generative AI functionality to their solution roadmap.
AI, Generative AI, and Salesforce AI Cloud
What is the difference between traditional AI and generative AI? This article goes into more detail, but a basic summary is that:
- Traditional AI is built for pattern recognition, analytics, and predictions
- Generative AI uses LLMs (large-language models) with natural language processing and analytics to recognize patterns and produce content that is in line with a user’s request; it is also known for its ability to create a human-like response
- When people think about generative AI, GPT (Generative Pre-Trained Transformer or generative AI) usually comes to mind; GPT is a type of LLM
- Generative AI can be used to address business needs and customer expectations in ways that AI is already helping with, but in a better and faster way
What does this mean in practical terms?
Generative AI tools can make intelligent suggestions based on what they’re seeing, automate processes in a more flexible way, and provide a customer experience that is more finely tuned for an individual customer. For example, for higher education, it could help create a more adaptive and personalized learning environment by analyzing a student’s past performance and interests.
Where does Salesforce and Salesforce AI Cloud fit into this?
Salesforce has been investing heavily in AI technologies since 2016, starting with Einstein; so, its AI innovations serve as a great example of AI and generative AI for use in a business environment.
- Think of Salesforce AI Cloud as an umbrella term for all of Salesforce’s generative AI/GPT applications (Sales GPT, Service GPT, etc.)
- The current list of Salesforce technologies it can integrate with includes Einstein, Data Cloud, Tableau, Flow, and MuleSoft
- It’s how Salesforce is integrating generative AI into their existing applications
- How it functions offers a great breakdown of generative AI associated risk management
Salesforce AI Cloud as an Example of GPT Business Use and AI Risk Mitigation
As outlined above, Salesforce AI Cloud’s GPT applications use a GPT LLM with natural language processing and analytics to recognize patterns and produce content that is in line with a user’s request.
Where does business use come in?
- To use a Salesforce AI Cloud GPT application the user starts with a prompt or question, similar to using ChatGPT or other publicly available generative AI
- Because the applications sit on top of the Salesforce platform, the data that combines with the user prompt for process and analysis is customer and business data from across the platform (including other Salesforce Clouds or integrated applications/systems)
- This means that the output that results from the prompt is highly personalized for that individual organization
Functionality isn’t limited to the examples listed below, but the current Salesforce GPT lineup and examples of their automation include:
- Sales GPT – automate sales related tasks like composing emails and scheduling meetings
- Service GPT – for service/field service tasks like creating work orders based on data and service history
- Marketing GPT – for marketing tasks like generating accurate recommendations for targeting
- Commerce GPT – business and customer related automations, including individualized product descriptions to increase orders
- Slack GPT – conversational interface; can do things like build no-code flows to simplify deployment of AI automations
- Tableau GPT – simplifies data and analytics use and processes with things like generation of analytics-based visuals and recommendations to meet targets
- Flow GPT – everything workflow related, including creation of workflows via prompts to improve automation across your business
- Apex GPT – for coding faster and scanning for errors or vulnerabilities more thoroughly
To get a better idea of the benefits, think about your own organization’s customer and business data combined with the automation examples listed above, including the time, effort, and resources it currently takes teams or individuals to complete those tasks.
Where does risk mitigation come in?
The entire process involves something called the Einstein GPT Trust Layer, which keeps sensitive data separate and helps ensure customers maintain governance over their data with both data and privacy controls.
The Einstein GPT Trust Layer adds automated security best practice steps to the generative AI process that make it a major differentiator and ideal for businesses, as it puts Salesforce AI Cloud ahead of the game in terms of generative AI legislation.
- It ensures Salesforce’s GPT applications use techniques like secure data retrieval, dynamic grounding, data masking and zero retention — automating important steps like auditing to ensure output is free from any bias and toxicity
- It enables users to securely access the information they need while improving processes — but ensures Salesforce AI Cloud’s GPT tools don’t retain personal data (due to its private, zero-retention architecture)
- It helps ensure data remains completely private and under the organization’s control
Salesforce has an open ecosystem, meaning it’s designed to play well with other technology, and is one of the reasons it integrates so well with other systems — and also means that customers that adopt Salesforce AI Cloud can choose to utilize an alternate LLM (large-language model; GPT is a type of LLM), while still benefiting from the Einstein GPT Trust Layer.
Next Steps
If your organization needs Salesforce or Salesforce AI Cloud solution advice or assistance — contact us. We’re here to help and always happy to provide insight on how innovations like generative AI can impact your industry and business.
Want to learn more about Salesforce AI Cloud?