Trying to keep up with Salesforce AI can feel like drinking from a firehose. Every week there’s a new tool, a new feature, a new promise. If you’re finding it hard to separate the real deal from the marketing hype, you’re definitely not the only one. The thing is, Salesforce AI isn’t some magic, all-in-one solution; it’s a mix of different capabilities.
This guide is here to cut through that noise. We’re going to break down the three main types of AI you’ll find in Salesforce: predictive, generative, and agentic. We’ll look at what they actually do, where they tend to fall short, and how you can pick the right kind of AI for your business without getting tangled up in a costly and complicated system.
A quick look at the landscape of AI capabilities in Salesforce
Before we get into the different types of AI, let’s quickly clear up the branding. You’ll mainly hear two names tossed around: Einstein and Agentforce.
Think of Einstein as the original AI layer that’s been part of Salesforce for a while now. It’s the engine behind a lot of the "smart" features you see in Sales Cloud, Service Cloud, and Marketing Cloud. Its main job is to give you insights from your data and handle some simple automation.
Agentforce is the newer, more ambitious play. This is Salesforce’s big push into creating autonomous "digital workers" that, in theory, can manage complex, multi-step tasks all on their own.
While these tools sound impressive, it’s important to know that they’re designed to work best inside the Salesforce ecosystem. And that’s a big deal when it comes to cost, flexibility, and how long it takes to get everything up and running.
The three main types of AI capabilities in Salesforce explained
Getting a handle on these three categories is the key to understanding what Salesforce AI can realistically do for you. Let’s walk through them one by one.
1. Predictive AI: Forecasting based on your historical data
Predictive AI does pretty much what it says on the tin: it uses your past data to make an educated guess about what’s going to happen next. It sifts through your CRM data, spots patterns, and uses them to predict future outcomes. This is the oldest and most established type of AI in the Salesforce world.
Salesforce examples:
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Einstein Lead Scoring: This tool analyzes your past leads, both the ones that converted and the ones that didn’t, to score new leads. This helps your sales team figure out who to call first.
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Einstein Opportunity Scoring: This works in a similar way for sales deals, looking at your history to guess the probability of closing an open opportunity.
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Churn Prediction: By examining customer behavior and history, it can flag accounts that might be about to leave, giving you a heads-up to step in.
The limitations:
Here’s the catch: predictive AI is completely dependent on the data you give it. For it to work well, you need tons of clean, historical data sitting right there inside Salesforce. A lot of businesses simply don’t have that, which can make the predictions unreliable. Plus, it’s stuck with blinders on, only seeing what’s in Salesforce and missing all the useful information in your other apps. This is a huge blind spot. It’s why some modern AI support platforms, like eesel AI, are built to connect to all your knowledge sources, from your help center to past tickets and internal wikis, giving your agents a much fuller, more accurate picture.
2. Generative AI: Creating content on demand
This is the AI everyone’s been talking about lately. Generative AI creates brand new content, like text, summaries, or emails, based on a prompt you give it and the data it can access. The whole point is to save your team time on writing tasks.
Salesforce examples:
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Einstein for Service: This feature can generate "Service Replies," which are suggested responses that agents can use in chats and emails to answer customers faster.
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Einstein for Sales: It can help your sales team by drafting personalized outreach emails, pulling customer details straight from the CRM.
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Knowledge Article Generation: Once a support case is closed, it can create a draft for a knowledge base article that explains the solution.
The limitations:
Unfortunately, getting it to produce high-quality, on-brand content isn’t as simple as flicking a switch. You often have to wrestle with tools like Prompt Builder, which can be a real pain if you’re not a Salesforce developer. Even after all that work, the output can still sound a bit robotic or generic if it hasn’t been trained heavily on your company’s specific voice.
Instead of making you mess around with complicated prompt engineering, eesel AI just learns your brand’s voice automatically by looking at your team’s best historical ticket responses. You can get a fully customized AI copilot running in a few minutes, not a few months, with no developers needed.
The eesel AI Copilot drafts a personalized response within a support ticket, showcasing an alternative to complex prompt engineering.3. Agentic AI: Taking autonomous action
This is where things get really futuristic, or at least, that’s the idea. Agentic AI doesn’t just predict things or write text; it takes action. The goal is to have an AI that can manage a multi-step task from beginning to end without a human stepping in. This is the big promise behind Salesforce Agentforce.
Salesforce examples:
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Agentforce for Service: The dream here is an AI agent that can handle a customer support ticket all by itself, from figuring out the problem to processing a refund and closing the ticket.
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Agentforce for Sales: An agent like this might be able to qualify a new lead, book a demo on a sales rep’s calendar, and update the CRM record, all without any help.
The limitations:
And this is where things get a bit shaky. The reality doesn’t quite live up to the hype just yet. Even Salesforce’s own data shows these AI agents can stumble on more complicated tasks pretty often. That’s a massive risk for your customer experience. If an autonomous agent messes something up, it ends up creating more work for your human team and leaves you with an angry customer.
Letting one of these agents loose can feel like a total leap of faith. You need to be able to trust your automation. That’s exactly why eesel AI built a powerful simulation mode. It lets you test your AI agent on thousands of your past tickets, so you can see exactly how it will perform and what your resolution rate will be before it ever talks to a real customer. Then, you can roll it out slowly, starting with just one type of ticket to build up confidence.
The eesel AI simulation mode allows users to test their AI agent on historical data to predict its performance before deployment.Unpacking the cost of AI capabilities in Salesforce
One of the trickiest parts of Salesforce AI is just figuring out the price tag. It’s not a single product; it’s a bunch of add-ons with a pricing model that can feel deliberately confusing.
The basic features are often rolled into the most expensive Salesforce plans (like Unlimited Edition) or sold as separate add-ons. For instance, Sales Cloud Einstein can cost around $50 per user per month.
For the newer generative and agentic AI, Salesforce uses a credit system. You get a certain number of "credits," and each time the AI does something, it uses up some of them. This can make budgeting a total nightmare. If your support team has a busy month, you could get hit with a massive bill, basically getting punished for doing a great job.
The eesel AI alternative: Transparent and predictable
eesel AI has a much simpler philosophy. The pricing is straightforward and predictable. You pay a flat rate for a certain number of AI interactions each month, and there are absolutely no fees for each ticket it resolves. You’ll always know exactly what to expect on your bill.
| Feature | Salesforce AI | eesel AI |
|---|---|---|
| Pricing Model | Confusing add-ons and unpredictable usage credits. | Simple, transparent monthly or annual plans. |
| Per-Resolution Fees | Often yes (paid for with credits). | No, never. |
| Predictability | Low. Your costs can spike with more usage. | High. You get a fixed cost for a set capacity. |
| Flexibility | Usually requires a long-term annual commitment. | Offers month-to-month plans you can cancel anytime. |
The challenge of being "all-in" on Salesforce
The other big thing to know is that to really use Salesforce AI, you have to be all-in on Salesforce. Its AI is built to live and breathe inside its own world, working best with data that’s already in the platform. If you want to connect it to outside tools or knowledge bases, you’re often looking at expensive, custom development work.
And getting it all set up isn’t a weekend project. It takes real Salesforce expertise and a lot of your team’s time. It is definitely not a plug-and-play tool.
In contrast, eesel AI is made to work with the tools you already use and like. With one-click integrations for help desks like Zendesk and Intercom, and knowledge sources like Confluence and Google Docs, you can be up and running in minutes. eesel AI fits right into how you already work, without making you change a thing.
The eesel AI platform showing various one-click integrations with popular help desks and knowledge sources.Making the right choice for your team
So, let’s wrap this up. Salesforce AI really breaks down into three camps: predictive AI for forecasting, generative AI for creating content, and agentic AI for taking action. If your company lives and breathes Salesforce and has the budget and experts to manage it, it can be a powerful set of tools.
But for most teams, especially in customer support, going all-in on a single platform isn’t the best move. A more flexible and dependable tool is often the smarter way to go. If you want strong AI automation without the headaches, risks, and vendor lock-in of a giant, all-in-one system, you need a tool that’s built for that specific job.
eesel AI gives you the power of a fully customized AI support agent with the simplicity of a platform you can set up yourself. It connects to all your tools, learns from your best agents, and lets you test everything out so you can feel confident before you go live.
Ready to see what a simple, powerful, and risk-free AI support agent could do for your team? Get started with eesel AI for free and automate your first support tickets in minutes.
Frequently asked questions
The guide breaks down Salesforce AI into Predictive AI (forecasting based on historical data), Generative AI (creating new content like emails or summaries), and Agentic AI (taking autonomous, multi-step actions). Each serves distinct purposes within the Salesforce ecosystem.
Salesforce AI features are often add-ons to expensive plans or sold via a credit system, where usage consumes credits. This model can lead to unpredictable costs, especially for newer generative and agentic AI features, making budgeting difficult.
Predictive AI relies heavily on clean, internal Salesforce data, often missing external insights. Generative AI can be complex to configure for on-brand output, requiring prompt engineering. Agentic AI, while promising, still faces reliability issues with complex tasks, posing risks to customer experience.
For customer support, Generative AI (like Einstein for Service creating replies) and Agentic AI (Agentforce handling full tickets) are particularly relevant. Predictive AI can also help by identifying potential churn or lead scoring, indirectly aiding support efforts. This is how you can improve customer support.
Salesforce AI is designed to work best within its own ecosystem. Integrating it with external tools or knowledge bases often requires expensive, custom development work, as it isn’t typically a plug-and-play solution.
Consider your primary goal: do you need to forecast outcomes (predictive), automate content creation (generative), or fully automate multi-step tasks (agentic)? Evaluate your existing data quality, budget, and internal expertise, as each type has different requirements and limitations.
