A practical guide to Salesforce AI projects in 2025

It seems like every company is talking about AI. But here’s the part that often gets left out of the conversation: most of these big AI projects just don’t work out. In fact, one study found that a wild 80% of corporate AI initiatives fail to deliver on their promises. It’s a familiar story, a team gets excited, dives in, and then their Salesforce AI projects get swamped by hidden complexities, messy data, and costs that just keep climbing.

If you’re thinking about launching an AI project on Salesforce, you’re in the right spot. This guide is a no-fluff, practical look at what you’re really getting into. We’ll cover the main tools in Salesforce’s AI kit, walk through a simple framework for planning your project, and point out the common tripwires to watch for.

What are Salesforce AI projects?

First things first, "Salesforce AI" isn’t just one magic button. It’s a whole collection of tools built right into the platform you’re already using, all designed to automate work, find useful patterns, and generally make life easier for your customers and employees. A Salesforce AI project is just any effort that uses these tools to fix a specific business problem.

These tools mostly come in two flavors:

• Predictive AI: This is all about using your past data to make smart guesses about what will happen next. A classic example is Einstein Opportunity Scoring, which looks at all your past deals to predict which of your current leads are most likely to turn into customers. It’s basically a crystal ball that uses your own sales history to work.

• Generative AI: This is the creative side of the coin. It makes new content from scratch based on what it’s learned. For instance, Salesforce Einstein can whip up a personalized sales email or suggest a reply to a support ticket, giving your team a solid starting point.

Underpinning all of this is the Salesforce Data Cloud. You can think of it as the fuel tank for the whole AI operation. Its main job is to pull all your scattered customer data into one place, which gives the AI the context it needs to be helpful instead of just making things up. Without good, clean data, even the smartest AI is just flying blind.

The core components of Salesforce AI projects

Before you can kick off a project, it helps to know the main players in the Salesforce AI lineup. Here’s a quick introduction to the tools you’ll be working with.

Salesforce Einstein: The core AI technology

Salesforce Einstein is the brand name for Salesforce’s core AI tech. It’s not some separate app you have to open; it’s a layer of smarts that’s woven directly into the tools you use every day, like Sales Cloud and Service Cloud.

Here are a few things it can do:

• For your sales team: It can draft follow-up emails, summarize call notes so you don’t have to, and flag deals that seem to be going cold, helping reps know where to focus.

• For your service team: It can automatically create summaries of long, confusing support cases, write draft replies for agents, and even help turn solved tickets into new knowledge base articles.

The big idea with Einstein is that it lives right inside Salesforce, so it can use all your CRM data to understand your business and your customers.

A screenshot of Salesforce Einstein AI summarizing a customer support case, demonstrating a key feature for successful Salesforce AI projects.

Salesforce Data Cloud: The fuel for your AI

Like we said, AI is pretty useless without good data. That’s what the Data Cloud is for. It’s Salesforce’s attempt to solve the age-old problem of fragmented information. Most companies have customer data living in a dozen different spots, the CRM, a marketing tool, an e-commerce platform, you name it.

Data Cloud’s role is to connect all those dots. It pulls data from all your different sources to build a single, complete profile for each customer. This gives Einstein the rich context it needs to make accurate predictions and helpful suggestions. It uses things like Dynamic Grounding to pull context from both structured data (like fields in your CRM) and unstructured data (like random notes or documents) to make the AI’s output even more on-point.

Agentforce and builders: Creating custom AI solutions

This is where you get to roll up your sleeves and really make the AI your own. Agentforce is Salesforce’s platform for building autonomous AI agents and copilots that can handle specific tasks for your team. You do this with a few key tools:

• Agent Builder: This lets you design AI agents that can actually do things, like route a support ticket to the right person or update a customer’s record.

• Prompt Builder: This gives you a way to write and save really good prompts that are based on your company’s data. This helps make sure the AI’s responses are always consistent and sound like your brand.

• Model Builder: If you have a more technical team, this tool lets you bring your own custom predictive AI models into the Salesforce world.

This ability to customize is powerful, but it’s also where things can start to get complicated. Building agents that work well requires a clear plan and, often, a decent amount of technical skill.

The Salesforce Prompt Builder interface, a key tool for custom Salesforce AI projects.

How to plan and execute your first Salesforce AI projects

Going into an AI project without a solid plan is asking for trouble. Here’s a simple, six-stage framework to get you from an idea to a finished product, inspired by Salesforce’s own Trailhead guides.

Stage 1: Define a clear problem and SMART goals

Don’t start with "we need AI." Start with a real business headache you’re trying to fix. Are your support agents wasting hours on the same simple questions? Is your sales team having a hard time figuring out which leads to call first?

Salesforce uses a great example in their documentation: a resort chain wanted to "reduce check-in time by 50%" without letting customer satisfaction drop. That goal is perfect. It’s specific, measurable, achievable, relevant, and time-bound (SMART). A clear target like that gives your project a direction and makes it easy to know if you’ve succeeded at the end.

Stage 2: Prepare your data (the biggest hurdle)

This is the step where most Salesforce AI projects get stuck. Your data quality is everything. Salesforce’s AI is designed to work with clean, complete data that lives neatly inside Salesforce objects. If your data is a disorganized mess, your project is in trouble before it even starts.

But let’s be honest, the biggest challenge isn’t the data you have in Salesforce. It’s the mountains of valuable knowledge that live everywhere else. Think about it: internal wikis in Confluence, project plans in Google Docs, and years of customer conversations in help desks like Zendesk. Shoveling all of that into the Salesforce Data Cloud is a massive, months-long data migration project.

Frankly, that sounds exhausting. What if you could skip that whole step? Instead of moving all your data, tools like eesel AI connect directly to the places where your knowledge already is. You can plug it into your existing tools in a few clicks and start training an AI on your team’s real-world knowledge, no painful migration required.

Stage 3: Build, test, and deploy gradually

Whatever you do, don’t roll out a new AI tool to your entire company at once. Start small. Use a sandbox environment to build and test your ideas. Let a small group of users play with it, give you feedback, and tell you what’s working and what isn’t. Then, make it better and repeat.

Salesforce gives you tools for this, but getting feedback can be slow, and it’s often hard to guess how the AI will behave in the real world. This is another spot where you can get stuck. With eesel AI, you can sidestep that uncertainty. It has a simulation mode that lets you test your AI on thousands of your actual, historical support tickets before it ever interacts with a real customer. This gives you a surprisingly accurate forecast of its performance and lets you roll it out with confidence, starting with just one type of issue and expanding from there.

An eesel AI simulation report, which helps in testing Salesforce AI projects before they go live.

Common challenges and limitations of Salesforce AI projects

Now for the reality check. While Salesforce gives you a lot of powerful tools, launching Salesforce AI projects comes with a few common headaches that can easily throw you off course. Here’s what to look out for, and how a different approach might help you avoid them.

Challenge 1: The long, complicated setup

A full Salesforce AI implementation is not a weekend job. It’s a major IT project that can take months of planning, setup, and work from certified Salesforce experts. Just getting the Data Cloud up and running can be a huge obstacle for many teams.

For a lot of us, that kind of timeline just isn’t practical. You need something that can start helping your team this quarter, not next year.

The eesel AI alternative: In contrast, eesel AI is designed to be completely self-serve. You can connect your help desk and knowledge sources with one-click integrations and have a working AI agent in a matter of minutes, not months. You don’t have to sit through mandatory demos or deal with long sales calls just to see if it’s the right fit.

Challenge 2: Rigid workflows and needing developers

While Salesforce’s builder tools let you customize things, they often keep you locked inside the Salesforce world. If you want to make a big change to how an AI works, you might find yourself needing to write custom Apex code or hire a developer.

This can make it tough to do one of the most important things when starting with AI: automating selectively. You don’t want an AI that tries to answer every single question. You want one that handles the easy, repetitive stuff and knows exactly when to pass a tricky issue to a human.

The eesel AI alternative: eesel AI gives you total control with a fully customizable workflow engine. You get to decide precisely which tickets the AI should touch. You can set its personality and tone with a simple prompt editor, and you can create custom actions, like checking an order status in Shopify or tagging a ticket for a specific team, all without writing a single line of code.

Challenge 3: Confusing and unpredictable pricing

Trying to figure out what a Salesforce AI project will actually cost can be surprisingly hard. For most of their AI tools, like Einstein AI and Data Cloud, the pricing page just says "Contact Sales." The costs are often bundled into different product tiers, which makes it almost impossible to know the true price of your AI project.

This lack of clarity makes budgeting a nightmare and can lead to some nasty surprise costs down the line as you start using the tools more.

The eesel AI alternative: eesel AI uses transparent and predictable pricing. Our plans are based on a flat monthly fee that includes a generous number of AI interactions. Most importantly, there are no per-resolution fees, so you never get a surprise bill just because you had a busy month. You know exactly what you’re paying, and you can even start on a month-to-month plan that you can cancel anytime.

Challenge with Salesforce AI Projects	The eesel AI Approach
Long & Complex Setup: Can take months and often requires certified consultants.	Go Live in Minutes: A completely self-serve setup with one-click integrations.
Data Silos: Requires a huge project to get all your data into the Salesforce Data Cloud.	Unified Knowledge, Instantly: Connects to your existing docs, tickets, and wikis.
Rigid Workflows: Customization can be hard and may require developers.	Total Control: A flexible workflow engine with a no-code prompt editor.
Opaque Pricing: Hidden costs and a "Contact Sales" button for most AI products.	Transparent & Predictable: Flat monthly fees, no surprise per-resolution charges.
Risky Rollouts: It’s hard to test and predict how it will perform in the real world.	Test with Confidence: A powerful simulation on past tickets before you go live.

Start smart with your Salesforce AI projects

Salesforce offers a deep and powerful set of AI tools, but they bring a lot of complexity with them. A successful project there demands careful planning, a serious commitment to cleaning up your data, and a clear-eyed view of the potential costs and timelines.

But for many teams, the key to winning with AI isn’t some massive, multi-year project. It’s about finding specific, high-impact problems where AI can start delivering real value, right now.

So instead of getting bogged down planning a huge Salesforce AI project that might not deliver for a year, maybe the better question is: what could you automate this week? eesel AI plugs right into the tools your team already uses, like Zendesk, Slack, and Confluence, to automate support and bring all your knowledge together from day one. Start your free trial today and see how simple enterprise AI can actually be.