Category: Salesforce Marketing Cloud

AI is becoming a pretty big part of Marketing Cloud Next. And if you’ve been following the Salesforce ecosystem lately, you’ll know that we now have everything from Agentforce agents creating campaigns to Einstein features deciding when and how often we should communicate with customers.

Cool, right?

But there’s something I think is worth paying a bit more attention to when we talk about these features: what actually happens to the data behind the scenes?

Salesforce has published some useful information about AI data usage in Marketing Cloud Next, including which customer data each feature uses, whether it uses a generative AI model, and whether it uses what Salesforce calls a global model.

And there are some interesting differences between the features.

So, let’s have a look.

First, what does Salesforce mean by AI data usage?

Before jumping into individual features, there are two types of data Salesforce calls out:

• Customer Data — data submitted by the customer to Salesforce services.
• Usage Data — data relating to users’ interactions with Salesforce.

Salesforce also makes an important statement in the documentation:

Data submitted to an AI feature can be used to train AI models, improve Salesforce services and features, or develop new features that customers can access without additional cost.

That sentence is definitely worth knowing if you’re involved in Salesforce architecture, security, governance or client conversations around AI.

But here’s where it gets interesting.

Not every AI feature works in the same way.

Some use generative AI. Some don’t.

Some use a global model. Some don’t.

And the actual customer data being used varies quite a bit from feature to feature.

Let’s get into it.

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Agentforce – Campaign Creation Agent

If you’ve spent any time looking at Agentforce in Marketing Cloud Next, this is probably one of the features that immediately catches your attention.

The Campaign Creation Agent is designed to help with the campaign creation process. Rather than starting with a completely blank canvas, the agent can work with information already sitting in your Marketing Cloud environment.

Salesforce identifies the following data as being used:

• Campaigns
• Briefs
• Engagement Data from Data Cloud
• CMS Assets
• Flows

And here’s the first interesting bit.

The Campaign Creation Agent uses a generative AI model, but Salesforce identifies it as not using a global model.

So when we hear “generative AI”, we shouldn’t automatically jump to the conclusion that our customer data is being sent into some single global model.

There’s a distinction here.

Generative AI model: Yes

Global model: No

That’s a pretty useful distinction to understand when you’re having those inevitable AI governance conversations with customers.

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Agentforce – Content Creation Agent

Now we get to something that is probably going to feel very familiar to Marketing Cloud users: creating content.

The Content Creation Agent is designed to help marketers create campaign content using information that’s already available to them.

Salesforce identifies these as the data sources used:

• Campaigns
• Briefs
• CMS Assets

And, just like the Campaign Creation Agent, it uses a generative AI model.

But again, Salesforce identifies no global model usage.

This is one of those areas where I think it’s worth being precise with the language.

It’s easy to say:

“Salesforce is using our marketing content to train AI.”

But the actual documentation gives us more detail than that.

The feature uses customer data as part of its AI functionality, and Salesforce says data submitted to AI features can be used to train models or improve services. However, Salesforce specifically identifies this feature as not using a global model.

That’s an important distinction.

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Segment Creation

Now this one is particularly interesting if you’re working with Data Cloud.

Segment Creation uses segments in Data 360 as the customer data involved in the feature.

The idea here is pretty cool: AI can help marketers work with audience segmentation rather than requiring them to manually construct every piece of logic themselves.

And, once again, Salesforce identifies it as:

Generative AI: Yes

Global model: No

So we’ve now got three AI capabilities where generative AI is being used, but Salesforce doesn’t identify a global model:

• Campaign Creation Agent
• Content Creation Agent
• Segment Creation

This is a useful pattern to keep in mind.

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Einstein Send Time Optimization

Now things get a little more interesting.

If you’ve worked with Marketing Cloud for a while, Einstein Send Time Optimization probably doesn’t need much introduction.

The idea is fairly straightforward: rather than sending an email at the same time to everyone, the system looks at engagement behaviour and determines when an individual is more likely to engage.

The data Salesforce identifies includes:

• Sends
• Clicks
• Opens
• Unsubscribes
• Spam complaints
• Associated timestamps
• Data and metadata about customer sending patterns
• Information about how campaigns are executed

But here’s the important part:

Send Time Optimization does not use a generative AI model.

Instead, Salesforce identifies it as using a global model.

And in Growth Edition, Salesforce says this global-model usage is required.

Wow — that’s quite different from the Agentforce examples we’ve just looked at.

This is a good reminder that “AI” doesn’t automatically mean generative AI.

There are plenty of predictive AI capabilities in Marketing Cloud that have been around well before the current generative AI boom.

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Einstein Metrics Guard

Metrics Guard is another interesting one.

If you’ve ever looked at Marketing Cloud engagement numbers and thought, “Hmm… are all these opens and clicks actually real?”, this is where Metrics Guard comes in.

Email security systems and automated scanners can sometimes generate opens and clicks that don’t represent genuine human engagement.

Metrics Guard is designed to help identify and account for that kind of activity.

Salesforce identifies the following data as being used:

• Email sends
• Opens
• Clicks
• Unsubscribes
• Bounces
• Spam complaints
• Data and metadata about customer sending patterns

And here’s the important distinction:

Generative AI model: No

Global model: Yes

Salesforce marks the global model usage as required.

So, again, this isn’t about an AI assistant writing your next email campaign.

It’s a predictive AI capability working with email engagement data.

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Agentforce – Journey Decisioning Agent

Now let’s jump back into Agentforce territory.

The Journey Decisioning Agent is aimed at the customer journey side of things. Instead of AI simply helping us write content, we’re starting to see AI involved in decisions around customer journeys.

Salesforce identifies:

• Journeys
• Subscriber content

as the customer data used.

And, like the other Agentforce features we’ve looked at:

Generative AI model: Yes

Global model: No

This is where things start getting really interesting for Marketing Cloud practitioners.

We’re moving from AI helping with individual tasks to AI becoming part of the actual decision-making and orchestration experience.

That’s a pretty big shift.

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Einstein Engagement Scoring

Engagement Scoring is another feature that takes a more predictive approach.

Rather than generating content, it’s looking at engagement behaviour and trying to understand how likely a contact is to engage.

The data involved includes things such as:

• Email sends
• Opens
• Clicks
• Unsubscribes
• Bounces
• Spam complaints
• Data and metadata about customer sending patterns

And here’s the interesting part:

Generative AI model: No

Global model: No

So despite being an Einstein AI feature, there’s no generative AI and no global model identified for this feature.

This is a great example of why we shouldn’t lump every Einstein feature into the same bucket.

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Einstein Engagement Frequency

And finally, we have Einstein Engagement Frequency.

The name probably gives away what this one is about.

The feature looks at engagement behaviour to help determine how frequently an individual contact should receive communications.

Nobody wants to be that brand that sends five emails in two days and then wonders why engagement has dropped!

The data involved includes:

• Email sends
• Opens
• Clicks
• Unsubscribes
• Bounces
• Spam complaints
• Data and metadata about customer sending patterns

Salesforce identifies:

Generative AI model: No

Global model: No

So, once again, we’re looking at predictive AI rather than generative AI.

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So what should we actually take away from all this?

For me, the biggest takeaway is this:

“Marketing Cloud Next uses AI” isn’t really a useful statement anymore.

It’s a bit like saying “Salesforce uses data”.

Well… yes. But what data? Where? For what purpose?

The same thinking applies to AI.

When you’re looking at a particular Marketing Cloud Next feature, I’d ask four questions:

1. What data does the feature use?

Is it campaign information?

Subscriber content?

Data 360 segments?

Email engagement?

CMS assets?

The answer is different for each feature.

2. Is it generative AI or predictive AI?

This is a really important distinction.

Agentforce features such as Campaign Creation Agent and Content Creation Agent use generative AI.

Features such as Send Time Optimization and Engagement Scoring are predictive.

They’re doing very different jobs.

3. Does Salesforce identify it as using a global model?

This is probably the question I’d pay the most attention to when discussing data governance.

And the answer varies.

For example:

Send Time Optimization and Metrics Guard → Global model: Yes

Whereas:

Campaign Creation Agent, Content Creation Agent, Segment Creation, Journey Decisioning Agent, Engagement Scoring and Engagement Frequency → Global model: No

4. Is the global model usage required?

This is another important detail.

For Send Time Optimization, Salesforce identifies global-model usage as required in Growth Edition.

For Metrics Guard, it’s marked as required.

That’s very different from simply having an optional AI feature switched on somewhere in your org.

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The bigger picture

I think we’re going to have more and more of these conversations as Salesforce continues bringing AI into Marketing Cloud.

And honestly, that’s a good thing.

But we need to get a little more precise in how we talk about it.

Instead of asking:

“Does Marketing Cloud use our data for AI?”

I’d ask:

“Which Marketing Cloud feature are we talking about, what data does it use, what type of model does it use, and does Salesforce identify that model as global?”

That’s a much more useful conversation.

Because, as we’ve seen, the answer changes depending on the feature.

And that’s probably the most interesting thing about Salesforce’s documentation here.

AI isn’t one big bucket.

Marketing Cloud Next has generative AI, predictive AI, customer-data-driven features and global-model-driven features — and they don’t all work in the same way.

So the next time someone says, “But Salesforce AI uses our customer data, right?”, you can give them the much more useful answer:

“Well… which AI feature are we talking about?”

And that’s where things get interesting.

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Check out my recent post on different AI tools that could be helpful if you are into technology