Hey Smart Marketers!
It’s been a while! It seems like there will be a before and an after this Dreamforce!
Before talking about Agentforce, let me just shamelessly push our latest innovation… We’re bringing TikTok Ads to Data Cloud audiences!! 🎉 We’re launching officially soon but with Salesforce last quarter and its usual madness coming we chose to onboard clients per batch… If you know about an interested company, there’s a waitlist here.
Back to Dreamforce…
It was not Dreamforce this year.
It was AgentForce.
AI Agents everywhere.
AI Agents doing all sort of things.
All sort of things but not Marketing…
Most of the use-cases were focused on Sales or Service…
But why?
The Platform “issue”
Let’s talk about the elephant in the room: Salesforce Marketing Cloud Engagement and Account Engagement are not built on the Salesforce Platform…
And Agentforce sits on the Salesforce Platform.
That’s the main reason why we only saw use-cases with Salesforce Marketing Cloud Growth and Advanced Editions.
AI Agents need data sources and a channel to communicate with the user. Neither is easy to manage with “legacy” Marketing Cloud products.
Add to this that Marketing Cloud current users are not Salesforce Core users and you’ll get why proposing a demo on Core makes less sense than for Sales or Service.
Where’s the Data?
One of the biggest challenges in using AI agents for marketing is the scattered nature of marketing data.
Some of our data lives in various platforms—email systems, advertising tools, social media analytics, CRMs—and it’s often unstructured.
And some of our data is just outside the systems: marketing plans, resources, budgets, approval processes and so on… in spreadsheets, slide decks or meeting minutes.
Except, to work efficiently, AI Agents need structured data or at least unified data pipelines.
I’ll just pause here to explain what I mean by “need”. You can build an AI Agent without gathering and structuring all the data you can. But then, you’ll have to deal with hallucinations.
Hallucinations are a real threat when it comes to using AI at scale for a company.
However, AI Agents are the future
Let’s imagine for a moment that we overcome this hurdle. Suppose all of our marketing data—structured and unstructured—can be collected and integrated into Salesforce’s Data Cloud.
Suppose AgentForce is able to “activate” segments based on prompts or predefined actions.
AI Agents could automate complex tasks like campaign management, lead nurturing, and customer segmentation—freeing marketers to focus on high-level strategy and creative decision-making.
Automation vs. Decision-Making in Marketing
For ages, Marketing Operations pain point was Time-to-Market… and Marketing Automation was the go-to solution for reducing it.
Tools like email campaigns, social media schedulers, and CRM-driven workflows were created to streamline repetitive tasks and let marketers focus on higher-value activities. Marketing automation was, and still is, the key to keeping campaigns running at scale, without needing to micromanage every single action.
And therefore, the job for us was to set-up these marketing platforms. Helping automate everything. Creating templates. Automations. Code snippets.
Delivering asset libraries so marketers could “quickly” set up new journeys, send an email and so on…
But that’s static isn’t it?
We’ve been spending weeks (months) during framing to figure out with the client how they were going to run their marketing. Their target structure, way of work and even relational patterns. And then we would deliver and they’d have to stick to it for years.
They can change a couple of things of course… but through Jira tickets and they’d have to wait for weeks.
In fact, the more we automate, the more we freeze the company’s marketing operations.
If AI Agents can handle the “delivery” part, do we still need automation?
Marketers’ roles will change from building and managing automation workflows to focusing on critical strategic decisions that require human intuition and creativity.
While the AI agent handles the nuts and bolts of execution, the marketer becomes the orchestrator of strategy, using AI as a powerful decision-support tool rather than a rule-execution tool.
I think we’ll see a marketing department that’s more agile and data-driven, empowered by AI to act quickly and intelligently. The shift from automation to AI-powered decision-making transforms marketing from a reactive discipline (responding to data after the fact) to a proactive one (predicting and shaping future strategies).
What’s your take on AgentForce?
Do you think we’ll see more marketing use-cases?
Other ways I can help you: