If you’re leading a marketing team in 2025, chances are you’ve been pitched AI from every direction: image generators, chatbots, virtual influencers. You’ve probably seen demos of dazzling creative tools that promise to slash costs or rewrite campaigns in seconds. And maybe—just maybe—you’ve assumed that your Salesforce investment already has “AI” baked in, and it’s doing its thing behind the scenes.
But here’s the problem: Most organizations aren’t actually using the AI they already have. And worse, many marketing leaders still misunderstand what AI is—and what it isn’t.
1. AI Isn’t Magic. It’s Modeling.
Too often, AI is perceived as a black box—a magical engine that just knows what to do. But in reality, AI is far more mundane—and far more powerful when treated accordingly.
At its core, AI in marketing is about probability. Predictive models use historical and real-time data to make statistically informed guesses:
- Who’s likely to open this email?
- Which image will get the most clicks?
- What time should we send this message?
- Which product should we recommend next?
It’s math, not magic. And treating it like a wizard instead of a workhorse is one reason so many “AI transformations” stall out.
2. The Problem with Chasing Hype: Generative Gets the Buzz, Predictive Gets Results
Right now, generative AI gets all the attention. From executives wowed by AI-generated photos to internal teams experimenting with ChatGPT for copywriting, it feels like the future is here.
But most of the business value in AI today lies in predictive tools—not creative ones.
Global adoption of generative AI technologies—including text, image, and chatbot generation—has risen dramatically, increasing from 33% in 2023 to 71% in 2024 according to McKinsey. However, only around 10% of companies have fully integrated these tools into core operations (McKinsey, 2024).
By contrast, personalization and predictive AI tools—such as Salesforce Einstein—have seen slower adoption. Reports indicate only 39–59% of Salesforce Marketing Cloud customers actively use features like predictive scoring, content selection, or send-time optimization (Damco, 2025 | Redress, 2025).
Put simply: Generative gets headlines. Predictive drives revenue.
3. What Salesforce Einstein Actually Does (That Your Team Might Be Ignoring)
Salesforce’s Einstein brand includes a broad set of AI features, most of which support automation, personalization, and optimization—not image generation or chatbot content.
- Einstein Content Selection (ECS): Swaps in personalized banners, CTAs, or promotions based on individual-level data.
- Einstein Engagement Scoring (EES): Prioritizes active, high-intent users in your CRM.
- Send Time Optimization (STO): Dynamically staggers send times for each person.
- Predictive Lead Scoring: Improves sales and dealer handoff strategies.
Yet Salesforce’s own ecosystem data shows that fewer than 40% of Einstein-enabled organizations use more than one of these features—and that usage is rarely integrated across customer journeys.
4. Creative Bias: The Quiet Killer of AI ROI
In many brand-driven organizations, creative still holds disproportionate influence over campaign direction.
That’s not a critique of design. But when aesthetics take precedence over testing, targeting, and decisioning, the full value of AI is often left untapped.
Classic direct marketing wisdom: 60% of success comes from the list, 30% from the offer, and only 10% from the creative.
Contemporary data backs this up:
- Adobe’s 2024 Digital Trends report found that dynamic personalization strategies drove 2–3x more revenue per send than campaigns emphasizing creative refreshes alone.
- A Fortune 100 retailer discovered that triggered emails using Einstein Content Selection generated 4x the revenue of their highest-profile seasonal campaigns—but struggled to secure sustained executive support.
5. You’re Already Paying for AI—Even If You Don’t Know It
Whether you're at a global brand or a scrappy startup, chances are you’re already paying for AI.
In enterprise platforms, tools like Salesforce, Adobe, and HubSpot bundle predictive AI features into their licensing fees. But platform inclusion does not equal operational impact.
Instead, AI is frequently treated as a compliance checkbox:
- ✔️ AI-enabled CRM
- ✔️ AI-ready platform
- ❌ Actually driving business impact
Salesforce CEO Marc Benioff recently positioned Agentforce—its next-gen AI framework—as the centerpiece of the company’s future, noting on CNBC:
“We have pivoted our company hard and fast... The power of Agentforce is that our apps, agents, and data cloud are all on one unified platform. The agents are fluid, available anytime, in any app, in the flow of work.”
It’s an ambitious vision—but one that risks being undermined by foundational gaps in adoption. Many organizations haven’t even fully leveraged baseline features like STO or EES, much less deployed agentic AI workflows.
6. The Fix: Start with Strategy, Not Shiny Objects
If you’re a marketing leader, the real unlock isn’t another AI pilot. It’s integration.
- Start with a business question: How can we recover more cart abandons? How can we upsell service plans in the ownership phase?
- Work cross-functionally to connect CRM, content, and analytics.
- Shift creative thinking toward modular, testable assets.
- Measure incremental lift from personalization, not just campaign performance in aggregate.
AI isn’t a strategy—it’s an amplifier. If your core execution is sloppy or misaligned, AI will magnify that dysfunction. If your processes are disciplined, AI can generate real operational lift.
Closing Thought
AI in marketing isn’t about creating more content. It’s about creating better outcomes.
If your team is obsessed with campaign visuals but ignoring send time optimization, engagement scoring, or dynamic decisioning, you’re not leveraging AI—you’re decorating it.
Now is the time to move beyond the hype and start realizing the potential of the tools already within your reach.
No comments:
Post a Comment