From "If/Then" to Intelligent Predictions: The AI Revolution in Email Personalization

 For years, email personalization has been the holy grail of digital marketing. We've all strived to send the right message to the right person at the right time. But how we achieve that "rightness" is undergoing a massive transformation. We're moving beyond the laborious, often brittle world of "if/then" rules and embracing the power of predictive AI.

At my core, I'm all about making marketing smarter and more effective. And the shift to AI-driven email personalization is one of the most exciting developments I've seen in years.

The Era of the "If/Then" Labyrinth

Think about how we've traditionally approached email personalization. It's been a massive, intricate flowchart.

• "IF a customer bought Product A, THEN recommend Product B, UNLESS they also bought Product C, AND EXCLUDE anyone who visited the returns page in the last 7 days."

Sound familiar? This "if/then" approach, while foundational, comes with significant baggage:

1. Scalability Headaches: As our customer base grows and their behaviors diversify, the number of rules explodes. Managing this becomes a full-time job, leading to inevitable errors and missed opportunities.

2. Rigid & Reactive: These rules are static. They can only react to predefined conditions. They can't anticipate a customer's evolving needs or adapt to subtle shifts in behavior in real-time.

3. Limited Personalization: We're often personalizing to broad segments, not individuals. A rule might apply to thousands, but does it truly resonate with each one? The depth of personalization is inherently limited.

4. Maintenance Nightmares: Updating, debugging, and ensuring these complex rule sets don't conflict is time-consuming and prone to breaking existing logic. Exclusions and suppressions add layers of administrative burden.

5. Underutilized Data: We collect vast amounts of data, but with "if/then" rules, we only leverage the data points that fit our predefined categories. Nuance and subtle signals are often lost.

This creates a self-perpetuating cycle of complexity, where our time is spent managing rules rather than innovating.

Enter Predictive AI: Your Intelligent Email Navigator

Now, imagine an email system that learns from all your customer data – not just what you explicitly tell it to look for, but also the hidden patterns and correlations. This is the magic of predictive AI. Instead of us dictating every step, the AI acts as an intelligent navigator, predicting the most relevant and impactful content for each individual.

Here's how this intelligent approach transforms email personalization:

1. True 1:1 Personalization at Scale: AI moves us from segment-level personalization to individual-level personalization, creating highly relevant experiences for millions of customers simultaneously, without manual effort for each new permutation.

2. Proactive & Adaptive: AI anticipates customer needs and adapts to changing behaviors. If a customer's interests shift, the AI recognizes this and adjusts its recommendations accordingly, far faster than we could update manual rules.

3. Unlocking Data's Full Potential: The AI can process and learn from massive, complex datasets, identifying subtle signals and opportunities that human analysts might miss.

4. Reduced Operational Burden: The laborious task of creating, maintaining, and debugging complex rule sets is largely eliminated, freeing up marketing and IT teams to focus on higher-value strategic initiatives.

The Crucial Role of Confidence Scores and Guardrails

One of the most powerful aspects of predictive AI is its ability to assign a confidence score to its predictions. The AI doesn't just say, "Send this." It says, "I'm 92% confident that Customer A will click on an email about Product C, but only 30% confident Customer B will open a promotional email today."

This confidence score is a game-changer because it allows us to set intelligent guardrails:

• Confidence Thresholds: We can decide to only send an AI-personalized email if the AI's confidence score for a positive engagement (e.g., open, click, conversion) is above a certain percentage (e.g., 70%). If the confidence is lower, we can default to a more general email or a human-curated message. This prevents "bad" or irrelevant recommendations from going out.

• Business Rules (Blacklists/Whitelists): While AI is powerful, human oversight is still critical. We can implement strict "blacklists" (e.g., never recommend a product a customer just returned) and "whitelists" (e.g., always send a welcome series to new subscribers). These are essential for brand safety, compliance, and ethical AI use.

A Salesforce Perspective: Streamlining the Tech Stack

For those of us entrenched in the Salesforce ecosystem (Marketing Cloud Engagement, Data Cloud, Marketing Cloud Personalization, Einstein Content Selection), this shift has profound implications, particularly for reducing AMPscript complexity.

Today, our email templates are often laden with dense, nested AMPscript for "if/then" logic. With AI, AMPscript transforms from a complex decisioning engine into a cleaner, more efficient tool for rendering dynamic content supplied by the AI.

• Less Logic, More Efficiency: Instead of hundreds of lines of AMPscript deciding what content to show, our scripts primarily focus on how to display the AI-determined content.

• Data Cloud as the Brain: Salesforce Data Cloud performs the heavy lifting of unifying customer data and deriving intelligent insights, which then feed into Marketing Cloud Engagement, vastly simplifying the AMPscript needed.

• Einstein Content Selection's Power: For dynamic content blocks, Einstein Content Selection eliminates the need for AMPscript to make content choices, as it makes these decisions in real-time based on AI predictions.

This results in cleaner, more readable AMPscript, faster email development cycles, and improved email performance. It's a move away from brittle, template-level logic to a centralized, intelligent AI layer.

The Path Forward: A Collaborative Effort

Transitioning to predictive AI isn't about replacing human expertise; it's about augmenting it. It requires close collaboration between marketing and IT. Our IT teams will be crucial in:

• Building robust data pipelines within Salesforce Data Cloud.

• Implementing and managing the AI/ML platforms.

• Defining and enforcing the critical guardrails and confidence thresholds.

• Monitoring AI performance and refining models.

By embracing predictive AI, we're not just improving our email personalization; we're investing in a scalable, adaptive, and truly customer-centric approach that will drive significantly higher engagement, satisfaction, and ultimately, business growth.

What are your thoughts on the shift to AI-driven personalization? Share your experiences in the comments below!

Predictive AI: The "Chicago" of Artificial Intelligence – Building the Future, One Prediction at a Time

 When you hear "Artificial Intelligence," what's the first thing that comes to mind? For many, it's the flashy, creative power of generative AI: think stunning AI art, captivating chatbot conversations, or even hyper-realistic deepfakes. These innovations are undeniably exciting, grabbing headlines and sparking imaginations. They are, in a way, the Hollywood of AI – dazzling, innovative, and highly visible.

But beneath the glitz and glamour, another, equally vital form of AI is quietly, relentlessly building the foundations of our modern world. It's the AI of "big shoulders," the tireless worker, the backbone that ensures everything runs smoothly and efficiently. This, my friends, is Predictive AI, and if generative AI is Hollywood, then predictive AI is most certainly the Chicago of Artificial Intelligence.

Why Chicago? Why "Big Shoulders"?

Think of Chicago's historical legacy, powerfully captured by Carl Sandburg in his iconic poem "Chicago":

• Industrial Might: "Hog Butcher for the World, / Tool Maker, Stacker of Wheat, / Player with Railroads and the Nation's Freight Handler;" Sandburg's lines evoke a city built on hard work, logistics, and the efficient movement of goods and people. It was the engine room, the processing hub, the city that got things done.

• Foundational Infrastructure: While other cities might boast of their cultural flair, Chicago was – and still is – a marvel of urban planning and infrastructure, connecting vast regions and enabling commerce.

• Reliability and Resilience: "Stormy, husky, brawling, / City of the Big Shoulders:" Through booms and busts, Chicago has consistently proven its ability to adapt, endure, and continue its vital work.

Predictive AI shares these same fundamental characteristics. It's not always the star of the show, but its power is immense, its impact far-reaching, and its role absolutely indispensable.

What Does Predictive AI Do? The Workhorse in Action

While generative AI creates new things, predictive AI excels at anticipating what will happen next based on vast amounts of historical data. It analyzes patterns, identifies trends, and forecasts outcomes, empowering businesses and organizations to make smarter, more proactive decisions. It is truly the "City of the Big Shoulders" for the digital age.

Here's where predictive AI flexes its "big shoulders":

• Optimizing Supply Chains: Imagine a world where every delivery is delayed, every shelf is empty. Just as Chicago was the "Nation's Freight Handler," ensuring goods flowed smoothly, predictive AI takes on the monumental task of optimizing supply chains. It analyzes weather patterns, traffic, demand fluctuations, and supplier performance to predict disruptions and optimize logistics, ensuring products get where they need to be, when they need to be there.

• Forecasting Demand: Retailers use predictive AI to anticipate exactly how many units of a given product will sell, preventing both costly overstocking and frustrating stockouts. This helps businesses manage resources, reduce waste, and maximize profits – pure efficiency, echoing the work of a "Stacker of Wheat."

• Preventing Equipment Failure: In manufacturing plants or critical infrastructure, unexpected equipment breakdowns can be catastrophic. Predictive AI monitors sensor data from machines to predict when a component is likely to fail, enabling proactive maintenance and preventing costly downtime. It's the ultimate "Tool Maker," ensuring the machinery of industry hums along.

• Detecting Fraud: Banks and financial institutions rely heavily on predictive AI to flag suspicious transactions in real-time. By identifying patterns of fraudulent activity, it acts as a silent guardian, protecting billions of dollars and countless individuals from financial crime.

• Personalizing Experiences: Ever wonder how Netflix knows exactly what movie you might like next, or how Amazon recommends products you actually want? That's predictive AI analyzing your past behavior and preferences to anticipate your future interests.

• Improving Healthcare: From predicting disease outbreaks to identifying patients at high risk of readmission, predictive AI helps healthcare providers make more informed decisions, leading to better patient outcomes and more efficient resource allocation.

The Unsung Hero: Why "Not Sexy" is Actually Profoundly Powerful

Perhaps predictive AI isn't "sexy" in the way a viral AI art generator is. You don't necessarily interact with it directly in your daily life, and its successes are often defined by things not happening – no delayed packages, no fraudulent charges, no unexpected machine failures.

But this quiet, consistent impact is precisely what makes it so profoundly powerful. Predictive AI doesn't seek the spotlight; it enables the systems that make our lives easier, our economies stronger, and our industries more resilient. It's the steady, reliable engine room of the AI revolution, making sure the entire ship stays afloat and moves forward. It works "under the terrible burden of destiny laughing as a young man laughs and eating up the years like a hungry fighter," as Sandburg put it, relentlessly building a more efficient and predictable future.

So, the next time you marvel at a piece of generative AI art, take a moment to appreciate its steadfast cousin. Predictive AI, the "Chicago" of AI, is busy building, optimizing, and predicting the future, one data point at a time – and that, in its own enduring way, is incredibly impressive.

Underground Leaders: Why Gen X Is Quietly Powering the AI Marketing Revolution

It was a classic Chicago spring afternoon—clear skies, a good game, and then suddenly… a sharp wind off the lake. We were in a suite at Wrigley Field for a corporate marketing event, enjoying the game from the balcony seats, when the temperature dropped nearly 20 degrees in what felt like five minutes. That’s early May in Chicago for you. As everyone filed back inside for warmth, the shop talk began.

A few of us—Gen Xers who are now leaders on major corporate marketing teams—started comparing notes. Not on the weather, but on how we manage. How we lead differently than the generations before us. The conversation quickly shifted to transformation, risk, and how we’re approaching AI—not as a buzzword, but as a toolset.

That conversation stuck with me.

It reminded me that, just like we did in the early days of our careers, Gen X is once again working just outside the spotlight. But make no mistake: we’re driving the real transformation. Especially when it comes to AI in marketing.

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From the Margins to the Middle Seat

Back in the '80s and early '90s, I was grinding my way through undergrad at Saint Louis University while working full-time. It took me eight years to finish, but I didn’t see that as a failure—I saw it as survival. My friends and I weren’t tuned into the mainstream. Our culture lived off the dial.

One of my closest friends used to order import records from the UK and loan them to DJs at WMRY 101.1 FM, a progressive rock station out of Belleville, Illinois. WMRY was unlike anything else in the market—owned by the Catholic Shrine of Our Lady of the Snows, but with no real religious programming. It was commercial-free, playing alternative and underground music, and the DJs programmed their own shows. It was pure Gen X: DIY, under the radar, and full of signals the mainstream hadn’t caught yet.

In contrast, KSHE ruled the St. Louis radio market with a Boomer-focused classic rock format. We didn’t fit there. And that’s the point.

That yellow WMRY bumper sticker? It’s still taped to the wall of my old room at my parents’ house. I see it every time I visit. It reminds me that we weren’t raised to follow trends—we were raised to find what mattered and amplify it ourselves. That mindset never left. It just evolved.

We grew up finding our own path—whether it was music, media, or work. That mindset didn’t go away when we got promoted. It’s what’s powering how we lead today.

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Why Gen X Is Built for AI Leadership

AI adoption in marketing doesn’t succeed because you added “machine learning” to a slide. It succeeds because someone—usually behind the scenes—is testing, iterating, refining, and proving real value. That’s where Gen X shines.

We're the ones who:

• Evaluate tools not by hype, but by lift.
• Ask the awkward questions early.
• Launch test pilots before asking for budget.
• Measure performance, not impressions.

We came of age learning to fix our own problems. We don’t expect AI to be magic. We expect it to be useful.

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AI Isn’t Aspirational—It’s Operational

In corporate marketing departments, there’s often a lot of energy spent on AI optics. Executive decks, vision statements, innovation summits. But while that theater plays out, Gen X leaders are in the trenches:

• Setting up predictive models in Salesforce and actually checking the regression strength.
• Using ChatGPT to automate creative briefs—not to write the campaign.
• Testing dynamic content frameworks that reduce review cycles by 40%—not just adding personalization tokens and calling it a day.

We’re building systems. Not hype.

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Bridging the Generational Divide

We also play a unique role in today’s multigenerational workforce. We understand the Boomer emphasis on structure and reliability. We appreciate the Millennial and Gen Z desire for transparency and flexibility. And because we’ve had to operate in both worlds, we know how to translate between them.

That makes us natural integrators—a vital role as AI touches every part of the marketing org, from creative to ops to compliance.

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The Bottom Line

Gen X leaders aren’t waiting to be anointed the “AI transformation generation.” That’s not how we work.

We’re shipping MVPs. We’re building content ops. We’re automating backend workflows so creative teams can breathe again. We’re not reinventing marketing. We’re making it work better—with AI as a tool, not a trophy.

We didn’t grow up expecting a seat at the head of the table. But don’t mistake that for passivity. We’re not the loudest voice in the room—but when the AI revolution in marketing hits its stride, look around.

Chances are, a Gen Xer built the engine.

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Let's Talk:

Are you a Gen X marketing leader quietly rolling out AI inside your org? Or someone who came up through the same analog-to-digital shift with an eye for impact over applause? I’d love to hear your story—and how you're putting AI to work without the theater.

The Kid’s Table Isn’t a Punishment—It’s a Prototype Lab

While the grown-ups were busy chasing shiny GenAI demos and making splashy announcements at board meetings, something interesting was happening in the corners of the enterprise. The teams without headcount. The projects without press releases. The initiatives not on the CEO’s radar.

Welcome to the kid’s table.

For anyone who’s worked in a large company, you know the feeling. You’re not at the “main table” where the enterprise-wide AI vision is being defined. You’re not the face of the transformation initiative. You're just the person in the back, trying to do something useful with the tools already in front of you—like a dusty personalization model or an underused CDP license.

But here’s the thing: the kid’s table is often where the real innovation happens.

Because nobody’s watching too closely. And that’s your advantage.

Why Innovation Thrives at the Margins

When you’re outside the spotlight:

• You can experiment without being micromanaged.
• You’re not locked into executive-imposed deadlines or artificial KPIs.
• You can build for real impact—not just optics.

This is how real transformation often begins inside big companies. It doesn’t start with a 100-slide PowerPoint. It starts when someone quietly builds a working prototype using tools the company already owns—and proves that it drives results.

These “skunkworks” projects don’t get budget—at least not at first. What they get is permission through obscurity. And that’s often all you need.

From Skunkworks to Superstars: Real Impact from the Edge

History is filled with examples of big companies giving small, autonomous teams the space to operate like startups—and reaping huge rewards in the process.

• Lockheed Martin’s Skunk Works: Developed the U-2 and SR-71 in secret, pioneering stealth technology and setting a gold standard for agile innovation under pressure.
• Apple’s Macintosh Team: A renegade group launched one of the most disruptive personal computing platforms ever—paired with the “1984” Super Bowl ad that redefined tech marketing.
• IBM PC Division: Escaping corporate inertia, this team created the IBM PC in record time, establishing the architecture that dominated computing for decades.
• Toyota’s Prius Project: Despite internal skepticism, a small team launched the first mass-produced hybrid vehicle, creating a category and boosting Toyota’s green credentials.
• 3M’s Post-it Notes: A side project from an adhesive experiment turned into an iconic product—thanks to internal evangelism and bottom-up marketing.
• Google X (now X, a Moonshot Factory): Created industry-shaping innovations like Waymo and Project Loon—projects too risky for the main org to touch.

In many of these cases, what started as “off to the side” experiments—hidden from corporate micromanagement—eventually transformed not just product portfolios, but company cultures and market leadership positions.

Agile Teams in Legacy Giants: Not Just for Tech

It’s not just the Apples and Googles of the world. Even deeply established, non-tech brands have built agile, startup-style teams to break free from red tape:

• John Deere’s XI teams slashed innovation cycles from 3 years to 8 months—and saw a measurable rise in team morale and delivery quality.
• Roche Korea empowered patient-focused agile squads that delivered a 30% boost in sales and improved outcomes in a year.
• Constellation Brands’ Mission Bell Winery used agile pilots to 10x problem-solving speed in its distribution unit.

These efforts weren’t announced on stage with fanfare. They started by trusting small teams to operate with speed, creativity, and autonomy—and they worked.

But Let's Be Honest: Not All Skunkworks Succeed

A word of caution: the “kid’s table” can turn into an unused playroom if the organization only supports innovation in theory. Skeptics rightly point out common failure modes:

• Innovation Theater: Projects launched for optics, not outcomes
• Integration Wall: Breakthroughs that can’t scale or fit into the mothership
• Cultural Backlash: Perceived favoritism or “special teams” that erode trust

That’s why these efforts must be grounded in customer problems, tied to clear outcomes, and championed by leaders who protect the space to build—even when the results aren’t immediate.

Acting Like a Startup Inside a Corporation

If you’re trying to shed the perception that you’re “too corporate” for startup life, here’s the truth: The best startup operators in big companies already act like founders. They:

• Navigate constraints instead of complaining about them
• Use existing resources in new ways
• Build lean MVPs before asking for permission
• Understand how to scale only after product-market fit is validated

Working from the margins is not a weakness. It’s training.

Closing Thought: Reclaiming the Kid’s Table

As we wrap this “AI at the Kid’s Table” series, I’ll leave you with this:

Being at the kid’s table doesn’t mean you’re immature or irrelevant. It means you’re not stuck defending the status quo. You have permission to play, to prototype, and to break the rules just enough to build something better.

And sometimes, the biggest AI breakthroughs don’t come from the teams with the biggest budgets. They come from the people who stopped waiting for permission—and just started building.

AI Isn’t Magic. It’s Math. Why Marketing Execs Miss the Real Power of Tools Like Einstein

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.

Agentic AI vs. Structured Automation: Where the Real Traction Is--and Where the Hype Still Outpaces Reality

The term "agentic AI" has officially crossed from technical discourse into vendor marketing decks. Platforms like Salesforce and Qualtrics are pushing agent-based intelligence as the next frontier of enterprise automation. Benioff claims Agentforce is resolving 380,000+ help tickets autonomously. Qualtrics is promising orchestrated customer journeys led by intelligent agents.

But while the term sounds futuristic (and let's be honest, budget-worthy), it’s creating more confusion than clarity.

So what exactly are we talking about when we say "agentic AI"?
And how does it actually compare to the structured automation that’s already part of most MarTech stacks?

Let’s unpack it.

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What Is Agentic AI?

Agentic AI refers to AI systems that can take goal-directed action based on context. Unlike traditional automation--which follows fixed logic trees--agentic systems can reason, plan, and act semi-autonomously. Think:

• An AI agent that doesn't just recommend next steps, but executes them
• A system that adapts based on evolving user inputs
• A "co-pilot" that goes beyond assisting--it orchestrates

The promise? Fewer humans in the loop. More intelligent workflows. Better outcomes with less manual oversight.

The problem? It’s still incredibly hard to do outside of narrowly defined, tightly scoped environments.

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Structured Automation: What Most Companies Actually Use

Structured automation is what powers most enterprise workflows today. These are systems that:

• Follow logic defined in journey builders, rule engines, or workflows
• Require human-defined segmentation, triggers, and content
• Can personalize at scale, but within guardrails

Salesforce Marketing Cloud, Adobe Journey Optimizer, Braze, Iterable, and other common platforms are great at structured automation. They can:

• Send the right message to the right person based on behavior
• Trigger re-engagement flows
• Deliver personalization via content blocks or recommendations

But these systems are reactive, not proactive. And they rely heavily on humans to define and optimize the experience.

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Where Agentic AI Is Actually Working Today

The places where agentic AI is gaining traction share three characteristics:

1. Narrow domain
2. Clear success metrics
3. Low business risk if it fails

Examples:

• Customer service chatbots that can resolve billing issues or reschedule appointments without escalation
• Internal knowledgebase agents that can find policy documents and summarize answers
• AI-generated summaries in CRM tools like Gong, Salesforce, or HubSpot
• Cold lead SMS agents built with HighLevel + ChatGPT that carry on structured, semi-autonomous conversations with prospects and book meetings -- especially useful in B2C and small business use cases where fast, persistent follow-up matters more than nuance.

Even Benioff’s Agentforce--for all its marketing swagger--is working because it’s operating in a narrow, structured support domain with defined resolution rules and tight escalation paths.

These are not "do-everything" agents. They’re well-trained interns that follow scripts, learn fast, and know when to ask for help.

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Where the Hype Outpaces the Results

Take the now-infamous case of Chevrolet of Watsonville’s website chatbot. In late 2023, screenshots went viral showing the AI agent—powered by Fullpath’s GPT-4-based dealership integration—agreeing to sell a new Chevy Tahoe for $1 and recommending a Ford F-150 over its own inventory. Users were also able to jailbreak it into solving advanced fluid dynamics problems in Python.

The story, first flagged on Mastodon and later picked up by outlets like Inc. and The Detroit Free-Press, highlights what happens when agentic systems are deployed without strict guardrails. While it made headlines for humor, it underscores a serious point: without role restrictions, domain limits, and oversight, agentic AI can behave in ways that feel “smart” but create real risk.

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So What Should Marketers Do Now?

Here’s the pragmatic view:

• Don’t wait for full autonomy. Focus on augmentation.
• Look for AI features embedded in tools you already use (Einstein, Generative Content, predictive scoring)
• Identify tasks where automation saves time but retains oversight (summarization, optimization, reactivation)
• Invest in AI readiness: clean data, consent strategy, content modularity, test-and-learn culture

The goal isn’t to hand over the wheel. It’s to make your team faster, smarter, and more responsive--today.

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Final Thought

Agentic AI has potential. But most of what’s working in enterprise marketing right now is still structured, supervised, and human-enabled.

The danger isn’t missing the agentic wave. It’s mistaking marketing hype for operational reality.

Smart teams aren’t trying to leapfrog the maturity curve. They’re building trust, use case by use case.

More on that soon.

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📍 Read more at mikehotz.com
🔗 Connect with me on LinkedIn

From Hype to Habit: Why AI Adoption Requires Trust, Coaching, and Embedded Value

From Hype to Habit: Why AI Adoption Still Stalls—and What Actually Works

In my last post, I shared a reflection that resonated: AI is still sitting at the kids’ table.

After a dinner conversation with marketing and IT leaders from some of Chicago’s largest companies, it was clear: everyone was experimenting with AI -- but few were moving beyond isolated pilots. The tech was present, but trust, integration, and impact were still lagging.

So the next question is: Why?

And what separates companies that dabble in AI from those that actually scale it?

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The Real Problem: It’s Not the Tech—It’s the Trust

Most employees don't adopt what they don't understand. If AI feels like a bolt-on tool -- or worse, a threat -- it won't gain traction, no matter how good the tech is. I've seen firsthand that the bottleneck isn’t capability, it's comfort. Most of your workforce doesn’t need more demos -- they need clarity, context, and confidence.

That’s where the idea of an AI Coach comes in.

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What Is an AI Coach?

I’m not talking about someone who trains the models.

I’m talking about someone who helps the humans:

• Guides employees in how to use AI in their daily workflows
• Translates complex concepts into actionable tips
• Builds bridges between experimentation and execution

And no, your AI Coach doesn’t need to be a developer -- or someone obsessed with terms like Vertex AI, Snowflake, or orchestration pipelines. They need to be business-oriented, relatable, and grounded in the reality of how people actually work.

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Early Adopters Still Matter -- Just Don’t Overcomplicate It

One of the more interesting comments I received on my last post suggested that internal AI adoption could be dramatically improved by applying psychographic segmentation -- essentially segmenting employees the same way we segment customers.

The strategy isn’t wrong. In fact, it sounds a lot like Crossing the Chasm -- start with the right early adopters, build momentum, and let influence drive the early majority.

I’ve seen this work over and over: with franchise owners, car dealers, insurance agents, even in the early BEV Model e pilot at Ford. The key wasn’t a fancy segmentation model. It was picking stakeholders who were open to experimentation -- and respected by their peers.

So yes, start with the right people. Just don’t over-engineer it.

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What About the Agentic AI Hype?

We can’t ignore the noise coming from vendors. Salesforce’s Marc Benioff recently said he might be the last CEO to manage only humans. He cited their new Agentforce product as having autonomously resolved 380,000 support cases with an 84% resolution rate.

Impressive? Absolutely. But let’s be clear: Agentforce is operating inside a highly structured, narrow use case.

It’s not orchestrating multi-touch marketing journeys or running GTM strategy. It’s automating help desk flows -- and doing it well.

Qualtrics is making similar claims, but so far, most examples remain confined to polished demos and pilot environments. The leap from structured support automation to full-scale AI-led customer experience? We’re not there yet.

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The Real Work Ahead

• AI adoption will continue to stall if it’s treated as separate from day-to-day work
• Employees need enablement, not just access
• Early wins come from clear value, not just cool tech
• Start with people who are motivated and credible—not just available

Whether you call them “AI coaches,” “Swiss Army Knife talent,” or just smart operators, the people who bridge tech and workflow are the ones who’ll make AI adoption stick.

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What’s Next

In my next post, I’ll dig into the idea of agentic AI vs. structured automation, with examples of where AI is gaining real traction today -- and where the hype still outpaces the results.

In the meantime, I’d love to hear how your org is navigating this shift. Are you embedding AI into everyday work? Or are your teams still stuck in pilot mode?

Let’s keep the conversation going.

Stay tuned at blog.mikehotz.com, or follow me on LinkedIn to get it first.