Saturday, May 24, 2025

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.


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.


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.


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.


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.


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.


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.



Saturday, May 17, 2025

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

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.

Wednesday, May 14, 2025

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.

Saturday, May 10, 2025

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

Agentic AI vs. Structured Automation

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.


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.


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.


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.


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.


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.


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.

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

Wednesday, May 7, 2025

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?


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.


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.


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.


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.


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.


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.

Friday, May 2, 2025

Is AI Still at the Kids’ Table? And what that means for your marketing strategy.

I recently attended a dinner with a group of senior marketing and IT executives from some of Chicago’s largest companies. The conversation was lively and centered around, you guessed it, AI.

Everyone in the room was doing something with AI:

  • A chatbot pilot here
  • Some copy generation there
  • A lead scoring experiment in progress

But the pattern was clear: a dozen disconnected pilots and proof-of-concepts (POCs), very few with executive sponsorship, and even fewer tied to clear business goals.

That’s when I used a phrase that I’ve found myself repeating lately: AI is still sitting at the kids’ table.

Why That Analogy Still Works

AI has been formally “invited” to the organization, it’s no longer fringe. But it hasn’t earned a full seat in strategic planning. It's not driving GTM strategy, customer experience design, or budget allocation in most orgs.

If you’ve been in marketing long enough, you’ve seen this pattern before:

  • Mobile: Once treated like a novelty (“We need an app!”), now an integral part of the customer lifecycle.
  • CRM: Formerly siloed within Sales, now the marketing backbone.
  • CDPs: Once seen as overbuilt infrastructure, now powering personalized journeys.
  • Personalization Engines: From “Hi, [First Name]” gimmicks to real-time, data-driven relevance.

In each case, the technology remained sidelined until someone tied it to customer outcomes, business impact, and operational strategy.

The McDonald’s Example: When High Profile Isn’t Enough

A perfect case in point: McDonald’s and IBM.

In 2021, McDonald’s launched an AI-powered voice ordering pilot in more than 100 drive-thru locations, developed in partnership with IBM. This wasn’t a rogue side project, it had executive-level visibility, strong vendor backing, and real customer exposure.

But the system struggled:

  • It failed to recognize accents and natural speech
  • It bungled complex orders
  • It frustrated customers and employees

By mid-2024, the pilot was shut down.

Even with scale, budget, and public support, it lacked the operational readiness and accuracy needed to create value. It wasn't a back-office test, it was customer-facing. The stakes were high. And it fell short.

This is what happens when AI is treated like a bolt-on experiment rather than embedded into process design and customer experience.

What the Research Says

And McDonald’s isn’t alone. According to CIO.com and TechSee:

  • 88% of AI pilots fail to reach production.
  • Many AI projects falter because they’re disconnected from revenue strategy and operational workflows.
  • CIOs are beginning to abandon custom in-house POCs in favor of commercialized AI platforms (like OpenAI, Vertex AI, Salesforce Einstein) that integrate faster and provide clearer value paths.

The problem isn’t the technology.

It’s that we treat pilots as strategy, and we confuse experimentation with execution.

The Risk of Too Many POCs

POCs are useful, but they’re not a strategy.

They become a problem when:

  • There’s no shared roadmap for what happens after “test”
  • They aren’t connected to KPIs or data infrastructure
  • No one owns scaling or operationalizing them
  • They aren’t part of the customer journey or marketing lifecycle

Disconnected AI = disconnected value.

So… Is AI Still at the Kids’ Table?

In most companies, yes.

But it doesn’t have to stay there.

If you're a marketing leader, ask yourself:

  • Is our AI roadmap connected to our customer journey?
  • Are we treating AI as infrastructure or just a novelty?
  • Who owns turning successful pilots into scaled programs?

AI moves to the main table when it drives outcomes, not when it wins headlines.

Welcome to the Blog & What's Next

Welcome to my new personal blog! I plan to explore topics like the practical application of AI in marketing further in the coming months.

I'll be writing more on how marketers can go from AI dabbling to measurable impact.

Stay tuned right here on mikehotz.com for future posts, or connect with me on LinkedIn to follow along.

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 Wrig...