The AI marketing stack in 2025 feels like martech in 2015 — how to avoid the same mistakes
This morning, I found myself drowning in an old, familiar sea—trying to build the marketing tech stack for my new consulting business in what should have felt straightforward. (I’ve spent years of my career working on building cohesive marketing stacks for the Arts and Culture sector.)
But instead, I was quietly—and sadly—waterlogged.
The maze of overlapping tools, endless options, and unclear categories across marketing tech today is overwhelming.
And then it hit me: This feels exactly like 2015.
A decade ago, the explosion of marketing technology promised to revolutionize how organizations connected with audiences. Instead, it often left teams overwhelmed—paralyzed by choice and stuck with fragmented, overlapping, and underutilized technology systems. (Thankfully, a few tools emerged purpose-built for arts and culture by conscientious digital firms, and they did make an impact.)
Today, AI is creating the same surge in choices and complexity—but bigger.
The frothy, AI-fueled marketing tool landscape is overwhelming, especially for resource-strapped arts and culture organizations navigating limited budgets and bandwidth.
As we stand on this fresh ground, I hope we can avoid repeating the mistakes I watched unfold a decade ago.
AI’s Exploding Toolset
AI has captured marketers' attention for good reason—it has the potential to assist and automate much of the manual lifting burdening today’s overworked teams.
But the tool landscape? More fragmented than ever. And worse every day of AI startup boom. Plus… we’re still carrying fragmentation from the last decade’s martech boom.
Let’s start by breaking down “the marketing stack” for arts and culture today. Here’s how I see it. (Not saying I’m entirely right—please comment if you see it differently!)
The Arts and Culture Marketing Stack Today:
Core Infrastructure
CRM (Customer Relationship Management) — First-party data about customers and transactions/donations. Primary source of truth.
Website Layer — Your content website + CMS (Content Management System) + shopping cart (TNEW, full build, etc.).
Digital Analytics — Usage intelligence around your digital properties (Google Analytics, Looker Studio, Fullstory, Amplitude...).
Activation and Campaigns
Marketing Automation — Lead nurturing and campaign execution (Marketo, Prospect2, HubSpot).
CDP (Customer Data Platform) — Traditionally about unifying customer data, but AI is turning CDPs into activation platforms that automate customer segmentation, trigger real-time actions, and generate personalized content—overlapping with CRM, marketing automation, and personalization platforms (e.g., Klaviyo CDP).
Email Marketing — Lines are increasingly blurring between email platforms and broader content creation tools (WordFly, MailChimp...).
Social Media Management — Publishing and engagement tools for social media (Sprout Social, Hootsuite, Buffer...).
AdTech and Performance Marketing — Digital ad buying, audience targeting, and now AI-driven creative, bidding, and optimization.
Content Creation, SEO, and Personalization
Prototyping and Design Tools — Cloud and AI-driven design capabilities (Figma, Sketch, BoltNew.ai, tailwindcss, shadcn, Canva, Gamma...).
Content Marketing/SEO — Search performance meets AI-generated content for optimal visibility (Jasper, Clearscope…)
Copywriting, Images & Personalization — Dynamic content recommendations and AI-generated media (Jasper, Typeface, Persado, Runway, OpenAI's Sora...).
Agentic Customer Interactions
AI-Powered Customer Support Agents — Interfaces for online support, trained on organizational data (Intercom, Zendesk, Double Eagle…)
The “Agentic Future” — Promising to revolutionize how we work…
I really struggled to keep these categories distinct—and that's exactly the point.
There’s so much blurring and merging as the landscape evolves daily.
What Arts and Culture can learn from 2015's martech boom
For overstretched arts and culture teams, the risk of shiny object syndrome isn’t just wasted budget. The bigger risk is distraction and rabbit holing. Every new tool, webinar, and conference session promises some New Utopia. The opportunity is real—but so is the distraction potential.
My current ratio of new AI hotness to real utility is something like 15:1.
But… what’s worse than wasting a lot of time testing new tools out with little to show for it? Missing the efficiency boost that WILL come.
One of the prime reasons I left Silicon Valley product development work to come back to Arts and Culture is BECAUSE of this “problem.”
Three Lessons from 2015 to Apply Now
1. Start With Strategy, Not Tools
When I work with organizations on AI strategy, we start by identifying the most urgent problems, organizationally and for your customer/visitor. There’s an entire book’s worth of commentary I have on this topic…but suffice it to say, regularly talking to your staff and customers/visitors is the foundation of all good tech strategy.
2. Consolidate Tools Where You Can
Technology is in a big expansion stage with AI and along with that comes data fragmentation. That’s ok! There are ways to deal with that. But time fragmentation and focus fragmentation is a much bigger risk to your organization. Fewer tools means fewer things to manage and with the rapid rate of change all technology platforms are undergoing, it pays to look first at what your existing tech provides (or soon will). But where you can’t leverage existing tech? Experiment your buns off. But keep it focused on the outcome you’re trying to create or you’ll get waterlogged fast.
> Consider running a proof of concept stage.
3. Experiment, But Be Intentional
There is real value in testing AI tools:
You get a first-class education on the art of the possible.
You sharpen the art of restraint and focus.
You might just stumble on a “never going back” capability that can change how your entire org works and thinks. In fact, I’m encountering this frequently as I am hands-on with AI daily.
To avoid overwhelm, focus on small, pointed experiments and keep your goal laser focused on the horizon.
Final Thoughts: The real potential of AI is in boosting efficiency
The key advantage of AI today is it’s potential to boost productivity. Figuring out HOW it can boost productivity can be a huge time waster if you’re not careful.
If you’re feeling overwhelmed by the AI noise, you’re not alone. I work with organizations like yours to figure out a strategic and practical path forward to leveraging AI effectively across your organization.
