3 ways AI is changing how people shop, marketers work and stacks evolve

Marketing in the age of AI feels outdated the moment you write about it. The field is massive — product, tech, operations, branding, pricing and growth. No single article can cover it all. And I’m not going to try.

I’m also not here to give you another list of the “Top 10 AI use cases in marketing.” We get it. 

• Generative AI can write copy. 
• Customer service chatbots can answer questions. 
• Predictive models can forecast what an individual is likely to do next. 

These are enhancements to the way marketing already works. They are table stakes.

The bigger story is structural. AI is changing how customers decide, how marketers work and how the technology stack gets built.

1. The consumer decision experience

For decades, marketing ran on reach and repetition. Get in front of the customer often enough, and familiarity would tip the scales. That worked when the buyer was the primary decision-maker. Now, AI agents are making the first pass before humans even see the options.

This shift rewires the buying experience, changing which brands get seen and, in some cases, reducing the customer interaction to a single, functional moment. For utility products bought to get a job done, not to inspire devotion, that moment may be the only one that matters.

Marketers have long optimized for human biases — choice paradox, availability bias, default bias. AI disrupts that by inserting itself between the product and the person, becoming:

• The filter.
• The recommender.
• The first (and sometimes only) layer of engagement.

Much like social media transformed word-of-mouth, this shift will transform shopping itself. These agents will weigh a customer’s needs, preferences, constraints and contextual data, filtering out what doesn’t match and surfacing only a few recommendations.

Dig deeper: AI tools are rewriting the B2B buying process in real time

This changes the dynamics:

• Differentiation becomes non-negotiable: If your product is too similar to others, you may never make the shortlist.
• Human bias plays a smaller role: The agent doesn’t care about your clever tagline if it doesn’t improve the match score.
• The volume game is less important: Bombarding customers with messages matters less than the AI agent positioning you as the safest, most relevant choice.

Many marketers are still planning for a human-first decision process — optimizing for awareness when they should be optimizing for selection readiness. Balance still matters. 

Not every interaction should become a push to “add to cart,” “book trip” or “submit complaint.” We can still make things beautiful and human while also marketing to the machines. Finding that balance will take time — and some hard lessons.

Key takeaway: When AI is the gatekeeper, it’s not just about convincing the person anymore. You also need the correct metadata, proof points and trust signals to influence the machine’s decision-making.

2. The marketer’s role

We’ve all heard the reassurance: “AI is here to help us, not replace us.”

I have mixed feelings about that. Yes, AI can take over the repetitive tasks that used to fill our week — segmenting lists, setting up tests, scheduling campaigns. But it’s naive to think that’s the whole story.

The marketers who will matter most are the ones who can orchestrate AI effectively:

• Creating guardrails so the technology operates inside a clear strategic frame.
• Holding the vision for how all the moving pieces — channels, campaigns, content, data — form one coherent brand experience.
• Knowing which levers to pull when the system can run itself, but still needs guidance toward a bigger goal.

There’s another side to this. The more AI-generated content we see, the more we’ll see AI slop — generic, recycled material that is technically correct but emotionally empty. In that world, genuine, human, unpolished moments will stand out more than ever.

Dig deeper: Why mindset, not just tech, defines AI success in marketing

Part of the marketer’s evolving role will be knowing when not to use AI — when the moment calls for something messy, complex and taste-driven, something with a point of view untethered from the statistical average of what’s come before. 

It might be a brand-defining campaign, a high-touch gesture for a key customer or a decision made on instinct because the data hasn’t caught up yet.

Key takeaway: AI will improve at patterns, but it still struggles with paradox, ambiguity and choices that can’t be reverse-engineered from past examples. Marketers must move fluidly between machine precision and human intuition — knowing:

• When to automate and when to get personally involved.
• When to scale and when to slow down. 

Some stories resonate across cultures and history without personalization — and those stories will always matter.

3. The modular martech stack

For years, the safest play was buying an all-in-one marketing platform — integrated, enterprise-ready and able to handle every function under one roof.

That model is cracking, and enterprise vendors know it, so we’re seeing a massive shift in AI investment and M&A. 

AI’s rapid innovation cycle — and the rise and fall of AI-native startups — is pushing marketers toward modular stacks. Instead of being locked into a vendor’s roadmap, teams assemble best-in-class tools to solve specific problems.

This shift is accelerating because the connective tissue is finally here:

• Agent-to-agent orchestration: AI tools that can pass tasks to one another and coordinate workflows without waiting for a human to click “approve.”
• Data mesh architectures: A decentralized way to manage data where each team owns its piece but shares it in a standardized, AI-ready format.
• Composable AI services: Smaller, swappable AI components (e.g., text generation, image creation, sentiment analysis) that can be mixed and matched as needs change.

What once felt like a fragile Frankenstein now looks like couture — stacks tailored to fit the business perfectly, built from components chosen for precision and fit, not for their vendor label. We’ll see even more highly specialized AI tools emerge, excelling at a single task, while larger platforms scramble to expand into adjacent capabilities. 

Dig deeper: Operationalizing generative AI for marketing impact

Infrastructure will be the hidden battleground. Multi-billion-dollar AI processing capacity will be required to support real-time personalization, multimodal content generation and advanced analytics at scale. As token-based billing becomes the norm, the economics of running your stack will matter as much as its features.

Marketers and CMOs will need to think like systems architects:

• Optimize for cost-per-output, not just subscription price.
• Route requests intelligently to the lowest-cost, highest-quality source.
• Build stacks that minimize redundant processing.

The ability to fluidly connect tools, resources and agents will be a competitive advantage. Imagine an architecture where an AI agent can pull from multiple specialized tools, tap a shared resource repository and hand off results to another agent for enrichment — all without human babysitting. That’s where stack efficiency starts to compound.

This also raises the bar for data producers. Whether it’s your own company or a third-party partner, data must be structured for AI to read, interpret and use. A poorly maintained dataset isn’t just a headache anymore. It’s a barrier to being retrievable in an AI-mediated market.

Key takeaway: The martech stack is becoming more fluid and programmable. The winners will be the teams who can:

• Stitch together the right tools.
• Optimize their cost-to-performance ratio.
• Ensure their data is ready for the AI agents increasingly running the show.

Where marketing goes from here

The most significant AI shifts aren’t about surface-level use cases — they’re foundational. Consumer decision-making is moving upstream to AI, marketers are shifting from executors to orchestrators and the martech stack is breaking into modular, composable pieces. These aren’t tweaks to how marketing works — they’re complete rewrites.