How agentic AI is changing the future of marketing | MarTech

At the MarTech conference in November, Scott Brinker said – editor of Chiefmartec.com – delivered a keynote on how agentic AI will transform marketing. Drawing on decades of experience in marketing technology, Brinker explained why AI is more than a speed boost, how agents fit alongside traditional automation, and what this shift means for marketers’ jobs in the coming years.

From scarcity to abundance (and a new creative offer)

Brinker opened with a simple metaphor: making slides. Forty to fifty years ago, producing a professional slide deck required specialist craftsmanship and weeks of work: physical planks, knives, rubber cement, film and no easy operations. The number of presentations a company could give was inherently limited.

Then came tools like PowerPoint, which democratized slide creation. Today, AI slide tools can create, design, and export entire presentations in minutes, from a short prompt. Brinker showed how he generated a deck outline, had AI apply a style, captured the aspect ratio with a single instruction, and then exported it to PowerPoint – “two or three minutes” from idea to working deck.

For Brinker, this is a metaphor for what is happening in the martech sector: we are moving from scarcity to abundance. It’s not just about more output or faster cycles; AI expands the creative scope of what marketers can reasonably attempt.

That abundance is visible in the marketing technology landscape. Over the years, the number of tools has exploded to tens of thousands, with AI entering a new growth spurt. The curve is starting to flatten somewhat, but the overall ecosystem was still growing about 10% year over year. Marketers now face a long line of platforms, horizontal tools, and specialized solutions, each adding more options for applying agentic AI.

The spectrum: from rule-based automation to agentic autonomy

Brinker urged marketers not to view “agentic” as a mandatory end state. Instead, he described a spectrum of possibilities:

• On the one hand: traditional rules-based automation – predictable, explainable, repeatable, but static.
• On the other hand: autonomous agents – probabilistic, variable, adaptive but riskier and less controllable.

Agents can make decisions and adapt in ways that static workflows cannot, but that autonomy comes with tradeoffs. They may do things differently than you intended, or in ways that are more difficult to control.

The key, Brinker emphasized, is that this is not a maturity ladder where everyone has to rush from rules to full autonomy. There’s a rich opportunity in the middle: embedding AI decisions into structured workflows, or letting agents perform specific tasks while keeping the guardrails, logic, and oversight firmly in human hands. Marketers don’t have to “agentize everything” to create value.

There are three categories of agents that marketers should think about

Brinker distinguished between three broad types of agents that already influence marketing.

Agents for marketers (backstage)

These are agents who work for the marketing team: AI-augmented features across major platforms, analytics tools, creative copilots, and agent-enabled workflow tools. They accelerate production and analysis behind the scenes.

Agents exposed to customers (you manage them)

Customer service bots, AI SDRs, and email agents communicate directly with customers while still under the brand’s control. When they work well, they provide faster resolution and better responsiveness. But they need to improve the customer experience, not just internal efficiency.

Customer agents (u not check them)

This is the most disruptive category. Consumers now conduct research through general AI assistants and “response engines” such as ChatGPT-like tools or AI-powered browsers. These agents read your content, reviews and prices – and then mediate how customers perceive you. Marketers cannot control them, but only influence them, similar to but more complex than traditional SEO. Brinker sometimes calls this AEO/GEO – answer/guide engine optimization.

He predicted that just as AI is reshaping search and web journeys, it will ultimately reshape the inbox as AI assistants review, summarize and rephrase marketing emails on behalf of users. Marketers start talking to customers Through agents more and more, not just directly.

Automation versus experience: a two-for-two worth keeping

When deploying customer-facing agents, Brinker urged marketers to be cautious. AI makes it tempting to automate anything that saves time or costs, but customer-centric automation must meet a two-by-two:

• Does it improve company efficiency?
• Does it improve customer efficiency and experience?

If it only helps the business while making the customer’s life harder (by forcing them through rigid bots, useless loops, or opaque flows) it becomes a negative marketing experience. Brinker’s Warning: Use agents to serve both sides of the relationship.

Vibe coding, no-code, and software you don’t realize you’re writing

One of the most mind-boggling parts of the keynote was Brinker’s discussion of “vibe coding.” In practice, many AI models now convert natural language instructions into actual code behind the scenes.

Brinker gave an example: asking an AI to collect data on the most valuable companies over a twenty-year period, broken down by technology versus non-technology, and display the result in a graph. The model returned a diagram in about 30 seconds, but under the hood it had written JavaScript, built a small web app (e.g. in React), and ran it. A marketer who communicated in plain English had essentially “created software” without realizing it.

He joked about “vibe coding” as a buzzword – calling it a modern cousin of no-code/low-code – but underlined its power for simple, low-risk internal tools:

• Small web apps or prototypes.
• Internal tools for a few users.
• Ephemeral tools for events or campaigns.
• Vague ideas that need to be explored quickly and practically.

These use cases have historically been underutilized, not because of a lack of ideas, but because the time, cost, and expertise to build them couldn’t be justified. Agentic AI changes that equation and moves more technology from centralized IT services to decentralized self-service in marketing. The result: more speed, more parallel experiments and more learning.

From centralized services to decentralized makers

Brinker described this as a major shift in the way marketing work is done:

• Historically, a lot of technology has been in IT or specialized operations teams; marketers had to queue for support.
• As tools became more accessible (low-code, no-code, now agentic AI), more options entered the hands of practitioners.
• The “cost” of building experiments – campaigns, micro-experiences, internal tools – continues to fall, allowing the volume and diversity of experiments to increase.

He quoted Linus Pauling’s statement: ‘The best way to get a good idea is to have many ideas’ – to argue for broad experimentation. Many experiments will fail, and that is acceptable; what matters is that as the flow of ideas increases, the absolute number of winners is also increasing.

For Brinker, this is the core of the AI ​​opportunity: empowering a broader group marketing makers to try more things faster, without waiting months for formal projects or specifications.

Will AI take over marketing jobs?

Brinker addressed the fear head-on by simply dividing the marketing work into three parts:

1. Strategy and creative.
2. Production and analysis.
3. MarTech and marketing activities.
   

Historically the most time has gone into production and analysis – even though most prestige is attached to strategy and creative. AI and agents will dramatically reduce the effort required for production and analysis: generating variations, retrieving reports, synthesizing data, and orchestrating flows.

Leaders now face a choice:

• Consider this a pure opportunity to cut costs and downsize teams, or
  
• Reinvest the freed up capacity in more strategy and creativity, more experimentation, and stronger operations to support a faster pace of learning.

Brinker argued that the second path is the only sustainable advantage. Efficiency gains through AI will soon become a commodity: every company will be able to produce more cheaply. Differentiation will come from how organizations use that freed up capacity: bolder ideas, more personalized experiences, faster test and learn cycles, and a more robust support infrastructure.

According to him, the marketers who focus on:

• Expansion of strategy and creativity,
• Building reliable, scalable marketing operations to support rapid experimentation, and
• Learning to work of agents as employees,

will be in a strong position for “many, many years to come.”

Important insights for marketers

A number of practical themes emerge from Brinker’s keynote:

• Think in spectra, not in absolute values. You don’t have to replace all workflows with agents. Combine rules-based reliability with agentic flexibility where it makes sense.
• Recognize the world with three agents. Agents for marketers, agents you expose to clients, and agents of clients (independent assistants) each require different strategies.
• Optimize for agents as new ‘target groups’. Structure content, data, and experiences so AI assistants can accurately parse, summarize, and represent your brand.
• Use efficiency gains to drive learning, not just cuts. The real benefit will come from smarter strategy, richer creative solutions and more robust experimentation – not just doing the same things cheaper.

In short, agentic AI is not just about automation; it’s about redefining what marketers spend their time on. Machines will increasingly be able to handle the heavy work in production and analysis. The marketers who get it right will be the ones who use this shift to think bigger, test bolder, and build the systems that turn abundant opportunity into sustainable performance.