Most of us were taught the same rules: Don’t get in cars with strangers. Carry cash for small transactions. Stand on the street and wait for a taxi.

Then, gradually, those beliefs changed—without most of us ever consciously deciding to change them.

Today, we get into cars with strangers without a second thought. We don’t carry cash. We don’t wait on street corners. Ride-sharing apps didn’t just change transportation—they changed what we expect and how we behave.

Marketing is undergoing the same kind of shift. Across the industry, marketing teams are stretched thin. In many organizations, ZS estimates that marketers spend more than 60% of their time managing activities—like reporting to leadership, coordinating vendors and distributing results—leaving little room for strategic thinking or innovation.

AI is now being pointed to as the answer to this pressure. It can automate reporting, streamline content reviews and speed approvals. But if those gains just make old processes run faster, they won’t make the organization any smarter or fully capitalize on the value that AI can bring to the table.

So the real question isn’t how we can get our marketers to use more AI, but how we can drive value by using AI to make better decisions.

To make the shift, we need to challenge our underlying beliefs that drive how we operate—just like how rideshare apps rewired how we think about how to get a ride. We need to ask ourselves what are the recurring decisions that drive value and how we can design for AI-native marketing workflows so that those decisions drive repeatable outcomes.

Marketing organizations that fail to ask this question will quickly become the taxi cabs of yesterday.

The real mistake marketing leaders are making

Many organizations treat AI as a way to accelerate today’s marketing model rather than reconsidering whether that model still fits.

Designing for a marketer using AI typically means optimizing efficiency—making existing work faster or easier. The underlying structure stays the same.

Designing marketing decisions for AI is fundamentally different, because it means looking at the recurring decisions that drive outcomes and rethinking workflows to support them for continuous intelligence with bothclear human accountability for decisions and machine intelligence to accelerate how those decisions are made and executed.

Just as ride-sharing didn’t digitize taxi dispatch but reinvented transportation, leaders must be willing to challenge every assumption about how marketing operates today.

What actually changes when decisions are designed for AI

To design for AI-native workflows that support these decisions, start with reimagining what it means to be a marketer. For decades, marketing has been defined by sifting through reports, managing agencies, overseeing approvals and explaining results after the fact. But the future belongs to marketers who lead with insight, foresight and creativity, supported by intelligent systems that handle complexity at scale.

This transformation isn’t about mastering the latest tools. It’s about cultivating new skills and new kinds of talent: people who can adapt to a relentless pace of innovation. The goal isn’t to chase each new technology, but to design a marketing function ready for what’s next.

Think of today’s marketer as a data hunter, searching through static reports to piece together insights after the fact. Tomorrow’s marketer must become a decision navigator, using real-time intelligence to make better, faster and more repeatable strategic decisions.

Here are some of the ways the role of the marketer will evolve.

• From data hunter to decision navigator: moving from manual analysis to directing strategy with on-demand insights
• From retrospective thinker to prospective thinker: using predictive analytics to learn forward, not just look back
• From agency manager to strategic architect: owning and shaping strategy, informed by evidence
• From editor to author: crafting and evolving brand narratives dynamically with customers
• From stretched executor to empowered leader: leading connected activities with visibility into data, insights and rationale

This shift redefines the marketer’s value, which is no longer measured by how much work they manage or execute, but by how effectively they lead.

AI does not replace that leadership. It amplifies it.

Marketing and the machine: Leading an agency of AI agents

Meanwhile, AI takes on the supporting roles: connecting data, surfacing insights, preparing briefs and optimizing deployment. The human marketer leads; the machine empowers.

This relationship reframes marketing as a partnership between human ingenuity and intelligent systems. The marketer becomes not just a people leader, but a leader of technology, directing a “team” of AI agents through clear decision ownership, escalation paths and guardrails.

What does AI-native marketing look like in practice?

Imagine a marketing organization supported by an agency of AI agents, each tailored to specific roles.

A product strategist might be assisted by agents that act as diagnosticians, digital twins of other internal roles or functional experts that weigh in on key decisions. An engagement planner could be supported by content designers, story builders or budget optimizers.

These agents can accelerate data mining, brief development and performance optimization, allowing marketers to focus on high-value decisions.

This is more than a productivity boost; it’s an organizational redesign. The marketing structure of the future might still include insights teams, product strategists and engagement planners, but each will operate in concert with a dynamic ecosystem of AI collaborators.

Marketers are no longer limited by the capacity of their human teams. Instead, they are empowered by an expanded network of digital contributors.

How work flows differently in an AI-native organization

When marketing is designed for AI, recurring decisions are redesigned into continuous loops of sensing, deciding and acting.

Imagine a competitive data readout. One of the marketer’s AI agents alerts them to a new market development. The marketer triggers a competitive response flow, using AI to identify strategic response options and simulate outcomes. The team then convenes to evaluate scenarios, align on direction and move rapidly to execution.

That microcosm captures what AI-native marketing looks like: a continuously adaptive system where humans and machines sense, decide and act.

Across the end-to-end process, AI enhances every stage:

• Insight generation: seamless mining of customer and competitive intelligence
• Strategy: automated scenario planning and brief writing that drives stronger alignment
• Creative development: storylines and messaging optimized for resonance and compliance
• Execution: dynamic deployment and real-time optimization

This isn’t just greater efficiency; it’s the ability to adapt faster because the decisions that guide action are continuously learning.

The new marketing talent equation

Technology alone doesn’t create transformation; people do. The marketer of the future will need a new set of capabilities to thrive alongside AI.

Yes, they must be adaptive and data-driven, but most importantly, they must cultivate what can be called applied curiosity.

Applied curiosity goes beyond asking questions. It means turning inquiry into action: testing and iterating with evidence. Using experimentation to challenge assumptions and drive better outcomes. And maintaining a mindset of “inquiry before solutioning,” remaining open to possibilities and transforming “what if” into “let’s try.” Other key skills of the AI-ready marketer include:

• Data-driven decision-making
• Digital and analytical acumen
• Collaborative mindset, openness to technology and learning agility
• Strategic creativity and adaptability

Digital literacy will be woven through every competency, not treated as an isolated skill. The marketer’s role will evolve from executing campaigns to engineering growth, continuously learning and adapting in partnership with technology.

What marketing leaders must rethink for AI-native workflows

Designing marketing for AI is not a single initiative—it’s a sequence of interconnected leadership decisions. In practice, organizations that make progress tend to start by stepping back and examining the marketing workflows that drive decisions from end to end: where intelligence is generated, how decisions are made and where work slows or fragments. From there, leaders assess how AI is being adopted across key activities, identify the use cases that matter most and reengineer workflows so human judgment and machine intelligence work together by design.

Just as important, they redefine what is expected of marketers—shifting roles, capabilities and mindsets toward adaptability, applied curiosity and evidence-based decision-making. Addressing mindset barriers isn’t incidental; it requires visible leadership commitment, storytelling and early wins that build confidence in new ways of working.

Organizations that focus only on getting marketers to “use AI” without redesigning the decisions andoperating model that drive outcomes often encounter resistance and stall. The most durable progress comes when AI is treated as a catalyst for redesigning decision systems, not a software rollout.

Designing for impact—and what comes next

When human-AI collaboration is embedded across the organization, the payoff is tangible.

ZS estimates that this kind of transformation can foster between 10%-15% revenue growth, encouraging faster creative and content delivery, quicker response to evolving customer needs and strengthened cross-functional execution. Companies might see an estimated 20%-25% selling, general and administrative expense savings coming from reduced agency and operations spend, improved compliance and consistency and refocusing talent on high-value strategic work.

Perhaps most importantly, organizations designed for AI drive better engagement and personalization, leading to stronger relationships with customers and patients. These gains come not from the tools themselves, but from how effectively they are integrated into the marketing engine.

Marketers aren’t being replaced, but their roles are being redefined

AI is no longer a differentiator; it’s the new baseline. The real differentiator will be how effectively organizations redesign their operating models, empower their people and reimagine what marketing can achieve.

Leaders face a choice: pursue incremental productivity or redesign around the decisions that create value and the workflows that support those decisions.

The future belongs to organizations that do more than adopt AI—they design for it.