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The new marketer journey (and how to make it work for you)

The marketer’s journey has evolved from navigating fragmented tools to orchestrating outcomes by strategically reclaiming time, amplifying creativity, and delivering personalized experiences that matter.

By Jacqueline Baxter

4 minute read

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On this page

The rise of answer engine optimization
AI-powered content creation: from ideation to activation
Closing the gap between experimentation and impact
Creating customer journeys that are personalized and valuable
Charting the agentic future

New technologies and rising customer expectations have reshaped the marketer’s journey and what it takes to build, launch, and optimize digital experiences. If you are navigating this shift, you know it is more than technology. It is about reclaiming time, amplifying creativity and decision-making, and delivering personalized experiences that truly matter.

Understanding how AI search works is the first step. From there, answer engine optimization (AEO) helps brands improve how they show up in AI-generated answers, feeds, and the moments where discovery begins. No brand can direct an AI tool’s exact response, but there are huge opportunities to strengthen the public information it can find and use.

The rise of answer engine optimization

A hard truth: traditional search engine optimization (SEO) is no longer the only path to visibility. AI engines are increasingly shaping discovery, changing how and where brands need to show up.

Brands improve their visibility in AI-generated answers by answering real questions in useful detail. They make that information easy for AI systems to access and understand, then support important claims with evidence people can trust.

That helps explain why some brands are cited more often than others in AI search results. A polished point of view is easy to find. A source that answers the question clearly, brings a useful perspective, and shows its working gives an answer engine more to use. Optimizing content for ChatGPT, Gemini, Perplexity, AI Overviews, and other AI-powered discovery experiences starts with a familiar discipline: create useful content that answers real questions, make it accessible, and structure it so the right information can be retrieved and referenced with confidence.

This is part of the broader shift toward answer engine optimization (AEO): preparing for the AI-generated answers, feeds, and moments where discovery increasingly begins. It means designing content for people first, understanding how AI interfaces represent your brand, and measuring visibility beyond rankings and traffic.

Here’s where to start:
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Prioritize unique, fresh, valuable content.

AI systems have more to work with when a page contains something useful they cannot get from a generic summary. Bring a perspective shaped by experience, research, customer evidence, or practical detail. Keep it current enough to be trusted.

PRO TIP: Rich media can make a page more useful and more discoverable. Use images, video, and audio where they genuinely add context.

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Measure AI visibility and agent activity

Traditional analytics tell you what people do on your website. AI search adds another layer: how AI systems discover, interpret, and represent your brand. Track where your brand appears in AI-generated answers, which sources earn citations, how accurately your products and expertise are represented, and how your visibility compares with competitors for the prompts that matter. Then look at agent activity on your website.

Look beyond volume. A mention only creates value when it gives buyers an accurate, credible picture of your brand, so connecting AI visibility with agent activity helps teams understand which pages are earning attention and where the story is incomplete as well as what to improve next.
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Being found is just the beginning

Visibility earns its value when buyers encounter a representation of your brand that is accurate, useful, and credible. A brand can appear frequently in AI answers and still be misunderstood, under-cited, or associated with information that is no longer current.

That is why AI discovery needs ongoing attention. Track where your brand appears, what AI systems say about it, and whether the sources they use reflect the expertise you want buyers to find.

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Use HTML, structured data, and semantic markup.

Deliver primary content in clean, crawlable HTML to make your content machine-readable. Ensure structured data reflects what users see to avoid confusion or misrepresentation.

PRO TIP: Keep critical information available in crawlable HTML rather than relying exclusively on JavaScript or user interactions to render it.

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Optimize for conversational, intent-aligned queries.

Answer engines including ChatGPT, Gemini, Perplexity, and AI Overviews increasingly respond to natural-language, intent-rich questions. Build content around the “what,” “how,” “why,” and comparison questions your audience actually asks, and answer those questions directly.

Measure AI visibility and agent activity.

Traditional analytics tell you what people do on your website. AI search adds another layer: how AI systems discover, interpret, and represent your brand. Track where your brand appears in AI-generated answers, which sources earn citations, how accurately your products and expertise are represented, and how your visibility compares with competitors for the prompts that matter. Then look at agent activity on your website. Understanding which AI agents visit, which pages they access, and how they experience your content can reveal gaps traditional analytics don’t show.

 

PRO TIP: Connect those two views to help explain why some content earns visibility and citations to give your teams a clearer path from monitoring AI search to improving it.

Being found is just the beginning

See what makes brands believable. 

Digital Authenticity Index
Sitecore's Digital Authenticity Index report

AI-powered content creation: from ideation to activation

That work starts long before a page goes live. The quality of the brief, source material, and evidence behind a campaign shapes how well content can travel across channels and how confidently it can represent the brand.

Content creation isn't a linear process anymore; generative AI has morphed it into a dynamic, adaptive workflow. AI-native agents embedded into orchestration layers can enable autonomous execution across the stack and these agents don't just suggest. They're programmed to adapt experiences based on goals, signals, and context clues.

With agentic AI it's possible to turn a single campaign brief into ready-to-go emails, social posts, landing pages, and press releases in minutes. Long-form assets like webinars or whitepapers can be automatically repurposed into snackable content tailored to specific audiences.

Closing the gap between experimentation and impact

One of the most persistent challenges in marketing is the lag between experimentation and measurable impact. Campaigns take too long to build and launch; tools are disconnected, workflows are manual, and scale is limited. Signal-driven execution solves this by enabling agents to detect intent, activate content, and optimize journeys based on real-time decisions.

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To thrive in today’s marketing landscape, leaders must build strength across three foundational pillars: data, content, and agentic capabilities.

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Data: Marketers need unified, accessible data environments that connect signals to moments. This enables intelligent agents to make decisions based on real-time context, without relying on fragmented systems or manual intervention.

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Content: Most marketing content goes unused. AI can reclaim dormant assets and deliver them where discovery happens to dramatically increase reach and relevance.

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Agentic capabilities: Agentic systems empower marketers to move beyond automation into autonomous execution. These capabilities allow teams to build, customize, and extend intelligent agents that operate across the marketing stack without constant oversight.

Together, these pillars enable a shift from manual execution to strategic orchestration. You and your team can focus on creativity, messaging, and audience strategy while intelligent systems handle the operational complexity.

Creating customer journeys that are personalized and valuable

Personalization is still a necessity. But scaling personalization without losing your sanity requires more than segmentation and targeting - you need autonomous collaboration between human marketers and AI systems. There are some immediate, helpful steps you can take:

Audit and activate dormant content

Most organizations have a wealth of unused content that can be repurposed. Use AI tools to identify high-performing content and adapt it for different audience segments and channels.

Start with popular webinars, blog posts, whitepapers, and web copy. There’s bound to be snackable content opportunities that have a proven resonance with your audience.

AI-powered personalization can trigger personalized messages or offers based on time-sensitive behaviors like cart abandonment or repeat visits.

Map signals to moments

Start by identifying key behavioral signals and link them to meaningful moments in the customer journey. This allows you to trigger personalized experiences based on real-time customer behavior and customer interactions rather than static segments.

Deploy intelligent agents for orchestration

Use agentic systems to automate high-level outputs. These agents can detect audience intent, select the right content based on a next-best action analysis, and deliver it across channels without time-consuming manual intervention.

Collaborate across teams to unify data and messaging

Break down silos across marketing, sales, and customer experience teams. Align on shared customer data and messaging frameworks that turn performance insights into action. Use KPIs and metrics alongside human oversight to keep your hyper-personalized marketing strategy consistent from first touch through post-sale engagement.

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Charting the agentic future

Ready to redefine discovery in your organization?

Download our executive guide on AI-powered discovery and learn how leading brands are scaling personalization, improving outcomes, and staying ahead in the agentic era.

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Discovery is only the beginning. As AI agents become more capable, they will increasingly move from finding and summarizing information to comparing options, evaluating products, completing tasks, and participating in transactions on a customer’s behalf. That means digital experiences will need to communicate effectively with both people and the agents acting for them.

The urgency is real.

As digital ecosystems become more fragmented and customer expectations climb, marketing teams need marketing technologies that work the way they do. The new marketer journey is not about reinvention. It is about unifying disconnected initiatives into a cohesive cross-channel system that lets marketers work the way they think, not the way tools dictate.

As we enter the agentic era of marketing, CMOs and senior digital leaders must rethink their approach to strategy, execution, and personalization. This new journey focuses on orchestrating outcomes instead of assembling tools, giving teams the freedom to invest in creativity and strategy that turn good experiences into great ones. 

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