For decades, marketing was built around human attention. We created campaigns to tell compelling stories, inspire emotion, and design experiences that people would return to. That human-centric work still matters deeply. But it’s no longer the whole story.

In my role at SAP, I’ve watched a new invisible audience take over some of the earliest and most influential stages of the buying journey. That audience is not human at all. It’s AI.

AI doesn’t click or scroll. It doesn’t respond emotionally to a campaign. But it is increasingly deciding what gets seen in the first place. Today, many buyers don’t begin with a website or search engine — they begin with AI systems that interpret their problems, compare approaches, and recommend paths forward.

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By the time someone actually encounters your brand, a meaningful part of their decision has already been shaped by systems that determine what gets seen, compared, and recommended. In that reality, marketing is no longer just about persuasion. It’s about being considered at all.

For brands, that changes the work ahead. At SAP, we are treating AI not just as a tool, but as a critical audience — and using Adobe technology to turn that strategy into scalable, repeatable content marketing operations.

Speaking to an invisible audience.

AI systems, including large language models, now act as an early layer of interpretation. Before a buyer even visits a product page, AI has often already framed the challenge, narrowed the universe of possible solutions, and synthesized perspectives from across the web.

That means marketers now have to design for two audiences at once: the humans who feel risk, confidence, and trust, and the AI systems that reward clarity, consistency, and proof. At SAP, we’re increasingly treating AI as a marketing persona in its own right — one that shapes how buyers understand problems, evaluate options, and form opinions before they reach us directly.

From storytelling to “answer engineering.”

Traditional marketing models were built around campaigns and assets optimized for specific channels. That approach can still produce strong work. But AI does not evaluate content the way a human does.

Large language models look for patterns across everything your brand publishes. Are your definitions clear? Is your terminology consistent? Are your claims backed by credible third-party sources?

If those patterns are strong, AI is more likely to include your perspective in summaries, comparisons, and recommendations. If they are fragmented or contradictory, your brand can fall out of consideration. That’s why we describe this shift as “answer engineering.” Content doesn’t just need to persuade anymore. It has to be something AI can reliably use.

At SAP, we’re operationalizing this shift through a more structured and connected content foundation — one where messaging, definitions, and claims are consistent across every touchpoint. That alignment is what allows content to be interpreted clearly, both by people and by the systems shaping how it’s surfaced.

You don’t automatically own your own story anymore.

One of the most uncomfortable truths for many marketers is this: We no longer get to be the authority on our own brand by default.

AI systems treat our owned content as just one source among many. Our claims are weighed alongside analyst reports, media coverage, customer reviews, and even competitor content. The clearest, most consistent, and easiest-to-validate sources win.

For SAP, that creates both risk and opportunity. If our expertise is scattered across disconnected assets, inconsistent messaging, or unsupported claims, AI may default to clearer voices — including competitors. But if we express our expertise coherently and at scale, our perspective can become a reliable reference point for both machines and people.

Content as infrastructure.

The biggest mindset shift for my team has been to stop thinking of content as a series of outputs and start treating it as infrastructure.

Content now has to function as a connected system: structured so that meaning is explicit, reusable across formats, and consistent across channels. In practice, that means making sure the same definitions, claims, and proof points show up consistently across the places AI systems may look, from webpages and blogs to campaigns, sales content, and third-party sources.

Creativity does not shrink in that model. It expands. Our job is no longer just to tell a great story for one campaign, but to structure meaning so humans and machines can both interpret and reuse it.

Designing for how decisions are really made.

To win in this world, marketers have to design for both audiences at once. We need structured, validated content that AI can trust, and human storytelling that builds empathy, confidence, and reduces perceived risk.

This is where marketers need to be almost diabolically intentional. Every claim, definition, proof point, and third-party signal has to work together so the brand is clear to people and legible to machines. That means thinking beyond a single asset or campaign and looking at the full content ecosystem.

That level of discipline does not make marketing less creative. It gives creativity a stronger foundation. The brands that sharpen their signals, structure their content, and treat AI as a real audience will be the ones that show up in the moments that matter most.

See how leaders from VML, Intuit, the NFL, and more are tackling these same challenges — and what it means for the future of marketing. We’ll have a full Summit wrap-up coming soon.

Tracey Craft is vice president of content marketing at SAP, where she leads the strategy and evolution of a global content organization. Her work focuses on transforming the content supply chain into a more connected, scalable system — one designed to deliver clear, consistent, and high-impact content across every stage of the customer experience.

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