The world of search and visibility is unraveling faster than most boardrooms realize. For decades, SEO was the playbook. Companies paid agencies to obsess over keywords, backlinks, and metadata, and the fight for the first page of Google results was framed as an existential battle for relevance. But that formula is already breaking apart. The Harvard Business Review reported earlier this year that in a global survey of more than twelve thousand people, 58% of consumers now use generative AI systems for product and service recommendations, up from just 25% in 2023. At the same time, IDC projects that by 2029, organizations will spend up to five times more on large language model optimization than they do today on SEO, with overall generative AI spending climbing at a 59% compound annual growth rate between 2023 and 2028. These numbers are not abstract—they point to a future where your brand’s visibility is not measured by search rankings but by whether you even appear inside an AI’s response.
That shift comes with consequences. When a consumer opens ChatGPT or Gemini and asks about the best logistics provider or the most trusted financial services firm, they are no longer scrolling through dozens of blue links. They are reading a synthesized answer, where only a handful of names surface, and those names are often presented as authoritative truth. If your brand is absent, or worse, misrepresented, the chance to compete has already evaporated. This is the new asymmetry: instead of fighting for a share of a results page, you are fighting for inclusion in the very act of machine-mediated recommendation. The language of marketing is already evolving to capture this. GEO, or generative engine optimization, and its sibling LLMO, large language model optimization, are the frameworks now replacing SEO in industry playbooks. They mark the same kind of structural shift as when paid search rewired advertising economics in the early 2000s, only faster.
For the C-Suite, the strategic urgency is twofold. First, content needs to be structured in ways machines can reliably ingest—clear, authoritative, and updated regularly. Harvard’s survey work also shows how quickly GenAI referrals are shaping commerce, with AI-driven clicks to retail sites spiking by more than 1,300% during the 2024 holiday season. Outdated or poorly designed content doesn’t just vanish quietly; it feeds misinformation into the models that billions are now consulting. Second, governance must be elevated from a marketing concern to a corporate risk issue. Executives need to think about brand visibility in LLMs with the same seriousness as financial disclosure or regulatory compliance. If a large language model consistently misstates your pricing, omits your sustainability commitments, or positions your rival as the safer choice, the reputational damage compounds at scale.
This is precisely where the CMO, the CDO, and the CIO must lean in. The chief marketing officer has to abandon the idea that content is just brand storytelling for human eyes; it is now also a data stream for machines, and how it is written, formatted, and linked determines whether the brand is surfaced at all. The chief digital officer, where the role exists, becomes the translator between marketing ambition and technical execution, making sure the digital infrastructure is designed so content flows into the pipelines that AI systems read from. And the chief information officer, long seen as a steward of systems rather than of reputation, must recognize that clean architecture, APIs, metadata, and structured repositories are not just IT concerns but brand survival issues. If these three roles don’t operate in tandem, the gap between the story a company tells and the story the machine repeats will grow dangerously wide.
There is also a cost dimension that CFOs cannot ignore. IDC’s projection of a five-fold increase in spending on LLM optimization underscores that this isn’t a marketing fad but a budgetary reality. Organizations will have to fund audits to see how they appear inside generative engines, invest in teams that can build content pipelines designed for AI consumption, and deploy monitoring tools that measure “AI visibility” as a metric alongside revenue, churn, and brand equity. The return will not be in pageviews but in being named, trusted, and surfaced by systems that are fast becoming the default gatekeepers of consumer attention.
The temptation in many boardrooms will be to treat this as a downstream marketing exercise. It is not. Brand content optimization in the LLM era is a cross-functional responsibility. Chief executives must set the tone, chief marketing officers must rethink messaging for a machine-mediated environment, chief digital officers must rewire the company’s operating systems around that imperative, and chief information officers must ensure the technical scaffolding holds. Even operations and finance leaders have skin in the game, because efficiency, compliance, and measurable ROI are the levers that will separate those who treat this shift as incremental from those who embed it into corporate strategy.
What makes this moment more unsettling is that the metrics are still forming. Page ranks and backlinks were blunt but clear. Now, companies must measure how often they appear in AI answers, how favorable those mentions are, and whether the output aligns with brand positioning. They must audit sentiment in AI systems the way they audit financial statements, and they must do so with the recognition that the line between discovery and decision is collapsing. By the time a consumer sees an LLM’s curated response, the persuasion has already taken place.
The C-Suite should view this as both a risk and an opportunity. Risk, because invisibility in AI responses is the new death in search. Opportunity, because those who adapt early can dominate mindshare in a way that was impossible in a crowded SERP. The executives who understand this will not ask whether content is optimized for Google’s algorithm; they will ask whether their company is trusted enough, structured enough, and present enough to be pulled into the answers that shape choice. The ones who hesitate will discover too late that the machine did not even mention them.
