Search is being rewritten – and marketers aren’t ready

The digital marketing industry is standing at a decisive point. For years, traditional SEO, performance marketing, and content strategies were the main channels for brand discoverability.

Now, those pillars are eroding. Consumer search behaviour is shifting from keyword-centric queries to conversational models powered by AI. The result is that brands can no longer rely solely on Google or its search ranking algorithms to drive visibility.

We’ve entered what I call ‘Search Engineering’. This is a deeper, outcome-oriented approach to brand discoverability in AI-first, search-evolved environments. This is not about chasing rankings; it’s about being chosen by AI itself.

The decline of traditional SEO in an AI-first world

Classic SEO has long been about maximising search rankings in the search engine results pages (SERPs) through keywords, backlinks and content relevance. The model assumes a linear, predictable search journey — type a query, scan a list of links, click one. That journey is disappearing.

Generative AI platforms such as ChatGPT, Google’s Search Generative Experience (SGE), Microsoft Copilot and Gemini are replacing list-based results with conversational responses. These systems synthesise answers by pulling from multiple sources. A user may never visit a brand’s website unless that brand is explicitly cited in the AI’s response.

This shift moves visibility rules away from pleasing Google’s algorithm alone. Instead, the challenge is to structure a brand’s digital presence in a way that large language models (LLMs) can recognise, trust and include in their answers.

What search engineering really means

Search Engineering is the next evolution of SEO — but with a fundamentally different target. Rather than optimising solely for search engines, it’s about ensuring that AI-driven interfaces select your brand when generating answers.

This requires:

•          Structuring data and content so it is accessible to AI crawlers and LLMs.

•          Optimising for AI outputs, not just search results.

•          Building topical authority across trusted, verifiable knowledge sources.

•          Ensuring brand signals are visible in vector-based search models.

•          Prioritising context, coherence and semantic richness.

It’s not about cramming in keywords or chasing backlinks. It’s about embedding brand signals into the datasets that generative AI tools use. That means making sites, product listings, reviews and expert content machine-readable, up-to-date and credible.

Search behaviour has already changed

User behaviour is evolving alongside technology. People are no longer ‘searching’ in the old sense — they’re asking AI to solve problems, recommend purchases, and explain complex topics.

A user once might have searched ‘best CRM software 2025’. Now, they might ask ChatGPT, ‘What’s the best CRM software for a mid-sized B2B company with limited onboarding resources?’ The AI will combine multiple sources to generate a complete answer, often without linking back to the originals unless specifically prompted.

This is a fundamental shift in the buyer journey. Discovery now happens inside AI systems, which are curating — and in some cases gatekeeping — what information a user sees. If your brand isn’t identifiable in the AI’s source data, it might as well be invisible.

Why ROAS is losing relevance

Performance marketing metrics like Return on Ad Spend (ROAS), click-through rates and conversions were once gold standards. They still matter, but they’re increasingly shallow measures in a fragmented, non-linear attention economy.

Consumers are bypassing paid channels entirely. Instead of clicking search ads, they’re asking ChatGPT for product recommendations. Instead of browsing social ads, they’re engaging with Perplexity.ai for peer-informed summaries. These touchpoints rarely show up in conventional performance dashboards.

Outcome-based marketing — including search engineering — is becoming essential. The priority is no longer impressions or traffic, but ensuring brand presence at the precise moments when AI is influencing consumer choices at scale.

The pitfall of content for content’s sake

Many brands still churn out large volumes of content purely to ‘feed the SEO machine’. In AI-driven discovery, that approach is wasteful.

LLMs don’t reward keyword repetition or freshness alone. They value contextual authority, depth and consistency across sources. Brands that want to influence AI responses need to show up as credible voices, not just prolific publishers.

This means authoritative contributions to industry platforms, consistent use of structured data, inclusion in reputable third-party sources, and maintaining a transparent, technically optimised footprint. The relevant question is no longer ‘How much can we publish?’ but ‘How well are we shaping our digital presence to educate AI systems?’

Moving beyond the agency model

AI-first discovery requires a break from traditional agency models. Agencies have typically focused on execution — ad campaigns, content production, SEO deliverables. The reality now demands orchestration: aligning brand strategy, data science, creative and engineering in a continuous feedback loop.

Marketing operating systems (MOS) are emerging as the new framework. These integrate Search Engineering teams, AI signal intelligence dashboards, real-time brand visibility mapping across LLMs, ongoing content audits, and customer journey tracking across AI touchpoints.

The goal is to shift from reactive marketing to proactive visibility management — influencing the discovery process before a consumer even clicks.

This is as much a cultural shift as a technological one. Marketing, engineering, data science and AI strategy teams must work together. Siloed approaches — performance over here, SEO over there, content somewhere else — no longer make sense.

CMOs and growth leaders need to treat discoverability as an engineering challenge, investing in structured data, machine-readable formats, and the continuous feeding of AI training models with credible, relevant brand signals.

Traditional SEO, performance marketing and content strategies are still part of the toolkit — but they won’t get marketers far enough. The future belongs to Search Engineering: a blend of technical fluency and outcome-oriented thinking.

As AI becomes the default interface for information discovery, brands that invest early will not just remain visible; they’ll help shape the narratives AI platforms deliver. Visibility is no longer earned click-by-click — it’s engineered signal-by-signal, embedded deep in the neural systems that power AI.

- Senthil Kumar Hariram, founder and managing director, FTA Global