The plan that still assumes the click

Picture a 2026 marketing plan that assumes organic search will deliver roughly the audience it delivered in 2022. The budget, the agency retainers, the SEO roles and the attribution model all rest on one mechanic: a user types a query, sees ten blue links, clicks, and the brand captures the visit.

My argument is that this mechanic is breaking faster than most marketing plans assume, and that the danger is planning inertia more than ignorance. Most leaders can recite the AI-search story. Their budgets, roles, dashboards and agencies are still arranged around the click while buyers increasingly learn through answer engines and agents.

Three shifts are happening at once. The discovery layer is fragmenting across several AI surfaces in addition to Google. An agentic layer is forming, in which software researches, compares and sometimes buys on a user's behalf. Inside the marketing function, generative AI is cutting the cost of creative production while performance media concentrates in a few platforms. Each would justify a strategic response on its own. Together they amount to a reset of how marketing earns attention.

The click is shrinking

In February 2024, Gartner forecast that traditional search engine volume would fall 25% by 2026 as AI assistants became substitute answer engines. Studies from Pew Research, Seer Interactive, Digital Content Next, Chartbeat and SimilarWeb describe informational click-through rates falling where AI Overviews appear, and publisher traffic from search down by about a third year on year. The effect is uneven. Retail and product queries have been less affected than news and reference.

Google disputes the framing. In August 2025, Google's VP of Search, Liz Reid, said that "total organic click volume has been relatively stable." Both things can be partly true, because total volume and the clicks a particular publisher or brand receives are different measures. For planning purposes, the question is what is happening to your own informational traffic.

ChatGPT now reports around 900 million weekly active users. The mechanism that turned demand into a session on a brand-owned site is increasingly mediated by a conversational layer that can answer the user without sending them anywhere.

The discipline that grows from this is often called generative engine optimisation: being cited and recommended inside AI answers. The work is structural. It involves structured data, evidence-backed claims, clear question-and-answer formats and earned citations on sources that answer engines trust, such as reputable trade publications, Wikipedia, YouTube and community forums. The brands that do well will be the ones reliably named when a buyer asks what the best option is in their category.

The website becomes a source for agents

Corporate websites will persist, but their role is narrowing. For many buyers, the first interface is becoming an AI assistant, and the website increasingly serves two jobs: a source of truth that agents can read and act on, and a flagship experience for people who are already interested.

Early standards for how agents call business systems are already in use. Anthropic's Model Context Protocol and the Agentic Commerce Protocol from OpenAI and Stripe let agents query products, prices and support, and in some cases complete a purchase. OpenAI's Instant Checkout in ChatGPT launched with Etsy sellers and was announced for more than a million Shopify merchants. Morgan Stanley's AlphaWise survey found that 23% of Americans had bought something through an AI agent in the previous month. These are early-adoption figures, and they will move.

The practical test is simple. Can an agent read your product catalogue, understand your support documentation and, where relevant, complete a purchase? Each "no" is a small form of invisibility in a channel that is growing.

Augmentation and replacement

The most comfortable story for executives is that AI augments marketers rather than replacing them. The holding-company headcount data complicates it. WPP, Publicis, Omnicom and Interpublic have together removed tens of thousands of roles since 2024 against flat to declining organic growth, and Taligence's U.S. Marketing Job Postings Index for Q2 2025 showed entry-level marketing postings down sharply year on year. The agency model is restructuring around fewer people producing more output.

Microsoft's internal pilots reported 29% faster task completion and roughly nine hours saved per user per month, but only with structured training rather than casual exposure. McKinsey sizes the generative AI productivity opportunity at $2.5 to $4.4 trillion a year, with marketing and sales the largest functional value pool. That value arrives largely by reducing the labour needed per unit of output.

My read for the next few years is replacement at the routine layer, concentration of the remaining roles around brand strategy, agent and data platforms and judgement, and a growing "vanishing junior" problem. Companies that quietly stop hiring at entry level are storing up a senior-talent shortage for the early 2030s. The contrarian move is to keep the entry-level pipeline through the disruption, accept a higher unit cost for a while, and own the senior bench in the next cycle.

What changes for B2B and B2C

In both markets the change can be put in one sentence: the buyer increasingly meets an AI surface before meeting the brand, and reaches the brand only if that surface has judged it relevant.

In B2B, most buyers now use generative AI in pre-purchase research and do much of their evaluation before talking to sales, often with a preferred vendor already in mind. The fill-the-funnel, qualify and hand-to-sales motion has to assume a buyer who arrives informed and partly decided.

In B2C, AI Overviews and chat products increasingly shape the top of the funnel for considered purchases such as health, finance, travel and durables. When an agent does more of the choosing, loyalty is harder to build and more valuable once it exists. Category leaders that are reliably named in answers will gain, while unbranded competitors that relied on price comparison and last-click attribution face the steepest erosion.

The marketing spend that shapes a buyer's view before the AI surface intervenes was always the most leveraged. It is becoming the most important.

The strongest objection

The strongest objection comes from Google itself: total organic clicks are relatively stable, and much of the decline is concentrated in informational publishing rather than in the commercial queries that matter to a B2B brand.

That may be right for some categories, and it is a good reason to measure rather than assume. The planning risk lies in a different place. Budgets, roles and agency contracts take a year or more to change, while the discovery layer is moving now. A company that waits for its own traffic to confirm the shift will be reorganizing after its competitors. The sensible response is to test your own queries and treat the result as evidence.

What to do in the next ninety days

  1. Audit AI visibility. Test your top fifty buyer queries in ChatGPT, Gemini, Perplexity, Microsoft Copilot and Google AI Overviews each month, and track how often you are cited compared with named competitors.
  2. Give someone the mandate for generative engine optimisation, either by expanding the search team or by naming an AI-visibility lead. Start with structured data, question-and-answer formats and evidence-backed claims, and work on earning citations from sources answer engines trust.
  3. Inventory and govern the AI stack across creative, media, personalization and customer experience, and prepare for the EU AI Act's Article 50 transparency obligations, which apply from 2 August 2026.
  4. Strengthen first-party data. Audit consent infrastructure, and treat preference centres and loyalty programmes as data exchanges that buyers find worth joining.
  5. If you sell products, make sure your catalogue and feeds can be read by agents through the emerging protocols, and watch whether competitors start appearing in agent purchasing flows before you do.

Risks worth planning for

Three risks deserve explicit attention alongside the base case.

The first is model collapse. As a growing share of new web content is AI-generated, the training data future models depend on may degrade, and a visible drop in quality could trigger a move back toward verifiable, human-authored sources. Proprietary data, original research and provenance standards such as C2PA would become more valuable in that world.

The second is platform concentration. The agent layer could mirror the current search and social dynamic, with two or three operators holding pricing power over advertisers. OpenAI, Google, Apple and Meta have the most credible distribution. The remedies in U.S. v. Google and the EU Digital Markets Act will shape how concentrated the outcome becomes.

The third is regulatory fragmentation. The EU AI Act, U.S. state rules and Asia-Pacific approaches are diverging, so a global marketing operation cannot assume a single compliance regime.

Boards that take this seriously will commission scenario planning rather than rely on point forecasts. The direction of travel is clearer than the speed. Leaders who invest now in AI visibility, agent-readable infrastructure, first-party data and the human-led brand assets AI cannot copy will be in a stronger position than those who wait for their own traffic data to force the decision.

Monday move

Take the ten queries your best customers most often use to find a supplier like you. Run them in ChatGPT, Gemini, Perplexity and Google's AI Overviews, and record who is named in each answer and which sources are cited. Bring the table to the next marketing leadership meeting as the baseline for the ninety-day plan.

The full 33-page working paper, with figures, references and a staged programme of action across three horizons to 2030, is available below.