What is McKinsey saying about AI search?
McKinsey’s recent report- “New front door to the internet: Winning in the age of AI search” (Oct 16, 2025), says that AI-powered search is already reshaping how people discover and choose brands, and that businesses need to treat “gen-AI engine optimization” (GEO / AI SEO) as a core capability or risk losing substantial discovery and revenue. In short: AI search is moving decision-making upstream, putting 20–50% of traditional natural-search traffic at risk and funneling large swathes of consumer spend through AI overviews unless brands diagnose their exposure, broaden their content footprint beyond owned pages, and invest in LLM-aware content and measurement.
How McKinsey frames the AI-search shift
According to McKinsey, AI-powered search is no longer experimental; many consumers already use it as their default research source, and brands will face reduced clicks from traditional search as AI summaries increasingly answer queries directly. In practice this means: even if your site ranks well in Google, AI overviews and LLMs may synthesize other sources and answer the user without sending traffic, so visibility in the AI layer matters as much as (or more than) classic SERP rank. (McKinsey & Company)
McKinsey’s 5 main takeaways
1) AI search adoption is large today and will grow quickly
What McKinsey found: ~50% of consumers in their August 2025 survey intentionally use AI-powered search; the firm projects major growth and predicts AI will channel large consumer spend by 2028.
So…: User behavior is already shifting; waiting to react is a strategic risk.
Action: Treat GEO as a strategic priority now; audit which audiences already prefer AI answers and which product categories are most affected.
2) Material traffic and revenue are at risk (and opportunity)
What McKinsey found: They project 20–50% of traffic from traditional search is at risk, and estimate around $750B in spend will move through AI search by 2028.
So…: This is both risk (lost discovery clicks) and opportunity (being present in AI answers drives upstream influence).
Action: Quantify your “value-at-risk” by modeling lost conversions if AI excludes your content; prioritize categories with highest exposure.
3) AI systems draw from many sources; owned content is a small slice
What McKinsey found: Brand-owned pages often make up only ~5–10% of the sources AI uses for answers; publishers, affiliates, and UGC dominate many categories.
So…: Classic on-site SEO is necessary but not sufficient, you need an ecosystem strategy (third-party, partnerships, review sites, affiliate relationships, and community content).
Action: Map top sources that LLMs use in your category and develop tactics to influence or contribute to them (guest content, data releases, PR that yields authoritative citations, encourage structured UGC).
4) Practical GEO playbook: diagnostic, content, optimization, capability
What McKinsey prescribes: Four core moves, run an AI visibility diagnostic, adjust content investments, optimize content structure and credibility for LLMs, and build GEO as a cross-functional capability.
So…: Success requires technical, editorial, and organizational changes; not just rewriting pages.
Action: Start with an AI Visibility Audit (measure presence across ChatGPT/Gemini/Perplexity/Google AI Overview), then build prioritized projects (high-impact content, credibility signals, and data assets).
5) Measure differently and plan for agent-style AI
What McKinsey warns: LLMs will evolve to act as purchasing or recommendation agents and to test paid formats; measurement frameworks and KPIs must be updated to track AI-layer visibility and sentiment.
So…: Traditional KPIs (organic clicks, SERP positions) are incomplete; you need GEO KPIs (mentions/citations in AI answers, share of voice in AI summaries, AI sentiment).
Action: Define GEO KPIs, instrument AI visibility tracking, and tie those metrics to commercial outcomes.
Table
McKinsey takeaway | Why it matters | Concrete first 90-day actions |
|---|---|---|
AI search adoption is significant and growing. | Early mindshare shifts upstream; first impression happens in AI responses. | Run consumer channel surveys; identify categories with highest AI adoption. |
20–50% of traditional search traffic at risk; $750B opportunity by 2028. | Major revenue and discovery implications. | Model value-at-risk by category; prioritize top 10 product pages. |
AI pulls from diverse sources; owned site often small portion. | You must influence external content and communities. | Map the top 20 sources LLMs reference for your category; start outreach. |
GEO requires a 4-part playbook (diagnostic, content, optimize, capability). | Tactical roadmap; combines editorial and technical work. | Commission an AI Visibility Audit and create a 6-month GEO roadmap. |
New measurement: AI citations, sentiment, share of voice in AI summaries. | Existing SEO metrics don’t capture AI layer effects. | Implement tracking for AI citations, schedule monthly GEO review. |
Tactical checklist: practical steps to recover traffic lost to AI
Run an AI Visibility Audit (indexLab offers this as a specialist service) to quantify where you are cited across major LLMs (ChatGPT, Gemini, Perplexity, Google AI Overview).
Prioritize pages/categories with highest commercial value and highest AI exposure risk.
Create “AI-ready” content: clear headings, factual snippets, unique proprietary data, and structured summaries suitable for LLM extraction.
Expand footprint beyond owned pages: publish on relevant publishers, contribute to forums, optimize product feed metadata, and push structured data where possible.
Build a cross-functional GEO team (marketing + SEO + customer experience + data + PR) and define GEO KPIs (AI citation share, AI sentiment, AI-driven conversion rate).
Why the McKinsey guidance matters for business owners and heads of marketing
You’re competing for the human’s decision earlier in the funnel. McKinsey’s data shows many consumers now prefer AI summaries for discovery; if you aren’t represented in that summary, you lose the chance to influence.
Traditional SEO still matters, but it’s no longer the whole picture. Where brands relied on owned content, AI now aggregates from multiple sources; this is a structural change in information supply.
The cost of doing nothing is measurable. McKinsey gives credible projections (20–50% traffic at risk); treating GEO as a budget line and building capabilities now reduces risk and captures new revenue streams.
On credible sources and expert voices
According to McKinsey’s report authored by Elizabeth Silliman, Julien Boudet and Kelsey Robinson (Oct 16, 2025), brands must “rethink how they structure, produce and amplify content” and establish GEO as a strategic priority; that’s the central prescription repeated throughout the analysis.
Counterarguments and limitations (what McKinsey might understate)
Model source bias and variability: LLMs differ in source selection (OpenAI, Google, Anthropic, Perplexity), and those choices can change quickly. McKinsey warns this, visibility will vary by LLM and question type.
Transient advantage: Early GEO investments may yield outsized gains now; over time, as everyone optimizes for LLMs, differentiation could compress and require more unique data or brand authority.
Measurement challenges: Tracking AI citations and attribution is harder than classic analytics; tools are emerging but are immature, so expect measurement noise. McKinsey calls for bespoke KPIs and continuous benchmarking.
How this ties to your goals
If your core needs are to Recover Traffic Lost to AI, Get Cited by ChatGPT, and improve AI Visibility optimisation, McKinsey’s report validates that you should:
Treat generative engine optimisation as part of your roadmap (not a tactical addon).
Invest in the AI SEO pillars: diagnostic, content breadth, structural optimization, and capability building; the same pillars McKinsey highlights.
Consider hiring or partnering with an AI SEO consultant (or agency) to run the diagnostic, set GEO KPIs, and execute a prioritized plan; exactly the services indexLab provides.
Example: 6-month GEO roadmap
Month 0–1- Audit & Prioritise: AI Visibility Audit; value-at-risk model by category; GEO KPI definition.
Month 2–3- Tactical Wins: Fix top 20 pages for LLM extraction (clear headings, TL;DR, structured data); publish 4 high-authority guest posts and 2 data assets for publishers.
Month 4–6- Scale & Measure: Implement AI citation tracking; run A/B tests for AI-optimized snippets; onboard cross-functional GEO team and review KPIs monthly.
Frequently Asked Questions
How fast is AI search growing?
McKinsey’s consumer survey (Aug 2025) reported roughly half of surveyed consumers intentionally use AI search today and the firm projects adoption and spend through AI search to grow meaningfully by 2028.
Will GEO replace SEO?
No, GEO complements traditional SEO. McKinsey says owned-site SEO remains important, but those pages represent only a small share of AI sources; you must expand influence across external sources and optimize content for LLM extraction.
What’s the single highest-impact first step?
Run an AI Visibility Audit to identify where you are (or aren’t) cited across major LLMs, this quantifies risk and lets you prioritize the highest ROI fixes.
Conclusion
McKinsey’s message is a clear call to action: AI-powered search has changed the front door to the internet. Brands that act, by diagnosing AI visibility, widening their content footprint beyond owned pages, optimizing content structure and credibility for LLMs, and investing in GEO as a capability, will protect and grow discovery and revenue.
If you’re seeing organic traffic decline, that diagnosis and a prioritized GEO roadmap are the exact remedies McKinsey recommends. Start with an AI Visibility Audit, set GEO KPIs, and execute high-impact content and third-party influence plays to get cited by ChatGPT, Gemini, and other LLMs.
👉 Book your AI Visibility Audit with IndexLab today and make sure your brand isn’t invisible in the age of AI search