Alright — spoiler alert: SEO hasn’t died, but it’s definitely been through a glow-up (and maybe a midlife crisis). AI is rewriting parts of the playbook. Here’s how—and what you need to watch out for (and lean into).

What’s new / what’s changed

AI has introduced two major shifts in how we think about SEO:

1. From search engines → generative / AI-powered “answer engines”
   In the old days, SEO was about “getting someone to click on your link in a list of results.” Now, AI and generative models often answer questions directly, summarizing content in response boxes, chatbots, or “overviews” instead of just sending people off to pages. That means some users don’t even click through—and that changes how value is measured.

2. From isolated “SEO techniques” → integrated AI / automation tools across the workflow
   AI is creeping into every stage: keyword research, content ideation, on-page optimization, site audits, internal link suggestions, content refreshes, even outreach or link-building. You don’t have to do it all by hand anymore. Also, “Agentic AI” (autonomous decision-making by AI) promises to take some of these tasks off our hands entirely. 

Because of those two shifts, SEO as a field is evolving. Some key implications:

Key Impacts & What’s Changed in Practice

1. “Zero-click” search is now a thing (often via AI summaries)

AI-powered “overviews” or chat-style answers can satisfy user queries without any click. That means even if your content is “best,” you may not get the traffic. 
Some publishers have reported traffic drops of up to ~70–80% for certain queries when those summaries appear above the first link.

Bottom line: ranking first isn’t enough. You want to be the content that the AI cites / references.

2. GEO / Generative Engine Optimization is rising

To compete in the age of AI, a new discipline is emerging: Generative Engine Optimization (GEO). This is optimizing content specifically so that AI systems (ChatGPT, Gemini, Perplexity, etc.) are more likely to select and cite your content when generating answers. 

This includes structuring content clearly, giving solid factual backing, using schema, and emphasizing authority / citations. 

3. Content expectations are higher (and more precise)

Because AI is good at sniffing out fluff, content needs to be:

• Truly useful, not just keyword-stuffed filler
• Well-structured, with clear logic and subheadings
• Rich in original insights, research, data, quotes
• Tied to authority, expertise, trust signals
• Updated regularly (freshness matters)
  

Also, long-form content that covers the “questions behind the question” becomes more valuable (helping the AI “understand” your content is relevant).

4. Site and technical SEO have to be “machine-friendly”

When AI agents or crawlers scan content, they prefer:

• Clean markup, schema, structured data
• Clear metadata
• Semantic HTML (headings, lists, tables)
• Fast, accessible, navigable content
• Signal-rich pages (citations, references, authoritative links)

5. Backlinks / authority / earned media are even more critical

In AI-powered search environments, content that is cited by others (trusted sources, third parties) is likelier to be chosen. Thus, signals of external authority become weightier. GEO research confirms that AI systems favor earned media over self-promotion. 

6. Personalization and fine-tuning content per segment

AI makes it easier to dynamically tailor content to user segments, contexts, devices. So instead of one “generic best page,” you might optimize for multiple scenarios. 

7. Monitoring & adaptation must be more real-time

Because AI search systems evolve fast, you need to track not just “did my rank change?” but “am I being cited in AI answers?” and “has the AI’s answer for a given query changed?” This is more dynamic than before.

What hasn’t (or shouldn’t) changed

• The core principle remains: help humans first. AI might be scanning your content, but it’s humans who convert.
• Relevance, authority, trust, user experience are still front and center.
• Technical SEO fundamentals (site speed, mobile friendliness, security, crawlability) still matter.
• Internal linking, content silos, logical site structure still help (maybe even more now).

What to do (a “cheat sheet” for adapting)

• Aim to be cited — build your content so that AI systems will pull from you (i.e. clean data, clear structure, strong authority)
• Use schema / structured markup to make your data “AI-readable”
• Write for queries, not just keywords — especially long-tail, natural questions
• Refresh / update content constantly — don’t let it go stale
• Earn quality mentions / links / media coverage — that gives the “authority boost” AI likes
• Set up monitoring for AI-driven visibility — track if your content appears in answer boxes or generative outputs
• Balance automation with human oversight — yes, AI tools are helpful, but content must stay accurate, unique, brand-voiced
• Test for prompt versions of queries (different phrasings) — AI engines may select different sources based on phrasing
• Diversify your traffic channels — not just SEO; content, social, email, direct, partnerships.