If you feel like search got weird this year, you are not imagining it. Between Google’s AI Overviews, ChatGPT search and answer engines like Perplexity, users are skipping the old “10 blue links” workflow and jumping straight to synthesized answers. Google frames AI Overviews as a layer that pulls from multiple sources and still links out, not a chatbot replacement. The catch is that what gets summarized and cited looks very different in B2B than it does in B2C.
Here’s the simplest way to think about “AI search.” It is not one product. It is a behavior shift across interfaces:
- In Google, AI Overviews use a customized Gemini model alongside traditional Search systems like ranking and the Knowledge Graph.
- In ChatGPT search, the web becomes part of the conversation, with publishers and websites showing up as sources inside the answer.
- In Perplexity, the product is the answer itself, built from real-time web results with citations.
Same outcome, different wrappers: fewer clicks, more “decision made inside the answer,” and a bigger premium on being the source that gets cited.
The real difference: what the user is trying to avoid
B2C searchers are usually trying to avoid choosing the wrong product. B2B searchers are trying to avoid choosing the wrong career move.
That sounds dramatic, but it maps cleanly to what AI systems reward. B2B queries tend to be higher risk, longer cycle and full of internal politics, which pushes users toward “help me defend this decision” content. B2C queries skew toward speed, price and availability, which pushes users toward “help me pick fast” content.
One quick comparison table we use with clients:
| What changes in AI search | B2B | B2C |
| Typical query shape | Use case, role, integration, ROI | Best, price, reviews, near me |
| What the answer needs | Proof, nuance, tradeoffs | Clarity, shortlist, specs |
| What gets cited | Research, benchmarks, docs | Product pages, reviews, policies |
| Conversion path | Demo, sales call, security review | Add to cart, store visit, subscribe |
How AI search plays out in B2B
In B2B, AI answers behave like a first-pass analyst. Users ask questions they would normally dump into a sales call or an internal Slack thread: “HubSpot vs. Marketo for mid-market,” “SOC 2 requirements for vendors,” “best ERP for manufacturing with NetSuite integration.”
Which means your old SEO playbook can underperform even if rankings stay stable. You can rank top three and still lose mindshare if the AI answer summarizes competitors, cites a G2-style comparison and never needs your page for the “next click.”
So what actually works?
You win B2B AI search when you publish things the model can safely quote. “Safely” means specific, verifiable and not fluffy. Google explicitly positions AI Overviews as corroborating information from high-quality results. In practice, that rewards content that reads like documentation, a benchmark report or a clear explainer with numbers.
If you want a B2B-focused build list that is realistic for a lean team, prioritize these four assets:
- Comparison pages that name competitors and draw real lines
- Integration pages with setup steps, limits and screenshots
- Proof pages for security, compliance and procurement objections
- Benchmarks that include methodology, not just claims
Notice what is not on that list: another generic “ultimate guide” to a category. AI can write those in five seconds. Your edge is specificity and evidence.
A concrete example: if you sell analytics for ecommerce, “How to track ROAS” is table stakes. “GA4 vs. Triple Whale vs. Northbeam for blended ROAS in Shopify” is the kind of query that triggers citations because it is decision-shaped and easy to attribute to a source.
How AI search plays out in B2C
B2C is more ruthless. The buyer journey can be five minutes. AI answers become a shopping assistant: shortlist options, summarize pros and cons, call out pricing and return policies, then point to a couple of links.
Google’s own help documentation talks about AI Overviews as a “snapshot” with links when the system thinks an overview will be helpful for understanding a range of sources. In B2C, “range of sources” often means reviews, product specs, creator content and retailer policies.
So the question is not “How do I rank for ‘best running shoes’?” It is “When the AI creates a shortlist, am I on it, and is the summary accurate?”
For B2C teams, the highest leverage work usually looks like boring operations work, not creative brainstorming. These are the four things we push first:
- Clean product data: specs, variants, pricing, availability
- Policy clarity: shipping, returns, warranty in plain language
- Review velocity: steady volume, not one big push
- Category pages that answer selection questions fast
If you are in local, stack “near me” intent on top. The AI answer is trying to reduce steps. If your store hours are wrong or your inventory story is unclear, you get filtered out before a click even happens.
Measurement: stop waiting for perfect attribution
AI search makes attribution messier, not cleaner. Chat interfaces summarize, users copy answers into Slack, someone else searches your brand later, then the demo gets booked. The dashboard story is rarely linear.
So measure it like an influence channel:
In B2B, we care about three signals over a 30 to 60 day window: (1) growth in branded search and “brand plus category” queries, (2) increases in direct and dark social on high-intent pages, (3) sales call transcripts that start with “I saw you mentioned in…” You can literally add a checkbox in your CRM for “found via AI answer” and start building directional truth.
In B2C, watch assisted conversions, product page entrances and shifts in conversion rate on organic landings. If AI answers are pre-qualifying shoppers, your traffic might dip while conversion rate improves. That is not a loss, that is filtration.
A practical 30-day sprint you can run next week
Most teams do not need a six-month “GEO initiative.” You need a month of focused publishing and cleanup, then iteration.
Here’s the sprint we use:
- Week 1: Audit top queries where AI answers appear, note cited sources
- Week 2: Ship two citation-friendly pages per priority theme
- Week 3: Tighten product, schema and internal linking to those pages
- Week 4: Track mentions, rework pages that are close but not cited
If you do nothing else, do Week 2. Publishing the right two pages beats polishing twenty mediocre ones.
The bottom line
AI search is not “SEO is dead.” It is “SEO is being judged differently.” In B2B, the winners look like the most credible internal wiki on the internet. In B2C, the winners look like the cleanest product catalog with the least friction.
Once you accept that, the work gets refreshingly straightforward: publish what the AI can quote, make it easy to verify and stop hiding the details your buyers actually need.
