Here's how we are getting ready for AI-driven search: ->Content shift - 'Answer Assets': Rather than long-form keyword blogs, we are creating modular content (use-cases, crisp definitions, quotable insights). We can feed these assets directly into large language models(LLMs). ->Budget reallocation: We've pulled part of our paid search spend into AI distribution tests, optimising for visibility in ChatGPT, Perplexity citations, and Google AI Overviews. ->SEO meets product marketing: Schema markup, trusted sources, and structured context are baked into every asset, so AI can cite us confidently. The biggest challenge? Attribution. clicks drop when answers are surfaced directly in AI tools. And the opportunity? Brand authority. If our content becomes the "default answer" in AI search, that is even more valuable than a #1 SERP ranking. AI search is not just SEO 2.0 - it is a visibility + trust strategy. We are treating it first as brand-building, then lead-gen.
As CMO of Nine Peaks Media, I'm actively preparing our SaaS clients for the shift to AI-driven search. The biggest change is prioritizing "answer-first" content. Instead of long-form SEO copy alone, we now structure pages so that the core insight or solution is summarized in the first 100 words. This improves our chances of being cited in AI summaries from ChatGPT or Google AI Overviews. In practice, we've seen early wins when product comparison pages lead with concise, data-backed takeaways. We are reallocating budget from broad keyword expansion into entity-based optimization and brand authority building. For example, investing in thought leadership on DR 70+ sites and structured data markup has driven visibility across emerging AI engines. The opportunity is clear: AI will compress search journeys. SaaS CMOs who align content to direct answers and brand mentions will capture demand faster. The challenge is attribution, traffic will be less visible, so brand authority becomes the KPI.
For the last 20 years, search has rewarded volume, optimization, and spend. With AI-driven search, the game shifts to context, authority, and trust. The companies that win will be the ones large language models view as credible sources. Marketing teams today are excellent at optimization and scale, but the ability to build authority and trust has become a lost art. That expertise still lives with PR and communications. Ironically, those teams have struggled to prove their work leads to revenue, and they have less influence in the c-suite compared to their marketing counterparts. AI search changes that dynamic. Brand authority, media relations, and customer advocacy are now necessary activities to increase brand visibility, which puts communications back at the center. I believe we are on the cusp of a renaissance for PR and communications professionals as the natural owners of AI search strategy.
As a SaaS CMO, I've shifted strategy to treat AI search like a new distribution channel, not just an SEO tweak. We're investing more in structured data, entity-based content, and thought-leadership pieces that AI tools can confidently cite. For example, instead of publishing 20 keyword-driven posts, we now produce 8 deeply researched guides with SME input, then repurpose them into formats AI scrapers and answer engines prefer. Budget wise, we're reallocating ~15% from paid campaigns to AI-driven optimization and experimentation with platforms like Perplexity. The biggest challenge is measurement since traditional SERP metrics don't capture AI visibility yet, but the opportunity is clear: SaaS brands that provide authoritative, verifiable answers will be disproportionately rewarded.
I moved about 20% of budget from low intent paid search into content that fits how AI search engines show answers. In the first quarter of doing that, cost per lead dropped by around 12% and organic leads converted better. Traffic didn't shoot up, but the pipeline got stronger and less tied to ad auction costs. For SEO, I stopped focusing on single keywords and started building clusters around real problems and solutions. That format works better when AI pulls summaries, so I also added more FAQs and schema markup. That way the content reads cleanly and has a better chance of being surfaced. It feels less about ranking number one on Google and more about being the source AI uses when giving an answer. Budgets now go more into long term assets. I cut back on paid campaigns where CPC kept going up and put that money into SEO, CRO, and landing pages made for people with strong intent. The tradeoff was losing some quick wins on volume, but the results are steadier and less fragile when CPC jumps. The hardest part is tracking. Analytics don't clearly show when leads use AI search engines like Google Overviews or Perplexity, so CAC is harder to measure. Still, the trend is clear. Teams that build topic authority and structured content now will be the ones showing up in AI results later. For SaaS, that means putting effort into assets that grow value over time and keep CAC more predictable instead of paying more every quarter for the same clicks. -- Name: Josiah Roche Title: Fractional CMO Company: JRR Marketing Website: https://josiahroche.co/ LinkedIn: https://www.linkedin.com/in/josiahroche
For us, we are focusing on building relevant and authoritative brand mentions while also writing authoritative, helpful, and unique content. In our industry, prospects tend to ask detailed context-heavy questions. Since AI-models source answers from what they deem trustworthy and authoritative sources, we are making sure we position ourselves as such. We do this by talking with the sales team to gather insights on common questions prospects ask, analyzing industry forums like reddit threads to identify common questions, frustrations, or gaps in understanding. On the brand mentions side of things, we are less focused on always getting a backlink, but instead, if we are mentioned on an industry specific site or something similar, we consider that a win since its another signal to these Ai-models that we are a trust worthy source on the subject. The challenge is that lead attribution gets fuzzier since people might not always land on our site, but our expertise can still shape the buyer's journey in they see us in the AI overview. But the opportunity is still huge: if you're the SaaS company providing the most useful, trustworthy perspective, AI search will amplify your credibility rather than just drive traffic to your site. Prosepcts will see yoru brand name in the AI overviews and its like a marketing touchpoint. My advice to other SaaS companies would be to focus more on answering the questions that your ideal customer will be asking an LLM or Googling.
When I think about how SaaS CMOs are preparing for AI-driven search, the biggest shift I've made in my own strategy is moving away from chasing keywords alone and instead building content that answers questions in the most natural, conversational way possible. Tools like ChatGPT, Perplexity, and Google's AI Overviews pull context more than just rankings, so I've focused on creating content clusters that deeply cover a topic from multiple angles. For example, with one SaaS client we built out an entire knowledge hub that answered "micro-questions" users might ask AI rather than relying only on a single long-form article. That approach quickly increased visibility in AI snapshots and improved user trust. I am also reallocating resources into structured data, FAQs, and experimenting with conversational search prompts to see how content is interpreted. Instead of putting all budget into link-building, I've been shifting more into schema markup, multimedia content, and AI-friendly formatting like bullet points and clear summaries. The challenge is that AI search doesn't always credit the source, so measuring ROI is harder. But the opportunity is clear—if your content consistently answers the intent behind queries, you can still become the trusted voice AI tools pull from. My advice for SaaS CMOs is to audit existing content for gaps in how "human" and actionable it feels, and then invest in depth, clarity, and structured data that makes it easy for AI to feature your brand.
As a Marketing Manager at Favouritetable, a SaaS company, we are actively preparing for the rise of AI-driven search by adapting our content and SEO strategies to focus on Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). Our primary goal is to create authoritative, structured content that AI models can easily cite, moving away from a traditional keyword-centric approach. We are reallocating a portion of our budget and resources towards technical SEO enhancements, investing in expert content creators to produce high-quality, data-rich material, and using AI tools to increase our team's efficiency. While we recognize the challenge of zero-click search, we see the greater opportunity to establish Favouritetable as a definitive brand authority in our industry. By becoming the go-to source cited by AI, we can build trust and a defensible market position, even if it means rethinking traditional traffic metrics.
We've really changed the way we think about search. In contrast to traditional SEO, we have refocused our clients' digital strategies to position them as the most reliable source for answers. In an AI-driven world, the model itself is the interface, so our goal is to become part of that model's mental map. That means our content now comes from real, visible experts, with unique data and stats, and is published in ways that are easy for people and machines to understand. When we release a new report or insight, we also provide clear explanations, glossaries, and FAQs, so that wherever someone or some system looks, they find clean, authoritative information from us. We focus on creating original data and genuine insight rather than recycling what others say, because that's what large models crave and what makes them more likely to include us in their answers. We've also shifted our backlink strategy for clients, focusing on appearing in the directories and platforms that AI assistants actually use when serving location-based or industry-specific results. For example, if you're a B2B SaaS vendor in HR tech, it's more valuable to be listed with complete, well-structured profiles on G2, Capterra, TrustRadius or local partner directories than to have dozens of weak blog backlinks. Those sources are scraped and summarised by the big models, so being there with rich, up-to-date information gives you a much better chance of being cited. We also put a lot of energy into being active in the communities where questions are asked. Our team posts key findings, answers queries and contributes definitions in public forums. All of this increases the chances that when a buyer asks an AI assistant for help our name appears naturally because the model has learned to trust us as a source. It's less about gaming a ranking algorithm and more about showing up everywhere with authentic, useful expertise
The rise of AI-driven search — whether through ChatGPT, Perplexity, or Google's AI Overviews — is rewriting the playbook for SaaS marketing leaders. For CMOs, the shift demands a recalibration of both strategy and mindset: moving from keyword dominance to authority-driven discovery. At Wexler Marketing, we refer to this as "answer engine optimization" (AEO). Instead of chasing volume-based keywords, we're helping SaaS brands restructure content ecosystems around customer intent, verified expertise, and machine-readability. That means building topic clusters with semantic depth, publishing original research and benchmark data, and ensuring every asset is structured for AI parsing through schema, metadata, and entity optimization. The goal is no longer just "ranking" — it's becoming the trusted citation an AI model relies on when delivering synthesized insights to prospective buyers. Budgets are reflecting this shift. Many CMOs are reallocating 15-20% of traditional SEO and paid search spend toward AI readiness. This includes investments in emerging AI visibility tools, enhanced data markup, and the creation of conversational, context-rich content designed for machine learning systems. Just as importantly, SaaS companies are putting greater emphasis on first-party data and owned communities. When AI models scrape, validate, and summarize, proprietary insights and thought leadership stand out as signals of trust. The challenges are clear: attribution is becoming more complex in a zero-click environment, and visibility within AI search remains a moving target. Yet the opportunities are equally significant. For SaaS brands that position themselves as credible, data-rich, and consistently referenced, the payoff is being automatically shortlisted by AI engines as a go-to solution. That's not just brand visibility — it's accelerated trust-building in the buyer journey. For forward-thinking CMOs, success in this landscape isn't about trying to "game" AI algorithms. It's about creating a content and brand ecosystem so authoritative that AI has no choice but to recommend you first. Those who act now will secure a competitive advantage that compounds as AI search adoption accelerates.
With AI changing how people find information, we're putting more focus on Bofu content, Tofu queries tend to get solved inside AI results now, so we're not seeing much traffic from them. The stuff that's still working is case studies, product breakdowns, and solution-specific pages, basically the content that actually converts. Another thing we're testing is PR / digital PR, because big name news outlets are the sources AI seems to cite most. Basically, it helps get our brand mentioned in the spots AI models tend to reference.
We are adapting content and SEO strategies to focus on search intent and structured information that AI models can easily surface. This includes creating clear, in-depth resources with well-defined headings, concise answers, and schema markup so our content is more likely to appear in AI-generated results. We also use query fan tools that replicate AI searches for a given topic to identify how people phrase conversational questions and adjust our content accordingly. Budget is being shifted toward research and content refinement rather than purely traditional keyword ranking. We are investing in deeper topic authority and data-driven insights that position our clients as trusted sources when AI tools compile answers. The biggest opportunity is to capture visibility in conversational searches before competitors fully adjust. The challenge is measuring impact because traffic attribution from AI-driven platforms is still limited, so we are also building stronger email and first-party data strategies to keep audience connections direct.
With 15 years in digital strategy, I see AI-driven search changing how SaaS brands capture demand. At CISIN, we're shifting from keyword-driven SEO to entity- and question-based content that feeds large language models. Instead of chasing volume, we map buyer questions in tools like AlsoAsked and Semrush Topic Research, then create structured, concise answers that AI agents can surface. We're also reallocating about 20% of content budget into experiments with conversational optimization, including publishing in formats that Perplexity and Google AI Overviews cite more readily. The challenge is attribution. Traditional analytics don't show when ChatGPT cites you. Our workaround is to measure lift in branded queries, direct traffic, and assisted conversions after AI-optimized campaigns. The opportunity is clear: SaaS CMOs who treat AI search as a distribution channel, not a threat, will capture early trust and category authority.
Hi Scott, You've raised a really important question that many businesses are wrestling with right now. The bigger issue is that hesitation is holding a lot of them back from engaging fully. In my work with B2B SaaS companies, we've had to rethink SEO in light of AI search. Answer Engine Optimization (AEO) still matters — short, structured answers and schema markup often surface in AI summaries. But we're also testing what's now called Generative Engine Optimization (GEO), which is about writing content so tools like ChatGPT and Perplexity actually reference your brand. We've shifted the budget away from keyword chasing into entity mapping, schema, and direct experiments with AI platforms. The hardest part is measurement, but the companies that crack this first will have a real edge. Looking forward to seeing your article published.
As a CMO in the B2B SaaS space, I see AI-driven search as both a disruption and an opportunity. Traditional SEO strategies built around keyword rankings are no longer sufficient when platforms like ChatGPT, Perplexity, and Google AI Overviews are delivering synthesized answers instead of links. To adapt, we're shifting from keyword-heavy content to authority-driven, structured insights—content that AI systems can easily parse, cite, and trust. This means investing in original research, clear data visualization, and FAQ-style resources that directly answer the kinds of questions AI models are trained to surface. From a budget perspective, we've begun reallocating resources toward AI optimization and experimentation. That includes testing how our content appears in AI summaries, building schema markup for better machine readability, and training our teams to write with both human readers and AI systems in mind. We're also investing in partnerships with analysts and thought leaders, since third-party validation is increasingly influential in AI-driven results. The biggest challenge is attribution—AI search often removes the direct click-through path, making ROI harder to measure. But the opportunity is significant: if your brand becomes the "trusted source" AI tools pull from, you gain visibility and credibility at scale. For SaaS marketers, the future isn't about chasing rankings—it's about owning the narrative in your category so that when AI answers, your voice is the one amplified.
We're shifting from keyword chasing to authority building. Content is now built around first-party data, expert quotes, and original use cases—signals AI systems prefer to surface. Budgets that once went to volume blog output now back research pieces and multimedia that can be cited. The challenge is attribution, but the upside is clearer: if AI trusts your brand, demand capture becomes less about rank and more about relevance.
AI-driven search is changing the way prospects discover SaaS brands. At our company, we've shifted our SEO strategy from chasing keywords to creating topic clusters designed for answer engines like ChatGPT and Perplexity. We're investing more budget into thought leadership content, structured data, and conversational FAQs that can be easily pulled into AI summaries. The challenge is attribution; AI search often delivers answers without a click. But the opportunity is brand visibility. If your content is cited as a trusted source, you can win awareness at the exact moment prospects are exploring solutions. For SaaS CMOs, the key is balancing classic SEO with 'answer optimization' so we stay discoverable in both traditional search and AI-driven environments.
As the operating partner of SourcingXpro, I can see firsthand how AI search is already reshaping buyers' ability to find us. Previous SEO tricks still work, but it is far fewer current tool and platforms that is now competing with, like ChatGPT, and they will give less than10 links - maybe one straight answer. So, I have been tapping into content that is uncomplicated, and direct, that I know cases my clients experience - like how one client saved $4,200 on his Shenzhen order by consolidating a couple of suppliers. We took some budget away from generic ads and put those resources into FAQs and sourcing guide details because that is a type of content AI is actually picking up. The benefit for your authority is there if your response is clear, but the downside is you cannot publish just fluff anymore - you must solve a real problem.