Today (2025), we are in a new era of search, where generative AI and answer engines are reshaping how buyers find information. Gone are the days when winning at SEO meant simply ranking #1 on Google for your relevant keyword(s).
A CMO’s challenge today is twofold: deliver measurable ROI from digital channels while keeping pace with evolving search trends and digital transformation. In fact,71% of CMOS say their budgets are insufficient, and 75% are pressured to “do more with less”, and roughly 70% of B2B buyers now prefer to self-educate online. This means your content strategy needs to work smarter and harder, and increasingly, it needs to succeed in AI-driven search results as well as traditional search.
Why does this matter now?
Over the last 18 months, AI adoption has exploded, 78% of companies use AI in at least one business function, and 71% regularly use generative AI for tasks like content creation. Search is no exception: tools like ChatGPT, Bing Chat, and Google’s Search Generative Experience (SGE) are turning classic search into a dynamic Q&A experience.
This playbook will walk you through how the rules of SEO are changing in a world increasingly shaped by AI-powered search engines, and how to adapt. We’ll contrast the traditional SEO playbook with the new AI search paradigm, and show how our ARISE GTM methodology, a strategic, forward-thinking approach developed by Paul Sullivan (author of “Go To Market Uncovered”), helps B2B marketing teams accelerate results by combining the best of both worlds. (Paul created the ARISE GTM Methodology in 2019 to help businesses take stock of their past, research the present, and plan for the future, aligning marketing, sales and success teams for long-term growth.)
This guide is strategic (we’ll discuss big-picture shifts), forward-thinking (focused on where search is headed), decidedly human (no dense techno-jargon here, just clear insights), and even a little cheek (because hey, if we can’t poke a little fun at our past SEO habits, who will?). So buckle up, it’s time to embrace the new reality of SEO in the AI era, or as we like to say, “evolve or get left behind.”
Fixed Keyword Rankings: In classic SEO, success is often measured by stable rankings for specific keywords – e.g. being result #1 on the Google SERP (Search Engine Results Page) for “B2B lead generation tips.” These rankings tend to be relatively stable over time (barring algorithm updates or competitors’ moves). The entire strategy revolved around climbing those rankings for target queries and then defending that position.
Tools and Metrics: SEO teams relied on tools like Google Search Console and Ahrefs/Moz/SEMrush to track performance. Google Search Console provides data on clicks, impressions, and average position for your pages, while third-party tools track keyword rankings and backlinks. Visibility was straightforward: if you’re ranking on page 1 (especially top 3), you’re getting seen. An “SEO win” meant higher SERP rank and more organic traffic.
Content Strategy: The focus was on producing content optimised for search algorithms – think keyword research, on-page SEO, link-building, etc. In practice, that sometimes led to content volume over value. Many marketers pumped out blog posts to target every keyword variant, aiming to cover as much ground as possible.
But by 2024, this approach started to falter: SEO content became oversaturated. You can publish 50 blogs a year, but without a compelling brand or unique angle, they’ll rank poorly. In short: quality over quantity, brand over breadth.
In other words, generic content doesn’t cut it anymore – even in traditional SEO, Google’s algorithms favour original, authoritative content that stands out from the content farms.
Visibility = SERP Presence: In the traditional model, your brand’s visibility was directly related to its SERP presence. If you could own the top spot (or at least page one), you were golden. Users would see your title and meta description, click through to your site, and (hopefully) convert. The battle was all about those ten blue links on Google. SEO was a bit like climbing a mostly static ladder; once you got to a higher rung, you could hang out and enjoy the view (and traffic) for a while.
Example (Traditional): Think of how we used to celebrate “Rank #1 on Google for [XYZ].” If you were a marketing software company ranking #1 for “marketing automation platform,” you’d boast about that constantly, as it meant steady inbound traffic. The content on that page might remain unchanged for months, yet continue to rank and bring leads. Stable, predictable, and measurable define the traditional SEO positioning.
Dynamic Answer Mentions (Not Static Ranks): AI-powered search changes the game by delivering dynamic answers drawn from multiple sources. Instead of a list of links, users get a summarised answer (as if from a knowledgeable advisor) with citations or references to source content.
For brands, this means the goal is no longer just ranking for a keyword, but being included in the AI-generated answer. Your content needs to earn a mention or citation in the AI response. These mentions are highly dynamic – they can shift from query to query and day to day.
One moment, your blog post might be one of the three sources an AI cites; a week later, after new content is published on the web, the AI might cite something else instead. In short, the “ranking” is episodic – re-decided by the AI for each query in real-time, rather than a fixed position on a static results page.
New Metrics of Visibility: Because of this, visibility is measured differently. It’s not just “What’s our Google rank for keyword X?” but “Are we showing up in AI-powered search answers for topic X?”. You might hear terms like “AI Mention Share” or “Answer Presence” as emerging SEO KPIs.
For example, how many times out of 100 sample AI queries about “fintech compliance” does your brand get cited in the answer summary? This is a fundamentally different way to gauge success. Being included in an AI summary or overview is the new top spot. If traditional SEO was about owning real estate on page one, AI SEO is about earning a seat at the table when the AI “talks” to your audience.
Tools and Tactics Evolving: Our toolbox is expanding. We still use favourites like Google and Bing Webmaster tools for index coverage, and SEMrush/Similarweb for traffic and competitive insights, but now there’s a twist.
Similarweb, for instance, has been analysing the impact of zero-click searches and AI snippets on site traffic (measuring how user behaviour changes when answers are given directly).
SEMrush and other SEO platforms are beginning to integrate features for tracking featured snippets and AI results.
And new AI-focused SEO platforms are emerging, tools that simulate questions on ChatGPT or Bing to see which sources are cited, or that optimise content specifically for voice and AI assistants. (This is a nascent area, but watch this space – the SEO industry is quickly adapting with AI-powered analysis tools.)
For now, some of this “tracking AI visibility” might involve manual testing, literally asking Bing Chat a question and seeing if it mentions your brand, but expect more automated solutions soon. The key is that traditional rank tracking alone isn’t enough; we need tools that measure how AI is using our content.
Fluidity and Frequency: Unlike the relatively stable world of Google rankings, AI search positioning is fluid. Each user’s prompt to an AI search engine could yield a slightly different answer, with different sources cited. Mentions can be fleeting.
This means SEO work becomes more continuous; you can’t just land a #1 rank and relax, because your #1 can disappear if the AI finds a more relevant snippet elsewhere. It puts a premium on consistently providing the best, most relevant, up-to-date information on a topic so that the AI will keep picking you as a source.
It’s a bit like trying to stay in an ever-rotating panel of expert panellists that an AI might quote. You have to keep raising your hand with great answers.
Visibility = Inclusion in AI Answers: In AI-driven results, a user might ask a complex question (“What’s the best way to implement zero-trust security in fintech?”) and get a curated paragraph that blends insights from several articles, reports, or forums.
If your content is one of them, the user might see a little citation number or a mention of your brand (depending on the interface). The user might not even need to click through if they got the gist of the answer already.
This introduces the concept of brand presence without clicks. Even if the user doesn’t visit your site, there’s value in being referenced (for credibility, awareness, or a bit like being quoted in an article). Of course, we still want the click! But success might look like being one of the three sources cited in a comprehensive AI answer, rather than being the top of ten blue links.
Example (AI Search): For instance, if someone asks an AI search engine, “How do I accelerate B2B SaaS go-to-market?”, the AI might generate an answer like: “Accelerating B2B SaaS GTM involves aligning marketing and sales, adopting account-based strategies, and leveraging automation for speed…” and it would footnote a couple of sources.
Perhaps it cites an ARISE GTM blog post and a Gartner article. If your content is cited, your brand gets visibility as part of the trusted answer. That’s a win in the AI SEO world, even without the user clicking, you’ve made an impression. (And if they do click your citation, even better!)
Bottom Line: Traditional SEO was about climbing a ranking ladder and monitoring steady positions. AI SEO is more like being part of an ongoing conversation; you need to ensure your voice is heard whenever relevant topics come up. Next, we’ll explore how AI search engines decide whose voice to include, and how that differs from the old Google algorithm game.
Traditional search engines like Google and Bing (in their pre-AI days) rely on a massive index of web pages. Their process, simplified, is:
Crawl & Index: Bots (crawlers) scan the web, following links and reading page content. They store text and metadata in an index. Tools like Google Search Console let you ensure your pages are indexed (e.g., by submitting sitemaps and checking Index Coverage reports).
Rank by Relevance & Authority: When a user searches for “best CRM for fintech,” the search engine pulls relevant pages from its index and ranks them using hundreds of factors. Keywords on the page, freshness of content, and authority signals like backlinks all play a role. Google’s algorithms (think PageRank, BERT, etc.) try to determine which page is most likely to answer the query. The output: a list of links with titles and snippets. Each result is a separate page for the user to consider.
Interpretation is Limited: Traditional search might offer features like Featured Snippets or People Also Ask, which give a taste of direct answers, but mostly it leaves interpretation to the user. The search engine doesn’t synthesise answers from multiple sites (with the exception of those featured snippets, which are still just verbatim extracts from one page). It’s essentially saying, “We found these pages that might answer your question; pick one.”
Citation isn’t Explicit (by the engine): In normal search results, every result is implicitly a citation, but it’s up to the user to click. Google isn’t merging content; it’s just ranking it. There’s no single answer that needs to cite sources – the sources are the answer candidates, listed out for you.
AI search engines still start with an index (they need source content to draw from), but the user experience and retrieval method differ:
Natural Language Understanding: AI search (often powered by large language models like GPT4) interprets the user’s question in a nuanced way. Instead of matching keywords to pages, it genuinely “understands” the question (to the extent an AI can).
It might break a complex query into sub-questions or identify specific facts needed. For example, “What’s the best way to improve SEO for a SaaS company in 2025?” might be parsed into needing advice on content strategy, technical SEO, and AI search considerations.
Synthesis of Multiple Sources: The AI then fetches information from multiple pages. It might use a search API to find top relevant pages (similar to a normal search), but here’s the twist: the AI will read through those pages (or already have “knowledge” from training data) and synthesise an answer in its own words.
It’s as if it researched the question by quickly skimming a dozen articles, then wrote a little report for you. In doing so, it will pull specific facts or tips from various sources.
For instance, it might take a definition of “programmatic SEO” from one blog, a statistic from a research report, and a step-by-step tip from an ARISE case study, and weave them together.
Dynamic Content Assembly: Because the AI is generating an answer on the fly, it isn’t constrained to showing one source at a time. It can merge insights. This is powerful for users (“one-stop answer!”) but tricky for content creators as you’re effectively competing (or collaborating) with others within the answer.
It’s not winner-takes-all anymore; several sources can share the spotlight. However, if your content provides multiple aspects of a topic (e.g., your article contains stats, definitions and examples), the AI might even build a full answer primarily from your single page. That’s a big win, and you become the main character of that answer.
Citations and Attribution: To maintain transparency (and combat the infamous “AI hallucinations”), these AI search engines cite their sources for the information they present.
For example, Bing Chat will append footnotes (like the little numbers you see in this playbook) that link to the source of a particular fact or sentence. Google’s SGE highlights key phrases and, when you hover, shows which source they came from.
This is a new form of citation in search. The search engine itself is doing the attribution, rather than just listing the sources as in traditional SERPs. Being one of those cited sources is crucial, and it’s how your brand gets credit when an AI summarises your insights.
Implication: If your content is cited, the user may click the citation to learn more, driving traffic to you. If your content is used but not cited (maybe the AI considered it common knowledge or aggregated many sources), you miss out on credit. Ensuring your content offers unique insights or data can encourage AIs to cite you as the source of truth for that information.
Trust and Quality Signals: AI models don’t have an inherent concept of which site is more authoritative in the same way PageRank does; they rely on their training and the search engine’s retrieval.
However, the underlying search results still matter – if Google doesn’t show your page in the top 20 for a topic, the AI likely won’t see it as it gathers info. So traditional SEO signals (quality content, backlinks, etc.) remain relevant to get into the AI’s consideration set.
Additionally, the AI might favour content that is concise, structured, and factual (since it makes the synthesis easier and reduces the risk of wrong info). Using clear headings, FAQ sections, and schema markup to highlight key answers might help the AI understand and pick up your content.
In essence, technical SEO and content clarity become even more important – you’re optimising for a machine reader, not just human readers.
Ever-Changing Answers: Because AI answers can change based on new content or different question phrasing, the interpretation of what’s “best” can shift rapidly. Google’s AI might cite one source for “best project management tools” today and another tomorrow if one publishes a fresh 2025 review with updated info.
The AI isn’t loyal; it’s pragmatic. It will grab whatever content best fits the question at that moment. This is unlike a static ranking, where a strong page could dominate for months or years.
Freshness and ongoing relevance are key. You need to keep content updated and continue answering new questions that arise in your industry so that the AI has reasons to keep coming back to your knowledge.
User Interaction and Feedback: AI search is new, and these engines are learning from user interactions. If users frequently click on a particular citation after seeing an AI answer, that might signal the AI didn’t fully satisfy the query (or that the source had something extra valuable).
Over time, AI might adjust how it formulates answers or which sources it trusts. This means user behaviour plays a subtle role in AI answer optimisation, just as click-through rates and dwell time do in traditional SEO. Optimising your content to be genuinely helpful and engaging remains the winning strategy.
In summary, AI search engines act like a research assistant that reads widely and gives a composite answer with references. Traditional search was like an indexed library where each result is a separate book on the shelf. For SEO practitioners, the shift means we must ensure our “book” is the one the assistant pulls quotes from, not just one that sits on the shelf.
If you’ve been reporting SEO success purely by keyword rankings and organic traffic, it’s time to broaden the KPIs. Consider tracking brand mentions in AI outputs and organic visibility in these new search experiences. This is akin to tracking share of voice in traditional media – now it’s share of voice in AI-generated answers.
Recognise that click-through rates may drop for top rankings when the answer is given directly. (Users might not click any result if the AI fully answers their question – a phenomenon we already saw with featured snippets and now amplified.) That means the traffic value of a #1 ranking could be less than before.
To compensate, you want to be present in the answers and possibly adjust your content strategy to target queries where users are more likely to seek detail beyond the AI blurb (e.g. complex decisions, product comparisons, etc., where an AI answer might prompt them to click for more).
Content quality and originality are paramount. One of the biggest fears is AI making all answers sound the same by homogenising content. But the flip side is: the AI needs sources of true insight. If your content provides a unique case study, a novel data point, or a fresh perspective (something not everyone is saying), the AI is more likely to include it (“I haven’t seen this insight elsewhere, let’s plug it in and cite the source”).
This circles back to the advice that “the traditional B2B playbook will decline in effectiveness”, and we must invest in original research and thought leadership exactly because it makes your content stand out to both humans and AIs.
Now that we’ve covered the landscape of traditional vs. AI search, let’s talk about strategy. How do we combine these approaches and ensure our SEO (and broader marketing) is future-proof? This is where the ARISE GTM methodology comes into play, blending time-tested strategy with AI-powered tactics.
ARISE GTM (Assess, Research, Ideate, Strategise, Execute) is our proprietary go-to-market methodology designed to accelerate results across marketing, sales, and customer success.
It’s a framework that’s helped SaaS and tech teams launch and scale faster by ensuring alignment and data-driven decision making at every step. When it comes to SEO in the AI era, the ARISE framework provides a perfect scaffold to integrate new AI-driven practices without losing the solid foundation of traditional SEO.
Here’s how ARISE GTM can supercharge your SEO and content strategy for 2025 and beyond:
In the Assess phase, ARISE GTM focuses on getting a clear picture of where you stand. For SEO, this means auditing both your traditional search performance and your emerging AI search presence:
Audit Traditional SEO Metrics: We start with the basics – review your Google Search Console data and rankings. Which keywords do you dominate, and where are you losing ground? What’s your organic traffic trend? This establishes the baseline. (Use Google Search Console and analytics to benchmark current organic performance – clicks, ranking positions, conversion rates, etc.)
Also, evaluate site health (indexing status, core web vitals, etc.) because technical issues will impact your site's performance, regardless of how good your content is.
Assess Content Quality & Gaps: How strong is your existing content library? Is it up-to-date and differentiated? Remember, “quality over quantity, brand over breadth” is the mantra now. Identify which content pieces are truly valuable and which are thin or redundant. If your content wouldn’t impress a human expert, it certainly won’t impress an AI looking for authoritative info.
New – Assess AI Search Visibility (Qualitative): Here’s a new step we include in 2025: Check your presence in AI-generated results. This might involve some manual research: go to Bing Chat or another AI search tool and ask a few key questions in your domain.
For example, if you’re a fintech security company, ask “What are the top fintech security best practices?” or “Who are thought leaders in fintech security?”. See if the answers mention your brand or quote content that sounds like it came from your site. If not, note who is getting cited; those are now your content competitors just as much as those who rank above you on Google.
We also look at any available tools or beta features (for instance, Bing Webmaster Tools was starting to show some data on Bing Chat interactions, and there are third-party trackers for featured snippets, which can be a proxy).
Assess Competitors (Traditional & AI): Which competitors rank above you in Google for critical terms? And are those same competitors appearing in AI answers? You might find a competitor with a mediocre Google presence is punching above their weight in AI results because they have a very authoritative piece of content on a niche topic.
Document these findings. The goal is to highlight opportunities: maybe you’re rank #5 for “CRM integration tips” (not bad), but the AI answer to “How to integrate a CRM” cites positions #1, #3, and #6 – and you’re left out. That’s a gap to address.
Assess ROI Alignment: Since ARISE is about cross-functional impact, also check if your current SEO efforts are aligned with business goals. Are you getting the right kind of traffic (e.g., decision-makers vs. random visitors)?
In an AI-driven world, you may also assess brand sentiment. If an AI summarises reviews or commentary about your product, what does it say? This bleeds into reputation management, but it’s part of the holistic assessment.
By the end of Assess, you should have a clear benchmark: “Here’s how we perform in traditional search and where we appear (or don’t) in AI-driven search results.” This is your starting point, and it will help in setting goals (e.g., improve AI citation share by X%, maintain or improve key Google rankings, etc.).
The Research phase in ARISE is all about gathering insights to inform strategy. In the context of SEO + AI:
Deep Keyword & Question Research: Traditional keyword research now must expand to question research. Use tools to find not just what keywords people search, but what questions they ask voice assistants or forums. Look at Google’s “People Also Ask” and tools like AnswerThePublic to gather common questions in your space.
These questions are likely fodder for AI answers. For each important topic, list the natural-language questions users have. (Think in terms of conversational queries like “How do I boost SaaS product adoption quickly?” rather than just “product adoption tips".)
Competitor Content Analysis: Research what your top competitors (and industry thought leaders) are writing. This isn’t just about copying their keywords – it’s about finding content gaps.
What topics have they not covered deeply? Where can you create a piece of content so authoritative that even your bigger rival would get overshadowed? Also note the format of high-performing content: Are there whitepapers or original research pieces drawing lots of links (and thus loved by both Google and potentially cited by AI)?
For example, if no one in your sector has published a definitive 2025 industry benchmark report, that’s a golden opportunity for you to create something that both ranks well and gets cited widely.
Customer Intelligence: In ARISE, customer research is key – we interview customers and sales teams to learn their pain points and the questions they ask during the buying journey. This directly feeds SEO: the questions prospects ask your sales reps are the same questions you should answer in your content (and hence appear in search).
If CMOs in your target base keep asking “How do I measure ROI on brand marketing?”, you’d better have content on that – or an AI will happily pull the answer from someone else’s site in front of your potential clients. Leverage your buyer personas and update them with any new questions or decision criteria that have emerged.
AI Output Analysis: Here’s a novel research tactic: analyse AI outputs themselves. For a given query, if you see an AI answer, study how it’s constructed. What sources are used? What perspective is taken? Are there nuances the AI is missing (which your content could provide)?
Treat AI answers as both a research source (they aggregate info that you might have missed in your initial research) and a benchmark (is our content good enough to be in that answer?).
For example, if an AI’s answer on “2025 marketing trends” cites Forbes and Gartner but misses a critical point about, say, data privacy trends, that’s an opening for you to produce content on it and fill that gap.
Tech and Algorithm Watch: Research any news about how AI search algorithms work. For instance, if Google or Microsoft releases guidelines for content to be recognised by their AI (similar to how we had schema markup for featured snippets), pay attention.
In late 2024, Google hinted at using structured data to help the AI attribute sources correctly. Staying updated via SEO forums and updates is part of research. (ARISE GTM’s forward-thinking ethos means we’re continuously educating ourselves, always be researching, because the AI space evolves rapidly.)
Data, Data, Data: Collect quantitative data wherever possible. If you have access to Similarweb or other analytics, see if overall organic traffic in your industry is dipping (could indicate more zero-click answers).
Look at your own site’s organic CTR in Search Console – has it dropped for certain queries since the introduction of AI results? These data points help build the case and prioritise where to focus.
For example, if a keyword that used to bring you lots of traffic suddenly has a much lower CTR because an AI box now answers it at the top, you might need to focus on differentiating that content or targeting a different stage of that query.
By the end of Research, you’ll have a rich pool of insights: a list of critical questions/topics, an understanding of the competitive content landscape, and intelligence on how AI and users are behaving. This sets the stage for ideation, turning those insights into actionable ideas.
In the Ideate phase, we take all that research and brainstorm innovative ways to reach our audience. This is where human creativity and a little cheekiness come into play – remembering that while AI can crunch data, we understand our buyers’ emotions and the power of a great story or unique angle.
For our SEO/AI playbook, ideation could include:
Content Ideas that Bridge Traditional and AI: Come up with content pieces that are designed to rank well and feed AI answers. For example:
“Ultimate Guides” on core topics, structured with clear sections and summaries (which AIs can easily digest and quote). These guides could include TL;DR sections or Q&A sections at the end that directly answer likely questions – essentially pre-packaging answers for the AI to find.
Original Research & Thought Leadership: Perhaps conduct a survey or analyse proprietary data to create a report (e.g., “2025 B2B SaaS Marketing Benchmark Report”). This gives you unique stats that others will cite (feeding traditional SEO via backlinks) and that AIs will likely pick up (because it’s fresh data). Recall OpenView’s insight that original research is the new gold standard of thought leadership, and it can set you apart in search results of all kinds.
Case Study Library / Story-driven Content: Brainstorm stories that highlight your expertise. For instance, an article titled “How We Achieved 300% ROI in 6 Months with AI-Assisted SEO – A Case Study” could both rank for queries about AI SEO case studies and provide compelling material for an AI answer on the benefits of AI in SEO (the AI might say “an ARISE GTM case study showed 3x ROI in 6 months by combining AI and SEO…” if it cites such content). Weave narrative with data for maximum impact.
Format Innovations: Think beyond text. Ideate on creating infographics or short videos that explain concepts (Google’s AI might not “watch” a video, but if you provide a transcript or article version, it counts).
Also, consider using interactive content or tools, such as a calculator or an assessment quiz, to engage users who click through from an AI summary and want more detail. Part of ideation is ensuring that if someone does come from an AI answer, they find high-value, engaging content (so they don’t just bounce, thinking they already got the answer).
SEO + AI Technical Hacks (Carefully): In brainstorming, you might consider some “out-there” ideas, like developing a schema specifically for “AI answer suggestion” (not a real thing yet, but who knows), or structuring your content in a way that’s extremely snippet-friendly (e.g., consistently using FAQ sections with the question phrased exactly as a user would ask it).
One cheeky idea: adding a short section in your article titled “In Plain English (for the bots):” followed by a concise summary of the answer. While we wouldn’t label it that way publicly, the concept is to deliberately include a plain, direct answer to the question in your content, basically feeding the AI what it needs on a silver platter. We’ll refine these in strategy, but ideation is about considering all angles.
Cross-Channel Ideas: Recognise that AI search is blurring lines with voice assistants and even chatbots on your own website. So, ideate how your SEO content can be repurposed.
For example, if we have an “ultimate guide” content, could we also use it to train a chatbot on our site (so customers get instant answers from our content)? Or how can we promote our original research via PR (getting it cited on other sites, thus increasing its authority for SEO)?
The ARISE methodology encourages cross-functional thinking – marketing, sales, CS alignment – so maybe an idea surfaces like “Let’s create a series of LinkedIn posts or a webinar around our new research, then transcribe the Q&A from that and add it to our blog – those Q&As can rank and be picked up by AI.” We’re basically looking for synergistic ideas that maximise output from input.
During Ideate, no idea is too bold or too cheeky to consider. Perhaps we even joke, “Can we get ChatGPT to write an article about how ChatGPT is changing SEO, and then rank for that? Meta, but why not!” We might temper it later, but throwing these ideas out helps identify truly creative approaches. By the end of Ideation, we should have a list of prioritised content ideas and SEO/AI tactics, each mapped to the insights from Research.
For example: Idea, “Comprehensive FAQ Hub” mapped to Insight, our customers ask 50 different questions, let’s answer them all in one place. Or Idea, “Annual Martech Trends Report” mapped to Insight, no one in our space has authoritative data on this, and AI loves data.
Now, onto making a concrete plan.
In the Strategise phase, ARISE GTM turns ideas into a structured plan. This is where we align our shiny new SEO/AI tactics with the overall GTM strategy, set timelines, assign responsibilities, and define metrics. Essentially, we’re crafting our new SEO playbook – one that merges traditional strategy with AI-powered practices.
Key components of this strategy might include:
Content Calendar & Pipeline: Take the top ideas from Ideation and schedule them out. For instance:
January: Publish “2025 SaaS Marketing Benchmarks Report” (original research piece).
February: Publish “Ultimate Guide to AI-Driven SEO” (pillar page with Q&A, schema).
Every Month, Update and republish one existing high-value article to keep it fresh (signalling AI that it’s up-to-date and worth citing).
Weekly: Answer one common customer question in a blog FAQ series (to target long-tail queries and voice search).
Each content piece should have a defined purpose: the keyword it targets, the questions it answers, and the stage of the funnel it supports. We will also note if we expect it to likely be picked up by AI answers (e.g., the FAQ series might be our ticket into AI responses for niche queries).
On-Page and Technical SEO Plan: Strategise any site improvements needed to help with both traditional and AI SEO. This could include:
Implementing the FAQ Page schema on Q&A content so Google can easily pull FAQ snippets.
Ensuring Open Graph and metadata are solid, because AI might use meta descriptions or titles when forming an answer citation.
Improving site speed and mobile experience further – if an AI result encourages a click, we don’t want slow performance to turn the user off. Plus, core web vitals remain a Google ranking factor.
Setting up an internal process to keep content updated (perhaps a quarterly review cycle for key pages to add new stats or insights).
Also, decide on tracking mechanisms: set up Google Analytics events or use UTM parameters for links coming from AI (if identifiable) to measure traffic from those sources. This is new territory – maybe we’ll find a way to identify “Bing Chat” as a referrer in logs. It’s part of our strategy to measure what matters.
Distribution & Amplification: Great content needs visibility beyond just organic search. Strategise how you’ll promote each piece so it gains the authority needed for SEO and is on the radar of AI systems:
Plan outreach for backlinks (especially for that big original research report – e.g., reach out to industry publications to cover it, which earns backlinks and buzz).
Share content on social and communities. If an AI is trained on public internet content up to a point, widespread sharing increases the likelihood that your insights are “in the training data” of the next AI model update. It sounds abstract, but basically, being talked about on the web increases your authority.
Use email marketing to share key findings with your audience, which can indirectly lead to them discussing or sharing it.
ARISE being cross-functional means maybe loop in Sales: give them the new content as talking points to use in conversations (if prospects hear a stat from your report during a sales call and later see the same stat via an AI search answer, it reinforces your authority – a nice psychological advantage).
AI-Specific Tactics: If there are any specific AI search optimisation tactics identified (from research or creative brainstorming), integrate them. For example, if we suspect Bing’s AI really likes step-by-step lists for “how-to” queries, our strategy will include “ensure all how-to articles have a clear step-by-step section”.
If Google’s SGE tends to show three sources per answer, a strategy might be “try to be among top 3 in traditional rank, because likely those are candidates for the AI snippet – although not guaranteed”.
KPIs and Goals: Set clear goals so we know if the strategy is working. These could be:
Achieve or maintain top 3 Google ranking for 10 high-value keywords.
Get our content cited in at least 50% of AI answers for a set of 20 target questions (this could be measured by periodic testing).
Increase organic traffic by X%, despite potential zero-click effects (meaning our content is so appealing that even with AI answers, people click to learn more).
Improvement in brand metrics: e.g., higher direct traffic or branded search volume (if AI answers raise awareness of our brand).
Ultimately, pipeline/ROI metrics: e.g., double the number of SQLs (sales-qualified leads) attributable to organic search (this aligns with ARISE’s focus on measurable ROI and cross-team impact).
Alignment with Sales & Success: Since ARISE GTM is holistic, ensure the SEO/Content plan feeds the needs of sales and customer success, too. For example, some content might serve as sales enablement (case studies, etc.) or help existing customers (knowledge base articles that also rank in search).
A well-rounded strategy means SEO content isn’t created in a silo; it’s part of the overall GTM execution plan. Paul Sullivan, our founder, often emphasises breaking silos, marketing, sales, and CS should share goals and insights (a principle he details in “Go-To-Market Uncovered” and that’s baked into ARISE).
Risk Mitigation: Identify any risks and plan for them. For instance, reliance on AI traffic is tricky – what if an AI stops citing sources as much? Our strategy should not be only to chase AI mentions at the expense of traditional SEO. We hedge by doing both. Also, consider the risk of AI misinterpreting content; we might add clarifying notes in the content to prevent quotes out of context. And always have a plan for major Google algorithm updates (some things never change!).
By the end of Strategise, we have a concrete, calendarised SEO action plan that marries the old and new. We know who will create what content, when it will go live, how we’ll promote it, and how we’ll measure success. We’ve essentially written our playbook, and the next step is to execute it.
Now it’s go time. In the Execute phase, ARISE GTM is all about implementation and agile adjustment. This is where the rubber meets the road – content is created, optimisations are made, campaigns go live, and we start seeing results (and learning from them):
Content Creation & Optimisation: Our writers, designers, and SEO specialists get to work on the content pieces as per the strategy. We ensure each piece follows best practices:
Use the target keyword and related questions naturally in the text (for traditional SEO relevance).
Write in a clear, conversational tone that AI algorithms can easily parse (short sentences where appropriate, clear definitions of terms, etc.).
Include the structured elements we planned (tables of data, step lists, FAQs). For example, if we’re publishing “Top 10 AI SEO Tips”, we might format it as a list with each tip as a heading (making it snippet-friendly).
Optimise title tags and meta descriptions to be both keyword-rich and enticing – remember, even in AI results, sometimes the original meta description might show if the AI provides a clickable link preview. Plus, traditional SERPs still matter for traffic.
Double down on E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals: list authors with credentials, cite external reputable sources in our articles, and ensure the content is fact-checked. This can improve our credibility in the eyes of both Google’s algorithms and AI systems attempting to choose reliable info.
Technical Changes & Monitoring: Implement technical improvements (maybe our developers set up the schema markup, fix site issues found in the audit, etc.). We also set up monitoring: Google Search Console for indexing or error issues (checking that our new pages get indexed quickly, sometimes new AI content gets picked up fast by search engines hungry for fresh info). We might also monitor log files for any unusual crawl activity from AI bots or anything that gives clues.
Promotion in Action: We execute the promotion plan for each content piece. For instance, once the “2025 SaaS Marketing Report” is live, the team does outreach to relevant journalists and influencers, shares infographics on LinkedIn, pitches Paul Sullivan to discuss the findings on industry podcasts, etc.
This drives short-term traffic and backlinks, and in the long term, those backlinks boost our SEO. We’re effectively fueling the engine so that our content becomes the reference point online. If we do this right, over the next weeks and months, search engines and AI models will take note of our content’s popularity and authority.
Team Alignment and Enablement: We make sure Sales is aware of the new content (“Here’s a new blog series answering common client questions, share it with your prospects who ask those!”). We enable Customer Success with it too (“Here’s our guide on XYZ; feel free to send to customers who need help on that topic.”).
This ensures consistent messaging across touchpoints. It also indirectly helps SEO: the more people engage with our content (even via direct or email traffic), the more likely they might link to it or mention it elsewhere, further boosting our content’s prominence.
Measure, Learn, Iterate (Continuous): As soon as things go live, we watch the data. This is where a CMO’s beloved metrics come into play. We look at:
Rankings: Are our Google rankings improving for the targeted terms? If not, why? Do we need to build more links or adjust on-page?
Traffic & CTR: Did organic traffic rise? How’s the click-through on our search listings – better or worse? (If worse, maybe the AI is answering too much in SERP – then we might pivot to target more long-tail queries where AI doesn’t fully satisfy the user).
AI Mentions: This one may be manual, but we can sample queries to see if our new content is getting cited. For example, after publishing our “AI SEO Tips” article, we might ask Bing, “What are some tips for AI-driven SEO?” and see if it references us now. If yes, celebrate and note what worked (did we use a particular phrasing the AI picked up?). If not, perhaps our content isn’t yet considered authoritative enough. Maybe we need more backlinks or to tweak the content to be more directly answer-like.
Leads & Conversions: Ultimately, we measure leads/opportunities coming from organic search. If those go up, even if overall clicks didn’t skyrocket, it means our quality of traffic improved (possibly thanks to more targeted, bottom-funnel content or the credibility boost of being an AI-cited authority).
Feedback from Sales/Customers: If prospects mention “I saw in a search that you guys said…” or if customers say “I found your article via Bing’s answer,” that’s anecdotal gold that our strategy is working. We’ll capture those and share internally.
Agile Tweaks: Based on the data, we adjust. ARISE’s Execute isn’t a blind “launch and leave”; it’s iterative. Maybe we discover one article isn’t performing, we’ll go back and improve it (or combine it with another piece, pruning content that doesn’t cut it, which can boost overall site quality).
Or we find a specific FAQ we posted is getting traction, perhaps expand it into a full guide. If an AI answer consistently cites one competitor’s stat, maybe we’ll conduct our own study to have a competing stat and update our content. We stay nimble.
Sustain and Scale: Execution is also about sustaining the effort. SEO + AI optimisation is not a one-time project, it’s an ongoing process. We’ll integrate this new approach into our regular marketing operations. For example, content writers are now routinely thinking “how might an AI use this?” when writing.
SEO specialists might start incorporating AI-response tracking into their monthly reports. We build the muscle memory in the team to keep this going. ARISE GTM is deliverable in platforms like HubSpot (where we track all this through custom dashboards, workflows, etc., ensuring maximum ROI). That means as we execute, we’re also leveraging technology (maybe an AI content optimisation tool) to work smarter.
Execution is where we demonstrate human-first growth in an AI-driven world. We use the tech and data, but we also rely on team creativity and judgment to make the right calls. It’s worth noting that Paul Sullivan’s ARISE framework is all about plugging gaps quickly and completely. As we execute, we keep an eye out for any gaps, whether it’s a content gap or a process gap, and address them swiftly.
Finally, we celebrate wins (maybe a bit cheekily on our Slack channels: “Hey look, our article is literally what Bing is quoting for that question, who needs position #1 when you’re the only answer, haha!”). That kind of win means our playbook is working.
As Paul Sullivan puts it, “We understand how challenging it is to identify your best customers and improve acquisition and retention, that’s why we developed ARISE to plug the gaps easily and completely.”
In the context of SEO, the “gaps” are the new blind spots created by AI-driven search. ARISE GTM helps you plug those gaps by ensuring you’re not missing out on the conversations happening in AI answers and that your SEO strategy is firing on all cylinders (technical, content, distribution).
The SEO landscape is no longer a linear race to the top of Google, but a multifaceted game of being in the right places, be it a featured snippet, a conversational AI answer, a voice assistant response, or a traditional SERP, at the right times.
For B2B marketing leaders, this can sound daunting. How do we keep up with algorithms that constantly learn and change? How do we prove ROI when the clicks might not come through even if our information is used? The answer is by adapting our approach and metrics, and by staying customer-centric and data-driven as we always should.
Here are the new rules of SEO in a nutshell:
Think beyond rankings: Position #1 is great, but being visible in an AI-driven answer box might be just as valuable. Adjust your goals to include both. In an AI-first world, ranking #1 but not being referenced by the AI is a missed opportunity.
Prioritise quality, originality, and structure: These have always been good SEO practice, but now they’re critical. You’re not just competing for a user’s click, you’re competing for the AI’s “brainspace.” Give it something worth talking about. Unique insights are your currency.
Monitor and measure new metrics: Start looking at things like zero-click impressions, brand mention frequency, and engagement from AI referrals. It’s okay if the metrics are a bit experimental; you’re building understanding ahead of your competitors.
Stay agile and keep learning: The one constant is change. Today it’s Bing and SGE; tomorrow it could be something like an AI voice search on smart speakers that cites sources aloud. The best thing you can do is keep your team in learning mode. Continue to apply the ARISE cycle; assess and research continuously, so you can ideate and adjust strategy proactively.
Keep the human touch: Even as AI grows, remember that people still make the buying decisions. Ensure your marketing remains human-first. Use AI to assist (our co-pilot, not autopilot), but maintain a human voice and empathy in content. Our ARISE tone – direct, friendly, a bit cheeky – is there because it resonates with readers. An AI can summarise 10 articles, but it can’t easily replicate a compelling story or a genuinely empathetic answer that connects to pain points. Leverage that.
In the spirit of ARISE, integrating AI into SEO isn’t about ripping up the old playbook entirely – it’s about optimising and expanding it. You still need strong fundamentals (good website, relevant content, clear value propositions, those never go out of style). ARISE GTM simply ensures you cover all bases: you Assess the new environment, Research deeply, Ideate creatively, Strategise methodically, and Execute relentlessly. It’s a formula for not just adapting, but leading.
Remember, digital marketing is an ongoing journey. The rules will keep evolving, but with a forward-thinking approach, you’ll always be ready. By combining traditional SEO wisdom with AI-powered innovation, you position your team to win in both worlds – securing those top spots on Google and the coveted citations in AI-driven results.
So, as you go forward:
Keep your eyes on the SERPs and the AI answers.
Educate your team and stakeholders about these new dynamics (maybe share this playbook with them).
Don’t panic about AI, partner with it. In many ways, AI is just another user agent we need to optimise for (albeit a very smart one).
Finally, hold onto that cheeky optimism. Marketing in 2025 is as exciting as ever. We have new toys to play with, new puzzles to solve. Those who adapt will find themselves more visible, more credible, and more successful than ever. After all, when you marry a sound go-to-market strategy with cutting-edge tactics, you get the best of both worlds, and that’s exactly what ARISE GTM is all about.
Here’s to rising above the competition (pun intended) and conquering the new rules of SEO!
Next Steps: Take this playbook and run an ARISE-style sprint with your team, focusing on your SEO strategy. Audit where you stand, identify one or two quick-win opportunities where an AI-focused tweak could boost your visibility, and implement it. You might be surprised at how quickly you see results.
And of course, if you need a strategic partner in navigating this new landscape, the ARISE GTM team is here to help (we live and breathe this stuff). Now, go forth and dominate both the search rankings and the answer engines – your future customers (and their AI assistants) are waiting to discover you.