Navigating the Nexus: AI’s Impact on Marketing
I’m a bit late to the party here. In my defence, I did start writing this back in May. It was a lot longer, but even in the last month and a half, a lot of it was out of date. So I was looking at ‘search psychology’ from a broader perspective, now I wanted to look at online exploration in the age of AI. I find myself using ChatGPT or Gemini more and more, and I’ve started to see Google’s AI search at the top of the search page to be fairly useful (but nowhere near as good as just asking an LLM a question). My questions are getting more and more specific, too, and often broader. Where I once searched ‘Steak Restraunt (insert city here)’ in Google Maps, I now ask ChatGPT to give me a quick rundown of the available choices based on the style of steak I like the most. And it works, it works well. But it’s bloody annoying if you work in marketing.
I’m going to wax lyrical for a bit, because that’s just what I do; however, if you want a really good rundown of how AI is specifically affecting SEO, you can check out the latest whitepaper from my friends and partners, Growth.Pro, which is excellent and should give you a very good guide on how to fix up that lost SEO traffic:
You can find it here: The Death of Traditional Search
There are a lot of facts and figures there, so I won’t cover much of that. I want to explore the psychology we now need to start addressing as marketers.
Generative AI tools are Changing Consumer Search and Decision-Making
Generative AI platforms likeChatGPT, Perplexity, and Google‘s Gemini are rapidly becoming a default choice for search (in Google’s case, it’s literally being forced upon us!) These tools can understand natural language questions and produce direct, conversational answers that don’t require scrolling and clicking to assess content. People are getting used to this convenience: why sift through ten blue links and read them all when an AI assistant can synthesise the information into a quick answer or recommendation?
A Bain & Company survey shows that generative AI tools now fulfil many roles once served by search engines. In the survey, 68% of generative AI users report using these tools to research or summarise information, 48% use them for up-to-date news and weather, and 42% use AI assistants for shopping recommendations and decision support.
This trend indicates that AI-driven assistants are encroaching on traditional search for a broad range of tasks. Consumers are using ChatGPT or similar tools to get advice on what products to buy, plan trips, or answer everyday questions, effectively bypassing search engines for a significant portion of their queries. For example, tools like ChatGPTcan instantly answer “What’s the best budget smartphone right now?” What’s even more compelling is that it can also answer: “What’s the best smartphone right now with 16GB RAM, a 1080p display and Wifi 6 capabilities running on custom Android software?”. You used to have to find a comparison website to deal with this, now you can ask it. With your voice, if you’re so inclined.
Critically, AI assistants aren’t just informing users, they’re influencing decisions that you, as a company, can’t see through traditional marketing analytics tools. Many consumers trust the recommendations given by AI or at least use them as a starting point for decision-making. A McKinsey & Company-backed study in 2024 found that 41% of Gen Z consumers already rely on AI assistants for shopping and task management, a number expected to grow. These AI “agents” can execute tasks (like filtering product options or even initiating purchases via plugins), effectively becoming new decision-makers or advisors in the customer journey if your brand or content is not appearing in the answers provided by AI tools. In that case, you risk being invisible to a growing segment of consumers during their decision-making process. They become invisible to you (unless they happen to click a reference link), which, for marketing and its dependence on data, is a bit of a nightmare.
From Discovery to Validation: The New Search AI Journey
One of the most profound shifts driven by AI in search is the move from a “discovery” phase to a “validation” phase for consumers. Traditionally, a consumer might use Google to discover options or learn about a topic, then gradually narrow down their choices. Now, AI handles that initial discovery. Conversational AI encompasses a broader range of the discovery phase, providing users with immediate ideas, answers, and product options. For example, a user might ask an AI, “What are some good electric cars under $40k?” and receive, say, a 10-car shortlist of models with pros and cons. Armed with this AI-curated shortlist, the user’s next step is typically to validate those suggestions, often turning to Google or another source to verify details, check reviews, or mess around with configurators. However, whereas in the past a user would land on all 10 websites and each company could add the user to their marketing pipeline, which could have a significant impact on eventual car choice, now they may have no idea that they’re even being considered, or only find out when the user is in the final stages of the buying process, which negates a lot of the hardwork done by marketing teams further up the sales/marketing funnel.
In other words, consumers increasingly use Google later in the journey for validation rather than exploration. They might search for a particular product name or a direct comparison after the AI assistant has provided the initial guidance. This aligns with observations that users still rely on traditional search for high-investment decisions or local results, e.g., searching “best real estate agent in [city]” or checking if a recommended store is nearby; however, the way they arrive at those searches has changed. In e-commerce, this pattern is evident: AI might suggest a couple of camera models to a shopper, and then the shopper visits those product pages or Google reviews to confirm which to buy. Some experts predict that websites will increasingly focus on validating decisions made via AI assistants by providing rich reviews, detailed specifications, and social proof, rather than being the primary touchpoint for discovery.
For marketers, this means your content must be ready to serve two roles: one, to be present in the AI-driven discovery (so that your brand or product is among the initial suggestions), and two, to provide authoritative, trust-building information for the validation stage. AI’s tendency to occasionally err (hallucinate) further reinforces the validation step; savvy consumers double-check AI-provided info. Ensuring that when they do validate, your content is accurate and convincing is key. Marketing teams that ignore this “discovery to validation” shift risk missing out on the discovery phase entirely. If your brand only shows up when a user explicitly searches for it, you’ve lost the opportunity to be recommended by the AI in the first place.
I’m a marketer with a sales background. I think in funnels, so here’s a bit of an overview of how I think AI is reshaping the marketing funnel as we know it (I should have created an infographic for this):
Awareness Stage
AI-driven audience discovery enhances targeting accuracy (great in theory at the moment, but not sure the application is quite there yet)
Predictive analytics anticipate trends and customer behaviour proactively (this seems to be developing quickly, and I’ve been playing around with how you can use mass data (LinkedIn, Clay, etc. and then using AI to find trends that we may target)
Consideration Stage
Personalised recommendations and dynamic content improve relevance (this is really cool, once it can be fully implemented it’s going to change the game, imagine ads that are truly relevant at the point in time they’re needed, I’d even love to see the time when ads actually stop based on some sort of signal that I’ve made a purchase)
Conversational AI chatbots provide immediate, personalised user support (not really, but it’s getting better. Please people, use chatbots sparingly for user support)
High-quality, authoritative (EEAT) content builds user trust during validation (I’m still on the fence on the this, Google did say they were going to downgrade AI content, but it doesn’t seem to, which I still think could end up with just a dealth of AI-created content that could eventually lead to a spiral of infinite AI created content and… then my mind explodes)
Conversion Stage
AI enables continuous A/B testing and dynamic pricing to maximise effectiveness (I’ve seen a few examples of AI A/B testing, it doesn’t differ massively from normal A/B testing, but its pace and efficiency in change is a huge leap)
Conversational commerce compresses the funnel, enabling direct transactions within AI chat environments, significantly shortening the traditional sales cycle (yeah, this is going to change the game. Why search when you can have a specific conversation, add amendments, make amends and then get to the product that almost perfectly matches your needs. It’s still early days, and a lot is still based on traditional EEAT or SEO principles, but it’s adding a new dynamic to finding what’s right for the individual, and marketing is going to need to shift to address this).
Loyalty and Advocacy Stage
Predictive analytics identifies churn risks, prompting proactive customer retention measures (Not seen it yet, but a good idea, might be a bit overwhelming)
Sentiment analysis supports continuous reputation management, ensuring positive brand perception and fostering advocacy (we’ll see about this, the fact so much happens in a more private environment, just like search, we may end up with less knowledge about this).
Impact on Traditional SEO Practices in an AI-Driven World
We need to recognise that the old playbook (identifying a few high-volume keywords, producing static content, and waiting for Google rankings) is quickly becoming outdated. Several aspects of SEO are changing.
Search Queries and Keyword Strategy Reimagined
Keyword strategy is shifting from short, exact-match terms to understanding user intent and natural language queries. AI-driven search tools don’t just match keywords; they interpret the context and intent behind queries to provide more accurate answers. Users are also becoming more comfortable asking long, conversational questions (e.g., “What is the safest family SUV with good gas mileage and under $30k?”) because AI can handle them. In response, Google’s search is evolving, with features like multi-step AI reasoning in the Search Generative Experience, allowing users to ask complex questions that previously required multiple searches, and this is already baked into LLMs.
For SEO, this means content needs to cover topics in depth and anticipate multi-faceted queries. It’s less about singular keywords and more about the myriad ways a question could be asked. Indeed, Google has noted that with AI summaries, people are using more nuanced, multi-part questions in a single query. High-intent, long-tail terms are becoming especially important. As Bain & Co. recommends, “Adapt your content for semantic search, emphasising high-intent, long-tail terms”. In practice, this could mean targeting questions and phrases that reflect specific user needs or scenarios rather than generic head terms.
Additionally, voice search and visual search are on the rise, again powered by AI. Consumers might ask a question to Alexa or use Google Lens (or even maybe Apple Intelligence) on a product image. SEO now must account for these formats. Content should be optimised for conversational tone (for voice queries) and include descriptive metadata (for visual search). The bottom line: search queries are becoming more natural and contextual, so keyword strategy should focus on capturing the intent behind queries. Marketers who still chase pure volume on basic keywords may find their content bypassed by AI that’s looking for the best answer, not just the best keyword match.
Content Format and Structure for AI Visibility
Traditional SEO often emphasised formatting content for human skimming (with catchy titles, skimmable sections, etc.). Now, we must also format for AI comprehension. Generative AI systems “read” and synthesise content in ways humans might not. They favour clear structure, factual accuracy, and rich context. As one AI SEO specialist notes, “AI relies heavily on structured and semantically rich data to provide nuanced answers. For example, well-crafted FAQs and schema-marked content improve your chances of being featured as an authoritative source. Unlike human users, AI agents prioritise concise, factual, and well-sourced content over attention-grabbing headlines.” In practice, this means:
Use structured data (schema markup) on your pages to help AI identify key facts (prices, ratings, business hours, etc.) easily. Machine-readable content stands a better chance of being picked up in AI summaries. Google’s own AI search integrations draw on schema-tagged info for their snippets.
Provide direct answers and FAQs. Incorporate frequently asked questions and straightforward answers in your content. AI tools often quote or paraphrase text that directly answers a question. If your page has a concise Q&A section, it might be what the AI needs. Marketing teams should prioritise FAQs and authoritative content, as AI engines rely on content that answers queries with precision and reliability.
Write in a conversational yet informative tone. Because users ask AI in natural language, content that mirrors natural phrasing can align better. This doesn’t mean dumbing it down; it means structuring your headings or sentences similar to how a user might pose a question. For example, a blog post titled “How Does Electric Vehicle Range Work in Cold Weather?” is both user-friendly and AI-friendly, as opposed to a vague “Maximising Battery Efficiency.” What we need to do is optimise for ‘conversational SEO’ or natural language queries.
Long-form, authoritative content wins over thin content. AI will synthesise content from across the web. If your content is shallow, there’s little for it to pull. Deep dives that demonstrate topical authority are more likely to be referenced by AI. Google’s own guidance with its AI overview stresses “deep topical authority over shallow keyword tactics.” In other words, one comprehensive piece that thoroughly answers a topic is better than ten thin articles targeting various keywords.
Diversify content types. AI doesn’t just read text, it can also summarise data from tables, transcribe videos (YouTube’s transcript can be an input to Google’s algorithms), or describe images. Having a mix of text, infographics, videos, and even interactive tools on your site can increase your chances of appearing in AI-generated results. For example, if someone asks an AI, “How do I tie a bow tie?” the AI might favour a step-by-step text from one source and also mention a how-to video from another. Ensuring you have both formats (an article and a short video) improves your visibility in that AI answer.
Importantly, content must be kept up to date. While not a format issue per se, it’s worth noting that AI tools (especially those with browsing or connected knowledge like Bing Chat or Google’s SGE) will favour current information for certain queries. If your content is outdated, you risk AI summarisers preferring a more recent source.
Google vs. AI-Driven Platforms: Where Are Consumers Searching?
With the proliferation of AI search tools, one might think Google’s dominance is waning. In reality, Google is still very much in the game, but the dynamics are evolving. Recent data indicate that, despite ChatGPT’s explosive growth, Google Search usage is expected to remain robust through 2025. Statistically, 99% of people who use AI platforms continue to use traditional search engines as well. Google still serves trillions of searches per year, dwarfing the number of queries handled by ChatGPT or others. This means marketing directors cannot abandon Google-oriented SEO; the volume there remains huge and high-intent.
However, what has changed is how Google itself is incorporating AI and how niche search behaviours are fragmenting. Google’s Search Generative Experience (SGE), powered by its Gemini AI model, is integrating AI-generated answers into the search results. Users see an AI-generated summary with citations on the search page, above or alongside traditional results. This effectively turns Google into a hybrid AI platform. When Google’s Gemini AI overview is active, being one of the cited sources in that overview becomes as valuable as a page-one ranking used to be. Google reported that links included in their AI Overviews get more clicks than if the page had appeared as a regular search result for that query. This suggests that while overall clicks might decline with more zero-click answers, the clicks that do happen often go to the sources featured in AI summaries. Thus, an evolved goal for SEO is to earn a spot in those AI summaries or answer boxes.
Meanwhile, AI-driven search alternatives like Bing + ChatGPT, Perplexity, and others are drawing early adopters. Bing’s integration of GPT-4 in early 2023 and tools like Perplexity (which reached ~15 million monthly users by late 2024) signal that a segment of users is exploring non-Google search experiences. These platforms often cite sources for their answers (e.g., Perplexity displays reference links, Bing Chat provides citations). Marketers should treat these as additional search engines to optimise for. For instance, ensuring your content is accessible (no paywalls or login required) and well-ranked in Bing can increase the chance of being picked up by Bing Chat’s answers. Failing to monitor these emerging platforms is a mistake; they may have smaller user bases now, but they often attract a highly engaged audience (e.g. tech-savvy consumers, researchers, etc.). Furthermore, the growth rates are notable: generative AI search traffic was found to be growing 165 times faster than traditional organic search traffic (albeit from a smaller base). Marketing teams need an omnichannel search strategy, covering Google and the expanding array of AI-centric search tools.
In summary, Google remains critical, but how you optimise for Google is changing (with AI summaries, etc.), and simultaneously, AI-driven platforms present new search ecosystems where your content needs to appear. The role of Google vs AI platforms is not an either/or; it’s now a dual priority. More for your workload, but the natural language element should lend itself much more to those creative writing types.
Where Marketing Teams Are Falling Behind
I keep saying this, but people keep ignoring me. Reddit. God damn it, if you’re not on Reddit or paying attention to how you’re discussed there, potentially engaging with people talking about your products or services, you’re missing out on Al juice. The reason Reddit is so critical is that it’s one of the largest, most candid, and human-generated text sections on the internet. LLMs were trained on it and continue to use it as a source for understanding genuine human sentiment, jargon, and recommendations. An AI trying to find the “best” product or service will often weigh a detailed Reddit discussion more heavily than a polished corporate blog. Ignore it at your peril!
Here’s some other, more practical stuff:
Underutilising AI for Personalisation:Ironically, while AI is changing consumer behaviour, some marketing teams aren’t using AI enough themselves. AI can help analyse customer data and deliver personalisation at scale (product recommendations, personalised emails or web content, dynamic landing pages, etc.), yet many teams are only scratching the surface. Brands that fail to adopt hyper-personalisation will struggle as consumers come to expect content tailored to their needs. I’ve also yet to see this massive dip we were expecting from EEAT for using AI-based content.
Focusing on Old SEO Tactics: Some marketers continue to operate as if it’s 2015, obsessing over a handful of keywords (I still occasionally get people pitching me ‘one’ keyword on page one of Google), churning out numerous thin articles, or sticking to rigid content calendars detached from what users actually want. In the AI era, this just doesn’t cut it any more. A mix of AI and human-written content that understands a human need and a more natural approach will do wonders here.
Failure to Adapt to New Search AI Ecosystems: As discussed, search is no longer just Google. Marketers who ignore platforms like Bing’s AI search, voice assistants, or AI Q&A forums are losing out on traffic and brand presence. For instance, if your team isn’t monitoring how your content appears in Google’s AI results or Bing Chat (if at all), you’re missing signals to adapt. Not exploring opportunities such as providing content for voice search answers (e.g., optimising for Google Assistant/Alexa responses) is another gap. In addition, clinging to “walled garden” content strategies (like gating all valuable content behind lead forms or PDFs) might hurt in an AI-driven world. Bain’s research bluntly states: “Forget PDFs and gated content, they’re relics in an AI-driven ecosystem”. Marketers slow to embrace this will see their thought leadership invisible to the very tools feeding consumers information.
Brand: I’ve got to include this, but building a brand is still the best way to remain bomb-proof given the volatility of the marketing world. Build a brand, people, it works.
NB: I’ve added a new one in here, which will be the basis of my next blog (probably in another three months), and that is TRYING NEW THINGS and TESTING them properly.
Embracing the AI-Driven Future of Marketing
The rise of generative AI in search is not a distant prospect but a present reality, actively reshaping consumer behaviour and, consequently, the marketing landscape. Individuals are increasingly seeking advice from ChatGPT, utilising AI-generated summaries on Google, and expecting instantaneous, highly relevant answers. SEO and content marketing stand at a pivotal juncture. Rather than viewing AI as a threat, astute marketing leaders should perceive it as a new layer of opportunity, an opportunity to connect more deeply with consumer intent, deliver value more directly, and innovate in the dissemination of information.
Those marketing departments that adapt swiftly, by modernising their strategies, refining their content, and enhancing their skill sets, will not only avoid falling behind but will be positioned to leap ahead of competitors mired in outdated paradigms. By focusing on the insights and recommendations outlined, marketing directors can ensure their brands remain discoverable, credible, and persuasive throughout the AI-shaped customer journey, guiding consumers with confidence from initial AI-powered discovery to final decision. The future is not just about being found; it’s about being an answer to a question, asked by a human, as if they were asking another human, but actually a robot and then seamlessly integrating that into the intelligent systems that now guide so much of our digital lives. Sounds easy, right?!
