Well, that didn’t take long. The way people search for information online has definitively changed, and it happened quickly. As a writer-turned-SEO, I’ll officially miss the days when landing on the first page of Google was the ultimate measure of organic search success. With AI Overviews taking full ownership of the top organic results, searchers are now finding more robust answers to their questions, comparing products on a whim, and discovering brands well before discovering its website.
As AI search inevitably becomes even more prominent, brands are facing a new challenge: How do you take steps to keep your content discoverable when traditional rankings and website traffic no longer tell the whole story? And with consumers turning to ChatGPT, Gemini, and other large language models (LLMs) to strike increasingly conversational longform dialogues, how should SEO expand beyond Google’s traditional measurements?
In early October, I spent three days in Denver, Colorado at the Content Marketing World Conference, where these changes were, surprise surprise, a recurring topic of conversation. Between listening to industry experts, exchanging ideas with fellow marketers, and filling more than a few pages with notes, I noticed a trend:
The future of “being found” is probably not about SEO or Content Strategy… or even the latest AI optimization trend. It’s about how all these ingredients work together in your recipe to be seen.
What I found most encouraging was that many of the conversations being had reinforced the approach my team’s been exploring, testing, and refining at Smith over the past 2+ years. We’ve been fortunate to have a handful of enterprise-sized sites where we’ve been able to test and refine our approach to AI optimization.
Visibility Is No Longer À La Carte
Remember when SEO was about keywords, rankings, organic traffic, and appearing “before the fold” on the first page? Those days aren’t necessarily behind us, but the above reads like table stakes heading into 2027.
In these last two years, we’ve seen a real shift in what our Digital Marketing clients are asking for: “traditional SEO” is still important – and that may not change – but brands are increasingly interested in how their content can be discovered across multiple emerging platforms.
One of the themes echoing throughout Content Marketing World was that “optimization” can no longer be approached as streams of independent strategies. Searchers aren’t relying solely on one platform to find information anymore (whether they know it or not), and we need to think more comprehensively about how their searches are surfacing results.
Coming out of the conference, I’m challenging myself to think of visibility as more of a stew than an à la carte menu. Each ingredient brings something different to the equation, but it’s how they work in concert to achieve coverage that makes the final product appetizing. In my experience, you can’t skew traditional SEO, off-site, or GEO/AEO and expect each to deliver comprehensive visibility on its own.
Instead, these branches want to complement one another as part of a broader approach to discoverability.
- Traditional SEO provides the foundation for organic visibility through relevant content, technical optimization, and a strong website structure.
- Generative engine optimization (GEO) focuses on improving how content can be discovered, understood by both LLMs and users like you and I, and how content is referenced in AI-powered search experiences.
- Local SEO helps businesses connect with audiences searching for relevant products, services, and solutions in specific geographic areas.
During one particularly impactful conference session, a speaker emphasized that their success in AI search required a coordinated effort across SEO, content marketing, public relations, and other marketing channels. We’ve seen similar at Smith as we’ve looked at emerging optimization opportunities, though we lean towards success in any search being predicated on coordinated efforts across all.
As Search Changes, So Does Our Definition of Success
Searchers are increasingly confident that the information served to them in their app of choice is enough to warrant skipping a clickthrough to the website where information was retrieved from. Who can blame them? At face value, they found exactly what they were searching for – so why dig deeper?
For marketers, this has presented an interesting challenge: If people are finding the information they need without clicking through to a website, how do we measure the success of our work? Strictly on AI visibility? Seldom can that be linked to deeper business objectives.
Historically, organic traffic has been one of the primary ways we’ve measured success in SEO. The more qualified visitors we could bring to a website, the better. And while organic traffic remains an important performance indicator, declining click-throughs mean it doesn’t tell the whole story anymore. A brand can be found, referenced, and recommended in an AI search experience without that interaction resulting in a visit or even a trail of breadcrumbs.
During another session focused on personalized AI search, a speaker discussed how their industry is shifting away from traditional keyword-led strategies and into brand-led approaches that look a little like this: rather than “what strategic terms should we rank for?”, consider who you’re trying to reach, what exact problems you’re solving, and how you can work towards becoming a trusted recommendation. Consider the difference between appearing in a list of ten blue links, vs. an inclusion in a generated response that references you. Both represent visibility, but the latter is counting on you to provide a whole new host of qualifiers.
Because AI search is increasingly conversational and personalized, two people asking similar questions may receive different responses based on context and other factors. This introduces another layer of complexity for marketers who have traditionally relied on somewhat consistent ranking conventions to achieve and evaluate search success.
Where does that leave us when it comes to measuring success? That one’s tough, if only because AI reporting technology and clarity are in their infancy compared to old-school metrics – what’s undeniable is that we’ve got to be thinking beyond the click, and I’m particularly grateful for some of the creative ways of getting around AI attribution that my colleagues have cooked up in the lab.
Relevance, Authority, Trust – Still a Thing
At this stage, it’s safe to say that if AI didn’t dethrone the trinity of relevance, authority, and trust, no emerging technology will. And despite their entrenched role in all ranking endeavors, there’s never been more mediocre content out there than there is now. Chalk it up to early experimentation with AI as a replacement for writers and the pressure to publish the newest, shiniest thing that has plagued Marketing since Google Panda. For savvy content folks, the opportunity to create something of consequence that resonates has never been greater. An era where the algorithms, the robots, and the people prefer it when you’re real? Jackpot.
Several of this year’s Content Marketing World sessions emphasized the importance of moving away from content created to play nice with search algorithms and LLMs, towards resources that are genuinely helpful. That dovetails into brand-led content strategy nicely – with case studies, subject matter insights as opposed to generalizations, and firsthand expertise assets that aren’t gated. These provide something meaningful, and as an added benefit, are likely to perform well organically as a result.
Wil Reynolds nailed it earlier this month when he dug into the importance of creating content that delivers value beyond the website visit. His position is foundational: the way your audience searches is inevitably going to change and change again, but the importance of understanding your audience and creating value for them, has not. There will always be questions, problems to solve, and the need for trust.
To lean into the trendy AI angle, note that relevance, authority, and trust become even more important when assessing what we know about how AI search assembles information to answer increasingly specific questions. Content that offers relevant details, original perspectives, cited references, and genuine expertise provides value that generic, surface-level content lacks and will always perform better. As more brands resign themselves to relying on AI tools to produce content at scale, standing out becomes an even greater opportunity in the immediate future. Or at least until they figure out the misstep. After all, if everyone is using the same models to create eerily similar content, what makes one worth paying attention to over the other?
Where Do We Go from Here?
Not a bad set of takeaways from a single content conference. Between recognizing organic search as just one ingredient in a much larger discoverability recipe, rethinking how we define and measure success, and revisiting the lasting influence of Google’s Panda update, there was plenty to unpack.
For our SEO & Content Marketing team, though, two ideas stood out above the rest. First, it’s time to bring even more of our omnichannel expertise into how we measure and report on content performance. Second, as the lines between search, content, AI, and other discovery channels continue to blur, there’s a good chance that “SEO & Content” won’t fully capture what we do by 2027.
All that said, what I found especially encouraging about Content Marketing World wasn’t that every speaker introduced a completely new strategy we ought to adopt. It was that many of the conversations reinforced the direction we at Smith had already been moving – challenging marketers to think more comprehensively about how their strategies work together.