Sarah McDowell shares that surrounding yourself with the right people is a key part of maintaining personal growth.

Sarah says: “Community and your inner circle are not only essential for your well-being, but they are also so important for your career progression as well.”

Why is community relevant to SEO?

“When it comes to SEO and career progression, as in any industry, looking after your own well-being and looking after yourself is so important. It prevents things like burnout, imposter syndrome, not feeling confident enough to push yourself forward, and not having confidence in your own skills.

One of the reasons why Tazmin and I started the SEO Mindset Podcast is that there are lots of great podcasts out there that tell you how to do SEO, but there was a gap for a podcast talking about career progression, personal growth, and mental health for those who work in SEO.

Those softer skills are really important. They're at the core of all of that lovely growth.”

Our final chapter begins with Helen Pollitt taking a look at the skills you need to personally build, to remain as relevant as possible in the SEO workforce for many years to come.

Helen says: “Keep your SEO career on track.

When it comes to your skillset, you need to stop thinking of ‘SEO versus GEO’. Instead, you need to understand how to apply your organic discoverability skills to each and every channel that you may need to – and how you can use AI to enhance those skills.”

How much of your SEO time should you focus on developing those skills?

“SEO is a career that is constantly changing, and not just in the sense that what works in SEO is changing. The tools and educational platforms that are available are changing as well.

As a result of that, we really need to be investing a lot in our own careers and in our skill sets. I like to spend at least a little bit of time every day dipping into articles, watching webinars, or just making sure that there's no breaking news within the SEO industry that I'm missing.

Treat it like an investment. Make sure that you are spending a significant portion of your time investing in those skills and keeping current with what's going on, without succumbing to the hype and fear-mongering that also seems to go around the space at the moment.”

Ray Saddiq shares that you need to be both recognisable and discoverable in 2026.

Ray says: “If you want to seriously influence your consumer moving forward, brand alone is not going to cut it.

We got into this mindset that brand was going to be key to driving SEO success, and it plays a huge part, but if you want to grow and stay top-of-mind, you need to drive demand and be discoverable. It's not just an awareness thing anymore. You need a combination of both. It's not enough to be known; you need to be found as well.

That means building brand and category salience. You've got to own your category in search so that, moving forward – with LLMs, AI search, and social search – you are the brand that has salience to the category that you want to be known for. You’re the one most closely related to that category.

This can’t just happen across Google. This needs to be across every searchable platform, because that's where intent starts now.”

Nickolass Jensen shares that most sites have high search visibility but zero AI citations, and the gap is diagnosable, measurable, and fixable with a structured three-layer audit. Talking points include... The three-layer diagnostic: Why sites fail AI visibility at the human layer (UX/trust), the search layer (authority/structure), or the AI layer (machine-readable proof) - and why fixing the wrong layer first wastes months. AI crawler visibility: Server log analysis reveals real-time and historical crawl patterns from GPTBot, ClaudeBot and PerplexityBot - most site owners have no idea which AI bots visit them, how often, or what they fetch. The gap between what AI crawlers index and what gets cited is where the work happens. GEO in 2026 is still mostly theoretical. The only practical path forward is tools that enforce structured execution. Rankings remain infrastructure, but the real competitive moat is whether an LLM cites you when a buyer asks. Read the full transcript of Nickolass's interview at https://majestic.com/seo-in-2026/additional-insights/nickolass-jensen

When you’re analysing data, you shouldn’t necessarily treat it at face value, shares Dixon Jones.

Dixon says: “You need to think about what you are trying to measure.

Buyer behaviour is changing dramatically with the advent of AI. Users are going to be sending out their little AI bots to ask questions on your website instead of going there themselves.

If you're trying to buy a vacuum cleaner, you might ask, ‘What's the best vacuum cleaner for pet hair?’ Of course, Shark, Dyson, and Hoover all want to be in that list of recommendations, but it's the AI that's going to do the investigating.

Potentially, an AI is going to go and find these brands, have a look at those brands, compare the information about pet hair, and then come back and provide an answer to the user that says, ‘X brand is slightly better than Y brand for this particular type of pet hair.’ At that point, the user doesn't need to click on any of the websites, but they will still buy a Shark, a Dyson, an Electrolux, or whatever it may be. They'll have made their decision.

All of the metrics that we've been using for the last 20 years have measured the visitors that come to the website. That's been a key performance metric: has your visitor come from search, from pay-per-click, from direct, affiliate, etc.? That's been the mentality, but that doesn't really work in an AI-driven world.

Firstly, that’s because AI is doing the search for you. Secondly, the AI doesn't typically trigger a visit on most web analytics systems. Most web analytics systems are JavaScript-based: the web page loads, it triggers a JavaScript call, and that will record the visitor. However, LLMs are really lazy when it comes to crawling the site. They just want the text. If the text doesn't appear, they can't be bothered to call the JavaScript.

Often, it won't even come up as a click in your systems, so you're going to have to change the way you measure success.”

Something else that’s arguably changed a great deal in the past few years is keyword research. Andy Mollison advises on tracking more than basic keywords.

Andy says: “In a world of generative AI, keyword tracking as we know it is no longer good enough.”

Why is keyword tracking no longer good enough?

“The way we track performance in SEO has historically been through organic sessions, organic revenue, organic lead generation, and that kind of thing. Obviously, that is still very relevant, but it's also been about keyword position tracking as well – tracking your individual single keywords, or long tail keywords, or whatever it is you're tracking.

To an extent, that's still fine. However, you can’t ignore what’s going on. If you're not tracking outside of that, you're not tracking your performance as a whole. With AI overviews, AI Mode, and ChatGPT, people are searching in so many different ways now. Tracking keywords is not everything anymore.”

Following on from the previous ‘Testing’ chapter, Marta Szmidt begins chapter 19 with an introduction to newer metrics that you should be considering.

Marta says: “You need to add new metrics for measuring SEO success and face the reality that search has changed.

It's no longer the same traditional search engines we used to know, and you need to adapt to the reality. You need to combine classic traditional SEO metrics with new ones that target AI search visibility.”

Which metrics aren't as relevant anymore, compared to the metrics that you have to focus on in 2026?

“There has been a big shift in metrics. Clicks, organic traffic, and click-through rate have all been hit by what is happening with the shift in search behaviour, so they have become less relevant. They're not going to give you the picture in the same way they used to.

We are seeing a big rise in zero-click search results. People find the answer very quickly now with AI tools, and even if you appear in an AI overview, that doesn't mean that the user will click on to your site. This shift means that we’re seeing a drop in clicks and organic traffic.

In the same way, the rankings are not going to show us the whole picture because, according to some data, 36% of people in the US will be using AI for search by 2028. We need to adapt to it. That doesn't mean that the normal traditional search and engagement metrics are going to disappear, but we need to adapt and find the metrics that are relevant for us now, with AI search in the picture.”

There’s one tool that Priya Verma finds particularly useful at combining data from various sources and conducting analysis – BigQuery.

Priya says: “Gear up your SEO analysis through Google BigQuery.”

Why BigQuery in particular?

“SEO analysis in BigQuery is powerful because it lets you move beyond the existing tools like Google Search Console, Ahrefs, etc., and their limitations. It allows you to see the full picture at scale.

By analysing millions of queries alongside other data, you uncover deeper patterns, user behaviour insights, and real opportunities to grow your traffic. We are focussing on BigQuery because of a subtle limitation of the most widely used tool for SEO, Search Console: you can only look at the top 1,000 keywords.

For big businesses, that doesn't give a full picture. That's where tools like BigQuery come in. There are alternatives like Google Sheets plugins, where you can import 25,000 rows. However, if you are in an industry with thousands of keywords, that is still a huge limitation.

That's where bulk export comes in. By connecting your Search Console to BigQuery, through the BigQuery API and the BigQuery Storage API, you can capture and store all your data at scale, without hitting those raw limits.

This is where it really gets interesting, because the retention policy in Google Search Console is 16 months. After 16 months, the data is lost. In BigQuery, you can keep it for as long as you want.

Then, when you take those insights and combine them with your analytics data – in GA4, for example – suddenly you're not just looking at what people are searching for, but how they behave when they land on your site. You can connect the dots between search demands, journeys, and conversions, and it makes the game more interesting.

Not enough SEOs are aware of this. They just think of BigQuery as a database where you kind of store your data, which is true, but when you talk about the analysis of your historical data set, that's where it all changes.

When you go to legal, they will ask you questions about retention and what you are storing. You need to spread that knowledge around to say that this is something that could be changed in BigQuery, and not just for SEO, but for things like analytics as well.”