AI search optimisation: a 2026 primer for tech marketers

AI search optimisation has gone from a curiosity to a line item on most B2B tech marketing plans inside about eighteen months. We’ve spent a chunk of 2025 and the start of 2026 watching what actually moves the needle for MSPs, SaaS firms and IT consultancies. Some of it looks familiar. A lot of it does not.

This is a primer, not a victory lap. We will be honest about the bits we still do not fully understand, because anyone telling you they have AI search “solved” in 2026 is selling something. The category is young, the systems are changing weekly, and the data we can pull is genuinely partial.

What “AI search” actually means in 2026

When we say AI search, we mean a handful of distinct surfaces that answer a user’s question with generated text rather than a list of blue links. ChatGPT search, Perplexity, Microsoft Copilot, Google AI Overviews, Gemini and Claude all sit under that umbrella. They behave differently. ChatGPT and Perplexity expose citations prominently. Google AI Overviews summarise above the classic results. Copilot blends Bing’s index with the chat surface inside Microsoft 365.

The practical implication is that visibility now means two things. First, being retrieved as a source the model uses to answer the question. Second, being named in the answer the user reads. Those are related but not identical, and we cover the mechanics in our piece on how LLMs choose what to cite.

Why classic SEO still matters (mostly)

We get asked weekly whether AI search makes traditional SEO obsolete. Short answer, no. Long answer, the foundations carry. Crawlability, clean information architecture, fast pages and clear content structure all feed into LLM retrieval, partly because several of the engines still lean on conventional web indexes for grounding. Google AI Overviews draws from the same index as classic Google search. Perplexity uses Bing and its own crawl. Copilot uses Bing. Our technical SEO audit checklist for tech sites is still the right starting point if your fundamentals are wobbly.

What has changed is the weighting. Pages that read like rambling introductions with the answer buried halfway down do not get cited. Pages that state a clear, defensible claim near the top, with supporting evidence, do.

The four jobs of an AI search programme

In our experience working with clients like Littlefish and Codestone, an AI search programme breaks into four distinct jobs:

1. Make your content retrievable. Crawlable, fast, well-structured, with the right schema where it earns its keep.
2. Make your content quotable. Short factual statements, named entities, definitions that can be lifted into an answer.
3. Make your brand mentioned elsewhere. LLMs lean heavily on third-party signals to decide who counts as authoritative.
4. Track what’s happening. This is the hardest of the four because the data is partial and the tooling is immature.

Each of those breaks down into specific tasks. We’ve covered the writing side in writing content that AI search engines actually cite and the schema side in structured data for AI search.

What we recommend doing first

Before you commission anything new, audit what exists. We typically run three checks for a B2B tech client:

• Citation audit. Pull a list of buyer-intent prompts. Run them through ChatGPT, Perplexity, Copilot and Google AI Overviews. Note which sources get cited and where you appear, if at all. Our piece on auditing your visibility in Copilot and ChatGPT walks through this in detail.
• Content gap check. Look at the questions buyers ask the LLMs that you do not currently answer with a dedicated page. Comparison queries, “best X for Y” queries, definitional queries, pricing-shaped queries. We’ve written a more detailed walkthrough in finding AI search content gaps.
• Entity audit. Search for your brand name across the major LLMs. See what they think you do, who you serve and what your differentiators are. We’ve found the gap between what a company believes about itself and what an LLM repeats back is often surprising.

These three checks usually surface six to twelve weeks of useful work before you write a single new article.

The unsettled bits

Here is what we genuinely do not know with confidence in April 2026:

• The weight of llms.txt. None of the major commercial LLMs have publicly committed to honouring it as a ranking signal. Some crawlers respect it as a discovery hint. We’ve written about this in llms.txt: should your tech site have one? and the honest answer is that the cost of adding one is low, but you should not expect it to do heavy lifting on its own.
• How much schema matters. Schema clearly helps Google AI Overviews. Whether ChatGPT or Perplexity care is murkier. There’s evidence both ways and the engines do not publish their grounding logic.
• The half-life of citations. Once a model decides you are an authoritative source on a topic, how long does that stick? Days? Months? After every model update? We do not have clean longitudinal data yet.
• Attribution in any rigorous sense. Profound and Athena are doing useful work here, and Cloudflare logs help, but you should treat AI search analytics in 2026 the way you treated Google Analytics in 2007. Directional, not exact.

How tech marketers should think about budget

We have not seen a single client who should be moving their entire SEO budget into AI search optimisation. We have seen plenty who should be carving off ten to twenty per cent and running deliberate experiments. The right split depends on how much of your buyer journey already touches an LLM. SaaS evaluation queries are heavily LLM-influenced. Local IT support discovery is much less so, though Google AI Overviews is changing that.

If you serve enterprise IT buyers, expect a meaningful chunk of your shortlist research to happen in ChatGPT and Copilot before a single page on your site loads. That alone justifies a measured investment in AI SEO services and a hard look at how your content strategy reads to a model rather than only to a person. We’ve drilled into the IT services case specifically in AI search for IT services.

What “good” looks like in 2026

A B2B tech site that’s set up well for AI search in 2026 typically has a tidy technical foundation, a clear entity-based content model around the things it sells, schema where it adds value, a credible third-party footprint of mentions and links and a measurement habit that takes AI traffic seriously even when the data is rough. We covered the measurement side in tracking AI search traffic and rethinking content KPIs in the AI search era.

It also has a marketing team that will admit when something they tried did not work. That second part is harder than the technical work, because the discipline rewards experimentation and punishes confident-sounding theories that turn out to be wrong.

If you’d like a second opinion on your AI search strategy, drop us a line.