GEN AI
The Big Shift: 2026 is already the Year of the Agent
We are IN the Agentic Age.
Last year, Google and Walmart quietly confirmed something that should be on every brand marketer's radar. AI agents are now completing purchases on behalf of users inside closed ecosystems, without a human ever visiting a product page. If your brand is not architected for machine-readable decision-making, you will not appear in those flows.
Not deprioritised. Simply absent.
AI systems are no longer just generating content. They are recommending, shortlisting and transacting on behalf of users, which means your competitive set is being shaped before a person ever sees it.
The strategic implication is straightforward: optimising for search visibility is no longer sufficient. The question is whether your brand is visible to the agents making decisions upstream.
Two imperatives follow…
1. Design for agents, not just humans.
Your brand is now increasingly interpreted before it is experienced.
When a model answers a question, it is synthesising patterns from product pages, press coverage, reviews and third-party listings simultaneously. It doesn’t have much room for ambiguity. Fragmented or contradictory positioning across those sources creates the conditions for brands to be mischaracterised, or miss out on opportunities.
The task here is less about reinvention and more about alignment. Ask yourself:
Are your category descriptors consistent across channels?
Do your product names clearly signal what they are and who they are for?
Are your differentiators clear to everyone, or buried in brand language that only makes sense internally?
Do independent sources reinforce the story you are trying to tell?
Then test it. Take real, high-intent prompts from your category and run them across leading models. Notice how your brand is described and how competitors are framed. Are you positioned as premium or generic? Specialist or interchangeable? The answers are often surprising, and always useful.
This becomes a diagnostic layer alongside search and share of voice tracking. AI does not invent your positioning. It reflects the patterns it finds, and if those patterns are inconsistent, that inconsistency becomes part of how your brand is portrayed.
The practical starting point: Google and Microsoft have both confirmed that Schema markup is used to power their generative AI features, making it a strategic requirement rather than an SEO consideration. Audit your structured data estate as a priority.
2. Build intelligence advantage through GEO
If the first imperative is about fixing what agents read, this one is about understanding what they conclude.
Even with your house in order, there’s another question to get curious about: when people ask AI for the best option, where does it place you, and why?
LLMs are already compressing categories into quick, confident summaries. Do a few “best X for Y” searches and you’ll see the pattern. Brands get labelled and filed away: premium, reliable, good value, innovative, niche. Sometimes accurate. Sometimes lazy. Often inconsistent.
That’s where Generative Engine Optimisation (GEO) comes in.
Paying attention to these ‘compressions’ reveals how the market story is being told before anyone is reading your website. It shows where you’re coming through clearly, where you’re being blurred into the middle, and where competitors are simply easier to describe than you are.
Practical starting point:
Run a small set of high-intent prompts across a few models each quarter. Save the outputs. Look for themes: repeated descriptors, missing proof points, competitors getting credit for things you also do, or your brand being compared on the wrong attributes.
Those patterns give you a practical to-do list: what to clarify, what to evidence, and what to own more deliberately.
Five behaviours separating leaders from followers
Beyond this, we’re seeing five behaviours separating GenAI leaders from followers:
1. Listening to measured expertise, not hype
AI moves fast. Commentary moves faster.
Those leading here are not reacting to every announcement. They are developing the judgement to distinguish between platform marketing and genuine capability shifts, which is a harder skill than it sounds when every press release is written to blur that line...
Practical move: Before adopting a new AI feature, pressure-test it:
· Does this solve a defined business problem?
· Is it proven in live environments, not just demos?
· What changes operationally if we adopt it?
If the answers are vague, wait. Advantage rarely comes from being first. It comes from being right.
2. Testing across frontier models, not relying on one
A brand that appears authoritative in ChatGPT may be poorly represented in Gemini or Perplexity. The gap between those outputs is itself a strategic signal worth investigating.
Practical move: Consider building a testing cadence across at least three models. It gives you a meaningful picture of where your brand stands in the AI information ecosystem, and where targeted content or structured data investment will have the biggest impact.
3. Reframing AI as creativity and growth, not just productivity
The efficiency case for AI has been made, repeatedly and loudly. And whilst it’s valid, it is also just a fraction of the opportunities available.
The opportunity: The brands doing genuinely differentiated work are using AI to expand the solution space. Greater personalisation, faster iteration, more creative routes explored before narrowing. Work that was structurally impossible two years ago.
Think beyond doing the same work faster, and lean into the creative opportunities and imagination that AI tools afford us.
4. Using natural language coding tools to prototype faster than ever before
Recent tools are changing what is possible for non-technical teams, and the implications for marketing are underappreciated.
The opportunity: A senior marketer who can take a brief from idea to working prototype without a development queue is not just saving time. They are operating with a fundamentally different relationship to risk, because the cost of testing an idea has dropped to near zero.
5. They’re bridging the “AI Skills Gap”
Most organisations now have access to broadly the same AI tools. The differentiator in 2026 is not access, it is whether your workforce can actually use them to produce work that is better, faster or more considered than it was before.
That is a skills question, and it is one that many AI strategies overlook.
Practical move: Growth will increasingly be constrained or unlocked by how quickly organisations can adapt skills, workflows and ways of working, not by access to new tools. Upskilling cannot be a 'one-off' training project; it needs to be treated as a permanent operational imperative.
In summary:
The brands that treat AI as a capability shift rather than a feature upgrade will lead. The ones that do not will find themselves being summarised, compared and quietly deprioritised by systems that have no interest in waiting for them to catch up…