The real shift: you’re no longer just marketing to humans
Scan those headlines and a pattern jumps out: Google AI Mode, AI Overviews, ChatGPT outranking YouTube in search interest, AI CRM, AI voice agents, “Does AI trust you?”, user data in Google Search.
The internet is no longer primarily a human-to-human discovery system. It’s a machine-to-human system, where AI intermediaries decide what people see, hear, and buy.
That’s the issue that matters: your brand is now marketing to algorithms and humans at the same time. The winners will be the companies that intentionally become “AI-preferred” – the brands that models, ranking systems, and recommendation engines keep choosing by default.
This isn’t a philosophical point. It’s a media buying and growth problem. If you don’t adapt, your CAC quietly drifts up while your organic and word-of-mouth channels flatten, and you blame “competition” instead of the real culprit: you’re invisible or untrusted to the machines that route demand.
From SERPs and feeds to AI surfaces and agents
Historically, you optimized for:
- Search engines (SEO, SEM)
- Social feeds (creative, timing, engagement)
- Marketplaces and app stores (ASO, reviews, merchandising)
In 2026, there are three new surfaces that matter just as much:
- AI answer engines (ChatGPT, Perplexity, Google AI Overviews, Gemini, etc.)
- AI-native ad products (Demand Gen, ChatGPT ads, AI-assisted targeting and creative)
- AI agents and assistants (voice agents, CRM copilots, shopping assistants)
All three work on a simple principle: they compress the messy open web into a small set of “safe, relevant, reliable” options.
If you’re not in that compressed set, your media dollars work harder and harder just to maintain the same revenue. If you are, everything compounds: cheaper acquisition, higher intent traffic, better conversion, and stronger brand recall because you show up as “the answer,” not just “an ad.”
How algorithms decide who to trust
Across search, social, and AI systems, three signals keep showing up:
- Behavioral proof – what real users do with you
- Structural clarity – how cleanly your digital footprint explains what you are
- Message consistency – whether your story matches across channels and over time
1. Behavioral proof: your users are voting for you in the background
Headlines about “user data is important in Google Search,” “AI CRM use cases,” and “website conversion strategies” are all pointing at the same thing: systems now read user behavior as a trust score.
For AI surfaces and modern ranking systems, this includes:
- Click-through from AI answers or snippets to your site
- Dwell time and scroll depth on key pages
- Repeat visits and branded search growth
- Conversion rates and post-click engagement (logins, feature use, purchases)
- Reviews, ratings, and refund/return behavior
These aren’t just “CRO metrics” anymore. They’re training data feeding the models that decide whether you’re a safe recommendation.
If your landing pages are pretty but confusing, if your product pages don’t answer obvious objections, if your post-purchase flows are sloppy, you’re not just losing revenue today. You’re teaching the algorithms that sending people to you is risky.
2. Structural clarity: the boring stuff is now existential
Look at the headlines about cannibalization, 8,000 title tag rewrites, URL mistakes killing Black Friday, and domain-level signals. Underneath them is the same message: structure matters more in a machine-intermediated world.
AI systems and ranking engines need to confidently answer:
- What exactly is this brand?
- Who is it for?
- What problems does it reliably solve?
- Where does it fit relative to alternatives?
They infer that from:
- Clean site architecture (no cannibalized pages fighting for the same intent)
- Consistent naming and taxonomy across site, app, and marketplace listings
- Clear, specific title tags and headings that match the actual content
- Stable, meaningful URLs (not “/product-1234?ref=bf-sale” for your flagship offer)
- Coherent domain strategy (not scattered microsites and random subdomains)
When this is messy, AI models hedge. They either ignore you or describe you vaguely. Either way, you lose precision and preference.
3. Message consistency: taste, not templates
There’s a reason “Every marketer says you need taste” and “AI’s trust problem: the cost of outsourcing your message” are resonating. If your content looks like it was written by the same generic AI that trained the models, you’re not adding signal – you’re adding noise.
Models and ranking systems reward:
- Originality (new data, new angles, real stories)
- Depth (specifics, examples, numbers)
- Consistency (same point of view across time and channels)
That’s what “taste” actually is in this environment: the discipline to say something specific and consistent, in a recognisable voice, instead of shipping AI sludge because it’s faster.
The AI-preferred brand playbook
Becoming AI-preferred isn’t a moonshot project. It’s a series of unglamorous changes to how you plan, buy, and build.
1. Redesign your measurement around “algorithm trust”
Most dashboards stop at CAC, ROAS, and maybe blended MER. That’s not enough. You need a small set of “trust-adjacent” metrics that tell you whether algorithms are getting more or less confident about you.
At minimum, track:
- Branded search volume and share – by engine, by geography.
- Click-through from non-paid surfaces – AI Overviews, featured snippets, app store search, marketplace search.
- Post-click quality – scroll depth, time on key pages, micro-conversions (add to cart, demo booked, trial started).
- Review velocity and rating distribution – especially on marketplaces and app stores.
- Content reuse in AI answers – where you’re cited or summarised by AI tools.
Have your analytics or growth team build a simple “algorithm trust score” that rolls a few of these into one trend line. It doesn’t have to be perfect. It just has to be consistent and directionally honest.
2. Treat AI surfaces as channels, not curiosities
Many teams are still in “let’s see what AI Overviews does to our traffic” mode. That’s a good way to get buried.
Instead:
- Map your high-intent queries and check how AI Overviews, ChatGPT, and similar tools answer them today.
- Audit which competitors are being mentioned or implied in those answers.
- Identify gaps where the AI answer is shallow, outdated, or missing key angles you actually solve.
- Produce content and assets that directly fill those gaps: data, comparisons, implementation details, pricing clarity.
You’re not “gaming” AI. You’re feeding it better training material than what it currently has. Over time, that’s how you become the default example or recommendation.
3. Clean up cannibalization and structural noise
The 8,000-title-tag case study isn’t just an SEO war story. It’s a reminder that algorithms hate ambiguity.
Run a quarterly “structure sprint”:
- List all pages targeting the same or similar intent; decide which one is the canonical “answer.”
- Merge or redirect thin, overlapping content into stronger, consolidated pages.
- Standardise naming: products, features, and plans should have one clear name everywhere.
- Fix broken or meaningless URLs, especially for flagship offers and seasonal campaigns.
This is unsexy work. It’s also the difference between being seen as “the definitive resource” vs. “a messy site that might confuse users.”
4. Use AI to scale craft, not replace it
AI is already embedded in CRM, ad platforms, and creative tools. The temptation is to outsource judgment. That’s how you become a commodity in the training data.
Instead, set a simple rule: AI can draft, but humans decide:
- What we stand for (positioning, POV, non-negotiable claims)
- What we will not say (overpromises, generic fluff, off-brand tone)
- Where we go deep (original data, case studies, product truths)
Use AI to:
- Summarise long-form content into channel-specific snippets.
- Generate structured variations for testing (subject lines, ad hooks, CTAs).
- Surface anomalies in performance data you’d otherwise miss.
But keep humans in charge of the actual story. Algorithms are pattern matchers; they amplify what you feed them. If you feed them mush, you get mush back.
5. Make “superfans” machine-readable
“When customers create more customers” and “superfans” sound like brand marketing topics, but they’re deeply operational in an AI-first world.
Enthusiastic customers do three things algorithms love:
- They create content (UGC, reviews, tutorials, comparisons).
- They generate branded search and direct traffic.
- They defend and explain you in public threads (Reddit, forums, social).
The trick is to make that activity easy to see and interpret for machines:
- Encourage reviews on platforms that feed into search and AI surfaces, not just your own site.
- Curate and structure UGC: playlists, galleries, “best of” pages that clearly tie customer language to your official positioning.
- Build simple programs that nudge customers to answer questions where your category is being discussed (with guardrails, not astroturf).
Think of it as structured word-of-mouth. You’re not just hoping people talk about you; you’re making their advocacy legible to the systems that decide what gets recommended.
What this means for CMOs and growth leaders
The operational implications are clear:
- Media buying can’t be channel-by-channel anymore. You need a view of how paid, organic, and AI surfaces interact to train the same underlying systems.
- Brand and performance can’t be separate teams with separate truths. Algorithms reward coherence; internal fragmentation shows up as external noise.
- Analytics has to move beyond attribution wars and into “how are we training the ecosystem?” – what signals are we sending, and are they consistent?
- Creative and content need new briefs: not just “drive clicks” but “be the canonical explanation that models will keep using.”
The marketers who win the next five years won’t be the ones who adopt the most AI tools. They’ll be the ones who understand the simple shift underneath all the headlines:
You’re not just chasing demand anymore. You’re training the machines that route it.