4 Facebook ad templates that still work in 2026 (with real examples)

4 Facebook ad templates that still work in 2026 (with real examples)

Have you ever tried to find inspiration for ads by scrolling your own Facebook feed?

Then you know that most companies’ ads aren’t very compelling. Also, scrolling Facebook in this day and age is weirdly exhausting.

Here’s the truth: most high-performing ads in 2026 aren’t winning the day because they’re wildly original or uniquely “viral” (do we still call something that?). 

They’re winning because they follow the same repeatable templates that smart marketers have been using for decades. 

(Yes, even now. Even with AI. Even with “creative strategy” and words like “scrollable” being used non-ironically in business initiatives.)

This article goes back to basics, eschewing “inspiration” in favor of tried-and-true approaches.

Below are four Facebook ad templates you can use right now, regardless of what you’re selling, with real examples that show the strategy behind top brands’ creative.

1. Problem? Meet solution

Pain point → Relief → Simple next step

This is advertising 101. It worked in 1926, it works in 2026, and it’s still undefeated for a reason.

Despite what some business owners believe, customers don’t wake up thinking about your business. 

They wake up thinking about their life:

  • “I spent too much money.”
  • “I don’t have time.”
  • “I feel stuck.”
  • “I’m overwhelmed.”
  • “I can’t stay consistent.”

That means you’ve got to meet them where they are. 

If your customer doesn’t realize their situation is solvable, they won’t buy anything. 

That means, even if you’re the best solution in the world, until they recognize the problem, they won’t look for an answer.

Example: ClickUp

Facebook Ads - ClickUp

ClickUp takes a modern pain point that most tech workers struggle with on a daily basis, and reframes it into something that can actually be solved:

Overwhelmed by multiple tools and apps? Stop switching between them and use one platform that does it all.

This ad isn’t just selling “project management.” It’s selling:

  • Mental relief.
  • A single source of truth.
  • Less context switching, more productivity.
  • Team alignment.
  • The promise (though some might say illusion) of control.

Plug-and-play copy starter

Still dealing with [problem]?

You’re not alone – and you don’t have to stay stuck.

[Product/service] helps you [benefit] without [common objection].

Get started → [CTA]

Dig deeper: Meta Ads for lead gen: What you need to know

2. Can your competitors do this?

Unique selling point → Instant comparison → ‘Oh, hey’ moment

If you’re in a crowded industry fighting for market share (and in 2026, a lot of businesses are), the brands that stand out are the ones that make it easy for customers to answer one question:

  • Why should I choose you?

Let’s be clear: you don’t necessarily need a radical innovation or a show-stopping differentiator. 

Sometimes it’s how you do things, what you prioritize, or who you’re for. 

All that really matters is that you’re different in a way people can understand quickly and easily.

Example: The Woobles

Facebook Ads - The Woobles

Crocheting has been around forever. Beginner kits have existed for decades. Patterns have been sold in stores since before we were all born.

And yet, somehow, The Woobles managed to grab a huge chunk of market share in a craft that’s older than the automobile.

That’s impressive.

This ad shows exactly how they do it.

Instead of positioning crochet as “learn a new skill,” they highlight what makes them different, then continue stacking their differentiators in a way that makes the purchase feel almost inevitable:

  • Cute, modern projects people actually want to make.
  • Designed for true beginners.
  • Thicker yarn and a chunky hook.
  • Step-by-step video tutorials.

That’s really the point of a strong USP ad. It’s not just “we’re unique.” It’s “here’s why this is easier, better, and faster.”

Plug-and-play copy starter

Most [category] products do [expected thing].

Ours does [unexpected/uncommon benefit].

Here’s what makes it different:

  • [Differentiator 1]
  • [Differentiator 2]

Try it for yourself → [CTA]

Dig deeper: Rethinking Meta Ads AI: Best practices for better results

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3. Say more with less

Testimonial/UGC → Minimal brand talk → Trust does the selling

Not every ad needs to look and sound like an ad. In fact, some of the best-performing Facebook ads in 2026 are the ones that take you a second to realize they’re sponsored.

This is the “let the customer do the talking” template, and it’s everywhere on Instagram and TikTok because it works.

Think creator-style, user-generated content (UGC), testimonials, and review-driven ads that feel real, slightly imperfect, and way less polished than traditional brand messaging.

Oddly enough, the lack of polish is part of the appeal. It reads as “honest,” not “salesy.”

Example: Allbirds

Facebook Ads - Allbirds

Allbirds runs a simple, product-focused ad for the Tree Dasher 2, pairing a customer quote with a simple image of the shoe.

  • “Wore these @allbirds for 13 hours and could’ve gone another 13. I never want to take them off.”

That line pretty much does all the work for the ad.

It implies:

  • Comfort that lasts all day.
  • No break-in period.
  • Real-world wearability.

The creative itself is even straightforward: product image, a few lifestyle shots, and a clean layout. It’s not trying to be flashy, it’s trying to be believable. 

Plug-and-play copy starter

“I didn’t think anything would help, but this actually worked.”

[Show the proof]

If you’re dealing with [problem], try [product] → [CTA]

Dig deeper: How to test UGC and EGC ads in Meta campaigns

4. The ‘quick win’ checklist

3-5 bullets → Easy decision → Low-friction CTA

Sometimes people don’t want a story. They want clarity.

This template works especially well on Facebook because it’s built for how people actually scroll: fast, distracted, and looking for something that solves a problem right now.

Instead of writing paragraphs, you give them a handful of “yes, I want that” benefits they can absorb in two seconds. 

The “quick win” Checklist format:

  • Reduces decision fatigue.
  • Makes value instantly scannable.
  • Highlights benefits without over-explaining.
  • Works beautifully for cold audiences who don’t know your brand yet.

Example: Little Sleepies

Facebook Ads - Little Sleepies

Little Sleepies uses a simple visual and benefit callouts to answer the parent question underneath the question:

  • “Is this actually going to make my life easier?”

Instead of trying to be clever, the ad clearly lists the practical wins:

  • Double zippers for easier diaper changes.
  • Ultra-soft bamboo for comfort.
  • Fits longer (up to 3x) for better value.

This is a great reminder that in 2026, the ads that win aren’t always the funniest or most creative; they’re often the ones that make the buying decision feel effortless.

Plug-and-play copy starter

Everything you need to [achieve outcome]:

  • [Benefit 1]
  • [Benefit 2]
  • [Benefit 3]

Get it today → [CTA]

Dig deeper: How to get better results from Meta ads with vertical video formats

Templates beat inspiration every time

In 2026, the brands winning on Facebook aren’t the ones reinventing advertising every week or pouring money into slick branding campaigns.

They’re the ones who:

  • Choose a proven structure.
  • Write a clear hook.
  • Test variations quickly.
  • Let the results decide.

You don’t need inspiration every time you write a Facebook ad. You need structures you can trust.

Pick one template, write two variations, and test them against each other. Then repeat.

Read more at Read More

Why Search and Shopping ads stop scaling without demand

Why Search and Shopping ads stop scaling without demand

If you’ve spent any time in PPC communities, Reddit threads, Slack groups, or conference Q&As, you’ve probably noticed a recurring frustration: “Google Ads isn’t scaling. It’s not working, and we’re stuck.”

On the surface, everything looks fine. The campaigns are running, impression share is high, shopping feeds are clean, and budgets are flowing. But growth isn’t materializing.

This isn’t usually about “broken campaigns” – it’s about the limits of demand.

In niche markets or categories shaped by seasonality, growth is naturally capped.

Yes, running broad match or AI Max can expand your reach to adjacent queries, so impression share might not literally be 95%.

But these campaigns are still only capturing demand that already exists. Once you’ve covered the pool of relevant searches, you can’t spend your way into more.

That’s the uncomfortable truth: Google Ads doesn’t create demand. It captures it.

If fewer people are searching this month, or if your category naturally has a small audience, your results will reflect that.

You can dominate what’s there, but you can’t conjure demand out of thin air.

So when growth stalls, the real question isn’t “What’s wrong with Google Ads?” but “What are we doing to create demand that fuels future searches?”

Search and shopping = Demand capture, not creation

Let’s call Search and Shopping what they are: demand capture channels.

They’re excellent for getting in front of people when they’re ready to buy, or at least actively researching. But they are reactive by design.

Ads only appear once someone types a query. No query, no ad.

That’s why impression share (IS) can be deceptive.

A 90% IS looks like you’re winning (and you are). But if there are only 500 relevant searches in your market this month, you’ll never scale to 5,000 clicks just by raising bids.

Broad match and campaigns like AI Max can stretch coverage by surfacing adjacent queries.

But these still rely on intent. If nobody is searching for related terms, there’s nothing to match against.

Contrast this with platforms like Meta or TikTok, where more budget literally means more reach.

Search doesn’t work that way. It’s not a demand generator – it’s a closer.

Where demand really comes from

So if Search and Shopping can’t create demand, what does?

Marketers have long grouped channels into three buckets: owned, earned, and paid.

It’s old-school terminology, but it’s still the most practical way to break down where demand actually originates.

If Search and Shopping are just there to capture demand at the end, you need to understand which levers create it upstream.

Owned

These are the channels you control: your website, email, content, and CRM. They don’t usually create brand-new demand, but they’re critical for nurturing it.

Think of a D2C brand running a simple “VIP early access” sign-up before Black Friday. That list fuels branded searches once the sale goes live.

Or a SaaS company publishing an FAQ blog that shows up for early research queries, nudging prospects who later Google the brand directly.

Owned channels ensure that once curiosity is sparked, it’s effectively nurtured toward a search.

Earned

These are the channels you don’t directly pay for: PR mentions, SEO visibility, reviews, organic social, and word of mouth.

A product that lands in a holiday gift guide? Branded searches spike the next week.

A TikTok that goes viral organically? Google Trends charts it days later.

Positive Trustpilot reviews? They push people back to Google to check your site or compare pricing.

Earned channels matter because they carry credibility. They don’t just spark curiosity; they make people trust you enough to type your name into the search bar.

Paid

Paid media includes both demand-capture channels (Search and Shopping) and demand-creation channels.

Search and Shopping capture existing intent, but platforms like Meta, TikTok, YouTube, Pinterest, and Display create it.

These channels don’t wait for someone to type a query, they put your brand in front of people who weren’t already looking.

  • A TikTok showing your product in action.
  • A YouTube pre-roll highlighting your brand story.
  • A Pinterest ad that lands on someone’s gift board weeks before purchase.

These sparks generate curiosity, which later turn into branded searches.

While broad match and Performance Max might unearth “new” queries, they’re still intent-driven.

The real creation happens upstream, through paid channels designed to spark awareness.

Dig deeper: How paid, earned, shared, and owned media shape generative search visibility

The funnel without the fluff

You’ve likely heard this before, but it’s worth being specific about where Search and Shopping actually fit.

They’re strongest at conversion, but they also show up during the consideration phase of the buyer’s journey, when people are still comparing options.

Here’s how the funnel really works.

Awareness

This is the stage where people first notice you exist.

For example, a skincare brand could run TikTok ads showing its serum in action, or a B2B SaaS company might run YouTube pre-roll explaining a popular platform feature.

In retail, a promoted Pinterest pin could land on someone’s gift board long before purchase.

Tip: This is where Meta video campaigns, TikTok ads, YouTube pre-roll, Pinterest-promoted pins, PR placements, and influencer content live. These channels don’t wait for intent – they spark it.

Consideration

During this stage, people compare, research, and explore.

For example, that skincare shopper might read reviews, sign up for “early access,” and later search “best vitamin C serum.”

In B2B, a prospect could download a case study and then Google “top CRM tools for small businesses.”

Tip: This is where generic search campaigns (e.g., “best [product]” or “affordable [category]”), shopping ads with comparison queries, CRM nurture flows, SEO content, and retargeting via Meta/display/YouTube come in. This stage is about reassurance, education, and visibility while the prospect weighs their choices.

Conversion

The stage where people buy. For example, two weeks after first becoming aware of the brand, the skincare shopper searches “Brand X serum” and buys via Shopping.

After much comparison, the B2B prospect searches “[Vendor name] pricing” and completes a demo-request form.

Tip: This is where branded search, high-intent shopping queries, retargeting to cart abandoners, and PMax remarketing close the deal.

That’s why the funnel matters. If you only play at conversion, you miss those critical mid-funnel searches where people decide between you and your competitors.

Skip awareness and consideration, and your funnel isn’t a funnel at all – it’s a drinking straw. 

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What to do when search hits its ceiling

When growth stalls, the solution isn’t “spend more on search.” It’s fuelling demand earlier.

Here’s how to do exactly that, broken up by budget level.

If you’re working with smaller budgets, focus on high-leverage plays:

  • Grow your CRM list: Run simple lead-gen ads, like “sign up for early access” or “exclusive drops.” Even $300-$500 on Meta can build a list that costs nothing to email later.
  • Run warm-up campaigns: Low-cost video or carousel ads on Meta or TikTok build remarketing pools you can retarget with cheaper Google Display or YouTube Ads.
  • Optimize your site: Gift guides, FAQs, delivery cut-offs. A poor landing page wastes every click you’ve fought for.
  • Keep remarketing switched on: Display, YouTube, or PMax remarketing switched on is often cheaper than chasing new clicks in search.

If you’ve got bigger budgets, play full-funnel:

  • Run always-on awareness: Meta, YouTube, TikTok, Pinterest. Sequence your creative by teasing early, revealing mid-season, and then pushing offers when intent peaks.
  • Segment your CRM properly: VIPs deserve exclusives. Lapsed buyers need reactivation. Gift shoppers want bundles. Tailor the journeys.
  • Invest in influencers and PR: Gift guides, unboxings, trend-driven content. These placements fuel branded search demand faster than any keyword tweak.
  • Personalize your site: Recommendation engines and dynamic content keep people on the path to purchase.

Things everyone should check:

  • Check impression share: If you’re at 90%+, you’re near the ceiling. Broad match and AI Max might stretch coverage, but they won’t invent intent.
  • Track branded search: If branded queries aren’t rising, awareness is flat.
  • Keep remarketing on: It’s the lowest-hanging fruit.

Assets you need in place

Fix the basics before you pour money into awareness. Demand creation is wasted if your funnel leaks.

At a minimum, you need proper creative assets. Don’t just think about “a video” or “a few images.”

Different platforms require different formats and sizes, and if you don’t prepare variations, you’ll either be stuck with auto-cropping or miss placements altogether.

  • Meta: Vertical (Reels/Stories), square (Feed), and landscape (In-stream).
  • TikTok: Full-screen vertical, with captions/subtitles baked in as sound-off viewing is common.
  • YouTube: Horizontal 16:9 ratio for standard placements, but also vertical Shorts for mobile audiences.
  • Pinterest: Vertical lifestyle imagery tends to outperform product-only shots.
  • Display: Responsive formats mean you should plan both text + multiple image ratios so the algorithm has variety to test.

For small brands, this doesn’t mean expensive shoots. Scrappy user-generated content can be repurposed across platforms if you plan with aspect ratios in mind.

For bigger brands, building a creative matrix – every concept mapped across different formats and funnel stages – ensures consistency and saves on reshoots.

Landing pages

Don’t send awareness traffic to a generic homepage. Build pages that:

  • Answer FAQs
  • Highlight delivery cut-offs (critical in Q4)
  • Showcase bundles or gift guides for seasonal shoppers
  • For B2B: Tailor landing pages to industries or personas

CRM setup

Even a simple nurture flow is better than nothing. Capture the email at the awareness/consideration stage and follow up.

Larger brands should run segmentation and automated journeys:

  • VIPs: Exclusives.
  • Lapsed buyers: Reactivation flows.
  • Prospects: Educational sequences.

These assets make sure that when demand is created, it actually converts instead of leaking out of the funnel.

AI: Helpful, but not a shortcut

AI is everywhere right now. Tools like Performance Max, AI Max, and creative generators are powerful.

Used well, AI can save time and scale execution.

For example, generative AI can help brainstorm dozens of ad copy variations that you then refine for brand fit.

Or it can automate repetitive tasks, such as analyzing search term reports or adjusting bids, freeing you up to focus on strategy.

However, AI doesn’t change the rules of demand. It still relies on intent already being there. And if you let it run unchecked, you risk losing what makes your brand stand out.

Search Engine Land has repeatedly warned about this: over-reliance on AI can result in generic creative that lacks voice and originality, blending your ads into the crowd.

Think of AI as an accelerator: It can speed up execution, but it can’t define your brand, audience, or strategy. That still requires a human marketer.

Making it real for stakeholders, measuring demand creation

If you’re explaining this to a board or client, keep it simple:

  • Lead with this: Search responds to demand; it doesn’t generate it.
  • Show them impression share: If you’re already at 90%+, the problem isn’t coverage – it’s demand.
  • Point to branded search trends: Flat branded queries mean flat awareness.
  • Highlight competitor activity: Show where rivals are fuelling demand – Meta, TikTok, PR, or Pinterest. That’s why their branded search traffic is rising.

Don’t just show performance data. Show where the demand gap is.

Branded search is the clearest signal, but it isn’t the only one. Look at:

  • Direct traffic: More people typing your URL into their browser means brand awareness is working.
  • Organic search traffic (non-branded): If this grows, your content is pulling people in who may later convert via paid.
  • Social engagement and reach: Demand creation platforms build traction, even if the final conversion happens in Google.

Ultimately, owned, earned, and upper-funnel paid activity all create demand; Search and Shopping are there to capture it.

The ceiling isn’t Google Ads – it’s demand

The truth is, this is the direction PPC is heading.

Query growth is flattening, AI search is reshaping how results appear, and brand demand is becoming the real performance lever.

The next time someone says, “Google Ads isn’t driving traffic,” flip the question on them: Was there any demand to capture in the first place?

Because if you’re only running Search and Shopping, you can’t grow beyond the demand that already exists.

The brands that win aren’t the ones squeezing bids and obsessing over CPC swings.

They’re the ones consistently fuelling demand upstream: awareness, SEO, content, influencers, CRM, video, and social – all working together to prime the market.

So when growth stalls, the real question isn’t, “What’s wrong with Google Ads?” It’s “What are we doing to create demand that fuels future searches?”

Read more at Read More

EU puts Google’s AI and search data under DMA spotlight

Google vs. publishers: What the EU probe means for SEO, AI answers, and content rights

The European Commission has formally opened new proceedings to spell out how Google must share key Android features and Google Search data with rivals under the Digital Markets Act.

The Commission on Tuesday opened two formal “specification proceedings” to guide how Google must comply with key DMA obligations, effectively turning regulatory dialogue into a structured process with defined outcomes.

Why we care. The European Commission is escalating its oversight of Google under the Digital Markets Act, with moves that could reshape competition in mobile AI and search — and limit how much advantage Google can extract from its own platforms. If Google is required to share search data and Android AI capabilities more broadly, it could accelerate competition from alternative search engines and AI assistants, potentially fragmenting reach and measurement.

Over time, that may affect where advertisers spend, how much inventory is available, and how dependent campaigns are on Google-owned platforms.

First focus — Android and AI interoperability. Regulators are examining how Google must give third-party developers free and effective access to Android hardware and software features used by Google’s own AI services, including Gemini.

  • The goal is to ensure rival AI providers can integrate just as deeply into Android devices as Google’s first-party tools.

Second focus — search data sharing. The Commission is also moving to define how Google should share anonymised search ranking, query, click and view data with competing search engines on fair, reasonable and non-discriminatory terms.

  • That includes clarifying what data is shared, how it’s anonymised, who qualifies for access, and whether AI chatbot providers can tap into the dataset.

Between the lines. This isn’t just about compliance checklists. The Commission is signaling that AI services are now squarely in scope of DMA enforcement, especially where platform control over data and device features could tilt fast-growing markets before competitors have a chance to scale.

What’s next: Within three months, the Commission will send Google its preliminary findings and proposed measures. The full proceedings are set to conclude within six months, with non-confidential summaries published so third parties can weigh in.

The backdrop. Google has been required to comply with DMA obligations since March 2024, after being designated a gatekeeper across services including Search, Android, Chrome, YouTube, Maps, Shopping and online ads.

Bottom line. The EU is moving from theory to execution on the DMA — and Google’s handling of AI features and search data is becoming an early test of how aggressively regulators will shape competition in the next phase of the digital economy.

Read more at Read More

ChatGPT ads come with premium prices — and limited data

The agentic web is here: Why NLWeb makes schema your greatest SEO asset

OpenAI is pitching premium-priced ads in ChatGPT — with far less data than advertisers are used to getting.

What’s happening. According to a report, OpenAI is pricing ChatGPT ads at roughly $60 per 1,000 impressions — about three times higher than typical Meta ads. Despite the cost, advertisers will receive only high-level reporting, such as total impressions or clicks, with no insight into downstream actions like purchases.

Why we care. ChatGPT is emerging as a brand-new, high-attention ad environment — but one that comes with trade-offs. The high CPMs and limited reporting mean early tests will be more about brand exposure and learning than performance efficiency.

For marketers willing to experiment, this offers a first-mover chance to understand how ads perform inside AI conversations before the format scales or measurement improves.

The tradeoff. OpenAI has left the door open to expanding measurement in the future, but it has publicly committed to never selling user data to advertisers and keeping conversations private. That stance limits the kind of targeting and attribution advertisers expect from platforms like Google or Meta.

Who will see ads. The first ads will roll out in the coming weeks to users on ChatGPT’s free and lower-cost Go tiers, excluding users under 18 and conversations involving sensitive topics such as mental health or politics.

Between the lines. OpenAI is positioning ChatGPT ads as a premium, trust-first product — betting that context, attention, and brand safety can justify higher prices even without granular performance data.

Bottom line. ChatGPT ads may appeal to brands willing to pay more for visibility in a new AI-driven environment, but the lack of measurement will make performance-focused advertisers think twice.

Dig deeper. OpenAI Seeks Premium Prices in Early Ads Push (Subscription needed)

Read more at Read More

Google research points to a post-query future for search intent

Google intent extraction

Google is working toward a future where it understands what you want before you ever type a search.

Now Google is pushing that thinking onto the device itself, using small AI models that perform nearly as well as much larger ones.

What’s happening. In a research paper presented at EMNLP 2025, Google researchers show that a simple shift makes this possible: break “intent understanding” into smaller steps. When they do, small multimodal LLMs (MLLMs) become powerful enough to match systems like Gemini 1.5 Pro — while running faster, costing less, and keeping data on the device.

The future is intent extraction. Large AI models can already infer intent from user behavior, but they usually run in the cloud. That creates three problems. They’re slower. They’re more expensive. And they raise privacy concerns, because user actions can be sensitive.

Google’s solution is to split the task into two simple steps that small, on-device models can handle well.

  • Step one: Each screen interaction is summarized separately. The system records what was on the screen, what the user did, and a tentative guess about why they did it.
  • Step two: Another small model reviews only the factual parts of those summaries. It ignores the guesses and produces one short statement that explains the user’s overall goal for the session.
    • By keeping each step focused, the system avoids a common failure mode of small models: breaking down when asked to reason over long, messy histories all at once.

How the researchers measure success. Instead of asking whether an intent summary “looks similar” to the right answer, they use a method called Bi-Fact. Using its main quality metric, an F1 score, small models with the step-by-step approach consistently outperform other small-model methods:

  • Gemini 1.5 Flash, an 8B model, matches the performance of Gemini 1.5 Pro on mobile behavior data.
  • Hallucinations drop because speculative guesses are stripped out before the final intent is written.
  • Even with extra steps, the system runs faster and cheaper than cloud-based large models.

How it works. Intent is broken into small pieces of information, or facts. Then they measure which facts are missing and which ones were invented. This:

  • Shows how intent understanding fails, not just that it fails.
  • Reveals where systems tend to hallucinate meaning versus where they drop important details.

The paper also shows that messy training data hurts large, end-to-end models more than it hurts this step-by-step approach. When labels are noisy — which is common with real user behavior — the decomposed system holds up better.

Why we care. If Google wants agents that suggest actions or answers before people search, it needs to understand intent from user behavior (how people move through apps, browsers, and screens). This research moves this idea closer to reality. Keywords will still matter, but the query will be just one signal. In this future, you’ll have to optimize for clear, logical user journeys — not just the words typed at the end.

The Google Research blog post. Small models, big results: Achieving superior intent extraction through decomposition

Read more at Read More

From searching to delegating: Adapting to AI-first search behavior

From searching to delegating- Adapting to AI-first search behavior

AI Overviews, which place generated answers directly at the top of search results, are improving the search experience for users. 

For businesses that rely on content to drive traffic from search engines, the impact is far less positive.

Google has been moving toward more “helpful” results for years, and zero-click searches are nothing new. 

AI Overviews accelerate that shift, absorbing much of the traffic opportunity that search has historically provided.

How AI changes the work of search

For years, search followed a familiar pattern:

  • A user entered a short query, such as “team building companies.”
  • Google returned a page of paid and organic results.
  • The user did the work of reviewing and refining.

Most of the effort happened at the end of the process. 

Google organized results based on intent and behavioral signals, but users still had to click through listings, conduct follow-up searches, and piece together an answer.

AI reverses that flow:

  • The user asks a more detailed question.
  • AI runs multiple searches and processes the results.
  • AI delivers a summarized response.

Traditional search allows for refinement, but each new query effectively resets the experience. 

AI, by contrast, is conversational. Each interaction builds on the last, narrowing in on what the user actually wants.

The result is a faster, cleaner path to an answer – with far less effort required from the user.

The path of least resistance

This shift matters because it aligns with a basic human tendency.

People generally choose the easiest available option. If something is easier and produces a better result, adoption follows quickly.

This is how search replaced older marketing channels such as the Yellow Pages.

Seeking the path of least resistance is an evolutionary trait that likely served humans well in earlier eras. 

Today, however, it often shapes behavior in less intentional ways, including how people interact with ads and information.

AI is not perfect, but it is typically faster, easier, and more effective than digging through traditional search results. 

That advantage makes widespread adoption inevitable, especially as AI continues to be integrated into the websites, apps, and devices people already use.

What does this mean for search marketing?

Recent studies have shown that more users are beginning their research with AI tools rather than search engines. 

These studies always have their critics, but the broader point is something of a moot one: AI is everywhere.

AI is now so integrated into the tools people already use that it is becoming the default. 

Search engines, messaging platforms like WhatsApp, and mobile devices are all moving in this direction, and this is just the beginning. 

With Google having signed a multiyear deal with Apple, Google AI will power a significant share of mobile devices, accelerating the shift toward AI-first experiences.

It’s easy to envision an AI-first future, much like the shift from desktop to mobile and then mobile-first.

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What this change actually looks like

Generative answers are shifting where users enter the funnel, with engagement increasingly starting mid-funnel around content that demonstrates experience and expertise.

This is the type of content users historically would only engage with on a company’s website, or through other owned channels such as YouTube.

This does not mean top-of-the-funnel content is no longer important. Blogs, guides, and videos still matter, videos in particular. However, it may be worth reconsidering how that content is distributed rather than relying solely on traditional organic search.

With the rise of AI tools such as Gemini and ChatGPT, users can now handle much of this comparison work through AI, saving significant time.

For example, the shift looks like this:

  • From “Mid market ERP platforms.” Where the user must sift through results, compare options, build spreadsheets, and conduct extensive manual review.
  • To “Which mid-market ERP platforms work best for manufacturing firms, integrate with our existing stack of X, Y, and Z, and won’t collapse during implementation?”

This changes where the user must exert effort.

A more detailed question or input produces a far stronger response or output.

You could argue that traditional search had degraded into a form of garbage in, garbage out (GIGO), where short, generic queries produced ad-heavy, blended results that were time-consuming to mine for real answers.

The result is user fatigue. Endless clicking, avoiding ads, and sorting through widely varying content has become a chore.

And the experience often does not improve once users reach the destination. Traffic-starved, ad-heavy websites can be just as difficult to navigate and extract useful information from.

AI offers a cleaner, faster, and less cluttered experience, delivering summarized pros, cons, and supporting evidence at each stage of the decision-making process.

All of this can happen inside an AI tool, without the user ever needing to visit the site where the content originated.

AI is increasingly becoming the default interface for information. These are still early days, and the experience will continue to improve, becoming faster, smoother, and more effective over time.

The crux of the SEO vs. GEO/AEO/AIO conversation is often that, despite a changing landscape, SEO and GEO are largely the same.

This is broadly true and, if anything, feels similar to the early days of SEO, when long-tail opportunities were real. 

You can now go much deeper with mid-funnel content because it no longer requires humans to read it all. 

Instead, AI can consume it and summarize the relevant parts.

The tactics are largely the same. Much of AI still sits on top of traditional search, but SEO strategies and execution may need adjustment to ensure all bases are covered.

It’s also important not to throw the baby out with the bathwater. 

SEO, PPC, and related channels all retain value in the age of AI.

Dig deeper: SEO, GEO, or ASO? What to call the new era of brand visibility in AI [Research]

How to adapt in an AI-first search environment

The game has changed. Planning for 2026 and beyond requires accepting that change and making practical adjustments to thrive in the age of AI search

Website

In traditional SEO and PPC models, users often land on the most relevant page for their query. 

That may be upper-funnel marketing content that leads deeper into the journey or directly to product or service pages.

This still happens, but there is now a noticeable increase in homepage visits driven by brand searches after AI-based research.

As a result, website navigation and messaging must be exceptionally clear. 

You need to understand user needs and make the path to relevant content as simple as possible.

The ALCHEMY website planning framework can help restructure sites around the expectations of an AI-savvy user.

Content 

In the age of AI, the devil is in the details.

If you want AI to recommend your brand or include it in increasingly nuanced research, your most important content must be visible and accessible so it can be retrieved and used to generate AI answers through retrieval-augmented generation, or RAG.

Frameworks such as “They Ask, You Answer” (TAYA) by Marcus Sheridan are particularly effective here. 

The premise is simple: If customers ask the question, you should answer it.

The framework focuses on five core areas, identified through extensive research, that address customer needs, drive engagement, and provide AI with the detailed information it needs to map to real user questions.

This approach works because it makes sense. It benefits users, improves visibility, drives leads, and supports sales. It is not an abstract AI strategy. It is good marketing.

These are the five key areas that TAYA focuses on:

  • Pricing and cost: If users search for pricing and cannot find it, they do not assume they should call for details. They often assume the product is too expensive or that information is being withheld, and they move on, or ask AI for a competitor’s pricing. Even when pricing is custom, you should explain the factors that influence cost.
  • Problems: Address the obvious issues. This includes problems with your product, your industry, and the drawbacks of specific solutions. Being transparent about limitations builds trust more effectively than excessive positivity.
  • Versus and comparisons: Buyers are choosing between alternatives. If you do not create comparison content, someone else will. Be objective. If a competitor is better for a specific use case, say so and focus on your ideal customer profile.
  • Reviews and ratings: People look for the best options and trust peer opinions more than brand claims. Create honest reviews of products and services in your space, including competitors. This process is informative for both users and brands.
  • Best in class: Users frequently search for “best” solutions. Lists such as “Top AI marketing agencies in [city]” are effective, even when they include competitors. Including alternatives demonstrates that customer fit matters more than self-promotion.

From an AI and SEO perspective in 2026, these five topics represent some of the highest-value data points for RAG systems.

Tools such as the Value Proposition Canvas and SCAMPER can support ideation and content variation, helping AI better understand your offerings.

Checklist: RAG-friendly formatting tips

Do not break content into meaningless fragments. Instead, use formatting that helps RAG systems navigate comprehensive resources:

  • Use question-based headers: Mirror real user questions in H2s and H3s, such as “How much does X cost?”
  • Lead with the answer: Apply the inverted pyramid. Start with the direct response, then add context.
  • Use bulleted lists for attributes: Bullets help RAG systems extract structured information.
  • Define key terms: Provide clear, one-sentence definitions for industry jargon.
  • Link to evidence: Cite sources for statistics and results to support credibility.

Treat blog posts as a knowledge base for AI. The clearer and more specific the information, the more retrievable your brand becomes.

Write for humans, not for bots

It bears repeating: Content should not be simplified solely for AI. 

Google Search Liaison Danny Sullivan has clarified that Google does not want content rewritten into bite-sized chunks for AI consumption.

Modern search systems and RAG pipelines can extract relevant information from well-structured, long-form content. 

There is no need to dilute expertise or create multiple versions of the same page.

A familiar example is being deep-linked to a specific section of a page from search results. This is established behavior, not new technology.

Some formats, such as FAQs, naturally benefit from concise structure. Use judgment based on the question being answered.

SEO v2026.0

These are positive changes. SEO is becoming more closely aligned with marketing and less of a fringe discipline.

The environment is shifting, and new tools are changing how people find information and make decisions. Yet many fundamentals remain.

SEO tactics still apply, but AI now acts as a superconsumer and summarizer of the information that influences choice.

The task is to identify, create, and structure that information so that when users ask a question, you have already answered it and are part of the conversation.

Read more at Read More

Web Design and Development San Diego

Search Central Live is coming back to South America

Search Central Live is coming back to South America! After many successful events in the region,
we’re continuing our mission to help you enhance your site’s performance in Google Search.

Read more at Read More

How to Automate Marketing With 8 Simple Workflows

Everybody wants smoother workflows and fewer manual tasks. And thanks to AI models, automation is at the center of conversations in marketing departments across all industries.

But most rarely get the results they’re looking for.

According to Ascend2’s State of Marketing Automation Report, only 28% of marketers say their automation “very successfully” supports their objectives.

While 69% felt it was only somewhat successful.

How successful is your marketing automation

While this specific stat is from 2024, I imagine the broad idea is still true. Especially since there are so many more automation options and tools. It can get overwhelming to decide a go-forward plan and implement effectively.

So if you feel stuck in the camp of “not bad, but not great” marketing automation, you’re not alone.

The good news?

Once you understand the core building blocks, you can turn messy, half-automated systems into workflows that actually move the needle.

A good marketing automation usually involves four basic steps:

  • A trigger: A catalyst event that starts the automation
  • An action: One or more steps that happen in sequence after the trigger
  • An output: The end result
  • A loop or exit point: A new trigger, or an event that stops the automation

Four Basic Elements of Automation

In this article, we’re going to discuss how to use these steps to automate:

  • The mechanics of content creation (and no, we won’t just be telling you to “write it with AI”)
  • Beyond the basics of email nurtures
  • Your PR strategy
  • Social media engagement

Automate the Mechanics of Content Creation

Content marketers are creative people. We don’t want to automate away the creative work that drives results.

That said, we can automate marketing workflows that come before and after creating. (So we can spend more time on high-impact work.)

Here are some simple ways to get started.

1. Basic Brief Builder

Tools required:

  • Make (free for 1,000 credits per month, paid plans start at $9/month)
  • Your favorite keyword research tool (plans vary)
  • Project management platform (tools like Asana offer a free plan)
  • Google Sheets, Google Docs (free plan available)

Every week, content marketers around the world spend hours researching keywords, pulling search data, creating new briefs, and adding tasks to their project management systems.

What if you could do most of that with one automation?

Here are the basics of how this works:

  • Trigger: A new row is added to a Google Sheet (your new keyword)
  • Action: That keyword is run through your SEO tool, which pulls keyword difficulty, search volume, related terms, and top organic results
  • Output: A new Google Doc with the data inside, and a new task in your project management tool

In the end, the automation will look like this:

How automation look like

And if this seems scary, don’t worry: I’m going to walk you through each step to create this with Make. (Or, you can go ahead and copy this Scenario into your own Make account here.)

First, you’ll need a Google Sheet for your source.

Start with columns for your new keyword, status, brief URL, and task URL. To get started faster, copy this template here.

New Content Ideas Template from Backlinko – Empty

Next, add Google Sheets as the trigger step, and select “Watch New Rows.”

Watch New Rows

After that, select the Google Sheet you want to watch.

Spreadsheet ID

This runs the automation every time you add a new keyword to that sheet.

Now, it’s time to gather information from your SEO tool. For this example, we’re going to use Semrush. (You could also use an API like DataForSEO.)

Our first Semrush module will be “Get Keyword Overview.” (You might see different options depending on the specific tool you use.)

You can choose whether to see the keyword data in all regional databases, or just one region.

Get Keyword Overview

In this task, you’ll map the “Phrase” to the “Keyword” column from your Google Sheet. Then, choose what you want to get as an output. (In this case, I only want to see the search volume.)

Phrase field to the Google sheet

Now, let’s create another Semrush model to “Get Related Keywords” to gather relevant keywords from Semrush.

Again, you’ll map the “Phrase” to the keyword column from our Google Sheet, and choose what data you want to export. (I chose the keyword and search volume.)

Maping "Phrase" to the keyword column

You can also decide:

  • How the results are sorted
  • Whether to add filters
  • How many results to retrieve

Now, you’ll need to add a text aggregator into your workflow. This tool compiles the results from Semrush so we can use them in a Google Doc later on.

Here, simply map the source (our Semrush module).

Then, in the “Text” field, map the data as you want it to appear.

Map the data in the text field

Next, we’ll create a Semrush module that runs “Get Keyword Difficulty.”

Again, we’ll map the “Phrase” to our keyword from the Google Sheet, and choose to export the “Keyword Difficulty Index.”

Phrase Keyword & Keyword Difficulty Index

Next, run the “Get Organic Results” module from Semrush to export the sites that are ranking for your new target keyword.

Select the “Export Columns,” or the data that you want to see, and limit the number of results you get (we chose 10).

Export columns & Limit field

Since we’re getting multiple results, this module will also need a text aggregator to transform those results into plain text for our Google Doc.

We’ll set it up exactly the same way, but this time map the “Get Organic Results” module.

In the “Text” field, I’ve added “Bundle order position” (where that result is ranking in the SERP), and the URL of the ranking page.

Bundle order position & URL

Now, for the fun part.

It’s time to build your basic content brief in a Google doc.

Before you add this into Make, you’ll need to create a Google Doc as a template. This template should have variables that can be mapped to the results you get in your automation.

To show up as variables, you’ll need to wrap them in curly brackets. So, your template will look something like this:

  • Primary Keyword = {{keyword}}
  • Keyword Difficulty = {{difficulty}}
  • Related Keywords = {{related_keyword}}
  • Competing URLs = {{organic_result}}

Variables in curly brackets

(Want to save some time? Copy this template here.)

Now, you’ll create a new module in your Make scenario to “Create a Document from a Template.”

Create a Document from a Template

Once you connect the Google Doc template you created, you’ll see all of the variables you added in curly brackets as fields in the configuration page.

Now, all you have to do is map those variables to the results you’ve gotten from Semrush and your text aggregators.

Values fields

Now it’s time to add this new brief into your project management tool. Make lets you connect several tools, including Asana, Trello, Monday, and Notion.

In this scenario, I already have an Asana project for content production.

So I choose the “Create a Task or a Subtask” module for Asana, and map that existing project.

I can also add project custom fields (like a link to the brief in Google Docs), choose the task name (like the keyword), and automatically assign it to someone on my team.

Brief Link & Task Name

Lastly, I want to go back and update my original Google Sheet so that I can see which keywords have already been run, and where their briefs and tasks live.

So, I add Google Sheets again as the final step in the automation and connect the same spreadsheet that we had at the beginning. Under “Values,” I can map the brief URL from Google Docs and the new task URL from Asana to columns in my spreadsheet.

I also set this so the “Status” column is updated to “Done.”

Status column is updated – Done

Now, let’s run this scenario and see what happens.

First, I add a new keyword to my Google Sheet.

New Content Ideas Template from Backlinko – Add a new keyword

This triggers the automation to run.

The first thing that’s produced is a brand new Google Doc with all of the SEO data from Semrush. You’ll see this new doc appear in your Drive, and you’ll find the link in Asana.

Brand new Google Doc

Next, I’ll see a new task appear in my Asana project (with the brief link included).

New task appear in Asana project

And finally, the Google sheet will be updated to show us that the task has been completed.

Plus, it adds in the links to the new brief in Google Docs and the new task in Asana.

New Content Ideas Template from Backlinko

And there you go: you now have a basic content brief builder automation.

Are these complete briefs? No. But the information provides a great start, gives the writer SERP context, and frees up more time to fill out other important content brief elements.

 

2. Content Workflow

Tools required: Your favorite project management tool (paid or free options available)

Project management tools are great for organizing your content workflow.

But the more tasks you create over time, the harder it is to keep track of and manage those systems.

Many project management platforms give you built-in automation tools to help things run more smoothly. Let’s talk about automations that can help your content workflow specifically.

Triggers might include:

  • A new task is added to a project
  • A custom field changes
  • A new assignee is added
  • A subtask is completed
  • Due date is changed (or coming up soon)
  • A task is overdue

And actions could be:

  • Add to a new project
  • Auto-assign to a team member
  • Update a status
  • Move task to a new section
  • Create a subtask
  • Add a comment

For this example, we’re going to use the Rules system in Asana, but the same basic principles apply to almost any major project management tool.

To start, click the “Customize” button in the upper-right corner of your content management project, and create some custom fields.

Rules system in Asana

Especially important here is the “Status” field. The options here should follow the steps in your content process, and will probably mirror the sections in your Project.

Edit field – Save changes

Once your “Sections” and “Fields” are set up, you can create some rules.

These can help dictate what happens when a new brief enters your content workflow and assign it to whoever is in charge of moving it forward in the process.

Use a Rule to auto-assign someone on your team (for example, your content manager or editor) to the task.

Use a rule to auto assign someone on your team

Now, let’s say a new article is now in progress with a writer.

Create a rule that moves the task to the corresponding section of your project when the status is set to “Writing.”

Move the task to corresponding section

If your content tasks have subtasks (like “create outline,” “write article,” “edit,” or “design”), you can track completion and use that to move pieces forward.

In this case, you can set a rule that once all subtasks are complete, the task moves to the “Ready to Publish” section.

Ready for publishing

Once the task moves to that section, set a rule to auto-assign it to the team member who publishes posts.

Set a rule to auto-assign

Then, when the status is set to “Published,” the task could be moved into a separate project where completed tasks of published content are stored.

This allows you to clear the tasks from your main production workflow, but still keep them on hand in case the piece needs to be updated in the future.

Published Content

What if a piece of content isn’t completed by its deadline?

Set up an automation that checks in with the team to see what the status is.

Automation that checks

There are plenty of other automations you can run in Asana or other tools.

But these basic workflow automations will help your content production process have better handoffs and less friction.

We do this at Backlinko using Monday.com as our project management tool.

Semrush – Monday board

Read more about how we scale content creation here.

Go Beyond Basic Email Nurtures

Email nurtures are relatively easy to put together in any basic email tool: for example, sending a welcome email to a new newsletter subscriber, or a transactional email to a new customer.

But let’s talk about some ways to take those automations even further.

Email marketing automation involves:

  • A trigger: Such as someone signing up for an email list
  • An action: The new contact is added to a list or segment
  • An output: They new receive a series of pre-made emails
  • An exit condition: The sequence finishes once all the emails are sent, or once the contact takes a specific action, like buying a product

Essential Elements of an Email Nurture

Exit conditions are especially important, because you don’t want people to receive another email from you after they’ve already completed an action. (Hello, promo email that arrives after I already made a purchase.)

Let’s walk through how to use marketing automation tools for email.

3. Behavior-Based Nurtures and Follow-Ups

Tools required: ActiveCampaign (paid plans start at $15/month, although other email platforms offer automation capabilities too)

When you trigger an email sequence based on real behavior, you’re catching people in the moment when they’re more likely to engage.

For example, if you want to help a new user get to know your platform, you can trigger onboarding emails based on the actions they’ve taken so far.

Or, if you want to reduce cart abandonment, you can send a special promotion for customers who have items in their cart.

This improved targeting can lead to better engagement from your email list.

All you have to do is match the right trigger to the right action. For example:

Trigger Action
Someone downloads a resource They receive a series of emails on that topic
A customer purchased a product a few months ago They get a reminder to replenish their stock
A contact browses a product category, but doesn’t make a purchase They get an email reminding them of what they looked at
A new user subscribes to your platform They get a series of emails walking them through specific actions

Your exit condition could be when the person:

  • Completes their purchase
  • Books a call
  • Starts a free trial
  • Replies to your email

For example, let’s say you want to send a series of emails reminding someone that their subscription is reaching its end date. It could look something like this:

  • Trigger: End date is within 20 days from now
  • Action: Send series of three emails up to the last day of their subscription (we don’t want to send too many)
  • Exit condition: Customer responds to the email, or renews their subscription

Here’s a great example for home insurance renewal:

Home Insurance Renewal

​​Or, let’s say a new lead just signed up for a free trial or freemium account.

You could create a workflow that pulls information from the onboarding survey in your tool, and builds a personalized, 1:1 email sequence.

Check out this example from HubSpot:

Example from HubSpot

When I signed up for the account, I identified myself as a self-employed marketer. HubSpot pulled that information into this new trial campaign to make the email even more personalized.

So the question is: how do you get started?

Here’s a quick overview of how you could build a behavior-based email nurture automation in ActiveCampaign.

Let’s say you want to send an email sequence to a known contact who visited a certain page on your website. For example, imagine someone who subscribes to your email newsletter, but isn’t a customer, just visited your pricing page. (In other words, they may be close to signing up — they just aren’t quite convinced yet.)

Before you start this automation, you’ll need to enable Site Tracking on your account in ActiveCampaign. To do this, install the tracking code on your website so ActiveCampaign can see page views.

Enable Site Tracking in ActiveCampaign

To start the automation, you’ll add new contacts who enter through any pipeline.

Now, when a known contact (someone who’s already in your database) visits a tracked page, ActiveCampaign associates that page view with the contact’s record, and can start an automation.

Contact enters a pipeline

The real trigger is the next step: “Wait until conditions are met.”

Wait until conditions are met

In this case, the condition is that the contact has visited an exact URL on your website.

Pro tip: You can also adjust this so the email series only runs when the person visits a page multiple times, showing a higher level of interest.


Next, set a waiting period from the time the person sees the page to when the email is sent.

And finally, write your email and add it to the workflow.

Add your email to the workflow

After that, you could:

  • Wait a certain amount of time, then send another email
  • Set an exit condition if the contact replies or makes a purchase

All of this effort turns into an email like this one that I received from Brooks after visiting one of their product pages:

Brooks email after visiting their product page

This makes me way more likely to revisit the shoes I was looking at than a generic reminder email (or no email at all).

4. Webinar Lifecycle Automation

Tools required:

  • Demio (plans start at $45/month)
  • HubSpot (limited free plan available)

Webinars are an entire customer journey, including promotion, confirmation, reminders, and post-event follow-ups.

The trigger is normally one event: Someone signed up for your webinar.

The actions include:

  • Confirmation email
  • Day before and day-of reminders
  • “Happening now” email
  • Post-event replay email

For example, here’s a great reminder email from Kiwi Wealth:

Great email reminder

Immediately after the webinar is finished, you might send an email like this one from Beefree:

Email from Beefree

And you’ll also want to follow up later with a replay and some action items for people who attended, like this:

Action items for people who attended

Note: We got these examples from Really Good Emails, which is a great resource for getting inspiration for your own campaigns.


So, how do you create this automation?

Most great webinar tools allow you to do this. Demio, for example, allows you to automate marketing emails when you create a new event:

​​

Demio – Automate marketing emails

If you want to get really fancy, you can segment your post-webinar follow-up emails by whether or not the contact attended the webinar:

Segment your follow-up emails

Demio’s built-in email is somewhat limited beyond an actual event.

So, you can connect it to HubSpot to add a new layer of segmentation to your lists.

Connecting Demio to HubSpot

Once this connection is live, Demio will import webinar attendance data into HubSpot.

Demio import data into HubSpot

For example, you can import data like:

  • Contacts who registered for the webinar
  • People who registered, but missed the event
  • People who attended the event
  • How long a contact stayed in the webinar
  • People who watched the replay

HubSpot – Field Mapping

You can even add new contacts to lists directly in Hubspot if they don’t exist there already.

Add new conctacts to lists in HubSpot

This automation will help your pre- and post-webinar flows run more smoothly. And hopefully get you more valuable engagement with those webinars.

Grow Your PR Strategy

For small marketing teams, PR outreach can use up a lot of valuable time.

Here are some easy automations to keep doing inbound and outbound PR requests, without spending your entire week on it.

Resource: Get your free PR Plan Template to help you pick the right goals, discover journalists, and make pitches that get press coverage.


5. PR Radar

Tools required:

  • BrandMentions (paid plans start at $79/month)
  • Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
  • Google Sheets (free option available)

Want to keep an eye on new articles that are related to your brand that you could potentially get featured in or a backlink from? Let’s build an automatic PR radar.

Note: Most monitoring tools send alerts, but those notifications disappear into your inbox. This workflow creates a shared, searchable log your whole team can access without extra logins—plus you’ll have a historical record for spotting PR trends over time.


This workflow looks like:

  • Trigger: A new article mentions your brand or related topics
  • Action: Pull all new mentions into one place to scan through them easily
  • Output: A simple, regularly-updated list of PR mentions

There are several tools that do this, but for this example, we’re going to use BrandMentions.

Once you set up your account and your project, head into settings to adjust which sources you’ll collect data from.

Remove social media, and just leave the web option. That way, you’ll get a clean list of articles and webpages that mention your brand or the keywords you added.

BrandMentions – Settings

Once this is set up, you can connect your BrandMentions project to Zapier.

This will trigger the automation to start when any new mentions are added.

You can choose whatever output works best for you: whether that’s a Slack message, a new row in Airtable, or an addition to an ongoing Google Sheet.

For this example, I chose Google Sheets as my output. All I had to do was tie the data pulled from BrandMentions to the right columns in my spreadsheet.

From BrandMentions to Google Sheets

Once that’s done, the automation adds new articles like this automatically into my spreadsheet:

Automation ads new articles into spreadsheet

Pro tip: Want to add a reminder? You can add another step that sends a daily Slack message summarizing all the newly added rows.


6. Media Request Matchmaker

Tools required:

  • RSS.app (free plan available)
  • Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
  • Airtable (free plan available)

PR would be nothing without the relationships we build with journalists and writers.

But it’s hard to know who’s writing about a topic that’s related to your brand. Or where your company’s internal subject matter experts can add their thoughts to promote your brand.

So, let’s build an automation to match new requests to your internal experts.

This involves:

  • Trigger: A new media request that matches relevant topics
  • Action: Classify new requests and match them to the internal expert with the most relevant expertise
  • Output: New requests are automatically routed to the right person

One of the most frequently updated places to find PR requests is on X/Twitter.

Search the hashtag #journorequest, and you’ll see hundreds of writers asking for expert contributions.

X – Hashtag – #journorequest

To prepare this for your automation, start by setting up an RSS feed with the hashtag #journorequest or #prrequest along with a relevant keyword.

You can do this for free with RSS.app.

Setting up an RSS Feed

Then, you’ll get results like this:

RSS app – Results

For the simplest version of this, you can connect RSS.app directly to Slack and send a new message every time a new request is added to the feed.

But let’s be real: that could get overwhelming pretty quickly.

So, we’ll use Zapier for a more in-depth automation.

Start by adding “RSS by Zapier” as the trigger, and paste your RSS feed link into the configuration.

RSS by Zapier

Pro tip: If you want to track journo requests for multiple topics, change the trigger event to “New Items in Multiple Feeds.” Then, simply paste in all of the RSS feed links. That way, they’ll all run through the same automation.


Next use “Formatter by Zapier” to extract the necessary information from the tweets.

First, in Formatter, choose the Action event “Text.”

Then, in the Configure menu, select “Extract Email Address,” and map the input to the description from your RSS feed.

Extract email address

Next, with another Formatter step, select “Text,” and “Extract Pattern.”

The input is still the same description (the original tweet).

In the Pattern box, in parentheses, add the keywords you want to track separated by a vertical bar, like this:

code icon
(cybersecurity|fintech|pets|saas)

Make sure that IGNORECASE is set to “Yes” so that the search isn’t case sensitive.

Ignorecase is set to yes

Now, it’s time to add that to a system you can use to keep track of new requests and route them to SMEs.

For this example, I’ve chosen to use Airtable. If you want to use this exact database, you can copy it here and we’ll use it as we move forward.

Airtable – PR Requests – SMEs

This database has tabs to keep track of your SMEs, the topics they can respond to, and the new requests that come in.

So, let’s connect that Airtable base to Zapier.

Our first step will be to find the right SME for the topic of our journo request.

To start, set the Action as “Find Record,” and link your Airtable base. We’ll pull from the SMEs table, and for “Search by Field” we’ll choose “Topics,” where we’ve previously added our SME’s favorite topics into the Airtable base.

Lastly for this step, map the “Search Value” to the previous step’s result (the topic from the PR query on X/Twitter).

Map the Search Value

Now, we’re going to create a new row in our “Requests” table in Airtable.

Add Airtable as the next step in this Zap, and select “Create Record” as the action. Link the same Airtable base, but this time select “Requests” as the Table.

Then, map the columns in that base to the information you’ve gathered. In this case, that would include:

  • Source = X/Twitter
  • Raw Text = The “Description” from RSS feed
  • Contact name = The “Raw Creator” from RSS feed
  • Contact Email = The output from our first Formatter step, which pulled the email from the original post
  • URL = Link from RSS feed
  • Topics = The output from our second Formatter step, which pulled the topic from the original post
  • SMEs = The “Fields Name” from our Airtable search step
  • Status = New

In the end, it should look like this:

Information fields

And a new record is added into Airtable, like this:

Record is added into Airtable

If you want to get fancy with this, you can dig down into:

  • Which publications are requesting expertise, and rank them by their credibility
  • Automate messages to your SMEs to let them know there’s a new request for them

Get the Most Out of Social Media

For busy marketers, social media can be an incredible time-suck.

Keeping track of trends. Trying to post consistently.

All without getting stuck in an infinite doomscroll.

But a few simple automations can help you get back some of the time you spend on manually managing your socials.

7. Video Clip Automator

Tools required:

  • Zoom (free plan available)
  • Dropbox (free plan available)
  • OpusClip (plans start at $15/month)
  • Zapier (free for 100 tasks/month, paid plans start at $19.99/month)

Short-form video has been gradually gaining a bigger voice in marketing.

In fact, 39% of marketers said that videos under 60 seconds are the most effective.

The problem: they take time to make.

If you’re already creating long-form video (or even just doing recorded interviews with in-house experts), we have a handy automation to help you create video clips faster.

Here’s how it works:

  • Trigger: New Zoom cloud recording is ready
  • Action: Auto-create clips, burn captions, and create a new task in Asana
  • Output: You get social-ready video clips, and a new task to publish them

First, adjust your Zoom settings so your recordings upload automatically into a folder in Dropbox.

Adjusting Zoom Settings

Next, head over to Zapier.

Your trigger step will be a new video uploaded to that folder in Dropbox.

Clip your video by OpusClip

Your next step will use OpusClip, an AI video editing tool. Select “Clip Your Video,” and map that new video file to the one uploaded in Dropbox.

Dropbox step – Video file

OpusClip will then take your long-form video from Dropbox and use AI to clip key pieces. It also crops the video for vertical sharing and embeds captions.

You can also add your own brand template so that videos are edited with your brand’s colors and font.

Now that you have new video clips to share, it’s time to add a task to review and publish them.

So the final step in your Zap is “Create Task” in Asana (or your preferred project management tool).

Create task in Asana

You’ll tie this to a project you’ve already created in Asana, and link the project ID from OpusClip.

In the end, you’ll have a few video clips prepared and ready — all you have to do is download, review, and publish them to your social channels.

8. Comment & Community Nudge

Tools required:

  • Social media monitoring tool (like BrandMentions, paid plans start at $79/month)
  • Automation tool (like Zapier, free for 100 tasks/month, paid plans start at $19.99/month)

Are people talking about your brand online?

To keep positive sentiment high, you need to engage in those conversations. But finding the right conversations, and knowing how to reply, can take a lot of time.

Using a tool like BrandMentions, you can create a similar automation to what we built for the PR Radar earlier:

  • Trigger: A new mention of your brand appears on Reddit, Facebook, or LinkedIn
  • Action: Those new mentions are added to a Google Sheet, and you get a daily Slack message summarizing new mentions

To build this, all you’d need to do is swap out the Sources in your BrandMentions settings. Instead of Web, you’d include all of the social media channels you want to track.

BrandMentions – Social Media

After that, you can build an automation with Zapier, the same way we did in the PR Strategy automation above.

If you want to get notifications for every new mention, you could connect the workflow to Slack. Then, a new message will be sent in the channel every time your brand is mentioned.

Notification for every new mention

This basic automation could work for smaller brands.

But when you start getting hundreds of mentions per day, this will quickly become chaotic.

Here’s an example of how one company faced with this issue was able to automate this process in a deeper way:

Webflow was getting over 500 mentions per day. Their two-person team couldn’t keep up with monitoring and responding (alongside their regular workload).

So, they built an automation.

With Gumloop, they monitor, analyze, and flag only the posts that require a response.

They started with a Reddit scraper to pull relevant threads.

Starting with Reddit

Then, they added an AI analyzer to gauge sentiment, rank priority, and assign a category.

AI Categorizer node

After that, they added a step that would send all high-priority mentions to Slack for a team member to handle directly.

High priority post

The result?

After testing and scaling this process, they were able to build an automation that processes 500+ mentions per day and escalates only the 10-15 that need immediate attention.

If you’ve ever thought, “How can I use AI to automate my marketing tasks?”

This is a great example of an AI automation that works for you without taking over your job.

Is Automation the Right Move? Ask Yourself These Questions First

Automation is the hottest trend.

But it’s hard to know what’s going to save you time and money, and what’s just another fad.

If you’ve ever spent more time trying to automate a task than it would’ve taken you to do the task manually, you’ll know what I mean.

To weigh up whether an automation is worth building, ask yourself these questions:

  • How much time does it take me to do this task manually every week?
  • Is the automation available with a tool I currently use, or would I have to pay for a new tool?
  • Is there a documented automation/integration I can follow?
  • Would this task still require human intervention (even with automation)?
  • Does this fit easily into our current workflow or process?

If the task:

  • Doesn’t take much time to do manually
  • Would still require human intervention even when automated
  • Isn’t easy to build an automation for

…it may not be worth your time.

On the other hand, if the task:

  • Is repetitive
  • Uses up hours of your workweek
  • Can be automated in tools you already have in your stack

…it’s probably time to give automation a try.

Build Your Automation Foundations, Then Keep Growing

The hype cycle of automation and AI can be overwhelming.

But don’t feel like you’re behind just because you haven’t automated away your entire marketing team yet.

Instead, focus on the automations that save you time and are sustainable.

We’ve just discussed eight different automations. Why not choose one or two that are most relevant to your business and team?

Start with the foundational automations that help smooth out your existing processes.

Then, you’ll have a better basis for building more complex automations.

To automate even more areas of your marketing workflows, check out our curated list of our favorite AI marketing tools right now.

The post How to Automate Marketing With 8 Simple Workflows appeared first on Backlinko.

Read more at Read More

What Is Google AI Mode and How Does It Work?

Does Google’s AI Mode mark a real shift in how search works? There’s a strong case that it does. And all businesses with an online presence need to pay attention, not just SEO folks. 

Given how big the change is, you likely have a lot of questions. 

What does AI Mode mean for your site traffic? How do you get featured? Do you need to change your content strategy? What happens to organic visibility as AI-generated answers become more common?

If you’re feeling uncertain, don’t worry. This guide breaks down what Google AI Mode actually is, how it works, and what it means for your site.

Key Takeaways

  • Google AI Mode is a search experience that builds on AI Overviews, offering deeper answers, reasoning, and more personalized responses.
  • AI Mode is currently available in English, with rollout expanding beyond early U.S. testing.
  • Users can access AI Mode directly from the Google homepage, where it functions through a conversational, ChatGPT-style interface.
  • Appearing in AI Mode is largely driven by strong SEO fundamentals, but brand mentions, structured data, and off-site signals play a growing role.
  • While AI Mode changes how results are presented, early data suggests users still click through to source content, especially for complex or high-consideration topics.

What Is Google’s AI Mode?

AI Mode is a search feature from Google designed to give direct, well-reasoned answers to complex queries. It builds on AI Overviews but uses a similar process that combines AI-generated responses with content from traditional search results and the Knowledge Graph (Google’s database of factual information). 

It runs on a modified version of Gemini, Google’s core AI model, and analyzes information from multiple sources. It then synthesizes this information into a clear, concise answer that prioritizes reasoning and context, rather than just summarizing pages.

The interface feels a lot like an AI Overview—same layout and a similar answer—but with a box to ask follow-up questions at the bottom.

Google AI Mode example with the definition of what Google AI Mode is.

Here’s what Robby Stein, Google’s VP of Search, said about AI Mode in a post on The Keyword:

“Using a custom version of Gemini 2.0, AI Mode is particularly helpful for questions that need further exploration, comparisons and reasoning. You can ask nuanced questions that might have previously taken multiple searches — like exploring a new concept or comparing detailed options — and get a helpful AI-powered response with links to learn more.”

AI Mode integrates several elements from traditional search engine results pages (SERPs), such as Shopping listings and Maps.

Google AI Mode with a map of New York pizza places.

Finally, Google has said that it will continue to add new features. These include agentic workflows in conjunction with Project Mariner, increasing levels of personalization, and even custom charts and graphs. 

AI Mode Is Becoming an Interactive Application Layer

Google is actively turning AI Mode into a more interactive part of search, not just a place to read AI-generated answers.

Recent updates already point to deeper personalization, richer inline links, and more interactive result formats, including charts, comparisons, and visual outputs. With Gemini 3 now integrated directly into AI Mode, those interfaces are becoming more dynamic and tool-driven instead of purely informational.

 “We spend a ton of time focused on this question of when and how to show links, and how we can really make the web shine. It will continue to be an ongoing effort as AI Mode and the Search Results Page evolves,” says Stein.

Links in a Google AI Mode result.

This shift matters. Rather than sending users to external calculators, templates, or apps, Google is starting to surface that functionality directly inside search. For certain queries, AI Mode can simulate outcomes, compare options, or guide users through multi-step decisions without requiring a click to another site.

A graphic in a Google AI Mode result.

Over time, this opens the door to agent-driven experiences. In those scenarios, AI Mode does not just explain an answer. It helps users complete tasks, from planning and analysis to evaluation and execution, inside the search interface itself.

As Gemini becomes more tightly integrated across Search, AI Mode is moving closer to a default experience. For brands, this raises the bar. Content that wins in AI-first search needs defensible value, interactive depth, or proprietary insight, not just basic information.

How to Access Google’s AI Mode and Availability

Google AI Mode is now available beyond early U.S.-only testing, with a broader global rollout underway. Users accessing Google in supported regions can enter AI Mode directly from the Google homepage, where it appears alongside the main search experience rather than as an experimental feature.

Screenshot of the main Google search page.

When users tap “show more” on certain AI-generated results, the AI Overview expands. Once in the expanded AI overview users can click “Dive Deeper in AI Mode” to enter AI mode. This signals a shift toward AI Mode acting as a default exploration layer, not a separate destination.

Diving deeper in a AI Mode result.

Once inside AI Mode, users can interact with responses conversationally, asking follow-up questions that carry context forward. Links to supporting pages remain available, and users can access their “AI mode history” once inside AI mode, so they can continue conversations that they previously started. 

AI Mode history.
AI mode history.

Google has moved away from positioning AI Mode as a Labs experiment, and there is no longer a separate opt-in process. Access is tied to Google’s standard search interface, and availability is expanding as Google refines performance, localization, and personalization features.

Timeline of Google AI Mode

While most people think of AI as starting with ChatGPT, Google’s been building AI tools for decades. 

AI Mode is part of Google’s broader family of AI tools, which include Veo, a video maker, Imagen, a text-to-image model, Project Mariner, an agent that can automate tasks, and others. 

Here’s a short timeline that puts AI Mode in context:

  • May 2017: CEO Sundar Pichai announces the launch of a dedicated AI division called Google AI at I/O, the company’s annual developer conference. 
  • March 2023: Google opens up early access to Bard, its first gen AI chatbot. It is rolled out globally several months later. Global availability follows later that year.
  • December 2024: Google announces Gemini, a multimodal LLM that can work with different content inputs (images, voice, and text). 
  • February 2024: Bard is coupled with Duet AI, Google’s Workplace AI assistant, and rebranded to Gemini.
  • May 2024: AI Overviews, initially called Search Generative Experience, are first released.The feature reaches broad availability later in the year, combining generative AI with Google’s traditional information retrieval systems.
  • May 2025: Google releases AI Mode, a ChatGPT-style interface available on its homepage. It builds on the core functionality of AI overviews. It is available only in America.  Early access is limited, but usage expands rapidly.
  • August 2025: Google begins a more comprehensive global rollout of AI Mode, signaling its transition from a test experience to a core part of Search. Google also announced that they’re increasing the number of links in AI mode.  Searchers begin to see inline link carousels and contextual introductions explaining why a link might be useful to visit.
  • November 2025: Google integrates Gemini 3.0 and Nano Banana in AI Mode.

Using AI Mode: AI Overviews vs. AI Mode

Time for the unboxing. To illustrate how AI Mode differs from AI Overviews, consider a simple comparison scenario.

First, a general query is entered into standard Google Search: “What will be the most popular spring break destinations this year.” This triggers an AI Overview.

Google search results for "What will be the most popular spring break destinations this year."

AI Overview analyzes the query, considers general context such as location, and pulls information from multiple sources, stitched together into a quick summary. 

Next, the query becomes a bit more specific: “what will be the most popular spring break destinations this year with a 6-month-old baby.”

AI Overview adjusts the response based on the added constraint, returning suggestions that better match the scenario while still relying on summarization.

Google search results for "what will be the most popular spring break destinations this year with a 6-month-old baby."

The same queries are then entered into Google’s AI Mode using the dedicated prompt box.

The initial response looked similar but for a subtle shift. Instead of simply summarizing existing information, AI Mode applies additional reasoning to evaluate suitability and trade-offs.

Google AI Mode results for "What will be the most popular spring break destinations this year."

A follow-up question is then added without restating the full context.

AI Mode retains the earlier details, understands the added nuance, and returns a more detailed, logically structured set of recommendations. This ability to carry context forward highlights one of the key differences between AI Mode and AI Overviews.

Google AI Mode results for "what will be the most popular spring break destinations this year with a 6-month-old baby."

How Is AI Mode Different from AI Overviews and Gemini?

Simply put, AI Mode is an expanded version of AI Overview. It incorporates and builds on features of AI Overviews, and both of these run on Gemini, which is Google’s core model. 

Here’s how AI Mode compares to AI Overviews:

  • More advanced reasoning: While AI Overview summarizes information from across sources, AI Mode interprets that information, connects related concepts, and surfaces conclusions based on reasoning rather than aggregation alone.
  • Multimodal understanding: In the Google app (on Android and iOS), AI Mode can also answer questions based on photos and images. 
Meet AI Mode landing page.
  • Better handling of complex questions: AI Overview works well for simple, fact-based queries, but AI Mode is designed for nuanced, multi-layered, or exploratory questions that benefit from context and comparison.
  • Follow-ups: You can ask follow-up questions, and the AI will respond based on the ongoing context in a conversational style.

AI Mode is also evolving in how it presents sources. Searchers increasingly see inline links, carousels, and contextual explanations that clarify why a particular source may be useful, rather than a static list of citations.

Research conducted by NP Digital shows that these features match emerging user demand. We found, for example, that 72% of people are inputting very precise, “exactly what I want” queries. And 76% are opting for more human-like and conversational interactions. 

NP Digital Graph showing search trends by generative AI.

What Is the Technology Behind AI Mode?

LLMs are vastly complex entities, and Gemini, the model that powers AI Mode, is no different. However, three main technologies separate AI Mode from standard gen AI bots and AI overviews. 

Here are the three core processes that power AI Mode: 

  • AI Mode uses a query fan-out technique. This involves breaking a query into subtopics and researching them in parallel. It then combines dozens of information points into a single answer. 
  • Structured logic is a key part of how AI Mode works. It takes a query and then creates a reasoning chain (e.g., “user is looking for a water bottle for hiking, therefore features should include durability and size, therefore a minimum capacity of 3 liters is needed, etc.) and then validates answers against these steps to determine suitable outcomes. 
  • Personal context plays a significant role. This means that AI Mode records conversations over time and builds a picture of individual user preferences, adjusting responses based on past inputs. It does this by creating a sort of digital ID—called a vector embedding—that is included in the answer generation process. This is a form of background memory that works in much the same way as ChatGPT.

How to Optimize Your Site for AI Mode

So-called GEO—generative engine optimization—is big business at the moment. However, there’s still a lot of uncertainty about what directly influences visibility in AI Mode, and many claims go beyond what Google has actually confirmed.

Rather than chasing shortcuts, the clearer pattern is that AI Mode rewards the same fundamentals Google has emphasized for years — with a few emerging signals becoming more important as AI-generated results mature.

Let’s look at what we actually know about “ranking” in AI Mode.

1. Traditional SEO principles still apply

Google has been pretty unequivocal about this. Traditional SEO optimization is still the most important activity for appearing in AI Overviews and AI Mode. 

As long as you follow SEO basics—create useful content, generate natural backlinks, and optimize technical health—you’re ahead of 90% of the competition. 

Research also backs this up. Ziptie, for example, found that sites with a number one ranking in traditional search results are 25% more likely to be featured in AI Overviews. 

2. Indexed web pages are eligible to appear in AI Mode

On the technical front, there’s good news. As long as a page is indexed, it’s eligible to appear in AI Mode. There are no other requirements. You can check your pages are indexed using the URL inspection tool in Search Console. 

If you’re having issues, be sure to check you’re adhering to Google Search technical requirements. Make sure Googlebots aren’t blocked, pages return 200 success codes, and content doesn’t violate spam policies.

3. Forum and discussion board citations matter

Recent analysis across multiple large language models shows that discussion forums and Q&A platforms are frequently referenced when generating explanatory or opinion-based answers, particularly for queries that benefit from lived experience or peer discussion.

Reddit, in particular, continues to surface prominently across AI-generated responses, in part due to its scale, freshness, and breadth of first-hand commentary. However, the weighting of any single forum is dynamic and continues to evolve as Google refines how AI Mode sources and cites content.

Given Reddit and Google’s partnership, it’s likely that well-moderated, high-signal community content remains an important input for Gemini-powered experiences.

If you haven’t already, build up a presence on Reddit and other similar forums and discussion boards. This can help reinforce topical authority and increase the likelihood of being referenced in AI-generated answers.

4. Schema markup (structured data) gives you a boost

Schema markup, also called structured data, is a type of code that you add to your content. It gives search engines and AI systems additional information to help them understand what it’s about. One simple example of schema markup is identifying a recipe as “@type”: “Recipe.”

Research by Aiso has shown that LLMs extract more accurate data from pages with schema markup, with a 30% improvement in quality. 

Using schema markup helps reduce ambiguity for AI-generated answers and increases the likelihood that your content is interpreted correctly. Fortunately, adding schema to your web page is relatively straightforward.

5. Digital PR is important

LLMs access information in two ways. They are initially trained on a large amount of information—called training data—and they can also access new online content, such as news articles. 

Digital PR is all about acquiring mentions and backlinks from reputable third-party sources, especially media websites. 

Brand mentions boost visibility in LLM training materials and strengthen topical associations (a measure of the number of times you’re cited in relation to a specific subject), meaning you’re more likely to appear in responses. 

Digital PR involves creating share-worthy content and contacting journalists and site admins to ask them to feature you. Our research shows that original research and tools are especially good at encouraging people to talk about your brand. 

NP Digital graph showing how different content formats are proven to generate links.

6. Be Ready To Test and Track AI Visibility

As AI Mode becomes more integrated into the search experience, visibility is no longer limited to rankings alone. Brands need ways to measure whether — and how often — their content appears in AI-generated answers.

New AI visibility platforms, such as Writesonic and Profound, are emerging to help track citations, brand mentions, and source inclusion across large language models. These tools provide early signals about which content formats, topics, and entities are being surfaced by AI systems.

Monitoring this data allows teams to validate whether SEO, digital PR, and structured data efforts are translating into real AI exposure. It also makes it easier to spot gaps, test changes, and adapt as Google continues to evolve AI Mode.

Treat AI visibility tracking as a complement to traditional performance metrics, not a replacement. Both matter.

What Does AI Mode Mean for the Future of Search?

There are a lot of unknowns about how increased use of AI tools will affect the way people look for information. That said, emerging usage patterns are already pointing to meaningful shifts in how AI SEO is evolving.

With that in mind, here are five implications for the future of search as AI Mode becomes more prominent:

Searchers will still click through to websites: Early performance data from AI-generated results shows that clicks are reduced for some informational queries, but not eliminated. Users continue to seek out original content, particularly for complex decisions, comparisons, and high-consideration topics.

NP Digital graph showing the impact on clicks to websites from Google integrating AI.

Long-play brand building will become more common: LLMs use third-party brand mentions to measure the authority of publishers. Popular brands are cited more by gen AI search tools and, as such, long-term brand building with an outlook of five years and above will become much more common. 

NP Digital graphic showing the length of time to build a recognizable brand.

Marketing strategies will become more omnichannel: As AI Mode absorbs more discovery queries, brands will need visibility across multiple platforms, not just Google’s traditional results. This reinforces a broader “search everywhere” approach, where discovery happens across AI tools, social platforms, and communities.

NP Digital graph showing the number of daily searches per platform.

People will favor AI for more specific searches: Analysis of large query sets shows that AI-generated results appear more frequently for longer, more specific searches. Short, navigational queries may still rely on traditional results, while nuanced questions increasingly trigger AI Mode.

NP Digital graph showing the frequency of AI overviews by search query length.

Trust in AI will continue to grow: Hallucinations are a big problem with AI Overviews and AI Mode also makes mistakes, according to user reports. With that said, user adoption and satisfaction with AI-powered search tools are trending upward. As Google refines AI Mode, usage is likely to grow alongside improvements in reliability and transparency.

NP Digital graph showing the user satisfaction with AI overviews over time.

FAQs

What is Google AI Mode?

Google AI Mode is a conversational search experience powered by Gemini, Google’s core AI model. It provides more detailed, context-aware answers to search queries, similar in format to tools like ChatGPT, but integrated directly into Google Search.

Instead of returning a list of links first, AI Mode synthesizes information from multiple sources and presents a reasoned response, with links available for deeper exploration. Users can ask follow-up questions, and the system carries context forward, making the interaction feel more like an ongoing conversation.

AI Mode builds on AI Overviews but goes further by handling complex, multi-step, or exploratory queries more effectively.

How do you use Google AI Mode?

In supported regions, users can access AI Mode directly from the Google homepage. On some AI-generated results, selecting “show more” will also open AI Mode automatically, allowing users to continue their search without returning to traditional results.

Once inside AI Mode, questions can be entered conversationally, and follow-ups don’t require repeating the original context. Users can still click through to source pages or switch back to standard search results at any point.

AI Mode is no longer accessed through Google Labs, and there is no separate opt-in process.

How do you optimize your website for Google AI Mode?

Start with strong SEO fundamentals, which Google has confirmed remain the primary eligibility signals. Beyond that, sites that appear most often in AI-generated answers tend to share a few traits:

  • Create useful, high-quality content that fully addresses search intent.
  • Make sure pages are indexed and technically accessible
  • Use schema markup to clarify meaning and structure
  • Earn third-party brand mentions from trusted publishers and communities
  • Build topical authority through consistent, focused publishing

Visibility in AI Mode is not guaranteed, but sites that are trusted, well-structured, and frequently cited are more likely to be referenced in AI-generated responses.. 

Search Is Changing but the Fundamentals Still Apply

The way people search is changing, and Google AI Mode is accelerating that shift.

People are finding information across a host of different platforms, not just Google. AI-generated answers are reducing clicks. And traditional content publishers are under pressure as gen AI eats up demand. 

At the same time, AI Mode doesn’t discard the fundamentals that have always mattered. Google is still prioritizing relevance, authority, and usefulness — it’s just surfacing them in new ways. Sites that understand search intent, build credibility beyond their own domains, and structure content clearly are better positioned to stay visible as AI Mode expands.

From the very start, Google had one aim: to solve users’ needs. That’s also what AI tools seek to do, and their models will continuously be designed to that end. 

Understanding your customers—and providing what they want through high-quality, useful content—is the best way of futureproofing your business and ensuring long-term visibility in LLMs.

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How Marketers Are Spending in 2026

Marketing budgets aren’t collapsing in 2026, but they are making a shift. That’s the part many teams miss.

That distinction matters. Rising media costs, weaker attribution, privacy changes, and AI-driven search shifts have created real pressure, but the data shows budgets are still moving into marketing. They’re just moving with more intent.

Our latest NP Digital research on how marketers are spending their money in 2026 shows a clear pattern: teams are reallocating toward channels that defend ROI, compound value, and hold up under volatility. This article breaks down what’s changing, why it’s happening, and how to think about your own marketing budget for 2026 without relying on outdated assumptions.

Key Takeaways

  • Marketing budgets in 2026 are not shrinking. They’re being consolidated around confidence, efficiency, and defensibility. 
  • Channels tied directly to conversion, retention, and owned data are absorbing spend, while those with declining signal quality or unclear ROI are losing ground. 
  • SEO and content are not disappearing, but expectations have shifted toward extractability, authority, and measurable downstream impact. 
  • Paid media still plays a critical role, but marginal efficiency now determines where dollars stay or move. 
  • Teams that can reallocate budget quickly, based on real performance signals, are gaining a structural advantage.

The State of the Marketing Budget in 2026

Let’s start with the context that’s shaping every budget decision this year.

Media costs continue rising across search and social. CPCs aren’t coming down, and competition for attention keeps intensifying. At the same time, privacy changes have reduced signal quality, making it harder to target precisely and measure accurately.

Economic uncertainty is pushing marketers to defend ROI more aggressively than ever. Every dollar needs a clear path to revenue, and channels that can’t prove their value are getting cut.

AI adoption has accelerated faster than most teams can operationalize. Nearly everyone is experimenting, but few have figured out how to turn that experimentation into systematic advantage. The gap between “using AI” and “getting results from AI” is wider than you’d think.

Here’s the good news: budgets are not disappearing. They are being reallocated with intent. The marketers who understand where efficiency lives and where it’s eroding are the ones capturing share.

What’s Driving Budget Decisions

The shift in spending comes down to a few core factors:

Purchase journeys are more complex. 94% of purchase journeys now involve multiple touchpoints. Search and social are the most influential, appearing in 79% and 73% of journeys respectively. But they rarely operate in isolation. Budgets are being distributed to support visibility across the full path to purchase, not just the final click.

Information about purchase journeys.

Attribution is noisier. Third-party signals keep degrading, so budgets are following channels that stay measurable. Paid search, email, and CRO all offer clearer attribution than many emerging channels. In uncertain conditions, that clarity matters.

Organic reach is declining. Zero-click searches now account for roughly 58-60% of Google searches. Organic listings are being pushed below the fold by AI Overviews, ads, and SERP features. This is reducing organic click opportunities and increasing reliance on paid coverage.

Efficiency matters more than volume. When media costs rise and margins compress, growth comes from doing more with what you have. That’s why CRO, lifecycle marketing, and retention are getting more investment even as some acquisition channels face cuts.

The marketers who are winning in 2026 understand that budget decisions aren’t about chasing trends. They’re about matching investment to where performance can be proven and defended.

Common themes across budget reallocations

Where Budgets Are Growing, Holding, and Declining

Let’s look at the actual spending patterns across channels. We’ll start with the big picture, then break down what’s happening in each major category.

Overall Marketing Budget Direction

61% of B2B marketers are increasing overall spend this year, with 20% holding flat and 19% decreasing. B2C is slightly more cautious: 57% are increasing, 32% holding flat, and 11% decreasing.

The takeaway? Growth budgets still exist, but they’re being deployed more carefully than in previous years.

The Biggest Budget Shifts Since 2025

Here’s where the reallocation is happening:

SEO spend has rebounded sharply. After a softer 2025, 61% of marketers are now increasing SEO budgets (up from 44% last year). The return of confidence in organic search reflects a few things: better AI tools for content production, clearer ROI measurement, and recognition that organic visibility still matters even in a zero-click environment.

AI SEO investment is accelerating dramatically. 98% of marketers plan to increase AI SEO spend in 2026. This isn’t just hype. Teams have figured out that AI can accelerate research, content production, and optimization cycles without sacrificing quality.

CRO and UX remain a priority. 52% are increasing spend, and only 25% are planning decreases. When traffic is harder to earn, you optimize what you have. CRO delivers measurable improvements regardless of where visitors come from.

Content creation growth has slowed. Only 32% plan increases, while 31% plan to reduce spend. This reflects a shift away from volume-based content strategies toward fewer, higher-quality assets that can be repurposed across channels.

Organic social media is facing the steepest pullback. 64% of marketers are planning budget decreases. Organic reach has declined to the point where most brands treat social as a support channel, not a growth engine.

Email and lifecycle budgets have stabilized. 60% are keeping spend flat and 23% are increasing. Email remains one of the most reliable channels for retention and conversion, especially as first-party data becomes more valuable.

The pattern across all of this? Increased focus on channels tied to conversion and retention. Reduced investment in traditional advertising channels with declining efficiency signals. And a shift away from broad content volume toward targeted execution. 

Channel-by-Channel Breakdown

Now let’s get specific. Here’s what’s happening in each major channel category.

SEO and Organic Search

Information about SEO and Organic Search Budget Trends.

SEO budgets are rebounding, but the strategy is changing. Digital channels now represent 61.1% of total marketing spend, and organic search remains a major piece. But zero-click searches and AI Overviews are changing how value gets captured.

Search is becoming answer-first. Google increasingly resolves intent directly in the SERP through AI Overviews, featured snippets, and knowledge panels. This means fewer clicks but doesn’t make SEO irrelevant, just less predictable on its own. SEO needs to optimize for visibility and citation, not just click-through.

Treat rankings as one output among several that matter. Visibility in AI Overviews and featured snippets matters as much as position one. Prioritize topics tied to revenue intent and customer lifecycle stages. Build content that can win both ways: clicks and citations. Measure organic success across visibility, assisted conversion, and brand lift. More brands are pairing search with other channels, like community, that capture attention off the SERP.

AI systems increasingly resolve intent directly in the SERP, which concentrates click opportunities into fewer, higher-intent moments. Brands that show up consistently in AI-generated answers are building trust and authority even when users don’t click.

Content and Thought Leadership

Content budgets are being reallocated toward assets that influence discovery, trust, and conversion across channels. Thought leadership is increasingly used to earn inclusion in search results and AI-generated answers.

Content still fuels discovery, even when the click doesn’t happen immediately. Strong content is what AI systems summarize, cite, and pull into answers. In a noisy market, a differentiated perspective is one of the few advantages you can own.

Design content for multiple outputs: search, AI summaries, social, sales. Prioritize fewer topics with deeper authority and a clearer point of view. Shift from publishing volume to publishing leverage. Use AI for research acceleration and synthesis, but keep humans in charge of insight, brand voice, and editorial judgment.

Creators especially matter here as a result. They help brands move beyond renting attention and toward building long-term loyalty that holds up even as platforms and algorithms change. This is important because things like original insight, point of view, brand voice, and credibility are not things AI can manufacture on its own. Editorial judgment and prioritization are still very human decisions.

AI can help scale content, but the trust, experience, and perspective that influencers, creators, and SMEs offer gives content weight and relevance with an audience.

Paid Search

A graphic about paid search budgets.

Paid search remains a core demand capture channel, but expectations have reset. CPC inflation and competition continue to compress efficiency. Reduced organic click availability increases reliance on paid coverage.

Shift from keyword expansion to coverage efficiency. Prioritize high-intent, defensible queries over volume. Use fewer keywords with tighter control. Coordinate more closely with SEO and CRO. Put higher emphasis on marginal ROI rather than raw spend growth.

AI and automation now control bidding, targeting, and pacing by default. Competitive advantage shifts to inputs: structure, data quality, conversion signals.

Paid Social

Paid social remains the most flexible scaled reach channel. Platform-level shifts show TikTok leading growth at 57%, YouTube at 53%, and Instagram at 46%. Facebook is under pressure, with 36% decreasing spend and only 18% increasing.

Creative velocity matters more than audience hacks. Message clarity beats novelty. Platform-native formats outperform repurposed ads. Measurement focuses on incremental lift, not just ROAS. Close alignment with lifecycle and email capture turns paid social prospects into owned relationships.

Organic Social

A graphic aboutr organic social media budget direction.

Some cuts are dramatic—and predictable.

  • Organic social: 64 percent decreasing investment. 
  • Content creation volume: Only 32 percent increasing; 31 percent decreasing. 
  • Traditional display: Banner ads are essentially frozen (63 percent flat). 
  • Facebook paid: Thirty-six percent decreasing. 

The pattern is clear:
Teams are cutting channels with declining reach, opaque ROI, or inflated costs.

But that doesn’t mean content or social isn’t important—it simply means they’re no longer funded as volume engines. The strategy is changing, not disappearing.

Influencer Marketing

Community building is one of the strongest growth areas in 2026 budgets, with 69% of marketers increasing spend. Influencer marketing is seeing even stronger growth at 78%. These channels support retention, referrals, and brand defensibility.

Friend and direct traffic drive more conversions than any paid channel. Don’t just focus on the channels that cause direct conversions. Focus on the channels that create brand awareness and influence purchase decisions earlier in the journey.

Email + Lifecycle

A graphic about email and lifecycle marketing budget momentum.

Email and lifecycle budgets remain resilient because performance is driven by trust, relevance, and timing. 60% are keeping spend flat and 23% are increasing. First-party data enables consistent message delivery when paid reach and signal quality decline.

Customer acquisition isn’t the only scalable lever anymore. Retention is the controllable one. Retention programs stabilize margins as media costs, auctions, and platforms stay volatile.

AI enables real-time message sequencing based on behavior, dynamic content assembly across email and SMS, and faster iteration without rebuilding entire lifecycle programs.

CRO and UX

CRO, UX, and First-Party Data investment trends.

CRO and UX are treated as defensive investments that improve performance regardless of traffic source. 52% are increasing spend. Traffic is harder to earn and easier to lose. Fewer clicks mean every visit carries more revenue weight.

AI-assisted test generation allows faster signal detection across variants and continuous optimization tied to real behavior. Competitive advantage shifts to inputs: structure, data quality, and conversion signals.

A Simple Framework: How to Build a Smarter 2026 Marketing Budget

A framework on building 2026 marketing budgets.

Here’s a practical framework for budget agility.

Anchor spend in proven demand. Protect budgets tied directly to revenue and high-intent activity. These are your foundation channels.

Build flexibility around performance signals. Shift dollars based on real outcomes. Don’t lock yourself into annual commitments for channels that aren’t delivering.

Separate experimentation from core investment. Test intentionally without destabilizing what works. Set aside 10-15% of budget for testing new channels and tactics.

Reallocate faster than your competitors. Speed of adjustment becomes a competitive advantage in volatile conditions. Review performance monthly and be willing to move budget mid-quarter.

The winners in 2026 will be faster, not just bigger. Budgets are consolidating around fewer, higher-confidence channels. Efficiency and retention now matter as much as acquisition. AI is reshaping how value is captured, not just how work gets done. Visibility, conversion, and experience must be planned together.

Conclusion

Marketing in 2026 requires a different approach to budgeting. The channels that worked three years ago still work, but they work differently. The measurement that mattered in 2023 doesn’t tell the full story anymore. The strategies that justified budget in 2024 need updating for how search, social, and AI have evolved.

The marketers who thrive this year will be the ones who allocate budget where performance is provable, build systems that compound value over time, and move faster than their competitors when signals change.

If you need help translating these budget signals into a channel-specific growth plan, aligning SEO, paid media, content, and lifecycle into one system, or building measurement models that reflect zero-click and AI-driven behavior, we can help. Reach out to discuss your 2026 strategy.

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