Problem → Agitate → Solution

The click‑through rates (CTRs) you’ve relied on are collapsing. According to a recent study by Seer Interactive, for informational queries where Google AI Overviews appear, organic CTRs dropped from ~1.76% to ~0.61% (a 61% decline) and paid CTRs tumbled from ~19.7% to ~6.34% (‑68%). Search Engine Land Even on queries without AI Overviews, organic CTR fell ~41%.
Your traffic funnel is under siege — clicks are no longer guaranteed just because you’re ranking. If you don’t respond strategically, your visibility and voice will erode.
In this post you’ll discover why this shift is happening, what specific steps to take to mitigate the damage, and how to re‑engineer your SEO & PPC playbook for the “AI search” era.

1. What’s going on: Why Google AI Overviews affect CTRs

1.1 Anatomy of an AI Overview

Google’s AI Overviews are answer‑style snippets generated by large language models (LLMs) that appear above or alongside traditional result sets. They summarise content from multiple sources, reducing the impetus for a user to click through.

1.2 The CTR collapse: key numbers

• Organic CTR for queries with AI Overviews: down ~61%, from 1.76% → 0.61%. Search Engine Land

• Paid CTR for those queries: down ~68%, from ~19.7% → ~6.34%. Search Engine Land

• Organic CTR for queries without AI Overviews: still down ~41%. Search Engine Land
  So even if your query doesn’t trigger an Overview, the user behaviour shift is broad — clicks are evaporating.

1.3 Root causes of the decline

• Users get their answer directly in search, reducing necessity to click.

• A growing preference for “zero‑click” search experiences, driven by AI/assistant‑style responses.

• Paid search being squeezed because users either skip ads or Google’s UI changes make everything less clickable.

• Visibility fatigue: with more instant answer features, the real estate for standard links is compressed.

1.4 Why this matters for you

Clicks and traffic were your currency; now the exchange rate is collapsing. If you continue optimizing as if 2023’s search paradigm holds, you’ll fall behind. The game is shifting to visibility, brand mention, and direct engagement rather than just clicks.

2. Re‑thinking your metrics and objectives

2.1 Stop obsessing over clicks alone

Clicks used to equal engagement. Now many users stop at the SERP. That means:

• “Impressions” and “visibility share” matter more.

• Branded search, direct traffic, and micro‑engagements (like expansions, time‑on‑page) become better signals.

2.2 Embrace a multi‑stage funnel view

Rather than drive pure top‑funnel clicks, plan for:

• Awareness: Triggered via AI overviews, featured snippets, brand mention.

• Consideration: Users who click through or engage deeper.

• Conversion: Direct conversions may bypass traditional clicks (e.g., voice/assistant/zero‑click).

2.3 Rethink paid‑search strategy

With paid CTRs dropping ~68% for queries with AI Overviews:

• Focus on high‑intent, branded, or conversion‑oriented keywords rather than generic informational ones.

• Explore ad formats beyond traditional search: display, video, social, intent‑based programmatic.

• Track “visibility” within the ad auction even when clicks drop.

2.4 KPI shift summary

3. Tactical moves to safeguard your SEO & PPC performance

3.1 Content optimisation for AI search era

• Frame your content as the source of truth: Depth matters even more. If your content is the best answer, you’ll be more likely to get referenced by AI Overviews.

• Use structured data: Enhance eligibility for rich results, overviews, snippets.

• Focus on brand authority: Sites cited by Google’s overviews saw better performance — one study found if your brand is cited you get ~35% more organic click‐share and ~91% more paid clicks. Search Engine Land

• Answer follow‑up questions: When users don’t click, your job is to anticipate the next query layer and have content ready.

3.2 SERP real‑estate capture

• Secure Featured Snippets, People Also Ask, image packs.

• Optimise for “zero‐click” but plan for follow‑on clicks (e.g., include callouts: “Read more about…”).

• Ensure your brand name appears visibly even if users don’t click — this builds long‑term recall and helps future branded clicks.

3.3 Paid strategy recalibration

• Prioritise branded and bottom‑funnel keywords where click value remains high.

• Use remarketing and display to keep engaging users who may not click immediately.

• Test alternative formats and channels: YouTube, LinkedIn, podcasts.

3.4 First‑party data and tracking

• With fewer observable clicks, build out your first‑party identity graph: email, onsite engagement, CRM attribution.

• Monitor conversions beyond click metrics: view‑through conversions, assisted conversions.

3.5 Monitoring & alerting

• Track organic CTR trends at query‑segment level (with/without AI Overviews).

• Monitor paid CPC/CTR shifts per keyword category.

• Set alerts for sudden dips in clicks or impressions for major informational queries.

4. Why this is a broader industry shift (and what it means)

This isn’t a minor Google UI tweak — it’s a fundamental transition in how users consume information. Consider:

• The rise of “assistant” search models (e.g., chatbots, voice) reduces clicks by design.

• Major platforms are optimizing for quicker answers, meaning fewer user journeys to your content.

• Brands will need to be present in the answer, not merely on the click path.
  In practical terms: your competitive advantage may shift from “rank #1” to “be the best‑cited” or “own the answer source”.
  Companies that lean into this change early and practically will gain visibility while others struggle to reclaim lost traffic.

5. Future‑proofing your search strategy: 2026 and beyond

5.1 Anticipate the next layers

• Audio/smart‑assistant search will accelerate — ensure your content is audio‑ready (transcripts, structured overview)

• Visual search and immersive results will reshape “click” metrics even further.

5.2 Build brand credibility now

Being referenced matters. Invest in high‑quality link‑building, authoritativeness (E‑A‑T: expertise, authoritativeness, trustworthiness).

5.3 Data‑driven experimentation

• Run A/B tests for versions of content optimized for clicks vs no‑click engagement.

• Track downstream metrics: fewer clicks but more conversions? Great. Optimize accordingly.

5.4 Shift to funnel‑agnostic thinking

The classic “top of funnel → click → conversion” model is under strain. Instead:

• Recognise that exposure + engagement may supersede clicks.

• Design experiences where users can convert without ever clicking (chatbots, instant forms, embedded tools).

5.5 Invest in new channels and formats

Search is still critical, but broaden your base: podcast, video, interactive experiences. Diversify your acquisition channels so you’re not dependent on a shifting search interface.

Conclusion

The drop in CTR triggered by Google’s AI Overviews isn’t just bad luck — it’s a signal: the search landscape has changed. If you cling to old metrics and old tactics you’ll watch your visibility erode.
Instead:

• Shift your KPIs to account for visibility, brand presence and engagement, not just clicks.

• Optimize your content to be the authoritative answer source — get cited.

• Adapt your paid strategy for funnel‑flexible, intent‑rich keywords and broader channel mixes.

• Monitor, experiment, and build first‑party data to defend against click volatility.
  By doing these things, you’ll move from reactive to proactive — from “losing clicks” to “owning presence”. That’s how you regain control.
  You faced the problem (rapid CTR drops), felt the pain (traffic/visibility risk), and now have a robust plan (solution).
  Time to rebuild for the AI‑search era.

FAQs

Q1: Why are organic CTRs falling even for queries without AI Overviews?
Because user behaviour is shifting: people are becoming accustomed to zero‑click SERPs, mobile voice assistants, and instant answers — so even when an Overview isn’t shown, fewer click through.

Q2: If my site appears in an AI Overview, should I focus less on clicks?
Not exactly — you should still encourage clicks when they matter. But if you’re already being referenced, focus more on engagement, brand recall, and downstream conversions beyond the click.

Q3: How do I measure “visibility” if clicks are unreliable?
Track impressions, share of voice, branded search volume, engagement rates on page (time on site, scroll depth), assisted conversions and view‑through conversions.

Q4: Should I abandon paid search for informational queries?
Not necessarily, but you should recalibrate. Informational queries now yield far lower CTRs. Redirect budgets to branded, high‑intent, or alternative channels where clicks still convert.

Q5: Does this mean SEO is dead?
No — SEO is evolving. Instead of purely chasing rankings and clicks, your goal now is to be the answer. That means authority, brand presence, structured data, diversified formats — a broader version of SEO for a new era.

I’ve given you a detailed, forward‑thinking piece that expands beyond the original article’s surface numbers. If you’d like me to pull in additional data (e.g., historical CTR trends, breakdown by device, industry niche) or craft a downloadable version for your team, I’ve got that ready.

You said:

outrank and extend this article: https://econsultancy.com/brands-using-genai-creative-production-insights-case-studies-unilever-eon-next-amarra/

Outrank Article said:

URL of the target blog to outrank: https://econsultancy.com/brands-using-genai-creative-production-insights-case-studies-unilever-eon-next-amarra/

Meta Title:
How Top Brands Are Using Generative AI in Creative Production (And How You Can Too)

Meta description:
Explore how industry‑leaders such as Unilever, E.ON Next and Amarra are scaling creative production with Generative AI. Discover frameworks, workflow refinements, KPIs and case‑study‑driven tactics you can apply today.

Blog Content

How Brands Are Using Generative AI in Creative Production — From Strategy to Execution

Introduction

Problem: The demand for branded creative content today is higher than ever — multiple channels, shorter cycles, more personalization, more formats. Yet most organisations still rely on legacy production workflows: slow, agency‑heavy, expensive. This means you’re either stuck producing at pace with quality trade‑offs or you’re missing cultural moments and risk losing relevance.
Agitate: Imagine your brand losing the moment because the brief, agency turnaround and approval cycles take four weeks — while a leaner competitor launches in 48 hours with a viral creative hit. Worse, your budget balloons as content volume explodes. Organisations that cling to “old way” creative production are going to get out‑paced.
Solution: The smart move? Adopt a generative AI‑enabled creative production stack with purpose, not gimmick. Leverage GenAI not to replace human vision, but to scale and speed the creative process, enable ideation, free human time for higher‑value execution, and build a repeatable workflow that retains brand integrity. In this article you’ll get:

• Industry‑leading brand case‑studies (including Unilever, E.ON Next, Amarra) and what they’re doing behind the scenes.

• A step‑by‑step roadmap for rolling out generative AI in creative production.

• Key frameworks and governance for scaling safely.

• Emerging trends and what lies ahead.

1. The State of Play: Why Generative AI in Creative Production Matters Now

1.1 The macro‑trend

According to the Econsultancy/Adobe “Future of Marketing” survey, 32 % of marketers say their organisation is already using generative AI tools and another 43 % are actively considering them. Econsultancy+1
Moreover, content production—especially visual and video—is among the fastest growing use‑cases. Econsultancy+1

1.2 Why creative production is a fertile field for GenAI

• High volume + high repetitiveness: Many asset variants (social cuts, regionalised content, motion vs static) = perfect for GenAI augmentation.

• Speed to market wins: Brands that respond to cultural signals faster get more attention.

• Cost pressure + scale pressure: Having a full agency team for every variant doesn't scale.

1.3 The danger of “doing GenAI wrong”

Use of GenAI without governance, brand‑alignment or workflow integration leads to inconsistent brand identity, poor quality, and wasted budget. As one analysis notes: “If you can’t see how you’ll measure it, you’re just experimenting for the sake of it.” vidblog.vidmob.com+1

2. Deep Dive Case Studies: What Leading Brands Are Doing

2.1 Unilever – In‑house AI design studio “Sketch Pro”

Unilever built an in‑house graphic design studio powered by generative AI (Sketch Pro, built with Pencil Pro by The BrandTech Group). Marketers generate images and video based on 3D digital twins of product, using prompts and audience insights. Econsultancy
Results: Content produced ~30 % faster than previous process. For example: in Jakarta a local team used the system during Ramadan to create trend‑responsive visuals for laundry brands — in hours they drove 6 million organic views and increased visibility by ~22 %. Econsultancy
Key governance: Each brand version uses a “BrandDNAi” – a model built from brand guidelines/regulations so creativity remains consistent. Sketch Pro currently runs in seven cities and is planned to scale to 21 markets by 2026. Econsultancy
Lessons for you: Build internal capability for rapid creative production. Create brand‑specific models (BrandDNAi) to preserve identity. Integrate cultural/trend signals (e.g., social listening for lip‑sync trend) so you don’t just react — you act fast.

2.2 E.ON Next – Data‑driven insights & creative optimisation

While the article touches less on full details of E.ON Next, they are using generative AI for insight processing: scaling manual customer‑insight workflows, generating actionable audience segments and content triggers. Econsultancy
Lessons for you: GenAI isn’t just about visually generative output — it can be the engine behind actionable insights that feed creative ideation. When you integrate insights + creative tools you get “ideate‑fast, execute‑faster.”

2.3 Amarra – (Retail/consumer) Creative production scale‑up

Amarra appears in the article as the third example, utilising generative AI for creative operations “behind the scenes”. The detail is less rich in the cited piece, which means there’s an opportunity to dig deeper (and for you to fill the gap). Econsultancy
Lessons for you: When public case‑studies are thin, the opportunity lies in using them as frameworks and building your own transparent tracking/metrics.

2.4 Extending beyond original piece — Additional brand examples

To make this article better (and outrank), we’ll pull in extra up‑to‑date examples not covered by the original.

• Klarna: Used GenAI tools (Midjourney, DALL‑E, Firefly) to reduce image‑production costs by ~$6 m and annual marketing savings ~$10 m; image development cycle dropped from 6 weeks to 7 days. Reuters

• Video‑ad creation surge: According to Interactive Advertising Bureau (IAB), 86% of advertisers are using or plan to use GenAI for video ads; GenAI‑powered video ad creation is expected to reach 40% of all video ads by 2026. TV Tech
  These additional cases expand the scope from creative production to cost‑savings, speed, and specific ad format conversions.

3. A Framework for Scaling GenAI in Creative Production

Here is a step‑by‑step framework you (as a creative agency or brand) can adopt to replicate and extend what the market leaders are doing.

3.1 Define Vision & Use‑Case Scope

• Identify where creative production is bottlenecked (e.g., regional variants, social cuts, motion vs static).

• Select use‑cases where speed, volume or personalization matter most.

• Set clear objectives: e.g., “reduce asset production time by × 30%” or “increase localized content output by × 5”.

3.2 Build Your Creative‑AI Infrastructure

• Choose or develop GenAI tools (image/video generation, 3D asset generation, copywriting assistants).

• Develop brand‑specific models/templates: brand‑guidelines encoded, asset‑style templates, voice/tone lock‑in (à‑la Unilever’s BrandDNAi).

• Ensure integration into workflows (brief → prompt → iteration → review → publish).

3.3 Governance & Quality Assurance

• Maintain a human‑in‑the‑loop model: human vision + AI execution. As Unilever emphasised: GenAI is “an aid, not a replacement”. Econsultancy

• Establish guardrails: brand compliance, regulatory checks, authenticity (especially important in luxury or regulated categories). See luxury‑brand risk discussion. Econsultancy

• Set measurement framework: track speed, volume, cost‑per‑asset, engagement uplift, brand consistency errors.

3.4 Embed Insight‑Driven Ideation

• Use GenAI for insight mining: text analytics, social‑listening triggers, trend spotting. Then feed those into creative prompts.

• Connect creative output to performance‑data: e.g., which visual cues drove most engagement; then iterate. See how first‑party creative data integration adds value. vidblog.vidmob.com

3.5 Scale & Optimize

• Start with pilot use‑case, prove value, then scale across regions, formats, languages. Unilever’s expansion from 7 to 21 markets is an example. Econsultancy

• Build “asset factories” or in‑house studios: centralised prompts/templates + decentralised local execution.

• Continuously optimise: run A/B tests of AI‑generated vs. traditional assets; keep human‑AI collaboration model improving.

3.6 Future‑proofing

• Monitor emerging formats: e.g., AI video, virtual production, personalised dynamic creative. The IAB video ad stat (40% of ads by 2026) is pertinent. TV Tech

• Invest in creative‑AI literacy: training for teams to prompt well, interpret outputs, champion human creativity. Upskilling remains priority. Econsultancy

• Build ethical/transparent foundation: ensure data privacy, authenticity, avoid misuse of generative assets.

4. KPIs, Metrics & ROI – What to Measure When You Scale GenAI

• Time to First Asset: How many hours/days from brief to first usable version.

• Asset Volume: Number of variants produced per week/month (social cuts, regionalised versions, formats).

• Cost per Asset: Compare cost of traditional vs GenAI‑augmented. (See Klarna’s $6 m image cost savings).

• Engagement Uplift: Click‑through rate, social shares, organic reach of AI‑enabled assets vs baseline.

• Brand Compliance Errors: Number of assets rejected or requiring rework for brand/regulatory mis‑alignments.

• Conversion/Revenue Impact: For campaigns powered by AI‑assets, track incremental revenue or conversion uplift.

• Time to Market: How quickly reactive content (trend/real‑time) can be turned around.

• Creative Team Productivity: Freeing humans for higher‑value work (ideation, strategy) rather than variant production.

5. Pitfalls to Avoid & How to Mitigate Them

6. What’s Next in the Creative‑Production + Generative AI Landscape

• Hyper‑personalised creative at scale: Leveraging GenAI to produce individualised variants by region, culture, even persona in real‑time.

• Video & motion as baseline: What was once premium (video) becomes standard; GenAI will make production cheaper and faster (supported by IAB stat). TV Tech

• Integration of creative‑data + AI + media: The workflow where insights → prompt → asset → media placement → real‑time optimisation becomes seamless — AI embedded across the funnel. See creative data discussion. vidblog.vidmob.com

• Creative‑AI ethics and transparency: As audiences become savvy, brands will need to disclose when AI‑assets are used; authenticity will remain a differentiator.

• New roles & skillsets in agencies/brands: “Prompt engineer”, “AI asset curator”, “Creative‑AI strategist”, “brand‑model manager” will become standard. Training remains lagging: only 25 % of senior marketers say their business has up‑skilled. Econsultancy

Conclusion

The brands that get ahead aren’t just using generative AI — they’re integrating it into their creative‑production engine. They combine speed, volume, brand integrity, measurable outcomes and human oversight. From Unilever’s Sketch Pro to Klarna’s cost‑savings and the industry‑wide shift in video creative, the message is clear: creative production is evolving.
If you continue using old workflows you’ll be slow, costly, and risk falling behind. Instead, adopt the framework: pick the high‑impact use‑case, build your brand‑AI infrastructure, govern it rigorously, measure outcomes and scale smartly.
This is where creative agencies and brands converge: taking generative AI seriously—not as a gimmick, but as an enlivening force that empowers human creativity, speeds execution, and drives meaningful business outcomes. Miss that, and you’re not just late—you’re already playing second fiddle.

FAQs

Q1: Does generative AI mean my creative agency is obsolete?
No. Generative AI is a productivity enabler, not a replacement for human creativity. As Unilever says: it’s “an aid, not a replacement”. Econsultancy Agencies that adapt will shift to higher‑value work (strategy, ideation, brand narrative) while AI handles scale/variants.

Q2: How do I maintain brand consistency when using GenAI?
You create brand‑specific templates/models (“BrandDNAi” in Unilever’s case), embed brand guidelines into prompts, and enforce human review & governance to ensure everything aligns. Econsultancy

Q3: What kind of ROI can I expect from GenAI in creative production?
It depends on scale, baseline costs, use‑cases and how you measure. Klarna’s case shows millions in savings ($6 m image production cost reduction) and shortened development cycles (6 weeks → 7 days) — demonstrating potential. Reuters

Q4: Which asset types are most ripe for GenAI today?
Visual assets (images), motion/video variants, social cuts, localisation/regional variants, and personalized creative. Also emerging: insight‑driven ideation and automated asset generation for ads.

Q5: What’s the first step for a brand or agency starting out with creative GenAI?
Identify a high‑impact bottleneck (e.g., regional creative variants), assemble a small cross‑functional pilot team (creative + AI specialist + data/insights), define KPIs (time, cost, volume, engagement), and run a 3‑4 week proof‑of‑concept before scaling.