Your feed is filled with companies that claim to know you…
So why is it that only 45% of consumers feel understood by the brands they interact with? Better yet – why does our data show that number is down YOY?
Consumers are tired. Tired of hearing about how AI is “reimagining” every product or service they use. They don’t care how sophisticated a tech stack is if the interaction still feels generic.
Business leaders riding the AI wave need to keep the consumer experience top of mind. While an AI rollout might look impressive on paper, what does it actually feel like for your customers?
Some things can’t be automated: empathy, trust, connection.
Or can they?
AI can absolutely help scale those human experiences. But only when it starts with a deep, genuine understanding of your customer. That foundation has to come first. Otherwise, you’re just automating noise.
AI isn’t magic. It’s a tool. And like any tool, its success depends on how – & more importantly, why – you use it.
Get that right, & you don’t just win attention. You win loyalty.
AI does not force us to buy. But it can make the invitation more personal, timely and difficult to resist.
Search for one product and similar recommendations can soon appear across shopping sites and social media. AI is increasingly helping businesses decide not only what consumers see, but which message may be most persuasive to them.
My new article for The Conversation UK explores:
• how AI is changing marketing from broad targeting to personalised persuasion
• why online shopping can shorten the gap between wanting and buying
• how this can contribute to overconsumption and waste
• how mindful consumption can help people pause and make more considered choices
The article draws on my research into mindful consumption, which views it through awareness, caring and temperance - recognising when enough is enough. My related academic work was published in the Journal of the Academy of Marketing Science.
The issue is not simply whether AI makes marketing more effective. It is whether personalisation can remain useful without weakening consumer autonomy or encouraging unnecessary consumption.
Read the article here: https://lnkd.in/eKRsamBs
Why AI wants you to buy more – and mindfulness could help you buy less
I would be interested to hear your view: where should businesses draw the line between helpful personalisation and excessive persuasion?
Cardiff Metropolitan University Cardiff School of Management British Academy of Management Sustainable and Responsible Business SIG The Conversation#ArtificialIntelligence#Marketing#ConsumerBehaviour#MindfulConsumption#ResponsibleMarketing#Sustainability
A third of consumers say they would trust a brand less if they knew its content had been generated using AI, versus only 15% who said they'd trust it more.
This is one of the findings of a recent YouGov and Meltwater report on trust in the age of gen AI, where they survey thousands of people in the UK, US, France, Australia, Singapore, Canada and Germany.
One of the most interesting things to me was that the use of the tech is more accepted depending on the context. While this may sound obvious, the responses from people surveyed show how widely varied that acceptance may be: for example, while 53% of respondents said gen AI is ok for entertainment, less than 20% said it's acceptable for political content, and for news content the acceptance is only marginally higher than that.
For influencers and creators, about 28% of people said it's ok for them to use gen AI in their content, but a whopping 55% of respondents said it's not acceptable at all.
Concerns around the use of gen AI are still high across all markets surveyed, but especially in the UK and the US – so if those are key audiences for you and your brand, it's important to be aware of that sentiment if you decide to use gen AI in any context.
Transparency is also a baseline expectation now: 86% of those surveyed across all markets said it's important to disclose the use of AI, while 59% said a lack of disclosure erodes trust. Nearly half of respondents (49%) across the seven markets surveyed added their trust would decrease if AI replaced human creators entirely.
My key takeaways are things I've been talking about for years, if you've been following me for that long:
> the best use of AI is not content creation
> always be transparent about using AI, and be prepared for some fallout
> humans are still needed to check AI outputs in every context
I'll link the full report in the comments as it's worth a few minutes of your time.
2026 is the year AI search either makes or breaks your traffic.
Here's exactly how to show up in ChatGPT, Perplexity, and Gemini before your competitors figure it out:
After analyzing successful AI optimization strategies from multiple client sites, here's your complete playbook for 2026:
1. Understand how AI search actually works
There are two separate systems at play:
• The LLM itself (trained on data up to a year ago): This is a popularity contest. The more your brand appears in training data, the higher probability of being mentioned.
• Real-time search retrieval: LLMs make actual Google searches behind the scenes to pull fresh information.
You need to optimize for both.
2. Track what matters in 2026
Forget traditional keyword rankings. They don't exist in AI search.
Instead, track:
• Extreme long-tail queries (7+ words): These mirror how people prompt LLMs. Filter Search Console for queries with 7+ words to see AI mode usage patterns.
• Brand mentions in commercial prompts: Create fresh LLM accounts (no personalization) monthly. Run commercial prompts like "best [your product] for [use case] 2026."
Track two metrics: Are you mentioned? (Yes/No) and Position within the response (1st vs 11th recommendation).
• AI referral traffic in GA4: Set up separate filters for ChatGPT, Perplexity, Claude, and other AI platforms. Track them as distinct traffic sources.
This gives you actual visibility data without expensive tools.
3. Build for retrieval
When LLMs need current information, they search Google and pull from top results.
You can see these searches using Chrome DevTools.
Check what queries LLMs are running, then optimize for those specific searches.
Your traditional SEO still matters here. Ranking high for searches that LLMs frequently perform increases citation odds dramatically.
4. Make brand mentions your new backlinks
The more places your brand appears online in relevant context, the better your odds in AI outputs.
Focus on:
• Third-party review platforms:
For local: Google Business Profile (80% effort), then Yelp, Angie, Thumbtack.
For ecommerce: On-site reviews plus Amazon, Etsy.
For SaaS: G2, Capterra.
• Industry publications and forums:
Get featured in articles, roundups, and discussions where your target audience already engages.
• Use AlertMouse for tracking:
Monitor new brand mentions across the web (better than Google Alerts).
5. Automate the grunt work
Use pandas (Python library) for data analysis. Learn basic skills to:
• Generate custom click-through rate curves from Search Console data
• Merge content categories with traffic data to identify top performers
• Create interactive visualizations without expensive tools
For non-coders: GPT for Sheets handles categorization, data cleanup, and analysis directly in Google Sheets.
The key: Good questions are expensive. Data is cheap.
Knowing what insights you're trying to surface is your competitive advantage, not the tools.
1 in 3 women feel pressured to change their appearance because of what they see online, even when they know the images are fake or AI-generated. 😔
By 2025 (NEXT YEAR!), 90% of content is expected to be generated by AI. This will require strong brand messages like Dove's: keep beauty real.
Dove's The Code campaign highlights the growing influence of AI on perceptions of beauty. The problem with Gen AI is that it creates images based on massive data sets, which often reflect societal biases. When prompts such as "beautiful women" are used, the results are often unrealistic and reinforce harmful beauty ideals-flawless, thin, and far from what real beauty looks like.
Dove has taken a strong stance by pledging never to use AI to distort images of women in their campaigns, and instead to promote real, diverse beauty. As part of their ongoing commitment, they introduced the Real Beauty Prompt Playbook - a guide to creating more inclusive AI-generated images. 🙏
I'm very happy that brands are paying attention to these algorithmic biases and not ending up like Mango's recent AI-generated campaign, which was criticized for featuring models that looked too perfect, too flawless, and lacking in diversity.
As brands continue to explore AI, they must consider its impact on societal standards. Dove's approach shows that AI can support creativity and inclusivity, but only if we use it responsibly. The playbook also has an "Inclusive Prompting Glossary", which provides practical ways for brands and creators to use AI in a way that represents the full spectrum of beauty!
How do you think brands should navigate the use of AI in advertising? 💡
#GenAI#Dove#AICampaign#AlgorithmicBias
🚨 I've been teaching personalization wrong.
After analyzing 1,000+ campaigns, I discovered what the 89% who see ROI actually do differently.
It's not what you think.
While most brands are personalizing EMAILS...
The smart ones are personalizing PREDICTIONS.
Here's what I found:
The $82 Billion Secret:
• Predictive analytics market exploding from $18.89B to $82.35B by 2030
• But 73% of companies still react to customer behavior instead of predicting it
• The winners? They know what you want before YOU do
3 Things the 89% Do That You Probably Don't:
1️⃣ Entity Optimization (Not Just Keywords)
→ They use schema markup to make AI understand their content
→ Result: 2x more discoverable in AI search results
→ While you optimize for Google, they're optimizing for ChatGPT
2️⃣ Predictive Personalization (Not Reactive)
→ They analyze intent data to identify prospects before they're ready to buy
→ Result: 5x faster lead identification and 300% better accuracy
→ While you send "personalized" emails, they predict customer lifetime value
3️⃣ Behavioral Forecasting (Not Demographics)
→ They track micro-behaviors across 12+ touchpoints
→ Result: 122% higher email ROI and 202% better conversion rates
→ While you segment by age/location, they predict next purchase timing
The brutal truth?
76% of consumers get frustrated when brands fail to deliver true personalization.
Your customers can smell "Dear [First Name]" from a mile away.
But here's what terrifies me:
71% of B2B buyers now EXPECT personalized digital interactions.
If you're not using predictive analytics, your competitors who are will capture your market share while you're still guessing what customers want.
The question that keeps me up at night:
Are you predicting customer behavior or just reacting to it?
What's the biggest challenge you face with implementing predictive analytics?
Meaning, Media & Machines: Reflections from dialogues at Cannes
There are two big realities shaping brands today
First, products are becoming interchangeable. Tech has flattened the playing field. What was once premium is now standard. From banking apps to beauty serums, functional differences are minimal. Brands struggle to stand out in a world of commoditised everything.
Second, meaning is the only real differentiator. But meaning needs media and media is now fragmented and hyper-personalised.
There’s no mass moment, no shared screen. One consumer is on TikTok, another on Substack, a third on Discord. Messaging is splintered across screens and seconds.
And now enters AI.
AI isn’t just another disruption but a total reset.
It’s not changing the rules of branding.
It’s rewriting the game board.
Here’s how:
A) Hyper-Personalisation is diluting brand identity.
AI tools like Meta’s Advantage+ or Google’s Performance Max optimise creative and placements in real-time. The same brand looks different to every user. Customisation scales, but consistency fades.
If every impression is unique, is there still a unified brand at all?
B) synthetic creativity is creating a sea of sameness.
Brands are now using tools like Mid journey, Adobe Firefly, and RunwayML to generate endless visuals, videos, and even voices. Great for scale. But risky because when everyone uses the same AI aesthetics, brands start to blend, not break through.
C) AI agents are becoming the new gatekeepers.
As users shift to voice assistants, search bots, and AI shopping tools, traditional brand equity loses ground. The algorithm - not you - chooses the brand. If you’re not optimised for AI discoverability, your story might never even show up.
🔹 Ethics and transparency will become brand signals.
Consumers are already thinking / questioning: Who made this ad? Was it a human?
Tools like Synthesia, ElevenLabs, and Jasper.ai make content creation seamless but brands that hide their AI use may suffer trust backlash
🔹 Human touch will be the new luxury
The more AI fills the feed, the more human made work will shine. We’re seeing a rise in “slow branding” with limitations woven in, handwritten notes, imperfect packaging.
Realness will be the new premium in an era of algorithmic perfection.
Some brands are already experimenting smartly
Coca-Cola launched the “Create Real Magic” AI platform letting users co-create ads using OpenAI + DALL·E.
Stability AI is working with brands on bespoke AI models tuned to their voice and tone.
AI won’t kill branding but it will kill lazy branding.
Brands must now define not just what they are, but how they show up through AI.
Your voice, your choices, your ethics, all matter more than ever.
In the age of the machine, the most human brands will win, warts and all.
#AIinMarketing#CreativeTechnology#FutureOfBranding
My article in Evonomic Times , Brand Equity
𝗔𝗜 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗮𝗸𝗲 𝗯𝗮𝗱 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗴𝗼𝗼𝗱; 𝗜𝘁 𝗷𝘂𝘀𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗲 𝗯𝗮𝗱 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗵𝗮𝗽𝗽𝗲𝗻 𝗳𝗮𝘀𝘁𝗲𝗿.
Most small teams spend 20+ hours/week on marketing tasks that could run themselves.
Here's the problem: They're trying to "AI-ify" everything instead of automating the RIGHT things.
I call this the ACT Method, and it's helped startups save 15+ hours/week while INCREASING results.
Step 1: ANCHOR (Pick Your Battle)
Don't automate random tasks. Find your biggest marketing bottleneck that's:
✅ High-impact (drives real leads)
✅ Repeatable (same process, different content)
✅ Time-consuming (eating founder bandwidth)
Step 2: CONSTRUCT (Build the Engine)
Encode your brand DNA into systems using:
→ LLMs (Claude/GPT) for smart decisions
→ Context storage (Airtable) for brand voice
→ Automation tools (n8n) for workflow
→ Output channels (LinkedIn/WordPress) for distribution
Step 3: TEST (Make It Bulletproof)
Add guardrails:
• Human approval checkpoints
• Quality checklists
• Fallback protocols
• Performance tracking
𝗥𝗲𝗮𝗹 𝗥𝗲𝘀𝘂𝗹𝘁: 𝗢𝗻𝗲 𝘁𝗲𝗮𝗺 𝗱𝗲𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝘁𝗶𝗺𝗲 𝗯𝘆 𝟴𝘅 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗲𝗿 𝘀𝗽𝗲𝗻𝘁 < 𝟭 𝗵𝗼𝘂𝗿/𝘄𝗲𝗲𝗸 𝗼𝗻 𝗮𝗽𝗽𝗿𝗼𝘃𝗮𝗹𝘀, 𝗮𝗹𝗹 𝘄𝗵𝗶𝗹𝗲 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗯𝘆 𝟰𝟯% 𝗮𝗻𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝟯.𝟱𝘅 𝗺𝗼𝗿𝗲 𝗾𝘂𝗮𝗹𝗶𝗳𝗶𝗲𝗱 𝗹𝗲𝗮𝗱𝘀.
The ACT Method doesn't just save time, it creates predictable, scalable marketing systems that work whether you're in the office or on a beach in Thailand.
What's your biggest marketing time-suck right now? Comment below 👇
Want more tips like this? Subscribe to my LinkedIn newsletter, AI Marketing Solution Architect: https://lnkd.in/gbbmMrBp#MarketingAutomation#StartupGrowth#AIMarketing