Personalization At Scale With AI Technologies

After months of collaboration with some of the smartest people in advertising, I'm excited to share the IAB AI Personalization Playbook, a practical guide for scaling AI-powered creative personalization. 👉 Here's the thing: many organizations are running pilots. Few are actually scaling those pilots to full enterprise personalization. This playbook is our answer to that gap, a practical, cross-functional guide for moving beyond experiments to repeatable, responsible AI-powered personalization. It's built around three core principles: 1️⃣ Human-centered AI – Because humans still need to be in the driver's seat 2️⃣ Cross-functional integration – Marketing, creative, ops, legal, and data all need to be at the table internally and externally, media and creative teams need to be tightly orchestrated 3️⃣ Risk-tiered governance – Not all content needs the same level of oversight We worked with platforms, brands, agencies, and publishers to build something that's practical and usable, versus simply aspirational. The framework covers everything from strategic briefing to scaled production to measuring what matters. It's designed to help companies figure out where AI fits in your workflow, how to maintain brand integrity at scale, and what good governance actually looks like in practice. Huge thanks to everyone who contributed feedback, challenged assumptions, and helped make this better than any one organization could have built alone. Specifically, Adam Buhler, Executive Vice President, Creative Technology Digitas North America, Adwait Walimbe, Digital Transformation Advisor Adobe, Brian Hull, Head, Global Creative Labs The Weather Company, Graham Wilkinson, EVP, Chief Innovation Officer, and Global Head of AI Acxiom, Kelly O'Brien, Senior Product Manager, NPR, Paul Longo, General Manager, AI in Ads Microsoft, Todd Hassenfelt, EGlobal Digital Commerce Sr. Director, Strategy & Execution Colgate-Palmolive and all the internal teams at IAB. ⭐️ 👉 IAB AI Personalization Playbook: https://lnkd.in/dDqU7p3G #ai #personalization #creative #roi

They said creativity couldn’t be automated. Turns out, they weren’t looking close enough... A few months ago, I sat down with a media agency that handled dozens of global brands. Their challenge? Personalization at scale.  Take a single campaign for a beverage brand.   - 50 cities.   - 15 demographics.   - Thousands of unique ad copies to make every customer feel, 'This is for me!'  The old way? Teams manually adjusted messaging for each target group, sifting through endless data to fine-tune language, imagery, and placement. It was exhausting, time-consuming, and—frankly—not scalable.  Enter AI Agents.  Here’s how we turned things around:   1. Creative Drafting: The AI Agent analyzed audience data and drafted tailored copy for every single segment in minutes. No generic templates—actual, on-point messaging.  2. Visual Personalization: It matched images and designs to local cultures and preferences—ads for a young Mumbai audience looked completely different from those in Delhi, but both hit the right notes.    3. Performance Insights in Real Time: As the campaign ran, the agent tracked performance, tweaking messaging and targeting on the fly. It didn’t just run the campaign; it optimized it live.  The results?   What used to take weeks now took days. The campaign reached 3x the audience with higher engagement rates and—this is the kicker—costs went down by 25%.  One of their senior creatives told me, “For the first time in years, we’re spending more time ideating and less time firefighting.”  This isn’t about replacing creativity. It’s about removing the barriers that hold it back.  The future of media and advertising isn’t human vs. AI. It’s humans, powered by AI. And the ones who embrace it will dominate the industry.  Are you ready to scale your creativity? Or will you let the old ways hold you back?  Supervity AI Agents are going to be leading the future

Your marketing playbook just expired. AI has rewritten every rule while most brands are still playing by 2019 strategies. The companies adapting fastest aren't the ones with bigger budgets or better tech teams. They're the ones who understand how AI has fundamentally changed customer behaviour. Here's what the winners are doing differently: 1. The New Search Landscape: SEO meets LLM Traditional keywords are the past. Conversational queries are everything. Example: REI shifted from keyword-stuffed descriptions to contextual content addressing specific use cases, increasing AI-summarised results visibility by 47%. Reality check: Google's AI Overviews now appear in nearly half of all search results. 2. AI Assistants as Gatekeepers Your brand must be recognised by AI as a category leader to enter consideration sets. Example: Best Buy organised product attributes to match natural customer questions, achieving 35% increase in organic traffic from voice searches. The shift: AI now filters options before consumers see them. 3. Attention Compression Consumer attention spans shrink as AI summarises everything instantly. Action point: Front-load your value proposition in all communications. The pattern: Customers want to digest information about products quickly, not hunt to understand what’s in it for them. 4. Hyper-Personalisation Without Creepiness AI enables true 1:1 marketing at scale, but only if you balance customisation with transparency. Example: Sephora's Skin IQ tool provides personalised skincare recommendations, driving 35% growth in skincare sales. The principle: Use preference-based content sequencing with full transparency about data usage. 5. Multi-Modal Content Strategy AI-driven consumers expect seamless experiences across text, voice, and visual channels. Example: Domino's "AnyWare" approach allows ordering through voice assistants, text, social media, and apps. The requirement: Build centralised content hubs ensuring consistent messaging across all channels. 6. The Human Advantage As AI handles transactions, authentic human connection becomes your competitive edge. Example: Lululemon's in-store community events resulted in 25% higher repeat purchase rates compared to online-only shoppers. The opportunity: Community-building programs generate 23% higher customer lifetime value. The brands that thrive won't be those with the most sophisticated AI tools. They'll be the ones that use AI to enhance human connection rather than replace it. Which of these shifts will you implement first? ♻️ Found this helpful? Repost to share with your network.  ⚡ Want more content like this? Hit follow Maya Moufarek.

The search bar is dead. And most e-commerce platforms don’t even know it yet. After working closely with AI systems and recommendation engines, I’ve learned one thing: “Personalized shopping” was never truly personal. It was pattern matching. It was collaborative filtering. It was reactive logic pretending to be intelligence. Now we’re entering a different era. → From personalized to personal → From search-based discovery to proactive intelligence → From browsing endlessly to AI agents working for you This is agentic commerce. Traditional e-commerce makes you do the heavy lifting: Search → Filter → Scroll → Compare → Hope Agentic commerce flips the entire model: Describe what you want → AI delivers with context One of the most interesting examples I’ve seen is Glance. They are not building another shopping app. They’re building a contextual, agentic AI commerce layer powered by multiple specialised agents working together. Instead of one algorithm guessing what you like, Glance deploys multiple AI agents working for you in parallel: → Weather Agent analysing real-time climate and fabric suitability → Trends Agent tracking global shifts and micro-trends → Occasions Agent anticipating upcoming events → Physical Agent understanding your skin tone, undertones, and body type → Lifestyle Agent decoding your aesthetic preferences All coordinated by an orchestrator that synthesises everything into a unified styling strategy. That’s not basic personalization. That’s contextual intelligence. And the most powerful shift? You see yourself in the generated looks. Not stock visuals. Not generic models. You. Commerce becomes a conversation instead of a search box. From personalized to personal. AI agents working for you. Learning with every interaction. Refining your style instead of just tracking clicks. This is the rise of agentic commerce. #Glance #AICommerce #AgenticAI

Sharing key learnings and insights from our Real-Time (In-Session) Personalization journey at CARS24 — a capability that has transformed how we personalize the car buying experience at scale. Leveraging advanced sequence-based neural networks and real-time Kafka streaming infrastructure, we've developed a dynamic machine learning pipeline that processes more than a million user interactions daily. Our deep learning models rapidly adapt to user behaviour, delivering personalized car recommendations with sub-200ms latency. Highlights: ✅ Advanced sequence-based neural network architecture  ✅ Real-time streaming and processing of user behaviour signals with Kafka  ✅ Rapid feature engineering and inference using optimized real-time databases  ✅ High scalability for continuous model retraining and deployment Performance Impact: 📈 Across all discovery widget we achieved a highest Impression-to-View (I2V) rate and on the 'Best Matches' recommendation rail on our car detail page and buyer home page. 📈 Delivered a strong Impression-to-Booking Initiation (I2BI) conversion rate across different discovery widgets, underscoring high user relevance and engagement. Business Outcomes: 🚀 Significant uplift in user engagement  🚀 Marked reduction in user drop-offs  🚀 Enhanced personalization and superior user experience The attached flow chart outlines the architecture behind this AI-powered personalization pipeline — from real-time clickstream ingestion to ML inference and personalized recommendations. #RealTimePersonalization #AI #MachineLearning #DeepLearning #Kafka #DataScience #RecommendationEngine #TechInnovation #AI  #Personalization #pubsub #CARS24 #transformers #llm #genai

There's no recipe to make customers happy. What delights one customer will piss off another. We sent the same QBR deck to three similar accounts last year: Account A → "This is exactly what we needed" Account B → No response, ghosted me for 2 weeks Account C → "Can you just send bullet points next time?" Same deck. Same outcomes highlighted. Three totally different reactions. But we still build playbooks like customers are the same. → Everyone gets a 30-60-90 touchpoint plan → Everyone gets quarterly business reviews → Everyone gets the monthly product update email We've confused "scalable" with "identical." The reality? Your customers don't operate the same way. Some execs want deep strategic sessions. Others want you to disappear unless something's broken. Some respond to Slack messages at 6am. Others won't open an email for three days. So stop scripting every interaction. Give your CSMs guardrails instead: → First business outcome in 45 days (let's guide them) → Two exec relationships per account (you decide which ones) → Flag risk at 60 days declining engagement (you determine the response) Define the outcomes. Let them handle the path. Here's what's wild though... AI actually makes this possible at scale now. Not the "AI will replace CSMs" nonsense. But real pattern recognition: This customer ignores emails but responds to video This exec engages most on Tuesday mornings This account prefers cost savings language over efficiency talk You can finally personalize without burning out your team. The best CS orgs in 2026 won't have the prettiest playbooks. They'll have the teams who can read the room and adapt accordingly. At scale. How are you addressing true customer-personalization?

How AI is Revolutionizing Cosmetic Formulation The cosmetic industry is undergoing a significant transformation with the rise of artificial intelligence (AI). As a chemist deeply involved in formulation and R&D, I have seen so many cases how AI is changing how we approach product development, research, and sustainability. The use of AI in the cosmetic industry is no longer a futuristic concept, it’s here and reshaping the landscape in ways we could not have imagined just a few years ago. -Ingredient Prediction and Optimization One of the most exciting developments in cosmetic formulation is AI's ability to predict and optimize ingredients. Traditionally, the formulation process involved trial and error, where chemists spent extensive time testing various combinations of ingredients. AI can now accelerate this process by analyzing huge datasets and predicting which ingredients work best together to achieve specific product goals, such as anti-aging, hydration, or sun protection. it's algorithms can evaluate thousands of ingredients, analyze their chemical properties, and suggest optimal combinations based on desired outcomes. This reduces the time spent on R&D and ensures formulations are more effective from the onset. Brands like Proven Skincare use AI to analyze skin profiles and create personalized formulations for their customers based on specific needs. -Personalization from Mass Production to Custom Solutions In the past, cosmetic products were largely mass-produced with minimal variation. However, AI is changing this by enabling brands to offer personalized solutions at scale. By analyzing consumer data, such as skin type, concerns, and environmental factors, AI can recommend customized formulations that cater to individual needs. Companies like Atolla and Function of Beauty have embraced AI to develop personalized skincare and haircare products. Personalized products lead to higher customer satisfaction and more effective results. As AI continues to improve, we can expect even more tailored solutions, potentially reducing the need for generalized formulations.

When most companies talk about “scaling customer experience,” what they really mean is: “Let’s add automation and hope people don’t notice.” But customers do notice. They notice when the response is fast but unhelpful. When the tone feels robotic. When their history gets ignored and they have to repeat themselves. When it’s obvious no one’s really listening. So the question isn’t how to scale faster. It’s how to scale without losing the personal layer that actually makes people stay. Here’s what we’ve learned from building AI agents at Quimbi for gaming studios: 1. 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫, 𝐧𝐨𝐭 𝐬𝐞𝐠𝐦𝐞𝐧𝐭𝐬.       Players don’t care if they’re part of Segment B. They care if you remember what happened in their last session.     2. 𝐔𝐬𝐞 𝐦𝐞𝐦𝐨𝐫𝐲 𝐚𝐬 𝐚 𝐟𝐞𝐚𝐭𝐮𝐫𝐞.       The best CX doesn’t guess. It recalls. A smart agent should know if someone wrote in two days ago—and why.     3. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐭𝐨𝐧𝐞, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐬𝐩𝐞𝐞𝐝.       Fast is good. But respectful and calm wins. You can do both.     4. 𝐆𝐢𝐯𝐞 𝐀𝐈 𝐚 𝐫𝐞𝐚𝐬𝐨𝐧 𝐭𝐨 𝐞𝐬𝐜𝐚𝐥𝐚𝐭𝐞.       Not everything needs a human. But the 𝘳𝘪𝘨𝘩𝘵 things do. Escalation isn’t failure—it’s care.     5. 𝐓𝐫𝐞𝐚𝐭 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐚𝐬 𝐟𝐮𝐞𝐥.       Every missed moment is a chance to improve. If you're not feeding that data back into your system, you're scaling blind.     Personalized doesn’t have to mean manual. At scale doesn’t have to mean distant. When you design CX to feel noticed, you earn trust, whether you’re serving 100 or 100,000.

Email marketing remains the bedrock of B2B success, consistently delivering for customer retention and new lead generation. Now, a transformative force is emerging to amplify these results: AI. 57% of larger B2B companies are integrating AI into their email strategies, recognizing its potential to revolutionize their funnels. AI isn't just about automation; it's about creating intelligent, hyper-personalized experiences that resonate with your prospects and customers at every touchpoint: - Hyper-Personalization at Scale: AI delves deep into individual buyer data, understanding their unique needs and behaviors to deliver tailored content that converts. - Intelligent Retargeting: AI dynamically re-engages leads based on their specific interactions, ensuring your message is timely and highly relevant. - Precision Optimization: AI continuously analyzes and refines subject lines and send times, maximizing open rates and engagement with scientific accuracy. - Dynamic Content that Adapts: AI generates personalized content blocks on the fly, ensuring each recipient sees the information most pertinent to their journey. While the potential is immense, successful AI implementation requires thoughtful consideration: - Authenticity: AI-generated content risks sounding robotic if not carefully trained on your brand's unique voice and the subtleties of human interaction. - Human Touch: AI is a powerful tool, but strategic oversight, creative input, and ethical considerations remain firmly in the human domain. - Quality Data: AI algorithms thrive on accurate and comprehensive data. Investing in CRM hygiene and data enrichment is paramount for AI to deliver meaningful insights and personalized experiences. Elevating Your Funnel: To move beyond simply adopting AI and truly leverage its transformative power, consider these strategic imperatives: - Pinpoint Your Bottlenecks: Identify the real friction points in your lead journey and CRM processes. Where are leads dropping off? Where is personalization falling flat? - Experiment with Purpose: Initiate focused pilot programs targeting those key bottlenecks. This allows for measurable learning and minimizes risk. Think: "Can AI improve our initial lead qualification response time?" or "Can AI personalize content to boost engagement in our nurture sequence?" - Fuel the Machine with Intelligence: AI is only as good as the data it consumes. Invest in data hygiene to ensure your CRM provides a rich, accurate foundation. - Infuse Your Brand DNA: Actively train your AI models on your brand voice, values, and target audience nuances. - Orchestrate with Human Expertise: AI should empower, not replace. Integrate human review & oversight into your workflows to ensure quality, ethical considerations, & brand alignment. - Build the Right Foundation: Recognize that successful AI implementation requires the right skills and support. Invest in marketing operations expertise..

The conversation around AI in customer experience is shifting, and I believe it's moving towards a more nuanced understanding of its potential. It's less about if and more about how to implement it ethically and effectively to drive tangible business outcomes. My perspective? The winners will be those who: Prioritize hyper-personalization at scale: This means moving beyond basic segmentation and using AI/ML to truly understand individual customer needs, preferences, and even predict future behavior. We're talking about dynamic content optimization, personalized recommendations, and proactive customer service that anticipates needs before they arise. Focus on AI-driven augmentation, not replacement, of human interaction: AI should empower human agents, not eliminate them. Think AI-powered tools that provide agents with real-time customer insights, automate routine tasks, and enable them to focus on complex problem-solving and relationship building. Build robust data governance frameworks and ethical AI practices: This is paramount. We need to ensure responsible AI use by prioritizing data privacy, mitigating bias in algorithms, and being transparent with customers about how AI is being used. This includes implementing explainable AI and putting in place mechanisms for ongoing monitoring and auditing of AI systems. What are your thoughts on the ethical considerations of AI in CX, particularly as AI becomes more sophisticated? #AI #CustomerExperience #Personalization #DataEthics #DigitalTransformation #ResponsibleAI