Generative AI In Creative Jobs

🔥 The Bundesliga Breakthrough: Personalizing Context, Not Content At the Sports Forum at Amazon Web Services (AWS) re:Invent 2025, I learned something rare… something most business leaders never hear: Everyone talks about personalized content. Almost no one talks about personalized context. And that’s where the Bundesliga — One of the top football leagues — is quietly years ahead of the market. Here’s the insight most executives miss: AI doesn’t scale content. AI scales understanding. Bundesliga’s AI system works because it doesn’t start with the match. It starts with the fan. Before a single line of commentary is generated, the system builds a real-time “context graph”: - who the fan is - how long they’ve followed the league - what commentary style keeps them hooked - which players they track - what cultural cues resonate in their region - and what emotional tone they respond to This is the whole magic. Gen AI is just the surface. The real breakthrough is the context engine underneath. >> Why this matters for executives? Most companies try to personalize by tweaking the message. Bundesliga personalizes by changing the lens through which the customer sees the message. That shift is massive. Because when you personalize context: - Engagement stops being random - Marketing stops being guesswork - CX stops being generic - Loyalty stops being an accident >> The uncomfortable question Most leaders ask: “How do we create more personalized content?” The better question — the Bundesliga question — is: “Do we truly understand the context our customers live in?” Because here’s the uncomfortable truth: > You can’t personalize content at scale unless you personalize context first. Bundesliga shows the future. The next decade of CX belongs to companies that invest not only in storytelling… …but in systems that understand their customers better than customers understand themselves. Your turn: 👉 How could your customer experience improve if your systems learned their context the way Bundesliga’s does? #CustomerExperience #AILeadership #GenerativeAI #Bundesliga #DigitalTransformation #Personalization

As artificial intelligence and immersive-tech evolve, we’re on the cusp of a major shift. Would you watch basketball like this? The global market for AI in gaming — which encompasses everything from smarter NPC behaviour and dynamic environments to procedural content generation — is projected to grow from ≈ USD 7.05 billion in 2025 to ≈ USD 37.9 billion by 2034 (CAGR ~ 20.5%) Meanwhile, immersive technologies (think VR, AR, mixed reality and related hardware/ software) are forecast to expand from around USD 13.2 billion in 2024 to as much as USD 175 billion by 2035 — a CAGR of ~ 26.5%. What does that mean for “watching” games? It means that passive spectatorship could soon feel a lot more like living — or being inside — a game world. ✅ AI-powered worlds — Developers are increasingly using generative AI to build expansive, dynamic 3D environments, intelligent NPCs, and procedurally generated content. These tools make large, intricate virtual worlds cheaper and faster to build. ✅ Immersive tech maturation — As AR/VR/MR hardware improves, and as the software ecosystem matures, immersive experiences are becoming more accessible and lifelike. The convergence of AI + immersive hardware dramatically lowers the barrier to “experiencing the game rather than just watching it.” The Implication: In the near future, “watching” gameplay could evolve into a fully immersive experience — where viewers don’t just see a screen, but feel present in the virtual world. For developers, this opens up new creative and monetization possibilities (personalized experiences, dynamic storytelling, virtual attendance). For players and spectators, it means deeper emotional connection, more interactive spectating, and heightened engagement. If you’re in gaming, entertainment, tech — or even beyond — now is the time to start thinking about what “spectator experience” really means. The boundaries between playing, watching, and being there are blurring fast. #AI #ImmersiveTech via @berkAI #GamingIndustry #FutureOfGaming #GenerativeAI #Metaverse #Holographics #VirtualReality #AugmentedReality #TechInnovation #DigitalExperience #AIRevolution #NextGenGaming #EmergingTech

As consumers seek more individual experiences and interactions, companies turn to #AI to deliver 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡𝙞𝙯𝙚𝙙 𝙥𝙧𝙤𝙢𝙤𝙩𝙞𝙤𝙣𝙨 𝙖𝙩 𝙨𝙘𝙖𝙡𝙚. For some time now, companies have been trying to address customer needs through #personalization, using data and analytics to craft more relevant consumer experiences. Using improved analytics models, brands and retailers can better provide valuable offers to micro-communities wherever they want to engage. Meanwhile, #genAI enables marketers to create tailored content that is relevant to those groups. According to McKinsey & Company, marketers should unlock personalization at scale, by upgrading five areas of their #martech stack and processes: 1. Data: by improving #data collection and analysis, marketers can gain deeper insights into customer behaviors and preferences. 2. Decisioning: to develop personalized promotions and content through more robust targeting, companies can also benefit from refreshing their #decision engines with new AI models. 3. Design: a sophisticated design layer that oversees offer management and #content production helps manage the process, fueling both operational excellence and agility. 4. Distribution: achieving true, real-time personalization requires a sophisticated #marketing architecture that delivers seamless and consistent messaging to the right audiences at the right time on the right channel. 5. Measurement: to validate the #ROI of personalization efforts, rigorous incrementality testing, standardized performance metrics, and measurement playbooks are essential. Are there other capabilities or technologies required for marketers to better target promotions and deliver individual content?

You probably heard about Google’s recently launched Nano Banana, a lightweight yet powerful image generation model. There’s more to share about it. Unlike traditional heavy models, Nano Banana focuses on efficiency without sacrificing quality. It can transform a simple text prompt or even an uploaded image into a polished, high-resolution output while maintaining consistency and safety checks. But how exactly does this AI take your words (or pictures) and bring them to life? This roadmap takes you through the complete journey of AI image generation, from the very first input stage to the final polished image, so you can clearly understand what happens behind the scenes. 1. Input Stage → You type a text prompt or upload an image for editing. 2. Text Processing → AI breaks text into tokens, converts them into embeddings (numbers). 3. Image Preprocessing → Uploaded images are split into small patches with features extracted. 4. Noise Initialization → Process begins with random noise, like a fuzzy blank canvas. 5. Concept Understanding → AI builds a mental plan of what the image should look like. 6. Multimodal Alignment → Text embeddings + image patches are mapped into a shared space. 7. Guided Transformation → Step-by-step, noise is removed, image becomes clearer. 8. Attention Mechanism → AI focuses on key details (e.g., makes “glasses” sharp & visible). 9. Iterative Refinement → Shapes gain textures, colors, depth, and realism. 10. Output Delivery → High-resolution final image is produced with an AI watermark. 11. Final Polishing → Shadows, reflections, and colors are enhanced for realism. 12. Safety & Consistency Check → Filters scan for harmful content and ensure subject consistency. With Nano Banana, Google is making image generation faster, safer, and more consistent, while keeping quality high. #AI

Scientists just invented a completely new way to make videos with AI. It’s called Interactive Motion Control. Instead of prompting the motion with words, you can literally drag your cursor on the screen to precisely control how different things move. But here’s the crazy part… This tool lets you control that motion in real-time. So as you draw it in, your new video is generating at the same time. It’s like having a creative magic wand that actually works. For example, let's say you have this photo of a wave coming out of a book. And you want to turn that into a video where the wave crashes on the pages. With this tool, you can literally draw in the exact motion path you want the wave to take. And your new video will generate at 29 frames per second as you draw. You’re not waiting for it to render. That generation is happening instantly. This is called real-time streaming and it’s a massive breakthrough. Not only can this method generate videos 900x faster than popular models, it can also make videos however long you want. There’s no maximum time limit. And so the question is…how is that possible? Cause these examples literally look like magic. Well it turns out, the researchers who invented this, had a crazy idea… Intentional Memory Loss. The reason AI video is so hard is because video models have to remember all the previous frames they generated before. An 8 second video is 192 frames. But here, they’re purposefully having the model forget all the frames in the middle. All it remembers is the first frame and whatever happened in the last few seconds. And by doing that, these videos require a lot less memory and compute. So they act more like a flexible canvas and less like a fixed render. It’s pretty insane. Now this model is the latest to come out of Adobe Research. And so it's probably gonna launch in Adobe products at some point next year. Imagine editing videos in Premiere Pro where you just draw camera movements and watch them generate instantly on your footage. This whole model is called MotionStream. And I think this is going to change the content creation game forever. Follow Kane K. for more videos on AI. #artificialintelligence #ai #tech #technology #creativity #video #creator #adobe

AI is transforming video editing. Let me explain: Last week, I found myself at Boardmasters music festival in Cornwall. I was decked out in my rave jacket, clinging to a swing ride for dear life. My Insta360 X3 captured every thrilling (and terrifying) moment. But when I got home, reality hit. I had over 2 hours of footage and zero editing skills. That's when I stumbled upon the latest Insta360 app update. It's like having a tiny AI film director in your pocket. Here's what blew my mind: 1. Scene Recognition: → This AI doesn't just see people, it understands context. It spotted me and my partner Lauren on the ride, but also captured the views of the tents and sea. 2. Movement Templates: → 40+ options to add dynamic motion with a few taps. Perfect for transitioning from POV to panoramic festival views. 3. Customisable Edits: → Adjust perspective, speed, and fine-tune end results for that pro look. 4. Works with all 360 cameras by Insta360: → Capture both your reaction and the scene—something your iPhone can't do (yet). My takeaway: AI is now helping amateurs like me elevate their editing skillset overnight. What tools are you using? Share your experience below. P.S. If you want more tips on using AI in your life, follow me Alex Banks for more.

Have you heard of an AI Playground? It’s a real or simulated environment that models use for training. Instead of learning from data, the model learns from doing and feedback like these ants are. Google found a way to use GenAI and YouTube videos to train robots. Videos allow the model to map thousands of real-world environments and variations on a task domain, like cleaning a countertop. Companies like Raytheon use advanced simulations to test aircraft designs in a digital environment before building an expensive prototype and running even more expensive real-world tests. Imagine being able to do that with any part of your business (physical and digital processes) and product. Testing, iterating, and improving in days vs. years at a fraction of the cost. When people say “AI training AI,” they’re talking about using AI Playgrounds or simulations to train models that can interact with the simulated digital or real-world environment. NVIDIA’s Omniverse is working to map physical and digital environments to build an AI Playground platform. Salesforce recently pivoted its Agentforce to map business workflows and develop a business process AI Playground platform. Simulations are among the most impactful AI applications that most people have never heard of. #ArtificialIntelligence #GenAI #Robotics

AI isn’t just a buzzword; it’s the engine that’s driving the next wave of marketing innovation. In today’s fast-paced digital world, AI is transforming the way we approach marketing, and Google's 'AI for Marketing Engine' (1) framework is at the forefront of this change. Let’s break it down: 1. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭 & 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 AI doesn’t just collect data; it makes sense of it. Take L'Oréal, for example—they used AI to analyse millions of customer interactions, resulting in an increase in ad recall (2). This kind of real-time, data-driven insight allows marketers to pivot and optimise strategies on the fly. 2. 𝐌𝐞𝐝𝐢𝐚 & 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐬𝐚𝐭𝐢𝐨𝐧 AI tailors media buying and personalisation at scale. Spotify is a perfect example, using AI to create hyper-personalised experiences like Discover Weekly, which led to users streaming more than twice as long (3). The lesson here? AI ensures that you engage the right person at the right time, every time. 3. 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞 & 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 AI isn’t just about crunching numbers—it’s a creative partner too. Unilever leveraged AI to optimise their product design process, accelerating development while aligning with their sustainability goals (4). AI enhances creativity, helping brands produce content that truly resonates. AI isn’t the future of marketing; it’s the now. Ready to transform your marketing engine? Let’s dive in and harness the power of AI together. Follow Dhawal Shah for more such content. (1) https://lnkd.in/gpvEvQ9h (2) https://lnkd.in/gsG_4NBR (3) https://lnkd.in/gWThbs_g (4) https://lnkd.in/gSV6ZDMX #MarketingStrategy #ArtificialIntelligence #DigitalMarketing #AIinMarketing #CustomerExperience #Innovation #BusinessGrowth #Personalization #CreativeMarketing

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

If your AI brainstorming starts with an AI prompt  such as “give me ideas about for X,” you’re limiting your imagination.  I learned this while working through IDEO U’s Human-Centered Design and AI certificate program, which keeps reminding me that AI only supports creativity when humans stay actively involved. To test this, I ran a small experiment tied to my design challenge: how can nonprofit professionals use AI to augment their thinking so their work becomes more strategic, creative, and human-centered? Here’s what happened. When I began with human-only ideation (my own brain or a brainstorming session with other humans), the ideas were grounded in mission, constraints, and real community needs. When I switched to AI with a clear creative direction to generate ideas, I asked for absurdity.  AI delivered: costume-based learning scenes, dramatic falling sequences, Play-Doh brains, even a human–AI tango. These weren’t solutions or a waste of time. They were creative provocations that loosened up the tight mental space we often operate within. The best ideas emerged only after I cycled through several layers of human grounding, AI variation, and human synthesis. It felt like a club sandwich of thinking modes. Humans brought mission and ethics. AI widened the possibility space. Humans shaped meaning. The infographic (created in Nano Banana) shows the practices that made this work: 💡Begin with human insight. 💡Give AI a clear creative direction. 💡Separate idea expansion from idea selection. 💡Use reflective checkpoints. 💡Treat AI as a partner, not a replacement. This experiment makes me think that the real value of AI in nonprofit brainstorming is less about efficiency and more about expanding imagination. When humans guide the process, AI becomes a thought-partner for more human-centered creativity. What would open up in your work if your organization treated AI as a creative partner instead of a shortcut?