AI Integration in Communication

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

Not getting the response you want from AI? It’s usually not the tool. It’s the way you’re approaching it. The biggest mistake people make is treating AI like a one-shot machine. They type one prompt, get an average answer, and assume the model is the problem. But that’s not how this works. Every interaction with AI is really an experiment. You start with a hypothesis. That hypothesis becomes your prompt. Then you test it, look at the output, and study what came back. If the result is weak, vague, or off track, you don’t stop there. You refine the prompt, add better context, adjust the goal, and try again. That’s where good results come from. Prompting is not just writing a request. It’s an iterative process. You’re shaping the outcome step by step. When I get a weak response, I don’t immediately blame the AI. I look at my own input first. ↳ Did I give enough context? ↳ Did I define the goal clearly? ↳ Did I frame the task for the right audience? ↳ Did I ask the model in a way that made a strong answer possible? And sometimes, the best move is to ask the model about its own response. ↳ Why did you answer this way? ↳ What assumptions did you make? ↳ What would make this prompt stronger? That’s where things get interesting. Because at that point, you’re not just using AI to get answers. You’re learning how to think with it, how to guide it, and how to get better results with every cycle. The people getting the most out of AI are not the ones asking once. They’re the ones who know how to iterate. ♻️ Share if this resonates. ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI. #Innovation #Technology #ArtificialIntelligence #GenerativeAI

In the rapidly evolving landscape of AI, data has become a pivotal asset, offering unique opportunities for commercialisation and monetisation. The groundbreaking deal announced yesterday between OpenAI and Axel Springer underscores the emerging trend of data deals and licensing in the AI sector, heralding a new era of AI-driven journalism and content creation. This unprecedented partnership allows ChatGPT to utilise and summarise news stories from prominent media brands like Politico and Business Insider. This agreement is significant for several reasons. Firstly, it enables AI models to access and use high-quality, current content, enhancing their capability to provide relevant and timely information. Secondly, it sets a precedent for how AI companies can legally use copyrighted material, a concern that has become increasingly prominent as AI technology advances. The value of a company's data has never been more apparent. In the AI context, data is not just a resource; it's the lifeblood that powers these sophisticated algorithms. By entering into data licensing agreements, content creators can open new revenue streams, and help ameliorate the adverse impact of AI outputs competing with their own work. This shift is essential in an era where traditional revenue models, especially in journalism and media, are under strain. The deal between OpenAI and Axel Springer also brings into focus the legal nuances of AI and IP. As AI models are trained on vast datasets, including copyrighted material, questions around ownership and infringement become increasingly complex. This partnership shows a path forward where AI companies and content creators can mutually benefit while respecting IP rights. Looking beyond this specific deal, the concept of data licensing in AI opens a myriad of possibilities. For AI models to be effective, they need diverse, extensive, and current datasets. Data licensing agreements can ensure a steady supply of this crucial resource while providing a fair compensation model for content creators. The OpenAI-Axel Springer deal is a harbinger of the changing dynamics in the AI industry. It represents a shift towards a more collaborative, ethical, and legally compliant approach to AI development and deployment. As AI continues to integrate into various sectors, the value of data will only escalate, making data deals and licensing an essential aspect of the AI ecosystem. This partnership is not just a business deal; it's a blueprint for the future of AI, data management, and the potential symbiosis between technology and content creation. Data deals in the AI context are new. Organisations will need expert advice on how these will need to be drafted, taking into account representations and warranties, indemnities and liability carve outs which are specific to AI. The licence grants will need to be carefully considered and limited to ensure that the licensor’s interests are maintained. I wonder who could help with that…

The biggest myth in AI today? That tools like LLMs, CoPilots, MCPs, and Agents will do the engineering for you. They won’t — because AI is engineering. LLMs. MCP. Agents. They’re all just that — tools. Yet many organizations are spending an extraordinary amount of time comparing, evaluating, and switching between tools — while missing the real essence of AI transformation. The real differentiator isn’t the toolchain. It’s the engineering mindset behind how those tools are used. Most organizations miss that AI is an engineering discipline — not a collection of experiments. It demands the same rigor as any mature system: design, development, testing, validation, rollout, and continuous optimization. Don’t go by leaderboards — they’re tested to work in controlled benchmarks, not in real-world, multi-system environments where context, latency, data, and cost all collide. And don’t fall for the misconception that AI will replace engineers. That’s a narrative being set — but having worked with top LLMs and chatbots, one thing is clear: they often fail when confronted with real engineering. Their code lacks depth, structure, and holistic system thinking. Tools never replace real engineering. They amplify those who understand it. Invest in the core. Invest in robust engineering practices. Upskill your teams. This will be your foundation in building scalable, responsible, and future-ready AI systems. Because tools will change. Frameworks will evolve. But engineering excellence — that’s what endures #aiengineering #ai #leanagenticai

Most people don’t realize: AI can coach you on how to prompt it better. Here’s how to turn AI into your personal prompt coach, so you get better results and learn how to use AI faster. Try this two-step fix: 1. State your goal and context. 2. Ask one of these questions: ➡️ "How would you rewrite my prompt to get more [specific, creative, detailed, etc.] responses?" ➡️ "If you were trying to get [desired outcome], how would you modify this prompt?" ➡️ "If this were your prompt, what would you change to make it more effective?" ➡️ "What elements are missing from my prompt that would help you generate better responses?" ➡️ "How might you enhance this prompt to avoid common pitfalls or misinterpretations?" ➡️ Or simply: "Improve my prompt." Before: "Explain force majeure clauses." After: "Analyze how courts in California have interpreted force majeure clauses in commercial leases since COVID-19, focusing on what constitutes 'unforeseeable circumstances' and the burden of proof required to invoke these provisions." The difference? A broad, non-jx specific, superficial overview vs. actionable legal insights for commercial leases in California. Not only will you get better outcomes, but you will learn how to improve your prompting in the process. What are your go-to strategies or favorite prompts to optimize AI responses?

A Comprehensive Guide to Seamless AI Implementation in Products Let me break down the critical stages that make or break AI integration success: 1. Problem Definition - Start by precisely identifying your business challenge - Set clear, measurable performance objectives - Align AI capabilities with actual business needs 2. Data Strategy (The Foundation) - Quality data collection is non-negotiable - Invest time in preprocessing and annotation - Maintain strict train/validation/test split protocols - Remember: Your AI is only as good as your data 3. Model Architecture - Choose algorithms based on problem complexity - Consider computational resources and constraints - Factor in deployment environment limitations - Set realistic hyperparameter configurations 4. Training & Evaluation Cycle - Implement robust validation procedures - Monitor for overfitting and underfitting - Use cross-validation for reliability - Test extensively on unseen data - Measure against predefined success metrics 5. Post-Deployment Excellence - Monitor real-world performance metrics - Implement continuous learning pipelines - Maintain ethical AI practices - Regular bias checks and corrections - Strict adherence to data privacy standards Key Learning: Successful AI implementation is 20% about algorithms and 80% about systematic execution and maintenance. Pro Tip: Always start with a small pilot before full-scale deployment. It saves resources and provides valuable insights. What steps in your AI implementation journey proved most challenging?

We need to continually upgrade our Humans + AI capabilities: in ourselves, our organizations, and embedded in the systems we use. The objective at all times is for humans to sharpen their cognition and grow through the interaction. This framework suggests 8 levels for Humans + AI engagement, defining the interaction style and value derived from each. This can be used both for developing skills and designing systems. The levels are: 1. TASK OUTSOURCING Vending machine AI completes discrete tasks via single prompts, providing instant results with minimal user learning or growth. 2. SMART RETRIEVAL Knowledge scanner Users retrieve targeted information or examples from AI, boosting fact-finding efficiency and potentially sparking deeper inquiry. 3. GUIDED DRAFTING Rapid composer AI drafts based on human framing, accelerating content creation while refining user judgment and voice. 4. REFLECTIVE PROMPTING Reasoning mirror Prompts elicit assumptions and counterpoints, improving argument quality and fostering self-questioning habits. 5. DIALECTIC EXCHANGE Sparring partner Human and AI engage in iterative probing exchanges, stress-testing ideas and increasing intellectual resilience. 6. COLLABORATIVE SYNTHESIS Multi-agent council Multiple AI agents present distinct views for human moderation, enhancing synthesis skills and embracing diverse expertise. 7. METACOGNITIVE ORCHESTRATION Process coach AI mirrors cognitive processes and suggests refinements, sharpening thinking workflows and bias awareness. 8. CO-EVOLUTION FLYWHEEL Symbiotic loop Continuous human-AI interaction builds an evolving knowledge graph and fosters mutual insight and mastery. How are you engaging at these levels or what improvements to the model do you suggest?

Disney 🤝 OpenAI — why this matters beyond the headline It isn’t just about a tech partnership announcement. It’s about how the next generation of consumer touchpoints gets built, and who owns the emotional interface with fans. From a consumer + licensing POV, this deal is quietly transformative. 1️⃣ IP becomes interactive by default Disney’s strength has always been story + scale. OpenAI adds intelligence + responsiveness. That combination turns passive IP into: ⌙ Conversational characters ⌙ Adaptive storytelling experiences ⌙ Personalized worlds that respond to you, not just demographics This shifts fandom from watching to participating. 2️⃣ The next licensing frontier isn’t just products, it’s experiences Think beyond toys, apparel, or collectibles. AI unlocks licensable formats like: ⌙ Branded AI companions (characters that “live” with fans across platforms) ⌙ Smart play patterns for kids & families ⌙ Educational + entertainment hybrids tied to core franchises Licensing moves closer to software + services, not just physical goods. 3️⃣ Every consumer touchpoint becomes a brand moment Theme parks, streaming, retail, games, even customer service — AI allows Disney to: ⌙ Maintain character voice consistency at scale ⌙ Localize tone and storytelling by market in real time ⌙ Extend IP life cycles far beyond release windows This is IP always on, not campaign-based. 4️⃣ Data gravity shifts back to the IP owner In an AI-powered world, whoever controls: ⌙ Narrative rules ⌙ Character behavior ⌙ Ethical guardrails …controls the fan relationship. The Walt Disney Company partnering early here is about protecting brand trust as much as accelerating innovation. 💡The big takeaway: This isn’t Disney chasing AI hype. It’s Disney reinforcing that the future of entertainment is intelligent, personalized, and emotionally resonant, and that their IP is the safest, most scalable playground to build it in. Licensing teams should be paying close attention. So should anyone who thinks the next Mickey Mouse won’t talk back. #Media #Disney #OpenAI

Last week, I was in a meeting where a young analyst presented an incredible AI-generated report. Beautiful graphs. Accurate predictions. Everything was perfect. But when the CEO asked, “So… what does this mean for us?” the room went silent. The AI had all the data. But it couldn’t explain the insight, connect the dots, or guide a decision. And the analyst — brilliant, talented — struggled to articulate the message. In that moment, one thing became painfully clear: AI can give you information. Only communication gives you influence. The future of work won’t be led by the people who know the most. It will be led by the people who can: • make others understand • simplify the complicated • inspire action • manage emotions • resolve conflicts • bring clarity when chaos hits AI can write, code, design, analyse. But it cannot build trust. It cannot speak with empathy. And it definitely cannot lead humans. If you want to stay future-proof, invest in: ✔ communication ✔ storytelling ✔ executive presence ✔ emotional intelligence Because in a world full of AI-generated noise, a human who can communicate clearly will always win. #CommunicationSkills #FutureOfWork #Leadership #ExecutivePresence #Anecdote #SoftSkills #AI #CareerGrowth

AI Media: From Zero to the top of every marketer's growth chart. The fastest growing ad channel doesn't even have a media plan template yet, but 21% of marketers plan to increase AI media spend by more than 25% in H1 2026. That's more than #search. More than #RetailMedia. More than #CTV. For a channel that barely existed 18 months ago, that's not growth. That's a land grab. Mediaocean's 2026 'Advertising Outlook Report' (n=320 marketing professionals, surveyed Nov 2025) puts AI media, specifically ads on AI agents and LLM platforms, at the top of aggressive investment intentions. The broader picture: 54% of marketers plan to increase AI media spend overall, surpassing search (47%) for the first time. Here's what stands out to me: 🛒 AI media leads all channels for planned spend increases above 25%, ahead of digital display/video and CTV (both 17%) 🛒 More marketers expect to boost AI media budgets than search budgets, despite (or perhaps because of) search being a $300B+ category 🛒 Gen AI remains the No. 1 consumer trend for the third consecutive wave, cited by 70% of respondents 🛒 Yet only 43% of marketers are using AI for data analysis, and just 31% for campaign optimization. Is the intent to spend outpacing the operational readiness to spend well? That last point is the tension worth watching. The Mediaocean data also reveals that 42% cite data quality issues and 41% say connecting AI insights across systems is their biggest barrier. So we have an industry racing to buy ads inside AI platforms while still figuring out how to use AI inside their own organisations. This is the "Defense vs. Offense" dynamic playing out in real time. Brands are going on offense (placing ads inside AI conversations) before they've fully built the defense (integrating AI into their own workflows and measurement stacks). The risk? Spending accelerates into a channel where attribution frameworks don't yet exist, brand safety guardrails are still forming, and the consumer experience is evolving weekly. For brands and retailers, the signal is clear: AI media investment is no longer a "watch & wait" line item. But the marketers who will win aren't just the ones spending first. They're the ones building the orchestration layer underneath, connecting AI media to their broader ecosystem so that when these platforms scale, their measurement and optimisation capabilities scale with them. The money is moving. The infrastructure needs to keep up. H/T to Debra Aho Williamson for sharing (and adding a foreword) to the report, check it out in full on the link in the comments below 👇 #AIcommerce #AI #media #marketing #agency Amazon QBurst Clevertar Thrad ChatGPT OpenAI ChatGPT Perplexity PubMatic Google Grok