Tech Wing Management

𝐓𝐡𝐞 𝐀𝐈 𝐃𝐢𝐯𝐢𝐝𝐞 𝐈𝐬 𝐑𝐞𝐚𝐥 — 𝐃𝐨 𝐘𝐨𝐮 𝐊𝐧𝐨𝐰 𝐖𝐡𝐞𝐫𝐞 𝐘𝐨𝐮𝐫 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐒𝐭𝐚𝐧𝐝𝐬? Businesses with a clear AI roadmap report 20% higher profit margins, while others remain stuck in pilot purgatory: scattered AI tools, shaky data, and zero enterprise ROI. That’s why leaders in Decryptogen like Sanjeewa Alwis are working with organizations to close the 𝐀𝐈 𝐃𝐞𝐥𝐭𝐚 — the widening gap between companies that thrive with AI and those that stall. At Decryptellix, we use our 𝐄𝐀𝐓 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲, a 3‑phase business‑first framework to kick off AI Transformation:  🔹 𝐄𝐧𝐯𝐢𝐬𝐢𝐨𝐧 your AI roadmap aligned to business outcomes 🔹 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐞 scalable foundations like Data Mesh and AI Factory 🔹 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 operations for sustained growth and “AI Future Ready” maturity Whether you’re a CEO, CTO, Founder, board member or IT decision maker, this isn’t about hype — it’s about closing the gap with strategy, data, talent, and governance. 👉 𝐒𝐭𝐨𝐩 𝐠𝐮𝐞𝐬𝐬𝐢𝐧𝐠. 𝐒𝐭𝐚𝐫𝐭 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠. Learn more and schedule your free AI maturity assessment and walk away with a clear, business‑aligned action plan: https://xmrrwallet.com/cmx.phubs.li/Q03Bkbj10 #ArtificialIntelligence #DigitalTransformation #Innovation #Decryptogen #EATStrategy #GenerativeAI

McKinsey Maps the GenAI System You’re Probably Missing👇 This travel booking example nails what real enterprise GenAI looks like when it’s orchestrated right: ✔️A chatbot triggers the flow ✔️LLM fetches user + policy context ✔️Talks to backend apps ✔️Coordinates systems like CRM, booking, and agent assignment ✔️Built on secure infrastructure with access controls This isn't “ask ChatGPT for ideas.” It’s AI as infrastructure: interwoven into real systems with real stakes (and real data). 💡The result? Tailored support, faster resolution, and human-AI handoff that actually works. - Tech teams, take note: the magic here isn’t the model, it’s the architecture. - Think caching, policy management, API orchestration, model hubs, and context-aware prompting. Want enterprise GenAI to work at scale? It takes way more than just plugging into an API. Are we investing enough in the invisible layers, the integration tissue that actually powers AI outcomes? Join the free course and future-proof your SaaS strategy https://xmrrwallet.com/cmx.phubs.li/Q03B5JRZ0 #enterpriseai #generativeai #artificialintelligence #machinelearning #aiagents

One of the most important questions in AI right now isn’t about compute or alignment. It’s about copyright. Should training AI models on copyrighted content be considered fair use? Courts are starting to weigh in. And while the rulings so far lean toward fair use, they’re also exposing how outdated the legal frameworks really are for this new era of machine learning. In two recent cases, federal judges ruled that scanning legally acquired books to train large models was “transformative” enough to count as fair use. But they drew the line at pirated content. And they left the door open for future challenges, especially around market harm. That last piece is key. Fair use hinges partly on whether the AI harms the market for the original work. But that’s hard to prove. Would a person have paid to read a book if the AI hadn’t been trained on it? Did the training actually reduce demand? Think of it like this: training an AI on copyrighted content is less like copying a painting and more like learning to paint by visiting a museum. The model doesn’t reproduce the originals, it absorbs patterns, styles, and techniques to generate something new. Trying to trace back its output to a single source is like asking which painting at the Louvre taught someone to paint a tree. In a world where models are trained on billions of tokens scraped from the public internet, this analogy isn’t perfect, but it captures the scale of the dilemma. Training data is fuel. The question is who owns the gas station. Billions of dollars and entire creative industries hang in the balance. These early rulings suggest that courts may favor innovation, but the fight isn’t over. As models become more powerful and outputs blur the line between inspiration and imitation, the legal gray area is only going to get messier. One thing is clear: the legal battles over training data are just getting started. And how they’re resolved will shape not just the future of generative AI, but the creative economy itself. If you’re building in this space, it’s time to pay attention.

📢 𝐓𝐡𝐞 𝐡𝐮𝐦𝐚𝐧 𝐞𝐝𝐠𝐞 𝐢𝐧 𝐚𝐧 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐰𝐨𝐫𝐥𝐝 Companies are spending millions to automate their customer experience - and losing billions in customer revenue in the process. Yes, AI is reshaping how companies serve their customers. And yes, everyone’s racing to automate. But one question stands out: Where do humans still matter most? No matter what your job title is today, what happens with Customer Success will define how your career changes. In this live conversation, Sabina M. Pons, MA, CEO & Chief Customer Officer at Growth Molecules dives into what AI can do, and more importantly, what it shouldn’t. Together we’ll explore where human connection still outperforms tech, what to delegate to machines, and what must stay human to drive real impact. Resisting AI is pointless. But leading with your strengths - and letting AI amplify, not replace, your role - is where the money is to be made. 𝐖𝐡𝐨 𝐢𝐭’𝐬 𝐟𝐨𝐫: Customer Success pros, CX leaders, anyone who works at a software company, or any curious minds navigating the AI shift. 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮’𝐥𝐥 𝐠𝐞𝐭: Insights, tools, and real-world methods to bring it all to life, starting the moment the session ends. 𝐖𝐡𝐞𝐧: 🗓️ August 4 🕒 10:30 am PT #customersuccess #artificialintelligence #cx #futureofwork