Every week we identify new ways AI could improve productivity. The challenge isn't finding ideas, it's knowing which ones will deliver the biggest business impact. How do you determine what AI initiative moves to the top of the list?

The best AI on the market has one job: keep you engaged. Not because it makes you better. Because engagement = data & data = profit. So it rewards: - The shortest, spiciest takes - Constant novelty over depth - Momentum over principle - Speed over discernment Your team watches. Your culture learns. You think you are stewarding your business in the advent of AI well. But your values start to drift. Pretty soon your values are lost in a bunch of AI slop. Furthermore, you didn't even notice the system was working against you. This is the silent cost of using an AI built for a different mission. UncommonOS is built differently. It's designed to make your values active in every decision. Not optional, not occasional, but baked into how your team thinks. It asks critical questions. It augments the human element, not replace it. It brings principled responses deeply rooted in what makes your company you. It slows you down when you need slowing down. Because sometimes the most strategic decision is the most principled one. So, what if your AI system actually reinforced what matters most? Would you use it?

AI is one of the biggest opportunities in our industry. It can save time, improve systems, and help you create better content. But it can also become the next shiny object. I've seen brokers spend hours trying to automate tiny tasks while avoiding the activities that actually grow their business. They'll build the perfect AI workflow... ...but they won't start 10 conversations. They'll refine prompts... ...but they won't post the reel. AI should amplify your business, not distract you from it. Master the fundamentals first. Then use AI to do them faster and better.

The more leaders I talk to about AI, the more one question keeps coming up: If we were starting our companies today, knowing what AI can do, would they look anything like they do now? Most conversations still sound like efficiency questions. How can AI help us write reports faster? Automate tasks? Reduce costs? Those aren't bad questions but I think they’re the wrong starting point. I like this one more: What would we redesign if we were starting from scratch? Where does judgment actually live in the org? Who has authority to make decisions? What are the learning loops that help people get better? How do humans and machines work together in a way that makes both more capable? When you keep the org the same and just add tools, your best operators become the human patch for every broken handoff. (and they burn out really fast)

Over the past year, we've watched AI become one of the most powerful tools available to businesses. It's helping teams move faster, uncover insights, organize information, and remove hours of repetitive work. We use it ourselves every day, and we'd be doing our clients a disservice if we didn't. What concerns us isn't AI. It's what happens when businesses stop thinking because AI is thinking for them. We're starting to see websites that sound interchangeable, strategies built on generic frameworks, and content that's technically correct but completely forgettable. When everyone has access to the same tools, the competitive advantage is no longer speed. It's judgment. Knowing which questions to ask. Challenging the obvious answer. Connecting ideas from different disciplines. Understanding people well enough to know what an algorithm will miss. AI should make us more human, not less. The businesses that stand out over the next decade won't be the ones using AI the most. They'll be the ones using it intentionally, while continuing to invest in the curiosity, taste, empathy, and critical thinking that no technology can replace. That's the future we're building HIVE for.

https://lnkd.in/dM92qdgc I truly believe human data will be one of the most consequential industries of our lifetime. The expert knowledge, task design, and evaluation we use to train and test AI only grow more important as we hand more thinking and action over to models. As models get better, that human expertise grows scarcer and more valuable, positioning this market to create some of the most valuable companies of our time. I lay out my thoughts on this market in the linked post. Would love to hear your thoughts!

AI is shaping how brands are discovered and trusted. Meltwater’s Mid-Year Release gives teams visibility into AI presence, smarter workflows, and deeper insights to stay ahead.

One of the questions I've been wrestling with lately is whether AI is actually changing how humans choose. At first, I thought the answer was yes. After all, AI can now answer questions, compare products, summarize reviews, and even recommend a business in a matter of seconds. What used to take hours of searching across websites can now happen inside a single conversation. It certainly feels like the selection process is changing. But the more I've thought about it, the more I've come to believe that something else is happening. I don't think AI is changing why humans choose. I think it's changing how quickly they gather the information that helps them choose. Those are two very different things. Whether someone spends fifteen minutes talking with AI or three weeks researching online, they still have to answer the same questions for themselves. Can I trust this? Is this right for me? Can I see myself on the other side of this decision? Those questions haven't disappeared. If anything, they've become even more important. That's led me to a hypothesis I've been exploring while developing Selection Science™. AI may compress the process of discovering information, but it doesn't eliminate the human work of constructing meaning, finding identity alignment, and building confidence. If that hypothesis proves to be true, then we've been measuring the wrong thing. The technology may be changing dramatically. The human journey toward selection may be far more enduring than we realize.

Most people don’t need more AI tools. They need a clearer way to use the ones they already have. It’s easy to get distracted by the next tool, the next feature, or the next big promise. But in reality, a lot of the challenge is much simpler: ✅ Knowing where to start. ✅ Figuring out what’s actually useful. ✅ Making time to test things properly. ✅ Building confidence. ✅ Working out how AI fits into the day-to-day. And for community teams, there’s another important point: AI can help, but it can’t replace the judgement, empathy, and relationships that make communities work. That’s why the teams who get the most from AI aren’t always the ones with the most tools. They’re the ones who know what they’re trying to do.

Recently, I've been thinking about data, context, memory, and reasoning. Now I'm asking another question: When should AI stop giving answers and start taking action? Most AI systems today are great at responding. They answer questions. Generate content. Summarize information. But in the real world, businesses don't create value from answers alone. They create value from actions. Think about it. Instead of saying, "This customer might churn." What if AI could recommend the next best action? Instead of saying, "Inventory is running low." What if it triggered the right workflow automatically? That changes the role of AI. It moves from being an information provider... To becoming a decision partner. As an AI builder, I'm starting to think less about building systems that know things... And more about building systems that help work move forward. Maybe intelligence isn't just knowing the answer. Maybe it's knowing what should happen next. #AI #AIBuilder #EnterpriseAI #MachineLearning #SystemDesign #DataEngineering #DigitalTransformation #Innovation