AI-Native Company Insights

For the last two years, the conversation about AI's impact has been dominated by the visible, the tactical, and the immediate. We are focused on new tools, evolving job descriptions, and the race to upskill our teams to use them. This is the "Surface Wave." It is real, it is important, and it is consuming all of our attention. But the real story, the one that will determine the winners and losers of the next decade, is happening beneath the surface. The "Undercurrent" is the deeper, strategic, and often invisible re-architecting of the organization itself. It's the shift in power structures, the creation of new data ecosystems, and the fundamental change in how decisions are made. This integration of human and machine intelligence is creating a new organizational physics, and most leaders are still using an old map. Think about it: A company builds a strategic intelligence unit designed to be "AI-native". The "Surface Wave" is giving the human analysts a suite of powerful AI tools for market research and data synthesis. But the "Undercurrent" emerges when the AI is integrated not as a tool, but as a de facto member of the team. Suddenly, the org chart is no longer a simple 2D hierarchy. You have a hybrid entity where the AI directly feeds insights to every team member, bypassing the traditional top-down flow of information from a human manager. The AI might even be given a "voice" in strategic meetings, presenting conclusions that directly contradict the team leader's intuition. The challenge is no longer about adopting a tool. It becomes a profound question of organizational design and leadership. What is the role of a human leader when the AI can provide more comprehensive data-driven direction? How do you manage a "team" that is a fluid network of human and machine cognition? And how do you measure performance when the most valuable output is a collaborative insight that is impossible to attribute to any single human or algorithm? This is the real transformation, and it requires leaders to move from being managers of people to being conductors of a complex, hybrid intelligence. I strongly feel that leaders who cannot distinguish between the two waves will be pulled under. But will organizations invest in the foresight this requires? #FutureOfWork #AIStrategy #TwoWaveTransformation

Get 50% of Your Time Back with AI: A Practical Framework for Business Leaders Two weeks ago, I had the opportunity to present alongside Ryan Markman to a group of elite fractional CMOs (thanks, Rajat Kapur and &Marketing!), sharing insights I’ve gained working with AI over the past year. In my portion of the presentation, I laid out a practical framework for business leaders looking to install that "first AI outlet" in their organizations. The key insight is that done right, integrating AI into workflows and processes gives knowledge workers back 50% of their time. However, most companies flounder trying to figure out exactly how and where to implement AI capabilities. My 3-level model offers guidance: ✅ Eager Assistant: Delegate rote information retrieval, documentation and other tedious tasks to AI. This is contingent on you knowing the goals. 🧠 Thought Partner: If goals aren't clearly defined, use conversational AI to provide clarity on objectives and strategy. ⚡️ Electricity: Take an "AI-native" approach where it's embedded in everything. Build custom workflows and software to save more time. Where should you start? Identify irksome tasks people have to do but don't enjoy. Find bottlenecks holding back core processes. Empower a champion user to demonstrate major productivity gains. Prove AI can deliver over 90% time savings. The key is iterating quickly at first. You don't need huge teams or long development cycles. Over time the capabilities compound, more users see the benefits and adopt the tools. Eventually your workforce hits the "screaming pain" point if AI is taken away. This practical approach allows AI to enhance human potential rather than replace jobs. The time savings let people focus on satisfying, high-leverage activities. Overall organization agility and responsiveness increase dramatically. I explain the framework in more detail in the accompanying video clip. Check it out and let me know your thoughts! Are you looking to install some "AI outlets" in your company? Send me a DM, and I’d be happy to provide guidance.

Generative AI applications have reached an inflection point. Led by companies like Glean (enterprise search), EvenUp (legal), and Typeface (content creation), the first AI-native enterprise apps broke from the pack, distinguishing themselves from the wave of AI-native applications that emerged last year following the rise of OpenAI and Anthropic. As a group, these companies grew faster than anything we’ve ever seen in the history of SaaS. Their rapid ascent blew past all prior measures of outlier potential. At Menlo Ventures, my partners Matt Murphy, Steve Sloane, and I studied these early winners and distilled the key strategies that made them successful in a new playbook designed to guide the next wave of AI-native enterprise apps: 👾 Displace services with software 🏆 Target work that is high-value, high-volume, or facing labor shortages 📈 Seek pattern-based workflows with high engagement and usage 🔓 Unlock proprietary data ✍ Embrace zero marginal cost creation 🏗 Build where incumbents aren’t, can’t, or won’t 🌟 Win with compound AI systems rather than models Read the full post here: https://lnkd.in/d_vpJNV9