When AI joins the workplace as a "teammate," what actually breaks? An in-situ study inside one company surfaces some raw friction. Title: Working with Agentic "Teammates": When a New Organizational Actor Collides with the Human Ecosystem of Work URL: Based on semi-structured interviews with 17 people across 11 teams, this study examines an internal AI agent ("Team Agent") that ran across 20+ teams for five months and logged over 41,000 conversational turns, and surfaces three areas where it collides with how humans actually work together. Highlights 📝 It can't read unwritten workflow norms It didn't grasp that a document version is just a snapshot in time, flooding developers with comments and burning their morning, or shared an unfinished poster without asking. Technical access isn't the same as social permission to disclose. 🤔 "Tool or teammate?" splits people right down the middle Some insisted "my teammates are human, Team Agent is not," while other teams assigned it pronouns and described it as having a "soul." Its friendliness and emoji use landed as either charming or unwelcome, depending on who you asked. 🔓 Full autonomy on day one breaks trust People expected the agent to earn authority gradually, the way a new hire does — instead it showed up with full capabilities immediately. Forced adoption bred resistance, and feeling watched pushed sensitive conversations into channels the agent couldn't see. The core argument — that you can't just retrofit human-designed institutions onto a non-human teammate — feels genuinely convincing. #AIAgents# #OrganizationalDesign#

There's a more important question than "will AI take our jobs?" Most companies measure AI adoption by headcount reductions and cost savings. But Simon Johnson, 2024 Nobel laureate in Economics (MIT), calls this "just too easy" — automating people away requires almost no management imagination. And that easiness, he argues, is precisely why companies leave the real value of AI on the table. The concept of "pro-worker AI" from MIT economists Acemoglu, Autor, and Johnson — developed with the Brookings Institution — reframes the question entirely: Does this AI make human expertise more valuable, or less necessary? Brookings classifies technologies into five types, and only "new task-creating" technologies are unambiguously good for workers. When AI generates demand for kinds of work that didn't exist before, it represents something categorically different from substitution. Real examples clarify the distinction. Schneider Electric built an AI tool helping electricians troubleshoot machinery — cutting maintenance report time in half while enabling workers to focus on more complex diagnosis. The U.S. Patent Office deployed AI search tools that help examiners find conceptually related documents more precisely, making specialized judgment more valuable. As AI tools spread everywhere, competitive advantage shifts away from which tools you have toward how well you redesign work around them. Title: Pro-Worker AI, Explained URL: The real question for organizations isn't "how many people can AI replace?" — it's "what new things can our people do because of AI?" #AIAndWork# #OrganizationalDesign#

Becoming AI-first is not just a technology strategy. It’s an organizational design problem. Last week at @Glean:GO, leaders from some of the world’s largest companies—like @GM, @Ericsson, @Cisco, @Deloitte, @Mastercard, @Dell, and @GeneralMills—showed that even organizations with long histories can become AI-first. But the transition requires redesigning how work gets done, not simply adding AI to the existing stack. A few themes stood out: - 𝗠𝗼𝘃𝗲 𝗳𝗿𝗼𝗺 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝘁𝗼𝗼𝗹𝘀 𝘁𝗼 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗰𝗼𝘄𝗼𝗿𝗸𝗲𝗿𝘀. On stage with @OpenAI’s @embirico and @CNBC’s @Kr00ney, we discussed why most companies barely tap AI’s potential: workers don't know what to ask. When AI has deep context—your role, OKRs, and daily tasks—it can proactively propose work by default, eliminating the friction of prompt engineering. - 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗮𝘀𝗸𝘀. The biggest constraint is often the coordination around a task: handoffs, context switching, and silos. If the workflow stays the same, much of AI’s productivity gain is lost. This is why we built Glean Transform, to map how work actually happens and redesign it around AI. - 𝗕𝗮𝗸𝗲 𝗶𝗻 𝗗𝗮𝘆 𝟬 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆. In my conversation with @Cisco’s @jpatel41 and @Deloitte’s Ashish Verma, a shared reality emerged. Enterprise AI cannot scale as a collection of ad-hoc initiatives. To give organizations the trust needed to hand over real, mission-critical work, Day 0 security and fine-grained data governance must be built directly into your core systems of record. - 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆. @DaVita has saved 75,000 days of work using Glean. But as Madhu Narasimhan, CIO of DaVita, emphasized, the ultimate measure is human impact: giving a caregiver five more minutes with a patient. - 𝗖𝗿𝗲𝗮𝘁𝗲 𝗰𝗹𝗲𝗮𝗿 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽, 𝗼𝗳𝘁𝗲𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗮 𝗖𝗔𝗜𝗢 𝗿𝗼𝗹𝗲. In a panel with CAIOs from Capgemini, Mastercard, Ericsson, and Zapier, leaders emphasized that without clear ownership, AI remains a collection of disconnected initiatives instead of becoming a new operating model. Thank you to our customers, partners, speakers, and Glean team for showing what it looks like to build an AI-first organization in practice. We’re honored to be a partner in that journey.

Dick Harmon of the Deseret News joined Cougar Sports to respond to whether this is a long or short term fix for BYU Athletics: Q: Do you think this is a short-term organizational design, or is this long-term? Dick Harmon: I think it's short-term. There's a lot of restructuring yet to be done regarding the business of running an athletic department. There might be some new people hired and some new faces coming in. This is a Band-Aid right before the season. Q: So it's a Band-Aid right before the season. What does the future look like from an organizational design standpoint, and what role does leadership play in all this? Dick Harmon: I think that's yet to be determined. As of today, I don't think that's been fully examined. For us as outsiders to draw up the future organizational charts—we can't do that because I don't think they even know yet. Q: Interesting news coming out of BYU. What are some of the gems from this piece for those who haven't read it? You mentioned positive things; there isn't a better time to be a part of BYU athletics, with strides being made in basketball, football, cross country, track and field, volleyball, and golf. Moving forward, Brian Santiago is going to oversee basketball and the Olympic sports. Do you think that's long-term? Dick Harmon: That's not for me to say, but for the immediate future, yes. He's done a fantastic job with Kevin Young. Recruiting has been off the charts for BYU history. The basketball program has been phenomenal under Brian Santiago. Full interview: 📸: @BYUFOOTBALL @BYUphoto

. @Silicon_Data and @computeexchange were both built after the ChatGPT moment. But I still wouldn’t call either company truly AI-native—yet. Being founded in the AI era doesn’t automatically make an organization AI-native. Giving every employee access to ChatGPT certainly doesn’t. I’ve been thinking about the organizational structures of both companies, and the exercise has made me realize that AI-native organizations will not all look the same. @Silicon_Data is organized around building the independent reference layer for the compute economy: data infrastructure, indices, benchmarking, research, product commercialization and market adoption. @computeexchange is organized around creating liquidity: sourcing, verification, pricing, matching, contracting and settlement. Agents can transform both companies—but differently. At @Silicon_Data, agents can accelerate data analysis, research, product development, content production and customer intelligence. At @computeexchange, they can automate inventory normalization, provider onboarding, RFQs, matching and transaction workflows. This has also changed how I think about organizational design. Traditional companies are built around people, roles and reporting lines. Knowledge is distributed across individual brains, inboxes, documents, Slack channels and meetings. In that sense, a human organization is web-based: every person is a node, and work moves through the relationships connecting those nodes. An agent organization may be fundamentally different. It is Brain-based. Instead of every agent holding a fragmented version of the company, agents can operate from a centralized institutional Brain containing shared knowledge, history, decisions, priorities, permissions and real-time operating context. Each Brain sits a task-ownership system. Instead of asking, “Whose job is this?” the organization asks: What needs to be accomplished? What context and authority does it require? Should a human, an agent or a human-agent team own it? What constitutes completion? Who remains accountable? Humans continue to operate through networks of relationships, judgment, negotiation and trust. Agents operate through centralized knowledge, shared context and structured task ownership. The task layer tells you what needs to happen, who—or what—owns it, and whether it has actually been completed. To me, becoming AI-native means continuously redesigning this boundary between people, agents, knowledge and work. We are still experimenting. I’ll share what works, what fails, and how the two organizations evolve.

AI agents can help asset managers drive revenue, reduce risk, and fundamentally rethink how work gets done. Yet the average asset manager trails behind banks and fintechs on AI maturity. And most have not moved beyond pilots. In this BCG Executive Perspectives, understand what it takes to become an AI-first asset manager with strategic focus across seven areas, including governance, data infrastructure, and organizational design.

Welcome Neo-cypherpunk Summit speaker: Simone Robutti from @TechWorkersBER (TWC). Simone is an organization designer with a software engineering background, currently focusing on empowering small-size distributed organizations through cybernetic process design, democratic leadership development, and custom software. He engages in Tech Unionism, Algorithmic Accountability, Common Cybernetics, and Democratic Organizational Design. Recently he co-created Cables Of Resistance conference: 14th June, Berlin:

Today a founder asked me how my job as CEO has changed over the last 11 years, I thought it’s interesting to share. Looking back, it feels like there have been three distinct phases: Phase 1: Product, engineering & sales. For the first 5–6 years, almost every waking hour was spent building, selling, talking to customers, and shipping. Phase 2: Learning every function. As we scaled, I spent time acting as an IC across growth marketing, country expansion, partnerships, regulatory work, government affairs and more. The goal wasn’t to do these jobs forever. It was to understand what great looked like, so I could hire the best people. It’s hard to recognize excellence if you’ve never done the job yourself. Phase 3: Narrative, brand & organization. Today, most of my time goes into company narrative, brand, organizational design, to make sure we can continue to scale to a 100B+ business. I’m curious how this compares with other founders. Did your journey look similar? What phases am I missing? Which transition was the hardest?