Enhancing Collaboration with AI-Powered Business Tools

AI is changing how we work. It's fundamentally reshaping team dynamics. From fluid roles to global collaboration, today’s team dynamics are evolving faster than ever. Understanding these 12 shifts isn’t optional; it’s critical to staying agile, competitive, and future-ready: 1/ From Fixed to Fluid Roles ↳ Teams swap tasks based on AI proficiency ↳ Skills matter more than titles 💡 Pro tip: Create a team skills matrix that tracks both AI and human capabilities. 2/ From Knowledge Silos to Open Learning ↳ AI tools democratize expertise ↳ Everyone becomes a teacher-learner 💡 Pro tip: Set up a shared prompt library where teams document their AI breakthroughs. 3/ From Linear to Parallel Processing ↳ Multiple projects run simultaneously ↳ AI handles routine tasks while teams focus on strategic thinking 💡 Pro tip: Use AI project managers to track parallel workstreams. 4/ From Competition to Collaboration ↳ Success = enhancing AI outputs ↳ Shared prompt libraries 💡 Pro tip: Create weekly "AI win sharing" sessions where teams present their best AI solutions. 5/ From Meetings to Async Intelligence ↳ AI summarizes discussions ↳ Continuous feedback loops 💡 Pro tip: Use AI meeting summaries as living documents that teams can enhance asynchronously. 6/ From Individual to Collective Problem-Solving ↳ AI provides initial solutions ↳ Teams refine together 💡 Pro tip: Start problems with AI-generated solutions, then use human wisdom to enhance them. 7/ From Status Updates to Strategy Sessions ↳ AI handles progress tracking ↳ Meetings focus on innovation 💡 Pro tip: Automate status reports with AI. Save meeting time for strategic discussions only. 8/ From Fixed Skills to Learning Networks ↳ Continuous AI upskilling ↳ Rapid knowledge sharing 💡 Pro tip: Rotate "AI champions" monthly to spread expertise across the team. 9/ From Task Completion to Value Creation ↳ AI handles the routine ↳ Teams focus on innovation 💡 Pro tip: Track time saved by AI and reinvest it in innovation projects. 10/ From Hierarchical to Neural Networks ↳ Expertise flows freely ↳ Innovation comes from everywhere 💡 Pro tip: Create open channels where anyone can share AI innovations. 11/ From Risk Aversion to Rapid Testing ↳ AI reduces experiment costs ↳ Faster iteration cycles 💡 Pro tip: Set up an "AI sandbox" where teams can experiment. 12/ From Individual Metrics to Team Impact ↳ Shared success metrics ↳ Focus on team outcomes 💡 Pro tip: Create team-based AI efficiency scores instead of individual performance metrics. These shifts are building a new foundation for how teams think, collaborate, and innovate. The key is to adopt change intentionally, not all at once. Start where your team has the most momentum, and let AI become a catalyst for stronger, smarter collaboration. Which team dynamic shift are you experiencing most strongly? Share below 👇 ♻️ Repost if your team is navigating these changes. Follow Carolyn Healey for more like this.

🛠️Your Organization Isn't Designed to Work with GenAI. ❎Many companies are struggling to get the most out of generative AI (GenAI) because they're using the wrong approach. 🤝They treat it like a standard automation tool instead of a collaborative partner that can learn and improve alongside humans. 📢This Harvard Business Review article highlights a new framework called "Design for Dialogue" ️ to help organizations unlock the full potential of GenAI. Here are the key takeaways: 🪷Traditional methods for process redesign don't work with GenAI because it's dynamic and interactive, unlike previous technologies. ✍Design for Dialogue emphasizes collaboration between humans and AI, with each taking the lead at different points based on expertise and context. This approach involves  📋Task analysis ensures that each task is assigned to the right leader — AI or human 🧑💻Interaction protocols that outline how AI and humans communicate and collaborate rather than establish a fixed process 🔁Feedback loops to continuously assess and fine-tune AI–human collaboration based on feedback. 5-step guide to implement Design for Dialogue in your organization 🔍Identify high-value processes. Begin with a thorough assessment of existing workflows, identifying areas where AI could have the most significant impact. Processes that involve a high degree of work with words, images, numbers, and sounds — what we call WINS work are ripe for providing humans with GenAI leverage. 🎢Perform task analysis. Understand the sequence of actions, decisions, and interactions that define a business process. For each identified task, develop a profile that outlines the decision points, required expertise, potential risks, and contextual factors that will influence the AI’s or humans’ ability to lead. 🎨Design protocols. Define how AI systems should engage with human operators and vice versa, including establishing clear guidelines for how and when AI should seek human input and vice versa. Develop feedback mechanisms, both automated and human led. 🏋🏼♂️Train teams. Conduct comprehensive training sessions to familiarize employees with the new AI tools and protocols. Focus on building comfort and trust in AI’s capabilities and teach how to provide constructive feedback to and collaborate with AI systems. ⚖Evaluate and Scale. Roll out the AI integration with continuous monitoring to capture performance data and user feedback and refine the process. Continuously update the task profiles and interaction protocols to improve collaboration between AI and human employees while also looking for process steps that can be completely automated based on the interaction data captured.  By embracing Design for Dialogue, organizations can: 🚀Boost innovation and efficiency, 📈Improve employee satisfaction 💪Gain a competitive advantage 🗣️What are your thoughts on the future of AI and human collaboration? Please share your insights in the comments! #GenAI #AI #FutureOfWork #Collaboration

Most teams are underperforming. Not because of laziness. Not because of bad culture. But because the people are stuck doing work AI should be doing. Want better meetings? Want stronger collaboration? Want better performance? Then free your team from doing things they shouldn’t be doing. Here are 5 simple, powerful ways AI is already helping teams build better ⬇️ ↳ 1. Summarizing conversations and meetings Let AI generate transcripts, write summaries, and pull action items. Tools like Fireflies(dot)ai, Sembly, or Cluely let humans listen deeply instead of multitasking. 👉 Deep listening creates better decisions. ↳ 2. AI-powered dashboards and alerts Use AI to surface KPIs, anomalies, and early warning signals. When AI tells the team what’s changing, the team can focus on what to do about it. 👉 Conversations shift from data-gathering to decision-making. ↳ 3. Detecting tone and team sentiment AI can analyze Slack, Zoom, or email data to flag mood swings, overload, or risk of burnout. This gives managers a chance to step in before trust or morale break down. 👉 Tech reads the room, so humans can step up. ↳ 4. Automating low-EQ, repetitive tasks Inbox triage, calendar management, ticket routing, and basic reporting don’t need a human. AI frees up emotional bandwidth so people can do higher-order, higher-empathy work. 👉 Free your team from the robotic parts of their jobs. ↳ 5. Drafting the first version of everything Project plans, presentations, emails, proposals—let AI write the messy first version. Humans can then refine, adapt, and build something great together. 👉 Collaboration thrives when people start from something, not nothing. This isn’t about replacing people. It’s about amplifying them. AI isn’t just a productivity tool—it’s a team design tool. ✅ Start mapping tasks that are low EQ, high repetition, and ripe for automation ✅ Pick 1 AI tool to test across a core workflow next week ✅ Train your team to co-create with AI, not just delegate to it ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. ⸻ 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).

We just built a commercial grade RCT platform called MindMeld for humans and AI agents to collaborate in integrative workspaces. We then test drove it in a large-scale Marketing Field Experiment with surprising results. Notably, "Personality Pairing" between human and AI personalities improves output quality and Human-AI teams generate 60% greater productivity per worker. In the experiment: 🚩 2310 participants were randomly assigned to human-human and human-AI teams, with randomized AI personality traits. 🚩 The teams exchanged 183,691 messages, and created 63,656 image edits, 1,960,095 ad copy edits, and 10,375 AI-generated images while producing 11,138 ads for a large think tank. 🚩 Analysis of fine-grained communication, collaboration, and workflow logs revealed that collaborating with AI agents increased communication by 137% and allowed humans to focus 23% more on text and image content generation messaging and 20% less on direct text editing. Humans on Human-AI teams sent 23% fewer social messages, creating 60% greater productivity per worker and higher-quality ad copy. 🚩 In contrast, human-human teams produced higher-quality images, suggesting that AI agents require fine-tuning for multimodal workflows. 🚩 AI Personality Pairing Experiments revealed that AI traits can complement human personalities to enhance collaboration. For example, conscientious humans paired with open AI agents improved image quality, while extroverted humans paired with conscientious AI agents reduced the quality of text, images, and clicks. 🚩 In field tests of ad campaigns with ~5M impressions, ads with higher image quality produced by human collaborations and higher text quality produced by AI collaborations performed significantly better on click-through rate and cost per click metrics. As human collaborations produced better image quality and AI collaborations produced better text quality, ads created by human-AI teams performed similarly, overall, to those created by human-human teams. 🚩 Together, these results suggest AI agents can improve teamwork and productivity, especially when tuned to complement human traits. The paper, coauthored with Harang Ju, can be found in the link on the first comment below. We thank the MIT Initiative on the Digital Economy for institutional support! As always, thoughts and comments highly encouraged! Wondering especially what Erik Brynjolfsson Edward McFowland III Iavor Bojinov John Horton Karim Lakhani Azeem Azhar Sendhil Mullainathan Nicole Immorlica Alessandro Acquisti Ethan Mollick Katy Milkman and others think!