Using AI to Improve Team Dynamics in Projects

The value of Humans + AI collaboration in the real world: an academic study of 776 R&D professionals at Procter & Gamble revealed not just substantial performance gains from AI, but a host of other gains, including in emotional state. Some of the stand out insights from the research paper (link in comments): 🚀 AI + teams unlock top-tier innovation. Teams using AI were 9.2 percentage points more likely to produce top 10% solutions compared to the 5.8% baseline—making them about three times more likely to generate standout ideas. This effect was not seen for individuals using AI, highlighting a unique benefit in combining AI with human collaboration. ⏱️ AI makes work faster and more detailed. Individuals with AI completed their work 16.4% faster, and teams with AI were 12.7% faster than their non-AI counterparts. At the same time, AI-enabled groups produced significantly longer and more detailed solutions, with higher average quality scores. 🧩 AI dissolves functional silos. Without AI, Commercial and R&D professionals proposed solutions aligned with their functional backgrounds—market-oriented vs. technical. With AI, this gap disappeared: both groups generated more balanced ideas, regardless of their original specialization. This pattern held across individuals and teams. 📈 AI lifts less experienced employees to team-level performance. Employees whose core job did not include product development performed significantly worse in the control conditions. However, when these non-core employees worked with AI, their performance matched that of teams containing core-role employees. 😊 AI improves emotional states during work. Participants using AI reported significantly higher increases in positive emotions—such as excitement, energy, and enthusiasm—and lower increases in negative emotions like anxiety and frustration. Individuals with AI experienced a 0.457 standard deviation increase in positive emotions, and AI-enabled teams saw an even larger 0.635 boost. 🏢 AI challenges traditional assumptions about team structures. The study found that individuals with AI performed as well as human teams without AI, while AI-enabled teams were significantly more likely to produce top-decile solutions. The authors conclude that this challenges long-standing assumptions about the necessity and structure of collaboration. They suggest organizations may need to rethink how they compose teams and allocate expertise in an AI-integrated environment.

🚀 Excited to share my latest Fortune column on truly groundbreaking academic work from my co-authors Professor Karim Lakhani and Fabrizio Dell'Acqua at Digital Data Design Institute at Harvard (D^3), where I serve as an executive fellow. This remarkable field experiment with 776 Procter & Gamble professionals fundamentally challenges what we thought we knew about teamwork. The research reveals the emergence of the "cybernetic teammate"—AI that doesn't just assist but actively participates in collaboration. Three breakthrough findings: 1. AI Can Replicate Team Benefits Individuals working with AI achieved nearly 40% performance gains—matching traditional two-person teams. AI is providing the same collaborative benefits we've long attributed to human teamwork. 2. Cross-Functional AI Teams Generate Breakthrough Innovation AI-augmented cross-functional teams were 3x more likely to produce top 10% solutions. This isn't marginal improvement—it's a multiplicative effect that neither human-only teams nor AI-enabled individuals could achieve alone. 3. AI Breaks Down Silos (For Real This Time) R&D specialists with AI proposed commercially viable solutions. Commercial professionals developed technically sound approaches. AI acted as a bridge, enabling each team member to think holistically across functions—achieving the "silo breaking" that leaders have struggled to accomplish through org chart reshuffles. Bonus finding: AI collaboration increased positive emotions by 64% in teams. This isn't cold, mechanical work—it's energizing and engaging. At Seven2, we're translating this research into practice with our portfolio companies, building these AI-augmented cross-functional teams to drive innovation and competitive advantage. This is the future of collaborative work—not AI replacing humans, but human-AI ensembles that combine the best of both worlds. Read the full analysis: https://lnkd.in/ef3f3pED #AI #Innovation #HBS #D3Institute #FutureOfWork #PrivateEquity #TeamDynamics

AI isn't just a tool; it's becoming a teammate. A major field experiment with 776 professionals at Procter & Gamble, led by researchers from Harvard, Wharton, and Warwick, revealed something remarkable: Generative AI can replicate and even outperform human teamwork. Read the recently published paper here: In a real-world new product development challenge, professionals were assigned to one of four conditions: 1. Control Individuals without AI 2. Human Team R&D + Commercial without AI (+0.24 SD) 3. Individual + AI Working alone with GPT-4 (+0.37 SD) 4. AI-Augmented Team Human team + GPT-4 (+0.39 SD) Key findings: ⭐ Individuals with AI matched the output quality of traditional teams, with 16% less time spent. ⭐ AI helped non-experts perform like seasoned product developers. ⭐ It flattened functional silos: R&D and Commercial employees produced more balanced, cross-functional solutions. ⭐ It made work feel better: AI users reported higher excitement and energy and lower anxiety, even more so than many working in human-only teams. What does this mean for organizations? 💡 Rethink team structures. One AI-empowered individual can do the work of two and do it faster. 💡 Democratize expertise. AI is a boundary-spanning engine that reduces reliance on deep specialization. 💡 Invest in AI fluency. Prompting and AI collaboration skills are the new competitive edge. 💡 Double down on innovation. AI + team = highest chance of top-tier breakthrough ideas. This is not just productivity software. This is a redefinition of how work happens. AI is no longer the intern or the assistant. It’s showing up as a cybernetic teammate, enhancing performance, dissolving silos, and lifting morale. The future of work isn’t human vs. AI. The next step is human + AI + new ways of collaborating. Are you ready?

In our new paper we ran an experiment at Procter and Gamble with 776 experienced professionals solving real business problems. We found that individuals randomly assiged to use AI did as well as a team of two without AI. And AI-augmented teams produced more exceptional solutions. The teams using AI were happier as well. Even more interesting: AI broke down professional silos. R&D people with AI produced more commercial work and commercial people with AI had more technical solutions. The standard model of "AI as productivity tool" may be too limiting. Today’s AI can function as a kind of teammate, offering better performance, expertise sharing, and even positive emotional experiences. This was a massive team effort with work led by Fabrizio Dell'Acqua, Charles Ayoubi, and Karim Lakhani along with Hila Lifshitz, Raffaella Sadun, Lilach M., me and our partners at P&G: Yi Han, Jeff Goldman, Hari Nair and Stewart Taub Subatack about the work here: https://lnkd.in/ehJr8CxM Paper: https://lnkd.in/e-ZGZmW9

We tried every AI team structure. They all failed. AI-first teams. Human-first teams. Hybrid models. Pair programming with GPT-5. Then we stopped thinking about AI as a team member. Here's the structure that finally worked: We organize around problems, not roles. Each "pod" has: - A Problem Owner (human): Defines success - A Solution Explorer (human + AI): Finds approaches   - A Quality Guardian (human): Ensures standards - An Implementation Sprinter (human + AI): Builds fast - A Context Keeper (human): Maintains knowledge Notice what's missing? "AI Engineer" or "Prompt Engineer." AI isn't a role. It's a tool each person uses differently. The Problem Owner uses AI for market research. The Solution Explorer for ideation. The Quality Guardian for automated testing. The Sprinter for code generation. The Context Keeper for documentation. Same GPT-5. Five different applications. The breakthrough: Stop asking "How do we integrate AI into our team?" Start asking "What problems need solving, and who's best equipped to use which tools?" Our velocity doubled when we stopped treating AI as a separate thing. Your team structure should mirror your problems, not your tools. What organizational antibodies are you fighting while implementing AI?

The Future of Teamwork is Human + AI, I just reviewed fascinating new Massachusetts Institute of Technology research by Prof Sinan Aral and Harang Ju on AI-human collaboration that has significant implications for innovation teams. Key findings from the study:- • Human-AI teams communicated 137% more than human-human teams. • Workers with AI partners focused 23% more on content generation. • Human-AI teams achieved 60% greater productivity per worker. • AI teams produced higher-quality text, while human teams created better images. • AI personality traits can be matched to complement human personalities for optimal results. Most remarkably, ads created by human-AI teams performed comparably to human-human teams in real-world tests with ~5M impressions! The researchers developed “MindMeld” - a collaboration platform enabling humans and AI agents to work together in real-time. Their field experiments revealed that AI agents reduce social coordination costs, letting humans focus more on creative output. As a builder and innovator working with agentic AI solutions, I find this research validates what I’ve experienced: the future isn’t about AI replacing humans, but about thoughtfully designing AI systems that complement human strengths. What’s your experience working with AI collaborators? Have you noticed changes in your productivity or communication patterns? #AICollaboration #FutureOfWork #AgenticAI #Innovation

The most powerful use of AI at work won’t be solo. It will be shared. Ben Thompson recently wrote about a compelling use case: how he and his assistant collaborated with a single LLM chat. An example of a shared assistant for team coordination and synthesis. I’ve been thinking about this a lot too. At Dropbox, we’re building toward this future with Dash, our new AI workspace, and specifically with Stacks, a way for teams to organize, track, and reason across all the work happening in a project. Stacks are designed for collaborative intelligence. Teams can pull in docs, links, and tools from anywhere, ask questions about the work, and get AI-generated summaries that evolve as the project does. It’s a persistent shared memory that helps teams move faster, stay aligned, and reduce the drag of context loss. But coordination is just the first step. There are four basic configurations for how humans and LLMs might collaborate: 1. One person working with many agents. The classic orchestration model. Think of a PM using agents for research, writing, and planning. Most solo AI workflows live here today. 2. One agent working with many agents. A tool-using agent. This is the core of agentic infrastructure work. AutoGPT, Devin, and others. A lot of current technical energy is focused here. 3. Many people working with one LLM. A shared assistant for a team. Ben’s focus. This supports team-level memory, project synthesis, and aligned decisions. It’s emerging now. 4. Many people working with many agents, all coordinated through a shared LLM. This is the frontier. Imagine a team approves a campaign plan. Their shared LLM doesn’t just spin up agents. It engages the creative director, strategist, and producer, plus their teams (human and AI). The LLM knows the full context. It routes tasks, surfaces blockers, loops people in, and maintains alignment across the entire system. This isn’t a person using a tool. It’s people and AI, working together, across roles and workflows, with shared direction and shared memory. The shift is from individual productivity to shared intelligence. And the opportunity doesn’t stop at coordination. Negotiation. Conflict resolution. Team morale. Goal tracking. These are the complex, often messy parts of work where tools today tend to disappear. But this is exactly where AI can help. Not by replacing humans, but by holding context, clarifying intent, and accelerating momentum. That’s the future we’re building toward with Dash. AI that doesn’t just respond to prompts. It shows up in the group chat. It remembers the project goals. It knows what’s next. And it helps the whole team move. The future of work is multiplayer. And the most powerful teams will be human and AI, together, all the way down.

𝗛𝗼𝘄 𝗜 𝗴𝗲𝘁 𝗼𝘂𝘁 𝗼𝗳 𝗵𝘂𝘀𝗹𝘁𝗲 𝗮𝗻𝗱 𝘀𝘁𝗿𝘂𝗴g𝗹𝗲 𝗶𝗻 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 I’ve always worked on large corporate and consulting projects throughout my entire career. I can really say that I know the pain points in project workflows and collaboration. Project work is full of hidden friction: 🔄 Repetitive updates 🧩 Misaligned communication 📄 Documentation that never gets finished 🤯 Mental overload from managing everything Project work shouldn’t be this hard. I discovered that AI can be a game-changer. It’s a toolbox that quietly removes the friction, so teams can actually focus on creating value. 👉 Here are 3 AI workflows I can’t imagine project work without: 📊 Project Status Report Drafting 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Creating regular updates is repetitive and often delayed. 𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: AI drafts weekly or monthly status reports from task data and notes. 𝗜𝗺𝗽𝗮𝗰𝘁 / 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Ensures consistent updates and professional formatting. 📍 Process Documentation Writer 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Documenting project workflows takes too long. 𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Converts bullet points into formal standard operating procedures. Rewrites complex content into plain simple language that everyone understands. 𝗜𝗺𝗽𝗮𝗰𝘁 / 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Supports scaling and standardisation. 👥 Meeting Summary and Clarification Generator 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Not everyone captures the same notes during meetings. Missing information or perspectives can lead to delays or conflicts. Hidden conflicts influence team collaboration in a bad way. 𝗔𝗜 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: AI creates a neutral, complete summary including action items and decisions. Lists missing information, reveals hidden conflicts. 𝗜𝗺𝗽𝗮𝗰𝘁 / 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: Ensures team alignment and saves time consolidating notes. Helps move forward faster and improves team collaboration by avoiding or solving conflicts. AI can really be a supporter for project teams, not replace them. And it is a true game-changer. I’m really happy to announce that Christoph Schmiedinger and I will start a content series about the practical usage of AI in project management and product management. We will keep you posted. Leave a comment about your experiences. Let’s learn together.

A new Harvard Business School study involving 700+ professionals at Procter & Gamble reveals AI is reshaping how teams work—moving to what researchers call a “cybernetic teammate.” Teams using AI (specifically ChatGPT-4 and 4o) consistently outperformed others, producing better solutions faster, while also fostering more cross-functional collaboration. AI broke down traditional silos, allowing people to contribute outside their usual expertise and enabling individuals to handle tasks that previously required entire teams. Interestingly, while AI users felt less confident, they did significantly better work and reported more positive emotions—hinting at a future where AI not only accelerates output but also improves the work experience. Key takeaway: Organizations that treat AI as just another tool are underestimating its impact. This study suggests we’re only seeing the floor of AI’s potential. As adoption and skills mature, the performance gap will widen—fast. The divide is no longer between teams—it's between those who embrace AI and those who don't. #ai

New research from Harvard Business School explores a big question: What if AI isn’t just a tool but a teammate? In a large-scale field experiment with Procter & Gamble, researchers tested how GPT-4 affected performance when used by individuals versus teams of experienced professionals working on real product development challenges. Some key findings: - AI-enabled individuals performed as well as teams without AI - Teams using AI produced the best and most exceptional results overall — not only did they outperform others, but they were significantly more likely to generate top 10% solutions - AI helped bridge expertise gaps and broke down professional silos - Participants using AI had better emotional experiences — more excitement, less frustration The takeaway? AI isn't just about individual productivity — it’s reshaping how we collaborate, think, and solve complex problems. It’s acting more like a cybernetic teammate, not just a more efficient tool. The working paper — “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise” — is worth a read. As someone interested in the future of work, this raises important questions: 1. How do we design teams when AI levels the playing field? 2. What happens to traditional boundaries between roles? 3. How do we rethink collaboration when AI enhances both performance and emotional engagement? Curious what you all think — especially if you’re leading teams or exploring how to integrate AI meaningfully into your org. #FutureOfWork #LinkedInWorkplace #LinkedInLife #WorkplaceResearch