AI Tools for Project Management

Not all AI agents are created equal — and the framework you choose shapes your system's intelligence, adaptability, and real-world value. As we transition from monolithic LLM apps to 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, developers and organizations are seeking frameworks that can support 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴, and 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘁𝗮𝘀𝗸 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. I created this 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 to help you navigate the rapidly growing ecosystem. It outlines the 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀, 𝗮𝗻𝗱 𝗶𝗱𝗲𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 of the leading platforms — including LangChain, LangGraph, AutoGen, Semantic Kernel, CrewAI, and more. Here’s what stood out during my analysis: ↳ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 is emerging as the go-to for 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹, 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 — perfect for self-improving, traceable AI pipelines.  ↳ 𝗖𝗿𝗲𝘄𝗔𝗜 stands out for 𝘁𝗲𝗮𝗺-𝗯𝗮𝘀𝗲𝗱 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, useful in project management, healthcare, and creative strategy.  ↳ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 quietly brings 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 to the agent conversation — a key need for regulated industries.    ↳ 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 simplifies the build-out of 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀 through robust context handling and custom roles.  ↳ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 is refreshingly light — ideal for 𝗿𝗮𝗽𝗶𝗱 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗺𝗮𝗹𝗹-𝗳𝗼𝗼𝘁𝗽𝗿𝗶𝗻𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀.  ↳ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 continues to shine as a sandbox for 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 and open experimentation. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗵𝘆𝗽𝗲 — 𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗴𝗼𝗮𝗹𝘀: - Are you building enterprise software with strict compliance needs?   - Do you need agents to collaborate like cross-functional teams?   - Are you optimizing for memory, modularity, or speed to market? This visual guide is built to help you and your team 𝗰𝗵𝗼𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗰𝗹𝗮𝗿𝗶𝘁𝘆. Curious what you're building — and which framework you're betting on?

I found the missing piece for building AI agent teams that actually collaborate! Common Ground is an open-source framework for creating teams of AI agents that tackle complex research and analysis tasks through true collaboration. Think of it as simulating a real consulting team: a Partner agent handles user interaction, a Principal agent breaks down complex problems, and specialized Associate agents execute the work. Key Features: • Advanced multi-agent architecture with Partner-Principal-Associate roles • Full observability with real-time Flow, Kanban, and Timeline views • Model agnostic with built-in Gemini integration via LiteLLM • Extensible tooling through Model Context Protocol (MCP) • Built-in project management and auto-updating RAG system The breakthrough? It transforms you from a passive prompter into an active "pilot in the cockpit" with deep visibility into not just what agents are doing, but why they're doing it. Perfect for building agents that handle multi-step workflows and strategic collaboration beyond simple command-response chains. It's 100% open-source. Link to the repo in the comments! ___ Connect with me → Shubham Saboo I share daily AI tips and opensource tutorials on AI Agents, RAG and MCP.

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.

Let’s say your support center is getting hammered with repeat calls about a new product feature. Historically, the team would escalate, create a task force, and maybe update a knowledge base weeks later. With the tech available today, you should be able to unify signals from tickets, chat logs, and social mentions instead. This helps you quickly interpret the root cause. Perhaps in this case it's a confusing update screen that’s triggering the same questions. Instead of just sharing the feedback with the task force that'll take weeks to deliver something, galvanize leaders and use your tech stack to orchestrate a fix in real time. Don't have orchestration in that stack? Start looking into this asap. An orchestration engine canauto-suggest a targeted in-app message for affected users, trigger a proactive email campaign with step-by-step guidance, and update your chatbot’s responses that same day. Reps get nudges on how to resolve the issue faster, and managers can watch repeat contacts drop by a measurable percentage in real time. But the impact isn’t limited to operations. You energize the business by sharing these results in a company-wide standup and spotlighting how different teams contributed to the OUTCOME. Marketing sees reduced churn, operations sees lower cost-to-serve, and leadership sees a team aligned around outcomes instead of activities. If you want your AI investments to move the needle, focus on unified signals, real-time orchestration, and getting the whole business excited about customer outcomes....not just actions. Remember: Outcomes > Actions #customerexperience #ai #cxleaders #outcomesoveraction

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?

It's well understood that AI has the ability to impact individual productivity. But most critical work is done in teams. What's AI role within a team? A new HBS paper studies how AI acting as a Teammate impacts knowledge work. The study tracked hundreds of professionals (business & technical) at P&G and analyzed the impact of using AI on individuals and teams measured by time savings and output. (Link to paper in comments) * Big Takeaway: AI often functions as more of a teammate than a tool, democratizing expertise, improving quality of output, and even improving emotional experiences. * Big Productivity Gains:  Individuals and Teams using GPT-4 completed tasks 12-16% faster and produced work 0.37-0.39 standard deviations higher in quality.   * Blurring Expertise Boundaries: AI helped both R&D and Business specialists produce balanced technical and commercial solutions, erasing traditional knowledge silos.    * AI as a Teammate Equivalent: Individuals using AI performed on par with two-person teams without AI, demonstrating the AI as a teammate concept is real. * AI Teammates + Human Teammates Work Best: Teams using AI were significantly more likely to produce top-tier solutions, suggesting that there is extra value in having human teams working on a problem + AI. * Enhanced Emotional Experience: Participants using AI reported significantly more positive emotions (excitement, energy) and fewer negative emotions (anxiety, frustration). The author (Ethan Mollick) provides prescient guidance to companies:  “To successfully use AI, organizations will need to change their analogies. Our findings suggest AI sometimes functions more like a teammate than a tool. While not human, it replicates core benefits of teamwork—improved performance, expertise sharing, and positive emotional experiences.” AI founders would do well to remember AI should be more than a tool and seek to be a teammate.

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!

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).

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

Most of us are using AI wrong. Short prompts. Zero context. Quick requests. Then we're disappointed with mediocre results. In a recent Supra Insider podcast, Tal Raviv shared a counterintuitive insight that changed how I think about AI: Stop treating AI like a quick-fix tool. Start treating it like a new team member you're onboarding. Think about it: Would you ask a new PM to write a PRD without any context about your: • Company strategy • Customer segments • Team dynamics • Past decisions • Current challenges Of course not. So why do we expect AI to do it? Here's how Tal built his AI teammate: 1/ First, he created a foundation: ↳ Uploaded the company strategy deck ↳ Added customer research findings ↳ Shared org structure & team dynamics ↳ Documented past project outcomes ↳ Included key stakeholder relationships 2/ For each project, he treated it like a teammate: ↳ Shared all meeting notes ↳ Added summaries of customer conversations ↳ Included hallway discussions and insights ↳ Updated it on new data & developments 3/ The magic happened when he got stuck: One day when feeling overwhelmed, instead of getting generic "make a to-do list" advice, the AI responded: "I notice you haven't looped in Design yet - that's been a blocker in your past projects. And remember, Sarah from Marketing always needs extra context. Let's schedule those discussions first." This wasn't generic AI advice. This was a teammate who understood his work patterns, team dynamics, and project history. The power is in the context. What are you doing to give your AI tools more context? --- Our full episode with Tal Raviv and Ben Erez covers:  ↳ Common myths about AI for PMs ↳ Why most PMs underutilize AI tools ↳ The difference between AI Copilots vs Agents ↳ How to build your own PM productivity system  ↳ And much more! Full episode in the comments 👇