How to Redefine Work Dynamics Using AI

Stop drawing boxes around people. Start mapping the work that creates value. The traditional org chart is dying. In the AI Age, hierarchical lines and boxes don't just slow you down, they actually obscure where the work is happening. If you try to retrofit AI onto your existing structure, you’re just paving the cow path. As I discuss in my new article with Jonathan Brill for the leading HR site TalentCulture (link in Comments), the future belongs to Octopus Organizations. Like an octopus that has a brain in every arm, AI-ready businesses use distributed intelligence. They don’t organize by jobs; they organize by tasks. This requires a shift from the Org Chart to the Work Chart. A Work Chart isn't about who reports to whom. It’s a dynamic map of what needs to happen to deliver value. It’s about workflows, outcomes, and the blended human-AI teams that make them a reality. Ready to build one? Here's how to start: 1) Deconstruct the Function: Pick a priority area and be brutally honest. What work actually happens? Focus on tasks, not titles. 2) Apply the AI Filter: For every task, ask: - Can this be automated? - Can AI enhance the human doing it? - If you started this business today from scratch, who (or what) would do it? 3) Define the Jobs to Be Done: Move past "Manager reviews X." Define the underlying motivation, like "Optimize pricing given customer capex/opex preferences." This reveals where AI can crunch data and where humans provide the strategic "last mile." (Yes, this is a new application of our 20-year track record with JTBD, and it really works) The goal isn't to replace people; it’s to liberate them from the drudge work that fills the boxes of an old-school org chart. AI should enable people to focus on the most human elements of their jobs. Is your organization a rigid hierarchy, or can it be an agile octopus? Work charts will loosen you up!

𝗔𝗜 𝗦𝗵𝗼𝘂𝗹𝗱 𝗗𝗼 𝘁𝗵𝗲 𝗖𝗵𝗼𝗿𝗲𝘀.  𝗛𝘂𝗺𝗮𝗻𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗗𝗼 𝘁𝗵𝗲 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴. This image captures the most practical view on #AI adoption. AI works best when assigned routine, repeatable tasks. Humans work best when focused on judgment, #creativity, and relationships. The problem starts when teams reverse this logic. Many organizations use AI - to write, decide, and speak. Then they leave people stuck - with admin work, coordination, and clean up. This approach drains value instead of creating it. A better operating principle for the future of work looks like this. • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. - Use AI for scheduling, reporting, data cleanup, and documentation. - Free your time for decisions, conversations, and problem solving. • 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗵𝘂𝗺𝗮𝗻 𝘄𝗼𝗿𝗸. - Writing, design, leadership, and strategy shape trust and meaning. - When machines replace these too early, quality drops and ownership fades. • 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗶𝗺𝗲 𝗿𝗲𝘁𝘂𝗿𝗻𝗲𝗱, 𝗻𝗼𝘁 𝘁𝗼𝗼𝗹𝘀 𝗱𝗲𝗽𝗹𝗼𝘆𝗲𝗱. The real ROI of AI shows up as fewer hours lost to low value work. Track time saved per role, per week. • 𝗥𝗲𝗱𝗲𝘀𝗶𝗴𝗻 𝗿𝗼𝗹𝗲𝘀, 𝗻𝗼𝘁 𝘁𝗮𝘀𝗸𝘀. When AI removes busywork, redefine expectations. Raise the bar on thinking, not output volume. The future belongs to teams who assign work with intent. #Machines handle #repetition. #People handle #responsibility.

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?

The HR ladder is gone. AI built a ramp in its place, and it moves faster. Most companies still use job levels from another time: Entry, Specialist, Expert, Senior, Master, Manager. Each step used to take years. You learned, you waited, you climbed. That system doesn’t fit anymore. AI doesn’t reward time. It rewards results. What took ten years to master before can now change in two or three. A skill that mattered last year might already be outdated. But HR still promotes people on long timelines. They still think experience means growth. It doesn’t. A new graduate who knows how to use AI well can work like a senior in half the time. Sometimes even better. The system has no way to measure that. In some jobs, skills last less than two years. But HR still acts like careers move slowly. Here’s what happens: → Top performers wait too long for recognition. → Average workers hide behind titles that don’t match their impact. → Managers can’t explain why an intern is producing senior-level work. If HR wants to stay useful, it has to change how growth works. Here’s one way to fix it: 1. Fewer levels, faster movement Skip the six-step ladder. Think in three levels: - Entry: learning and context - Core: steady results and ownership - Senior: proven impact with AI, systems, or people Someone can move from entry to senior in a year if their work shows it. Yes, even a college intern. ⸻ 2. Measure learning speed, not time served Ask, “How fast can you learn and use what’s new?” In today’s world, being quick to learn is the most valuable skill of all. ⸻ 3. Let titles match the work If someone delivers at a higher level, give them the title. Not because of age or years on the job. Because of what they produce. ⸻ 4. Redefine leadership Good leaders don’t guard knowledge. They help others get better and use AI to raise the whole team’s results. ⸻ Studies show that almost half of all job skills will change in the next few years. That means many job descriptions are already out of date. The future of performance isn’t a slow climb. It’s a ramp anyone can run up if they move early. HR’s job now isn’t to count years. It’s to reward speed, learning, and results. The next “senior” might not be the one with the longest résumé. It might be the intern who just automated half the team’s work. How long before HR stops measuring time and starts measuring impact?

AI is making people more productive. But most teams feel more chaotic, not less. The productivity gains are real. The workflow gains aren't. Most companies are applying AI to individual tasks while leaving the structure of work untouched. That's why it feels like everyone's moving faster but nothing's actually getting easier. The real shift isn't speed. It's reorganization. I use a framework called JAIT to break down work and redesign it for the AI era: → 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 – decisions (stays human) → 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 – coordination (stays human) → 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 – the knowledge work (AI compresses) → 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 – moving information between steps (AI compresses hardest) AI removes entire categories of work. That forces the workflow itself to reorganize. Here's how it plays out in product development: 𝗕𝗲𝗳𝗼𝗿𝗲: PM writes PRD → Eng interprets → Code → QA writes tests → Testing → Release Each handoff = Transfer. Each role re-learns the problem = Intelligence repeated. 𝗔𝗳𝘁𝗲𝗿: PM ↔ Eng ↔ QA working on shared artifacts, AI handling the translation layer. Intelligence is centralized. Transfer is automated. The loop tightens. The roles don't disappear. They elevate: → PM shifts from document writer → intent designer → Engineer shifts from builder → orchestrator → QA shifts from executor → risk strategist Tasks redistribute, but ownership deepens. The old model: sequential handoffs, siloed roles, repeated context. The new model: continuous loops, shared system, AI-mediated collaboration. This isn't about making each step faster. It's about removing the steps that existed only because humans couldn't share context efficiently. If you're struggling to figure out where AI fits in your workflows, use JAIT: 1. Map your current process 2. Label each step: Judgment, Alignment, Intelligence, or Transfer 3. The Intelligence and Transfer steps? Those are your compression targets 4. Redesign around what remains: Judgment and Alignment Stop asking "where can we apply AI?" Instead: "Which parts of our workflow exist only because of human limitations?" Those are the parts that collapse. What remains is where your people should be spending their time. How is your organization redesigning work structure, not just adding AI to existing tasks? Save 💾 ➞ React 👍 ➞ Share ♻️ Follow Jimi Li for AI Enterprise Adoption

People keep asking me for the "secret" to getting the most out of AI. They want the killer app, the perfect prompt library, the one tool that will change everything. They're asking the wrong question. After embedding AI into >100 clients' businesses, I've realized the greatest leaps in productivity don't come from adding the "perfect" new tool; they come from imposing constraints. You + the model figure out the "secret" only when you're forced to solve a problem under deliberately difficult conditions. When you remove comfortable ways of working, you and your team are forced to discover new means toward the same end. Here are the core insights from how we operate at GSD at Work LLC: 1. The 90/10 Tooling Rule: Generalize, then Build. Stop chasing niche AI tools for every little task. It's a rat hole. 90% of your leverage will come from masterful use of powerful, general-purpose tools like ChatGPT. The key is feeding the models curated context—transcripts, documents, salient data—and formalizing good goals. The other 10%? That's for building your own solutions. When a workflow is truly core to your business, don't look for a tool—build one. With models like Claude Code (for back-end business logic) and no-code platforms like Lovable (for front-end content), you can create lightweight software that does exactly what you need. 2. Engineer Your Environment for Deep Work, Not Meetings. My team is growing fast, but we operate with almost zero internal meetings. We have one optional sync every two weeks. My calendar is ruthlessly protected. It forces us to communicate asynchronously and document everything. This creates a massive repository of recorded context—the perfect fuel for AI. My team gets pings and AI-generated summaries from my external meetings, but they don't have to sit in them. They are judged on outcomes, not effort. 3. Redefine "Work": Outcomes, Not Hours. We don't sell hours. We sell "quick wins in a box"—a guaranteed outcome or your money back. This constraint forces my team to find the most efficient path to the result. They are incentivized to use AI to shorten the process, not to bill more time. This model aligns everyone—the client, my team, and me—around pure value creation. 4. Impose Personal Constraints: No Typing, No Excuses. Here’s a simple one: I've almost entirely stopped typing. I use dictation tools for everything from emails to prompts. This small constraint forces me to be clearer and more concise in my thinking. It's faster, and the output is often better. The best way to learn is to remove the alternative. Try This Experiment: Don't buy another AI tool this week. Instead, pick one recurring, time-sucking task in your business. Your challenge: Solve it this week using only AI, with two constraints: 1. You are not allowed to have a single meeting to discuss it. 2. You must use dictation for every prompt and instruction. Keep at it, and you'll stumble into the future of work. That's the real secret.

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.

The New Job of Engineering Managers When AI Joins the Team AI has quietly become the newest member of every engineering team. The question is no longer “should we use AI” but “how do we lead when AI is part of the team’s workflow.” Engineering managers now have a different job than even two years ago. It’s not about replacing engineers with models. It’s about building a team that works with AI the same way they work with testing tools, build systems, or cloud services. Here is what an AI native engineering manager actually does: 1. Shift the team from task output to system thinking Anyone can generate code with AI. Only strong teams can design systems. Your job is to help your engineers zoom out, reason about architecture, tradeoffs, failure modes, and long-term maintainability. AI handles typing. Humans handle thinking. 2. Build workflows where AI removes cognitive load Teams that win are the ones who stop treating AI as a “code machine” and start using it for reviews, scaffolding, debugging, documentation, architecture diagrams, and learning. Managers must set up these workflows so engineers spend their energy on design, not boilerplate. 3. Coach for judgment, clarity, and decision making AI can draft five options. Only an engineer with good instincts can choose the right one. Your role becomes less about unblocking tickets and more about strengthening judgment and reasoning under ambiguity. 4. Redefine collaboration norms AI creates parallel streams of work. Context gets scattered. Good managers create rituals where engineers explain decisions, record assumptions, and keep the team aligned even when AI is moving everything faster. 5. Protect quality and long-term health AI can generate ten times more code. Without stronger review, testing, and standards, you inherit ten times more tech debt. Your job is protecting the codebase from hidden risk while still unlocking speed. 6. Make experimentation normal AI workflows evolve weekly. The best managers create an environment where trial, error, and iteration feel natural. Teams learn together instead of pretending they have it figured out. AI will not replace engineering managers. But managers who ignore AI will slowly lose relevance. The teams that thrive will be the ones where humans design the system and AI accelerates the work. That’s the new job. And it’s a good one. #EngineeringLeadership #AINativeTeams #FutureOfWork #SoftwareEngineering #TechLeadership #AIInEngineering #EngineeringManagement #TeamCulture #AIProductivity #BuildBetterTeams

New evidence says discourse on how AI will reshape work is getting it wrong. It’s not that some jobs get automated away while others are augmented. Automation and augmentation are playing out in the same roles at the same time. In other words, AI is reshaping work within jobs rather than eliminating them. The “winners vs. losers” frame doesn’t hold. Our latest research at The Burning Glass Institute mines millions of job postings before and after the advent of LLM’s to track how AI is already reshaping skill demand. The finding is striking: we found a 0.87 correlation between the roles experiencing the greatest automation effects and those experiencing the greatest augmentation effects, meaning the jobs most vulnerable to automation are also those most empowered by AI. Tasks are disappearing and intensifying simultaneously—within the same roles, at the same time. In fact, we find that skills most exposed to AI automation were 16% more likely to see demand decline than baseline skills. Skills most exposed to AI augmentation were 7% more likely to see demand increase.   Project managers aren’t disappearing, but our analysis shows that spreadsheet-heavy tasks are fading while strategic, judgment-intensive work is growing. Financial analysts aren’t getting replaced, but model-building is automated while interpretation and decision-making matter more. The unit of change isn’t the job. It’s the task mix inside the job. Our paper, "Beyond the Binary", offers some of the first empirical evidence from the AI Tracking Hub, a multistakeholder initiative led by the Burning Glass Institute to move the AI–work conversation from forecasts to observation. If jobs aren’t vanishing but transforming from within, the real question isn’t “Which jobs are safe?” It’s whether our institutions—education, training, workforce policy—are built for continuous change rather than one-time transitions. You can find the report on https://lnkd.in/ej5FJu2J. I so enjoyed the collaboration with coauthors Benjamin Francis, Shrinidhi Rao, and Gwynn Guilford, and I am grateful as always to Gad Levanon and Stuart Andreason for their work to bring data-driven, empirical understanding to the workforce impacts of AI. #AI #artificialintelligence #jobs #economics #work.

It’s easy to think of AI as a time-saver that streamlines workflows and accelerates output. But the deeper opportunity lies in how it’s reshaping the nature of work itself. A new study from Harvard Business School’s Manuel Hoffmann followed more than 50,000 developers over two years, with half using GitHub Copilot. The results were striking: developers shifted away from project management and toward the core work of coding. Not because someone told them to, but because AI made it possible. With less need for coordination, people worked more autonomously. And with time saved, they reinvested in exploration—learning, experimenting, trying new things. What we’re seeing here isn’t just productivity. It’s a shift in how work gets done and who does what. Managers may spend less time supervising and more time contributing directly. Teams become flatter. Hierarchies adapt. This is just one signal of how generative AI is changing our org charts and challenging us to rethink how we structure, support, and lead our teams. The future of work isn’t just faster. It’s more fluid. And if we get this right, it’s a whole lot more human. https://lnkd.in/gaUgXnRY