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 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.

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

AI didn’t take my job. It gave me back the part of it that actually mattered - understanding people. For three decades, I believed I was doing "people work." I was wrong. My team was reviewing 50 resumes daily but never truly seeing candidates. Scheduling 20 interviews weekly but not preparing meaningful conversations. Drafting policy documents and communication instead of understanding employee concerns. With AI, now I can spend:  → Spend 2 hours weekly in deep career conversations with high-potential employees  → Conduct stay interviews that uncover real retention drivers  → Design onboarding experiences that create genuine belonging  → Make nuanced decisions about team dynamics and cultural fit  → Build mentorship programs based on individual aspirations If you’re in HR or leadership, here’s how to make the same shift: Step 1: Map your week. List every recurring task, from screening résumés to sending feedback reports. Mark what requires pattern spotting (AI’s domain) versus empathy or nuance (your domain). Step 2: Automate the repeatables. Let AI handle interview scheduling, résumé shortlisting, and pulse surveys. This frees up 10 to 15 hours that you can reinvest where human connection drives outcomes. Step 3: Guard human time. Block at least two hours every week to mentor, check in, or resolve team friction. These are the kinds of conversations no bot can replicate. Step 4: Track the intangibles. Instead of only measuring time saved, track retention, engagement, and internal referrals. That’s the real ROI of emotional bandwidth. It removed the excuse that administrative tasks were strategic work. Now I'm finally doing what HR was always meant to be about: understanding people. What is the biggest change you’ve made with AI?

Here is the real story. AI is not just reorganizing work. It is reorganizing identity. If leaders do not understand that, they will lose something far more damaging than headcount. They will lose the human center of their organization. Right now, most leadership strategies are focused on tasks, skills, and productivity. That is fine. Necessary even. But if that is where the conversation ends, then here is what is coming: A workforce full of people who can still do the job. But no longer know who they are inside it. Call it burnout, disengagement, quiet quitting. The label does not matter. The root cause is identity detachment. People do not perform well when their sense of self is slipping. This is not a soft topic. It is not a “nice to have.” It is the most strategic leadership mandate of the AI era. So what should leaders actually do? Start here. 1. Stop talking only about efficiency Just because AI can do something faster does not mean the humans feel better about their contribution. If the only message coming from the top is “look how much time we saved,” do not be surprised when no one feels proud of the work anymore. 2. Help people rewrite their worth If AI now does 40 percent of their tasks, help them understand what the 60 percent really means. Not in a buzzword way. In a “this is the human value that cannot be automated” way. 3. Make identity part of the work design conversation Ask the question out loud. How will people see themselves in the work once AI becomes another team member? 4. Build space for meaning, not just output If there is no room for ownership, curiosity, and contribution beyond task completion, you are not building a workplace. You are building a human assisted API. 5. Give people language to answer the new question Not “what do you do?” But: “Who do you become here?” If you cannot answer that as a leader, your people will quietly decide the answer is “someone who will eventually leave.” Here is the part no one wants to say out loud. AI will not break companies. Leaders who ignore identity will. The companies that thrive will be the ones who treat identity as a strategic asset. Who understand that purpose is not a poster and meaning is not a perk. It is the psychological contract that makes someone choose to care. Because work is not just a place people show up. Work is one of the deepest stories they tell about themselves. You want loyalty. Performance. Creativity. Resilience. Give people a story they want to keep living inside. Or someone else will. That is the end of this series. But it is not the end of this conversation. Because the real future of work is not about technology. It is about becoming.

𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘁𝗮𝗸𝗶𝗻𝗴 𝘁𝗮𝘀𝗸𝘀 𝗮𝘄𝗮𝘆. 𝗜𝘁’𝘀 𝗿𝗲𝘀𝗵𝗮𝗽𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗲𝘃𝗲𝗻 𝗺𝗲𝗮𝗻𝘀. Across industries, roles are being redesigned, not eliminated. The question is no longer “𝘞𝘪𝘭𝘭 𝘮𝘺 𝘫𝘰𝘣 𝘦𝘹𝘪𝘴𝘵?” but “𝘞𝘩𝘢𝘵 𝘸𝘪𝘭𝘭 𝘮𝘺 𝘫𝘰𝘣 𝘭𝘰𝘰𝘬 𝘭𝘪𝘬𝘦 𝘸𝘩𝘦𝘯 𝘩𝘢𝘭𝘧 𝘰𝘧 𝘪𝘵 𝘤𝘩𝘢𝘯𝘨𝘦𝘴?” When AI automates routine tasks, what’s left isn’t less work... it’s different work. • It demands new skills. • It challenges old identities. • It forces organisations to re-draw the boundaries between human and machine. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗛𝗥 𝗺𝘂𝘀𝘁 𝗹𝗲𝗮𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗮𝗴𝗲 𝗼𝗳 𝗿𝗼𝗹𝗲 𝗿𝗲𝗱𝗲𝘀𝗶𝗴𝗻: 𝟭. 𝗗𝗲𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁 𝗷𝗼𝗯𝘀 𝗶𝗻𝘁𝗼 𝘁𝗮𝘀𝗸𝘀 See clearly what’s human, what’s machine, what’s shared. 𝟮. 𝗥𝗲-𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 Design partnerships, not replacements. 𝟯. 𝗥𝗲𝘀𝗸𝗶𝗹𝗹 𝗳𝗼𝗿 𝗯𝗼𝘁𝗵 𝘀𝗸𝗶𝗹𝗹 𝗮𝗻𝗱 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆 Help employees find meaning in new versions of their work. 𝟰. 𝗦𝗲𝘁 𝗲𝘁𝗵𝗶𝗰𝗮𝗹 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 Ensure AI enhances trust, not erode it. 𝟱. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗽𝘂𝗿𝗽𝗼𝘀𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗼𝘂𝘁𝗽𝘂𝘁 Because productivity without meaning won’t sustain. The future of work isn’t about defending old jobs. It’s about re-imagining new ones. #DrJaclynLee #FutureOfWork #AIinHR #RoleRedesign #StrategicHR #PeopleStrategy

There won’t be a “job apocalypse” because of AI — but there will be job chaos. Here’s the key insight: by 2028–2029, the number of jobs created by AI will surpass those eliminated (source: Gartner 2025). However, more than 32 million jobs will change significantly every year. This isn’t the end of work — it’s the reshaping of what work looks like, the skills we need, and how teams operate. What this means for People and business leaders: Redesign the work, not just the roles. Break jobs into tasks and decide what to automate, what to augment with tools, and what must remain fully human (judgment, relationships, creativity). Enable internal mobility. Build bridges between roles at risk and emerging ones through short training paths, mentorship, and project-based transitions. Refocus learning. Emphasize critical thinking, data fluency, digital collaboration, and responsible use of tools. Simplify governance. Clear policies, human oversight for sensitive tasks, and strong data protection are essential. Measure what matters. Go beyond productivity — track employee experience, retention, and fairness in opportunities. A practical plan: 1. Rapid assessment: Identify 10 critical roles, map their tasks, and evaluate automation potential. 2. Pilot projects: Focus on measurable outcomes like quality, cycle time, or customer satisfaction. 3. Learning paths by role: Choose three essential skills and embed weekly practice. 4. Transparent communication: Share what’s changing, why, and how progress will be measured. Final thought: AI doesn’t erase jobs — it moves them. Those who plan now will lead the talent economy of tomorrow.