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Emerging Departments: How AI is Transforming Organizations Transformation in light of AI isn't just about digital change—it's strategic, cultural, and organizational. Early results of organizational optimization with AI reveal that traditional structures are evolving into new, combined departments that break down silos and enhance collaboration. Here are some emerging trends: 1. Human Experience Department (Led by the CXO) Combines marketing, HR, and customer service to create a unified experience approach. Focuses on customer and employee experience as a seamless continuum. Example: Airbnb and Starbucks blending internal and external engagement for holistic experience design. 2. The Intelligence Function (Led by Chief Data & Intelligence Officer (CDIO)) Merges IT, data analytics, and AI strategy into a unified intelligence function. Enhances decision-making with data-driven insights and technology integration. Example: Microsoft and Amazon use intelligence functions to support strategy and innovation. 3. Integrated Growth Department (Led by the CGO) Combines Marketing, Sales, and Customer Success to create cohesive client journeys. Prioritizes growth by aligning customer interactions across all touchpoints. Example: HubSpot and Salesforce driving client experience continuity. 4. Strategic Innovation & Transformation Office (Led by Chief Strategy Officer or Chief Transformation Officer) Combines strategy, innovation, and transformation initiatives for continuous evolution. Fosters agility by integrating foresight and innovation into long-term strategy. Example: Tesla blending innovation with strategic growth planning. 5. Technology and Digital Transformation Department (Led by the Chief Technology & Transformation Officer) Integrates IT, digital transformation, and cybersecurity under one strategic role. Embeds technology into workflows while ensuring security and compliance. Example: Cisco and IBM streamlining their digital transformation efforts. 6. Resilience and Continuity Department (Led by the Chief Risk Officer) Oversees Risk Management, Business Continuity, and Strategic Foresight. Ensures organizational resilience in an increasingly FLUX world. Example: JP Morgan building resilience to mitigate risks and ensure continuity. 7. Ethics and Responsible AI Office (Led by the CEAO) Ensures ethical AI use and compliance with regulatory standards. Maintains trust and integrity as AI becomes central to business strategy. Example: Microsoft and IBM proactively building ethics frameworks for responsible AI. In sum, AI is driving fundamental shifts in how we structure our organizations. To thrive, leaders must think beyond digital transformation and focus on strategic, cultural, and organizational evolution. The companies that succeed will be those that break down silos, integrate their functions, and embrace transformation as a continuous journey.

The 2026 Microsoft Work Trend Index points to a shift every leader should be paying attention to—AI is changing how work itself is designed at an organizational level, going beyond individual tasks.   For the past couple of years, many organizations have focused on AI adoption. Pilots. Use cases. Productivity gains. Now we’re entering the next phase: building the operating model for people and agents to work together in ways that create measurable business impact.   A few themes stood out to me:   ➡️ AI is expanding what people can do The report shows that 49% of Microsoft 365 Copilot conversations support cognitive work, including analysis, problem-solving, evaluation, and creative thinking. AI is not just helping people move faster. It is helping more people participate in higher-value work.   ➡️ Human judgment becomes even more important As agents take on more execution, people play a bigger role in setting direction, defining quality, and evaluating outcomes. Workers recognize this too: 50% of AI users say quality control of AI output is becoming more important, and 46% point to critical thinking.   ➡️ Copilot Cowork and Dynamics 365 help move AI from insight to action The latest innovations shared by Jared Spataro and Bryan Goode show how Microsoft is helping organizations connect people, agents, and systems in the flow of work. With Copilot Cowork, Dynamics 365 plugins, and connected data across business processes, AI can move beyond generating outputs to helping teams coordinate work, reduce friction, and drive real outcomes.   What’s becoming clear is that this next chapter is about reimagining how work moves, where human judgment matters most, and how teams come together to turn intelligence into meaningful action.   The organizations that pull ahead will be the ones that embrace AI as a foundational part of their operating model for how work gets done.

When anyone in an organisation can use AI to build a working system in minutes, the focus shifts from how to build to what should exist at all. The implications of this are counterintuitive because this isn’t just about democratisation, it’s about the not-so-subtle removal of a governing mechanism most organisations never realised they relied on. When building took weeks and required many departments, it was the scarcity that enforced selection. Now departments produce overlapping versions of the same capability because nobody is asking permission, and because each system works perfectly in isolation. Replit recognises both the opportunities and the difficulties and asked me to explore these dynamics and share what I found here on LinkedIn. In analysing how instant creation reshapes organisational structure, it's clear that traditional governance was never built for this condition. It is not trivial to govern what no longer depends on permission. The ability to create an application by describing it is remarkable, but it tells only half the story. Replit’s enterprise direction reveals the other half. Its security and production infrastructure are not simple features. They are designed for a landscape defined by abundance rather than scarcity. The platform assumes proliferation as the natural state and builds around it. What I consider may work, or may have to work, inside organisations moving at the speed of AI is an inversion of governance. Instead of controlling who builds, they will manage what persists. They accept that creation will happen and focus on what deserves to remain, identifying which solutions warrant institutional support, which local innovations contain enterprise value, which experiments should become standards. This inversion calls for different organisational abilities. Most firms currently lack the structures to develop this kind of judgment. Their systems assume that control occurs at the point of creation, not selection. That assumption is defunct. Organisations that recognise this inversion early will build the capacity to manage abundance. This area will remain a focus of my research. I’m grateful to partners like Replit, whose support enables the sustained analysis this kind of work demands. #Leadership #Strategy #AI #OrganisationalDesign #Governance #FutureOfWork

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-Augmented Organization Development: Redefining How We Diagnose, Analyze, and Transform Organizations. Today, with the rise of Artificial Intelligence (AI), OD is entering a new era: one that is data-driven, predictive, and deeply insightful. By augmenting OD with AI tools, leaders can go beyond assumptions and uncover patterns that were previously invisible. Key Applications of AI in OD 1️⃣ Organizational Diagnosis with OrgVue: It integrates organizational data—spanning roles, costs, skills, and reporting structures—to create a “digital twin” of the organization. With this, HR Leaders can simulate restructuring, forecast workforce costs, and test design changes before implementation. Instead of reactive restructuring, organizations move towards proactive design thinking at scale. 2️⃣ Culture Audits with Perceptyx: It uses AI-driven sentiment analysis across surveys, open-text feedback, and communication patterns to uncover the “hidden layers” of organizational culture. This allows HR leaders to move beyond engagement scores to understand micro-cultures, trust networks, and cultural blockers in real time. 3️⃣ Leadership Behavior Analytics with Eightfold.ai: It leverages AI-powered talent intelligence to map leadership behaviors, identify skill adjacencies, and even predict derailers. Organizations can design personalized leadership development journeys, ensuring succession pipelines are both ready and resilient. 🌟Benefits for HR & OD Professionals ✔ Accuracy – AI cuts through bias and reveals patterns missed by traditional surveys. ✔ Speed – Complex analyses that took months can now be completed in weeks—or days. ✔ Insights – Beyond “what’s happening,” AI provides the “why” and “what’s next.” ✔ Impact – OD moves from episodic interventions to continuous transformation. ⚠️Challenges & Ethical Considerations While the promise of AI is exciting, HR and OD professionals must navigate carefully: Bias in AI Models: If historical data carries bias, AI can reinforce it. Transparent, explainable AI (XAI) must be prioritized. Data Privacy: Employee trust is critical. How data is collected, stored, and used must be clear and ethical. Adoption Resistance: Leaders and managers may resist data-driven insights that challenge their instincts. Building AI literacy in HR is key to adoption. The leaders who embrace AI in OD today will build organizations that are not just future-ready, but future-resilient. #AI #OrganizationDevelopment #FutureOfWork #OD #HRStrategy #LeadershipDevelopment #DigitalTransformation

🚀 AI is transforming how we work—but so much of the focus is on the individual—individual AI use cases, individual productivity gains, and the individual skills required to capitalize on AI. Too often, we miss the bigger picture. What happens to our teams and org structures when AI enters the workplace? I’m excited to share new research (link in first comment below 👇) that my colleagues and I recently published in CSCW. Our 10-month ethnographic study of a fast-growing digital retailer unpacked how AI challenges traditional org charts and structures. Here's the problem: 🗂️ Traditional org charts divide work into silos—sales, marketing, product lines, etc. This structure is decades old, designed to keep complexity manageable by clearly assigning who does what. 🤖 But AI doesn’t like silos. 🔎 In our study, the algorithms couldn’t fully optimize because the org chart kept decision-making locked in silos. Once those constraints were lifted, AI delivered far better results—spotting trends and opportunities no single team could see on its own. Our research suggests that to get the most out of AI, organizations need to rethink three key areas: 1️⃣ Break Down Silos Don’t box in your AI—or your teams. AI is most powerful when applied at the cross-functional level, connecting insights across departments and uncovering trends no single team can see on its own. 2️⃣ Rethink Your Data Systems Rigid, fragmented data systems are AI’s kryptonite. Shifting to flexible, connected data systems ensures AI can analyze patterns across the entire organization. If your data’s stuck in fragmented systems, your AI will be stuck too. 3️⃣ Rethink Your Org Chart—Or At Least How It Might Be Constraining Your AI Build teams and processes that aren’t limited by static org charts. Rethink how roles and responsibilities are assigned. When you’re building your next team, don’t just grab the org chart. Look at what needs to get done and use AI to help you figure out the right roles. Have you thought about how AI might reshape your org chart? We know that hierarchy matters a lot—history shows us that too many attempts to dismantle it over the years have flopped. But with AI in the picture, I expect that rethinking parts of our org charts won’t be optional—it’ll be inevitable. So honored to collaborate with an incredible team of superstars on this piece: Amanda Pratt, Melissa Valentine, and Michael Bernstein.

Everyone’s racing to adopt AI. But most are missing the real unlock. According to a new research study, AI tools like ChatGPT are everywhere—but they’re not moving the needle on productivity, wages, or work hours. Why? Because AI isn’t a tech problem. It’s an organizational design problem. You don’t get ROI from AI by handing out logins. You get it by redesigning workflows, roles, and management systems to make the most of human+machine collaboration. The data is clear: Without clear organizational structure and support, average productivity gains from AI are just 2.8%. But, with training and leadership buy-in? Creativity, task expansion, and satisfaction rise 10–40%. The future of work isn’t about who uses AI—it’s about who redesigns their organization to unlock its power. Tools don’t transform companies. Structures, systems, and talent strategies do. Source: NBER Working Paper No. 33777, “Large Language Models, Small Labor Market Effects” by Anders Humlum & Emilie Vestergaard (May 2025). #FutureOfWork #AI #OrganizationalDesign #FreelanceTalent #UpworkResearch #ProductivityParadox #Leadership #HumanInTheLoop

The most revealing line in McKinsey's recent agentic AI report isn't about technology at all.⠀ ⠀ It's this: "𝘔𝘰𝘥𝘦𝘳𝘯𝘢 𝘮𝘦𝘳𝘨𝘦𝘥 𝘪𝘵𝘴 𝘏𝘙 𝘢𝘯𝘥 𝘐𝘛 𝘭𝘦𝘢𝘥𝘦𝘳𝘴𝘩𝘪𝘱, 𝘴𝘪𝘨𝘯𝘢𝘭𝘪𝘯𝘨 𝘵𝘩𝘢𝘵 𝘈𝘐 𝘪𝘴 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘢 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘵𝘰𝘰𝘭 𝘣𝘶𝘵 𝘢 𝘸𝘰𝘳𝘬𝘧𝘰𝘳𝘤𝘦-𝘴𝘩𝘢𝘱𝘪𝘯𝘨 𝘧𝘰𝘳𝘤𝘦."⠀ ⠀ This structural move hints at something profound happening beneath the surface of enterprise AI adoption. We're not just automating tasks anymore. We're redesigning the fundamental architecture of how organizations think, decide, and execute.⠀ ⠀ Consider what agents actually represent: persistent, autonomous software that can reason across complex workflows, maintain context over time, and adapt to changing conditions without human prompting. This isn't incremental improvement. It's a new category of organizational capability.⠀ ⠀ The implications ripple far beyond efficiency gains. When agents can proactively monitor supply chains, autonomously negotiate with external systems, and dynamically reallocate resources based on real-time conditions, they're not replacing human tasks. They're creating entirely new operational possibilities that didn't exist before.⠀ ⠀ What fascinates me most is the architectural challenge this creates. The report introduces the concept of an "agentic AI mesh": a distributed infrastructure designed specifically for autonomous software collaboration. This isn't about adding AI features to existing systems. It's about rethinking enterprise architecture around the assumption that software can act independently.⠀ ⠀ The cultural shift is equally significant. Organizations are moving from "human-centered design" to "human-agent collaboration design." The question changes from "How do we make this easier for humans?" to "How do we orchestrate optimal outcomes when both humans and autonomous agents are contributing to complex workflows?"⠀ ⠀ This explains why so many current AI initiatives feel stuck in pilot mode. They're trying to solve tomorrow's problems with yesterday's organizational models.⠀ ⠀ The companies getting this right aren't just deploying more AI tools. They're building new forms of organizational intelligence: hybrid systems where human judgment and machine autonomy create capabilities that neither could achieve alone.⠀ ⠀ That's the real transformation ahead. Not smarter tools, but fundamentally different ways of organizing work, distributing decision-making, and creating value.⠀ ⠀ How is your organization preparing for this shift from human-centered to human-agent collaborative models?

The more I engage with organizations navigating AI transformation, the more I’m seeing a number of “flavors” 🍦 of AI deployment. Amidst this variety, several patterns are emerging, from activating functionality of tools embedded in daily workflows to bespoke, large-scale systems transforming operations. Here are the common approaches I’m seeing: A) Small, Focused Add-On to Current Tools: Many teams start by experimenting with AI features embedded in familiar tools, often within a single team or department. This approach is quick, low-risk, and delivers measurable early wins. Example: A sales team uses Salesforce Einstein AI to identify high-potential leads and prioritize follow-ups effectively. B) Scaling Pre-Built Tools Across Functions: Some organizations roll out ready-made AI solutions across entire functions—like HR, marketing, or customer service—to tackle specific challenges. Example: An HR team adopts HireVue’s AI platform to screen resumes and shortlist candidates, reducing time-to-hire and improving consistency. C) Localized, Nimble AI Tools for Targeted Needs: Some teams deploy focused AI tools for specific tasks or localized needs. These are quick to adopt but can face challenges scaling. Example: A marketing team uses Jasper AI to rapidly generate campaign content, streamlining creative workflows. D) Collaborating with Technology Partners: Partnering with tech providers allows organizations to co-create tailored AI solutions for cross-functional challenges. Example: A global manufacturer collaborates with IBM Watson to predict equipment failures, minimizing costly downtime. E) Building Fully Custom, Organization-Wide AI Solutions: Some enterprises invest heavily in custom AI systems aligned with their unique strategies and needs. While resource-intensive, this approach offers unparalleled control and integration. Example: JPMorgan Chase develops proprietary AI systems for fraud detection and financial forecasting across global operations. F) Scaling External Tools Across the Enterprise: Organizations sometimes deploy external AI tools organization-wide, prioritizing consistency and ease of adoption. Example: ChatGPT Enterprise is integrated across an organization’s productivity suite, standardizing AI-powered efficiency gains. G) Enterprise-Wide AI Solutions Developed Through Partnerships: For systemic challenges, organizations collaborate with partners to design AI solutions spanning departments and regions. Example: Google Cloud AI works with healthcare networks to optimize diagnostics and treatment pathways across hospital systems. Which approaches resonate most with your organization’s journey? Or are you blending them into something uniquely yours? With so many ways for this technology to transform jobs, processes, and organizations, it’s important we get clear about what flavor we’re trying 🍨 so we know how to do it right. #AIAdoption #ChangeManagement #AIIntegration #Leadership

The dominant AI narrative still assumes a path toward a single monolithic superintelligence. “Agentic AI and the Next Intelligence Explosion” https://lnkd.in/ga2nDqDR considers something more important. Intelligence does not scale as a single system. It scales as a social system. Not one model getting smarter, but many agents interacting, debating, coordinating, and evolving together. A few ideas worth noting: + Advanced models already behave like “societies of thought,” reasoning through multi-perspective debate + The next leap comes from coordination across agents and humans, not just bigger models + We are entering “centaur systems” with dynamic human and AI collaboration Alignment shifts from model tuning to institutional design with roles, rules, and governance Where this becomes critical is through Conway’s Law. Systems mirror organizational structure. Now extend that to AI. Your agent ecosystem will mirror how your enterprise makes decisions, shares information, and governs itself. The consequences are real: + Siloed org → siloed agents + Fragmented org → conflicting agents + Weak governance → scaled risk and inconsistency You are no longer just shipping software that reflects your org. You are encoding your operating model into autonomous systems that will amplify it. The implication is clear. AI architecture is inseparable from organizational design. The orchestration layer, agent protocols, and governance systems will directly reflect: + Decision rights + Incentives + Trust boundaries + Information flows This is where most enterprises are underestimating the challenge. If you deploy agents into a broken operating model, you will not fix it. You will scale the dysfunction. We are moving from building software to building systems of coordinated intelligence. Conway’s Law is no longer just a diagnostic. It is a strategic warning. If you don’t redesign the organization, your AI will mirror its limitations and scale them. Worth the read. #AgenticAI #ConwaysLaw #AITransformation #OperatingModel #MultiAgentSystems #AIGovernance #SystemsThinking #EnterpriseAI James Evans Benjamin BrattonBlaise Agüera y Arcas Wesley Reisz Google Thoughtworks