Evolving Workforce Models

Two things happened today that inspired some thinking and research. First I spoke with Kanella Salapatas from QBE Insurance who shared a view that in the future of work everyone becomes a manager, even individual contributors (ICs). Then Riges Younan from Gloat posted about individual contributors needing to become 'Exponential contributors' (ExC's). I thought I would explore these concepts further and here is what I have discovered: ⚡As agentic AI is introduced to businesses, individual contributors will, by necessity, become work orchestrators. Their work will shift from execution and doing the tasks to defining them and orchestrating AI and humans to get the tasks done. (Note, even individuals will need to be task-conversant). ⚡Middle management layers will flatten as ICs manage more of the work and activities, as well as the swag of agents they deploy. ⚡Orchestration is the new world - defining the work, designing the work systems, choosing the agents, providing the context and codifying 'how we do work here', govern execution, monitor quality, overseeing synthesis and decisions What will this mean for HR / Talent and OD? 💡IC career path revolution > orchestration tracks (Agent Manager, Context Engineer, Human-Agent Superisor); IC execution work narrows & judgment, framing and synthesis expand 💡Critical need to upskill workers on task decomposition, prompt / agent design, data intuition, risk, QA, storytelling 💡Way fewer layers in businesses - more cross functional squads + agents & autonomy tiers by process 💡Less Management span of control over people, more span over agents. Management now includes orchestration (at scale), governance and decision velocity. The value moves from doing to deciding. In an Agentic infused enterprise, everyone becomes, at least in part, a manager of systems. (Kanella Salapatas - you are 100% correct) HR / Talent leaders need to start thinking about designing for orchestration, and fast. This will be a critical ingrediant in future workforce strategy. #futureofwork #workforcestrategy #talentstrategy #agenticAI #taskintelligence #workanalysis

Workforce management is evolving to workforce orchestration. What does orchestration actually mean?  At its core, workforce orchestration is the ability to continuously coordinate people, operations, and AI-driven decisions in real time. Instead of simply managing work, orchestration intelligently adapts work. Traditional WFM systems were built around transactions: → Track time, create schedules, process payroll, approve requests, generate reports.  Those capabilities remain foundational, but modern organizations operate in environments that change by the hour.  For example: a healthcare provider detects rising agency labor usage for overnight CNA shifts. Orchestration allows you to benchmark labor patterns against peers, identify burnout risk, recommend optimized incentives, and trigger staffing adjustments automatically.  Or a supply chain operation detects disruptions across distribution centers. Staffing plans then dynamically adjust based on shipment volume, labor availability, overtime risk, and delivery timelines — where supervisors can approve optimizations in one click and stay in control.  Automation executes tasks, but orchestration coordinates decisions across interconnected systems, people, and workflows. This matters to me because AI finally makes it possible at scale. Workforce orchestration will become one of the defining enterprise operating models of the AI era. 

I've been having some fascinating conversations lately—across industry forums and in candid discussions with customers and peers. One theme keeps surfacing: The future workforce won't be built around static roles, but around adaptable, skills-based teams aligned to solving problems. Here's what I'm seeing emerge: Traditional job boundaries are dissolving. With AI handling routine tasks, value now comes from how teams combine complementary capabilities—technical, domain, and interpersonal—to tackle real business challenges. Agility is becoming foundational. The most forward-thinking organizations are forming and reforming teams fluidly based on the challenge at hand, not the org chart. Talent is being redefined. It's no longer about deep expertise in one narrow area. Today's professionals need to adapt, collaborate, and contribute across diverse teams—especially as technologies multiply and problems grow more complex. Building for continuous evolution. The future demands more than one-time reskilling. It requires repurposing talent for future roles through a culture of continuous learning tied to business outcomes. This evolution goes beyond being AI-enabled—we're entering an AI-led world where intelligence isn't just in our tools, but in how we align people, skills, and learning. There's no universal playbook, but the direction is clear: Organizations harnessing intelligent, skills-based talent management frameworks and embracing team diversity are better positioned for what's ahead. Curious to hear your thoughts on building the Workforce of Tomorrow.

Headcount planning is becoming a dangerously incomplete way to understand workforce capacity. The future of workforce planning is not headcount planning. It is productive-capacity design. Because work no longer flows only through employees. It flows through: Employees. Contractors. Vendors. AI agents. Software. Workflows. Data systems. Decision rights. Approvals. Hand-offs. Bottlenecks. And yet many organizations are still trying to answer 2026 workforce questions with 1996 workforce math. “How many people do we have?” “How many people do we need?” “How much will they cost?” Those are still important questions. But they are no longer sufficient. The better question is: What productive capacity does the organization need to create, sustain, and improve the work that actually matters? That requires a shared model across the CHRO, CFO, COO, and CIO. Not four separate dashboards. Not HR counting heads. Finance counting cost. Operations counting throughput. IT counting systems. A shared model. Every work system should be evaluated across six dimensions: Cost: What does this capacity actually cost across labor, vendors, systems, and AI usage? Speed: How quickly can the work move without creating downstream fragility? Quality: Is the output reliable, useful, and decision-grade? Trust: Do employees, leaders, customers, and regulators trust the process? Resilience: Can the system absorb turnover, disruption, reorgs, and demand spikes? Learning: Does the system get smarter over time, or does it simply repeat yesterday’s process faster? This is where organizational design debt becomes visible. A company can add AI and still be slow. It can reduce headcount and still be expensive. It can automate tasks and still destroy institutional knowledge. It can generate more dashboards and still make worse decisions. Because the constraint was never just labor. The constraint was coordination. The organizations that win the next era of workforce planning will not be the ones with the leanest org charts. They will be the ones that understand how human capacity, technological capacity, operational capacity, and decision capacity actually combine to create value. That is the real workforce equation. Not “how many people do we have?” But: What capacity can we reliably coordinate? #FutureOfWork #PeopleAnalytics #OrganizationalDesign

From Chief People Officer to Chief Workforce Officer? From AI as a Tool to AI as a Teammate... In a world where the pace of change will never be slower than it is today, we have to be willing to rethink and relearn. Including rethinking our own ideas. Last summer I wrote an article called “Work Is Getting Deconstructed: Why AI Is Accelerating the Conversation from Jobs to Tasks.” My argument was that AI doesn’t replace a job; it replaces, enhances, or accelerates a task. I still believe that framework matters. But I’ve been turning something over in my mind for a while now, and I truly don’t think it’s the whole picture anymore. I changed my mind. I no longer think AI is simply replacing tasks. I think we are moving very rapidly from AI as a tool to AI as a teammate. This shift in my thinking crystallized for me today during a webinar hosted by CloneForce. In their model, each AI “Clone” is powered by a collection of skills that are orchestrated together. They’re multi-agentic, executing multiple tasks, performing what looks a lot like a role on an org chart. Some Clones have more skills than others. Some can take over an entire job function. That’s not task automation. That’s a digital teammate. It was wild to see. And they’re not the only ones thinking this way. BCG and MIT Sloan recently found that 76% of executives already view agentic AI more as a coworker than a tool. Workday has started talking about the CHRO evolving into a “chief work officer”, someone who shepherds both human and artificial intelligence. I don’t know if I agree with “chief work officer” because it’s not just about the work. It’s about how we manage an entirely new workforce. If the workforce is a blend of humans and AI agents we need to completely rethink workforce planning, performance management, culture, even onboarding… We’re talking about an entirely new workforce architecture. If the workforce of the future is a blend of human talent and digital teammates, does the Chief People Officer or CHRO become the Chief Workforce Officer? (or am I arguing semantics...?) Not because the people part matters less, I think it matters more than ever. But because the scope of who and what makes up the workforce is expanding. Someone has to own the strategy for how human talent and AI talent work together. Someone has to think about onboarding, governance, performance, and development for all of it. That sounds like the natural evolution of the people leader’s role to me. I don’t have this all figured out. But I’m convinced the conversation has moved past “which tasks can AI do” to “how do we lead a workforce that’s part human and part digital.” And I think the leaders who start asking that question now will be the ones who are ready when the answer becomes clear. HR leaders: does this resonate? What would you call this role? #AI #FutureOfWork #Leadership

The future of workforce strategy isn't solely about hiring more people—it's about deploying the right mix of talent. A significant shift observed in recent years is that workforce planning has evolved into a business strategy discussion, rather than just an HR concern. Organizations that focus solely on full-time equivalents (FTEs) are overlooking a major opportunity. Today's workforce encompasses: • Full-time employees • Contingent labor • Project-based specialists • Consultants • Outsourced services • AI-enabled work and automation The critical question is not "How many people do we need?" but rather, "What is the best workforce mix to achieve the business outcome?" This necessitates leaders to move beyond traditional recruiting metrics and consider: • Workforce segmentation • Capacity planning • Total labor spend • Skills availability • Geographic labor markets • Productivity and automation • Risk and compliance • Supplier governance • Internal mobility and upskilling Contingent labor often represents one of the largest workforce investments outside of payroll, yet many organizations still manage it separately from talent acquisition and workforce planning. When these strategies are aligned, organizations benefit from: ✔ Better visibility into total talent ✔ Greater workforce agility ✔ Faster response to changing business demand ✔ Improved cost control ✔ Better hiring decisions As AI continues to transform work, leaders who seek a competitive advantage will not only recruit faster but will also develop integrated workforce strategies that unify permanent talent, contingent labor, technology, and automation into a cohesive plan. This is the direction of next-generation talent leadership. I am interested in how others are navigating this shift. Is your organization planning talent holistically, or are FTE and contingent labor still functioning in separate realms? #workforcestrategy

🧠 Building an Agentic AI Workforce in the Modern Enterprise Enterprises are evolving from automation driven organizations to intelligence augmented ecosystems. The next leap in this evolution is the rise of the Agentic AI workforce digital agents that reason, collaborate, and act with autonomy under human and organizational guardrails. 🏗️ The Architectural Foundation: From LLMs to Enterprise Agents The backbone of this workforce lies in combining retrieval based intelligence with secure action frameworks. The Retrieval Augmented Generation (RAG) architecture ensures that agents have access to the latest, organization-specific knowledge, while the Model Context Protocol (MCP) gives them the ability to act securely within enterprise environments. 💡 Business and Functional Considerations From a business standpoint, Agentic AI redefines workforce economics. Instead of hiring for routine data synthesis or reporting tasks, enterprises deploy agents that continuously operate at scale, bridging knowledge silos and ensuring operational continuity 24/7. Functionally, these systems must align with: - Auditability through immutable logs and traceable decision chains - Interoperability with legacy systems - Ethical governance, ensuring fairness, transparency, and explainability ⚙️ The Leading Approach: Hybrid Integration The most effective implementation model emerging globally is the Hybrid AI Workforce Framework, combining centralized governance (to ensure standards and oversight) with decentralized execution. Key Success Factors Strategic Clarity – Identify which functions benefit most from Agentic AI: operational workflows, decision-support systems, security operations, etc. Human-Centered Design – Treat agents as collaborators, not replacements. The design must integrate human approval loops, contextual transparency, and traceable decision logs. Data and Access Governance – Implement zero-trust principles, immutable input control, and encryption to maintain data sovereignty. Iterative Maturity Model – Begin with guided agents (low autonomy), evolve toward supervised execution, and finally deploy autonomous systems for stable use cases 📊 Measuring Success Quantifying success requires a blend of operational, financial, and cultural metrics, such as: - Reduction in manual task volume and turnaround time - Employee augmentation rate (AI+Human synergy metrics) - Net productivity gain and innovation throughput 🚀 Benefits and Risks Benefits: - Continuous productivity and 24/7 operations - Shortened decision cycles and faster insights - Reduction in operational cost and cognitive burden - Democratization of intelligence across departments Risks: - Over-reliance on AI judgment without human validation - Model drift or bias amplification without periodic retraining - Shadow AI unsanctioned agent deployment outside governance #AI #AgenticAI #Automation #DigitalTransformation #AIGovernance #AIWorkforce #FutureOfWork #CIO #Innovation

The blended workforce isn’t coming — it’s here. Across the Middle East this week, leaders are asking the same question: “How do we redesign our organizations for a human + agent workforce?” This isn’t about deploying a tool. It’s about reconfiguring the enterprise to deliver measurable outcomes. Leaders are wrestling with: - Integrating agents into real roles with responsibilities and accountability - Reskilling people for the higher-order work only humans can do - Building new collaboration models for human–agent teamwork - Governing and managing agents as part of the workforce At McKinsey, we’re feeling this shift ourselves with nearly 60,000 “colleagues”, of which over one-third are AI agents. The future operating model is arriving faster than anyone expected. Organizations fully embracing AI are not automating tasks anymore. They are redesigning how organizations work. The leaders who act now will create advantage by unlocking the next curve of performance. How is your organization building its own blended workforce? What is working and where are you finding challenges?