Evolving HR Tech

🎙️ "Workforce planning is evolving - and in some organizations, being reinvented - to become a key differentiator in a dynamic, artificial intelligence-powered world." Workforce planning needs to evolve because the old model - forecasting headcount and roles based on stable assumptions - no longer holds in a world shaped by rapid AI adoption, skills decay and unpredictable markets. In this environment, workforce planning must anchor the future of work by aligning human, machine and organisational capacity in real time, rather than treating it as a static exercise. In their article for Deloitte, 'Reinventing workforce planning for an AI-powered, uncertain world', Susan Cantrell, Russell Klosk (智能虎), Zac Shaw, Kevin Moss, Christopher Tomke, and Michael Griffiths identify five key shifts to achieve this: 1️⃣ From planning for a single future to planning for multiple futures: 🔎 Build agility by modelling a range of scenarios, embedding resilience and alternative talent paths. 2️⃣ From planning based on jobs to planning based on work: 🔎 Move from fixed roles to tasks, skills and outcomes, including human-machine blends. 3️⃣ From visible capability to unlocking hidden capability and capacity: 🔎 Identify undervalued talent, non-traditional roles and internal mobility, as well as human-machine hybrids. 4️⃣ From static, manual planning to autonomous, dynamic planning: 🔎 Leverage real-time data and AI agents to monitor workforce signals, trigger interventions and continuously adjust. 5️⃣ From silos to synergies (horizontal and vertical): 🔎 Embed workforce planning across business units and levels, democratise data and involve people closest to the work in decision-making. These shifts reposition workforce planning from a support function into a strategic capability - enabling organisations to adapt faster, deploy talent smarter and harness human-machine potential for both business and human outcomes. 🔗 The article is featured in the November edition of the Data Driven HR Monthly, which you can access here: https://lnkd.in/ekVuREn8 🔗

HR is hitting a structural wall. The traditional model was built for a different era with different constraints. Today, HR teams are under significant pressure to optimize, enable enterprise-wide agility, and drive AI integration. Add in fragmented legacy systems, rigid hierarchies, and the old model simply cannot scale. But, we don't just need another layer of HR tech. We need a completely new operating system. The infographic presents Wowledge's blueprint, along with other leading voices we have seen publishing comprehensive models. There is something we all agree on: the future of HR is fluid, deeply integrated with AI, and built around human-machine teaming. While shifting away from static service centers toward agile, outcome-focused teams, how we propose structuring that shift varies significantly. Here is a look at how these four frameworks propose to reinvent the function, with links to each in the comments: The Human Readiness Operating System [Wowledge] Embraces a radically lean internal footprint via an "access vs. own" approach. It relies heavily on a curated external ecosystem for specialized expertise, enabling robust execution accountability directly by business leaders, aided through AI tooling. A New Operating Model for People Management [McKinsey] Fundamentally elevates IT and data capabilities directly within HR. It introduces "People Technologists" as a core structural pillar, aiming to replace traditional shared services with hyper-personalized "digital twins" or personal agents for employees. HR Reimagined [Deloitte] Treats AI as a literal component of the workforce. By emphasizing "Agentic AI" to execute complex, multi-step workflows, it advocates for HR to formally govern digital agents as part of the workforce strategy, freeing up to 25% of human capacity. Operating by Design [Mercer] Abandons process-based departments in favor of a structural fusion of HR, IT, and Finance by pulling budgets down to cross-functional "Outcome Delivery Teams," ensuring financial accountability is tied directly to operational demand. The model you choose depends heavily on your organization's scale, maturity, and specific friction points. But one thing is clear: as HR leaders, we must lead this transformation before the business begins driving it for us. ~ Click Carlos Larracilla and follow me [+🔔] for daily resources from Wowledge.

DEI Isn’t About Being “Woke”—It’s About Existing This holiday, I shared a beautiful photo of me and my sister standing in front of a Christmas tree, full of joy and warmth. Curious about AI tools, I used one to describe our photo and generate a “similar” version. But what I got back wasn’t us. The generated image erased everything unique about me and my sister. It replaced our individuality and vibrant presence with generic, stereotyped versions of people who didn’t look like us. This wasn’t just a technical glitch—it was a reminder of the deeply ingrained biases in AI. This experience hit hard. It’s not just about this one tool. It’s about the larger message: without inclusive practices, people like me are literally erased. DEI (diversity, equity, and inclusion) isn’t about being “woke.” It’s about ensuring that all of us—our identities, our experiences, and our existence—are represented and valued. When AI fails to represent people accurately, it highlights a systemic issue: Diversity in AI Development: AI tools must be built with diverse data sets and teams to reflect the richness of humanity. Equity in Representation: It’s not enough for AI to be accurate for some—it must work for all. Inclusion as a Core Value: This is not optional. If systems and practices aren’t inclusive, they exclude. Period. The gap between the original photo of me and my sister and the AI-generated result made it painfully clear: without inclusive practices, some of us are left out entirely. This isn’t about being trendy—it’s about existing in a world that sees us. We need better. We deserve better. #AI #DEI #InclusionMatters #Representation #BiasInTech #DiversityInAI

The 4-Layer AI-Native HR Stack Is on the Horizon: Precision, Power, and Personalization The HR landscape, long dominated by monolithic, platform-centric systems, will undergo a fundamental shift. Traditional HRIS/HCM platforms excel at transactional record-keeping and process automation—they’re strong systems of record. But the future requires more: a proactive, intelligent, and deeply personalized AI HR ecosystem. Enter the AI-Native HR Stack, built on four reinforcing layers. 1. Foundational Data Layer — The Source of Truth Combine existing HR systems with two powerful data structures: Vector Databases for deep semantic understanding of unstructured policies, job descriptions, and documents, and Knowledge Graphs to map complex relationships across skills, roles, projects, and people. This creates a structured, reasoning-ready foundation that connects context, meaning, and relationships. 2. Core AI & Reasoning Layer — The Intelligence Engine A powerful LLM is precision-grounded through Hybrid RAG, merging semantic search from the Vector DB with relational reasoning from the Knowledge Graph. This ensures responses are accurate, contextual, and explainable—drastically reducing hallucinations and elevating trust. 3. Service & Orchestration Layer — The Doers (Agentic AI) Specialized AI Agents take action. Workforce Intelligence, Talent Management, and Compliance agents autonomously execute multi-step goals using the LLM’s reasoning capabilities. The result: proactive compliance monitoring, predictive workforce planning, and automated decision support—moving HR from reactive to anticipatory. 4. Interface Layer — The Personalized Experience Generative UI transforms interactions by dynamically building workflows, pre-filling information, and delivering instant, expert-level guidance. Work Intelligence tools integrate directly into everyday platforms like Teams or Slack, providing real-time learning and assistance in the flow of work. Shifting from today’s legacy, platform-centric setup to an AI-native architecture requires rethinking HR entirely. The focus moves from transactional record-keeping to orchestrating intelligent, autonomous agents. For HR professionals, the role evolves too—toward strategic oversight, data interpretation, and strong human-AI governance.

AI is creating new opportunities for individuals across all walks of life to excel in their roles.   In Hong Kong, Lawrence Fong, Director of Digital & IT at Cathay Pacific, used to move emails to a "Follow Up" folder and hope to revisit them later. Now, with Copilot, he responds faster, drafts speeches with ease, and his team can summarize proposals and meetings in minutes – not hours.   In Australia, Julian Ockford, a Rail Operations Planner at GHD, with dyslexia, faced extra challenges in writing. With Copilot, he’s now able to write with clarity and confidence while keeping his unique voice. AI is also helping employees with temporary disabilities, like those recovering from surgery, get back to work more quickly.   For Australia Post, AI is reimagining accessibility. Anthony Moufarrege, Diversity & Inclusion Coordinator, knows firsthand how workplace adjustments can make all the difference. He’s also seen Copilot break down communication barriers for those who are deaf or hard of hearing - enhancing both virtual and in-person interactions.   The question is no longer if AI will change the way we work - it’s how we will use it to create more opportunity for everyone.   Read more on Lawrence’s story here: https://lnkd.in/e4uTRgFf Read more on how GHD and Australia Post are leveraging AI for inclusion and empowerment here:

A new era of biometric-driven training, powered by data once reserved for elite athletes, is taking hold as the obsession with longevity accelerates. Large-scale events like Hyrox have normalized data-rich preparation — and experts say everyday exercisers now expect feedback and recovery plans that mirror professional sport. In 2026 and beyond, this data-driven mindset looks likely to extend beyond the gym. Workplace wellness will move from tracking to adapting — using biometric signals to shape schedules, environments and team dynamics. Imagine offices that subtly respond to collective physiology, adjusting lighting, temperature and meeting length based on energy and focus patterns. Teams could even be assembled for circadian compatibility and recovery profiles, optimizing wellbeing and output. Burnout prevention could be the most compelling frontier — a predicament thought to be costing companies with more than 1,000 employees about $5 million a year. Data from companies like Whoop, Oura, Garmin and Apple Watch could guide flexible work policies, such as prompting later starts to avoid commuter rush hours. How do you feel about technology playing a bigger role in our daily health and routines? Weigh in below. And check out the rest of this year's Big Ideas here: lnkd.in/BigIdeas2026. #BigIdeas2026 ✍️ Aaron Toumazou

I was asked this system design problem in 3 out of 11 Big Tech companies I interviewed at this year, including Amazon, Google, Atlassian, Salesforce, Walmart, and others. For context, I landed 6 offers this year during my 3-month job switch journey: 1. Amazon (Senior Eng. L6) 2. Walmart (Staff Eng.) 3. Atlassian (Principal Eng.) 4. Salesforce (LMTS) 5. Confluent (Sr. SWE 2) 6. Deliveroo (Staff SWE) What was the problem? It was: Design a distributed job scheduler. I was given different requirements and constraints each time. If you’re ever asked this problem, never make these 13 mistakes, these will make or break your SD interview: 1) Starting with the architecture before the workload Most people jump straight into talking about queues and workers. Ask yourself: – What types of jobs? – How long do they run? – How many per second? – What failure rate can the system tolerate? Without understanding the load, any architecture you draw is imagination, not engineering. 2) Ignoring time as the primary axis A job scheduler is not only about work. It is about time. Candidates talk about compute, but forget the real challenge is scheduling jobs based on time windows, offsets, retries, delays, expirations, and deadlines. 3) Not separating control plane from data plane A scheduler that mixes job orchestration logic with job execution becomes impossible to scale. The control plane decides what should run. The data plane performs the work. Blending both is an automatic fail. 4) Treating the scheduler as a single component You never design a scheduler as one system. You design it as a set of cooperating parts: Job producer, scheduler, dispatcher, worker pool, tracker, storage, and monitoring. When you miss these layers, your solution falls apart under real load. 5) Ignoring clock drift Every distributed scheduler breaks when clocks drift, even by a small margin. Candidates never talk about this, that’s why always ask: who is the source of time, how consistent are the clocks, and how do we handle drift. 6) Hand waving consistency People say eventual consistency like it solves all problems. It does not. When a job must run exactly once at a specific time, you cannot shrug off consistency. You need to reason about write paths, read paths, and lease ownership. 7) Forgetting the hardest part: exactly once Most candidates assume exactly once means a worker runs a job only once. That is the easy part. The real version of exactly once is: The job must be scheduled once, dispatched once, acquired once, run once, and marked complete once. Each of these steps is a failure point. Continued here: https://lnkd.in/gA3p5h_5 — P.P.S: Feel free to reach out to me if you're preparing for a switch, want to chat about interview preparation or how to move to the next level in your career: https://lnkd.in/guttEuU7 For Mock interviews: https://lnkd.in/gKWbHmke

How to Get 10X More Productivity 🚀 The way we build teams is broken. For decades, org charts grew by adding headcount. If you needed more output, you hired more people. But that model is collapsing: • Budgets are flat or shrinking • Productivity growth is stagnating • Most employees spend 40% of their time on repetitive tasks (Deloitte) At the same time, the opportunity has never been bigger: • AI can now automate 60–70% of knowledge work tasks (McKinsey) • Early adopters see 20–30% productivity gains in year one • One person, with the right digital workforce, can match the output of an entire department => This is the rise of the digital worker: AI copilots, agents, and automations that sit alongside humans as part of the org chart. Hiring in the Age of Digital Workers When you hire someone today, you’re not just hiring a single individual. You’re hiring a conductor of an orchestra of AI resources. Take marketing as an example: Hiring one marketer should mean bringing in someone who knows how to activate AI agents for content, social, SEO, ads, analytics, personalization, and more. One human → multiplied by dozens of digital workers. Now extend that logic: • A finance hire who commands AI for forecasting, compliance, and reporting • An operations hire with AI copilots for supply chain, scheduling, and workflow optimization • A sales hire backed by AI prospecting, outreach, and CRM automation 5 Principles for Building a 10x Org in 2025 1️⃣ Hire for orchestration, not execution Your team should be experts at leading digital workers, not drowning in manual tasks. 2️⃣ Expect leverage, not headcount One skilled human + AI = output of 5–10 traditional FTEs. 3️⃣ Prioritize adaptability Tools will change fast. What matters is the ability to design workflows where AI compounds human creativity. 4️⃣ Measure outcomes, not hours Redefine productivity in terms of revenue, margin, CAC, cycle time, and customer experience — not time spent. 5️⃣ Redraw your org chart Start with the human role at the top, then map the AI digital workers that multiply their output. That’s your real architecture of the future. Next time you ask, “Should we hire one person?” Ask instead: “What digital workforce comes with them and how much more can they deliver from day one?” This is not the future. It’s the architecture of hiring today for any startup. Or any company that wants 10x productivity. If you had to hire one role right now, with 10x productivity in mind, which would it be? Drop your comment below and let's have a discussion. #AI #Productivity #Hiring #Career

💡 What big idea will define the world of business in 2024? Proactive compliance - powered by automation ⚙ The Fair Work Legislation Amendment (Closing Loopholes) Bill 2023 will create an unprecedented shift in workplace regulations in Australia. Proactive compliance is key to navigating this evolving industrial relations landscape. The new legislation will criminalise wage theft for intentional employee underpayments. This offence will carry penalties of up to 10 years in prison and fines up to $1,565,000 for individuals and $7,825,000 for corporations, or three times the value of the underpayment — whichever is higher. For civil provisions, serious contraventions now cover recklessness as well as intentional acts, with the maximum fine increasing to $939,000, or three times the underpaid amount if it exceeds the cap. These reforms are prompting employers and directors to think ahead about compliance strategies to avoid penalties resulting from honest mistakes or ignorance. Outdated processes and systems are the primary driver of underpayments, yet employers typically address underpayments with reactive, expensive and time-consuming remediation projects. ♻ The approach to compliance must shift from reactive to proactive, with automation as a driving force. 🔎 Implementing control mechanisms to rapidly identify, quantify and fix underpayments and give boards visibility could be the differentiator between an honest mistake and potential criminal liability. 🛡 Demonstrating due diligence will be the tangible proof that intentions are legitimate in this heightened era of enforcement. 🤖 Automation will serve as a crucial safeguard for businesses, mitigating the risk of human error that comes with manual audits. Embracing data through automation will empower employers to enhance their compliance processes and address systemic issues. #LinkedInNewsAustralia Misa Han

Your workout data will run your workday. From the gym to the boardroom, we won’t just track biometric data in the year ahead – we’ll translate it into action. A new era of biometric-driven training, powered by data once reserved for elite athletes, is taking hold. Large-scale events like HYROX have normalised data-rich preparation – and Bertie Wilkins, founder of tech-forward fitness studio One City, says everyday exercisers now expect feedback and recovery plans that mirror professional sport. In 2026 and beyond, this data-driven mindset looks likely to extend beyond the gym. Workplace wellness will move from tracking to adapting – using biometric signals to shape schedules, environments and team dynamics. Imagine offices that subtly respond to collective physiology, adjusting lighting, temperature and meeting length based on energy and focus patterns. Teams could even be assembled for circadian compatibility and recovery profiles, optimising wellbeing and output. Burnout prevention could be the most compelling frontier – a predicament thought to be contributing to UK firms’ £51bn annual mental health costs. Data from companies like Whoop, Oura, Garmin and Apple Watch could guide flexible work policies, such as prompting later starts to avoid commuter rush hours. Wilkins even imagines HR roles dedicated to interpreting biometric insights. Before long, wearables could be calling the shots better than any manager ever could. ✍ Aaron Toumazou 📷 Getty Images 💡 This is one of a several ideas LinkedIn News is highlighting in our annual list of predictions. Read it here: https://lnkd.in/BI26PanEurope Join the conversation in the comments or share your own prediction in a post or video with #BigIdeas2026.