Evolving HR Tech

I analyzed 8,000+ recruiting emails sent through our platform. Basic personalization (name, company) now delivers ZERO lift in response rates. None. Ten years ago, this was revolutionary. Today, it's table stakes. What's working? Our customers using AI-powered personalization are seeing 60% higher response rates.  But even this is about to become obsolete because the market is about to get flooded with AI outreach that can do exactly that. The next frontier isn't just better personalization based on someone's online profile or resume. It's using the entire relationship history your company already has with each candidate. I'm talking about: - "I saw you attended our recruiting event 15 months ago" - "You interviewed with us last year and based on your feedback, this new role addresses exactly what you were looking for" - "You had conversations with Jill and Sam on our team 3 years ago - they still reference your insights" This hyper-personalized approach based on relationship context is what separates elite recruiting teams from everyone else. Here's what most people miss: Generic AI tools can't do this alone. The magic happens when AI is married with a system that captures every single candidate touchpoint in one place (hint, hint: we’ve been building this for almost 10 years). We're seeing early adopters of this approach fill roles 2x faster with candidates who are far better fits. This is the future of recruiting personalization. The companies who get there first win the talent war. Everyone else will be left wondering why their "Hi {{first_name}}" emails aren't working anymore.

🎙️ "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 🔗

The first time I presented a data-driven HR strategy to the board… They didn’t ask about culture. They didn’t ask about performance reviews. They asked: “How does this move the business?” That moment shifted my mindset forever. As HR leaders, we often talk about engagement, inclusion, and retention. But unless we connect people to performance, it’s all just noise. That’s where HR metrics come in. Not dashboards for vanity. Not numbers for compliance. But people data that drives real business decisions. Here are the 10 essential HR metrics every strategic HR leader must watch: ✅ Headcount – Are we staffed to meet strategic goals? ✅ Turnover – Are we leaking talent, and what’s it costing us? ✅ Diversity – Are we building inclusive teams that attract top talent? ✅ Total Cost of Workforce – Are we balancing efficiency with value? ✅ Compensation – Are we aligned with market realities and internal equity? ✅ Spans & Layers – Are we structured for agility or buried in hierarchy? ✅ Engagement – Are our people emotionally invested in our mission? ✅ Talent Acquisition – Are we hiring right—or just hiring fast? ✅ Learning – Are we preparing for the skills of tomorrow? ✅ Workforce Planning – Are we ready for what’s next? I’ve used these metrics to launch cultural transformations, align HR with corporate governance, and deliver real ROI—not just HR wins, but business wins. Because here’s what I’ve learned: 👉 You can’t improve what you don’t measure. 👉 You can’t lead without insight. 👉 And you can’t expect impact without alignment. If HR wants a seat at the strategy table, we need to speak the language of metrics. Because in today’s world, the most human organizations… are the ones who understand their people through data. #PeopleAnalytics #HRStrategy #DataDrivenHR #HRMetrics #FutureOfWork #BusinessImpact

AI Innovation in HR: Listening to People at Scale Anthropic has piloted Interviewer, a new AI research tool powered by the Claude model that autonomously designs, conducts, and analyzes in-depth, qualitative interviews at scale. This tool is an example of how AI will change the methodology of collecting organizational insights. Key Features: 1) Adaptive Conversations: Claude Interviewer can engage employees in natural, 10–15 minute chats, dynamically adapting questions based on responses, simulating a human interviewer. 2) Achieving Scale: Conduct thousands of detailed qualitative interviews quickly and parallel, significantly reducing the cost and time limitations of traditional methods. 3) Full Pipeline Management: The solution manages the entire process, from initial planning to automatic thematic analysis of transcripts. This autonomous execution allows for outcomes to feed back into AI models to propose follow up actions. The power of scalable qualitative data is highly relevant for HR: 1. Performance Management: Collect deep insights on team dynamics, leadership effectiveness, and skill gaps. 2. Engagement Research: Move beyond survey scores to truly understand the contextual factors driving satisfaction and retention. 3. Job Analysis & Evaluation: Accurately map complex roles by gathering detailed data from incumbents on evolving responsibilities and workflows. Anthropic tested Interviewer on 1,250 professionals, demonstrating its capacity to deliver genuine, scalable qualitative perspectives necessary for informed strategic decision-making. As similar tools become standard, data privacy and control will be key considerations for adoption. See Anthropic publication. https://lnkd.in/eqPVrBqX

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:

Moderna Is Redefining Work With 3,000 AI Assistants In a striking move that shows how deeply AI is reshaping business, biotech firm Moderna has merged its human resources (HR) and technology departments under one leader: Chief People and Digital Technology Officer, Tracey Franklin. So, what’s behind this shift? ► At the core are AI assistants Moderna developed with help from ChatGPT’s creator, OpenAI. These custom GPTs are tailored to Moderna’s needs. ► Think of them as trained AI assistants that handle specialized tasks, from selecting doses during clinical trials to drafting responses for regulators. ► For instance, a “virtual HR agent” now answers employee questions that once required junior staff. Moderna currently uses over 3,000 custom GPTs, a clear sign AI is deeply embedded into daily operations. 🤯 A new way to think about teams: ► The success of these AI assistants has sparked a broader rethink of team structures: What still needs a human, and what can AI do? ► Some roles are being reshaped, others phased out. ► I would really like to understand how Moderna is retraining employees, so they’re not replaced but empowered with AI. One thing is clear: while many companies bolt AI onto old processes or build “digital transformation” teams, Moderna is going further. It is rebuilding its entire organization around a human-AI workforce. This is a wake-up call for HR leaders everywhere: Your role isn’t going away—it’s expanding. More complexity. More responsibility. Definitely learn more about AI and organizational change to take on this new opportunity. Watch this video where Moderna’s Tracey Franklin shares how custom GPTs help her communicate better with the Executive Committee. 📷Video: Fortune #AI #Technology

Talent is the buzzword nowadays. We all want to have it, show it and get more of it , even if we are not necessarily able to evaluate it, either in ourselves or others. We are living in the age of talent. In today's competitive landscape, talent management is more crucial than ever. But how can organizations optimize their talent strategies for maximum impact? Enter the "Rule of Vital Few," a transformative concept explored in Tomas Chamorro-Premuzic's insightful book, "The Talent Delusion." According to the Rule of Vital Few, a small fraction of inputs often generates the majority of outcomes. In the context of talent management, this principle emphasizes the importance of identifying and prioritizing high-impact contributors—the individuals who drive exceptional results and propel the organization forward. Following are the ways on how to leverage the power of the "Rule of Vital Few" effectively : 1. Identify Your High-Impact Contributors: Start by analyzing performance data and identifying the individuals who consistently deliver outstanding results. 2. Tailor Development Opportunities: Once you've pinpointed your high-impact contributors, tailor development opportunities ( training program, mentorship, stretch assignments) to their specific needs and aspirations. 3. Foster a Culture of Recognition: Celebrate and acknowledge the achievements of your high-performing employees in the form of Public recognition, awards, and praise. 4. Facilitate Knowledge Sharing: Encourage collaboration and knowledge sharing among your high-impact contributors. Create platforms and forums where they can exchange ideas, best practices, and innovative solutions, fostering a culture of continuous learning and improvement. 5. Align Incentives with Performance: Consider implementing performance-based rewards and recognition programs that incentivize and motivate your top performers to maintain their high levels of excellence. 6. Invest in Succession Planning: Develop robust succession plans that identify and groom high-potential employees for future leadership roles. Please share your insights on redefining talent in today's dynamic workplace. #talentmanagement #hr #leadership #talent #organizationalculture Pic Courtesy : To the respective owner

One-Size-Fits-All Learning is Broken. Personalization is the Fix. I recently met with the CHRO of a global enterprise software company. We both agreed on something critical: AI is changing not just what we learn, but how we learn. For too long, L&D has been built around “one-size-fits-all” programs. The result? Low engagement, uneven outcomes, and skills that don’t stick. AI gives us a way out. It can design personalized learning journeys for every employee—adapting to their role, career path, current skills, and even learning style. Personalized learning isn’t just more effective; it’s also more engaging. Employees feel invested when development is tailored to them. If you’re activating reskilling programs today, ask yourself: • Are we still delivering generic training, or are we tailoring to the individual? • Do we have the tools and data to make learning adaptive? • Are we embedding personalization into every upskilling initiative? Platforms like Draup are already enabling this shift. The companies that act now will see faster adoption of new skills, higher employee engagement, and a workforce ready for the AI era. The future of reskilling isn’t about more training. It’s about the right training, personalized at scale. Zinnov Shweta Rani (She/Her) Vamsee Tirukkala Vijay Swaminathan Manikandan PK, PCC(ICF) Hani Mukhey Charu Kapoor Dimple N Rakhiani (She/Her) Namita Adavi Dipanwita Ghosh

Scheduling in Kubernetes happens in various ways. Depending on the workload, you might need different algorithms like 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴. Volcano, a CNCF project, supports this and can optimize complex workflows such as AI training, inference pipelines, and distributed data processing.  🚀 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗚𝗮𝗻𝗴 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴? Gang scheduling ensures all pods in a group ("gang") start simultaneously or none do. This prevents partial execution, which is critical for interdependent tasks like distributed training or multi-stage AI pipelines. Without it, a single delayed pod could stall an entire workflow, wasting resources. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: In distributed AI training, if three worker pods are needed, Volcano’s gang scheduler waits until all 3 are available. If even one fails to schedule, the scheduler releases reserved resources to avoid cluster deadlocks. ⚡ 𝗪𝗵𝘆 𝗩𝗼𝗹𝗰𝗮𝗻𝗼? Volcano extends Kubernetes’ default scheduler to handle batch workloads and multi-pod dependencies. It’s ideal for: → AI/ML workflows (e.g., TensorFlow/PyTorch jobs). → Big Data processing (Spark, Flink). → High-performance computing (HPC). Key features: ✅ PodGroup orchestration: Treats multiple pods as a single schedulable unit. ✅ Fair-share resource allocation: Balances cluster resources across teams. ✅ Preemption/Reclaim: Prioritizes critical workloads without manual intervention. 🌟 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 Imagine training a large language model (LLM) across 3 GPUs. With gang scheduling: → Volcano groups all worker pods into a PodGroup. → The scheduler reserves resources only when all 3 GPUs are available. → If a node fails, Volcano retries or releases resources instantly, avoiding idle clusters. This eliminates "resource hoarding" and ensures cost-efficient scaling for AI teams. #Kubernetes #mlops