Evolving HR Tech

If AI can replace you as a recruiter, were you recruiting at the right level? Let’s be honest. If your role is only: • Posting jobs • Screening resumes • Sending bulk emails • Scheduling interviews AI will outperform you. Faster. Cheaper. 24/7. But Talent Acquisition was never meant to be administrative. The real value of a great recruiter is not in keyword matching - It’s in judgment. Influence. Business understanding. AI can: • Map talent markets in seconds • Identify skill clusters • Automate engagement • Predict hiring trends But AI cannot: • Convince a passive cybersecurity leader to leave a stable role • Challenge a hiring manager’s unrealistic expectations • Read between the lines in a leadership interview • Build long-term talent communities with trust The uncomfortable truth? AI is exposing transactional recruiters. The opportunity? It is elevating strategic Talent Advisors. The future belongs to recruiters who: • Understand business strategy • Use data to influence decisions • Leverage AI for scale • Focus their energy on high-impact conversations AI won’t take your job. But a recruiter who knows how to use AI might. #TalentAcquisition #AIinHR #HiringStrategy #FutureOfWork #Leadership #Recruitment

What if AI's biggest win in recruiting isn't speed but developing the humans you DON'T hire? I had the opportunity to work with Capgemini on a case study that may just change how you think about AI in talent acquisition. They hire up to 90,000 people annually. They replaced technical interviews with AI-powered conversational assessments powered by MakiPeople. And yes, they cut time-to-hire from weeks to under 10 days. But here's what actually matters: The candidates they REJECTED loved the experience. 95% satisfaction. 94% improved brand perception. 96% completion rates. Why? Because AI gave every candidate - hired or not - personalized, specific, real-time feedback on their strengths and skill gaps. The recruiting process stopped being a black box and started being a development opportunity. This is what our 4 E Framework of AI Impact looks like in action: Efficiency → Several weeks to 10-day hiring cycles Experience → Candidates got personalized, branded assessments that felt human, not robotic Effectiveness → Better hiring decisions AND recruiters focusing on high-value work instead of screening  Employee Productivity → Freed recruiters became strategic advisors, not administrative processors; better performance of every new hire because they fit the job better As one candidate put it: "The detailed feedback was really useful to see which skills I could improve for future missions in Capgemini." Here's my question for you: If AI in talent acquisition can develop EVERY candidate who touches your organization - not just the ones you hire - what does that mean for your talent pipeline, your employer brand, and the broader labor market? Are we measuring the right things - or is it time to think beyond efficiency? Would love to hear your perspectives. #TalentAcquisition #AIinHR #FutureOfRecruiting #CandidateExperience #TalentDensity Josh Bersin Stella Ioannidou Maxime Legardez Coquin Emmanuel Legros ♠ Capgemini Jihane Baciocchini Sebastian Paez

Why Benefits Need an Operating System — Not Just More Apps Most benefits solutions are like individual applications: great at one thing, but limited in scope. You can write a compelling strategy document in Word. But to build the financial model behind it? You need Excel. Each tool excels at its specific job — yet none of them can run the full ecosystem on their own. That’s where an operating system comes in. It’s the foundational platform that lets all those specialized applications work together seamlessly, reliably, and at scale. At Alight Solutions, we’re building exactly that for employee benefits: the Benefits Operating System. It brings together health, retirement, and absence management — not as disconnected point solutions, but as an integrated ecosystem that powers the entire benefits experience. Instead of juggling multiple vendors, siloed data, and fragmented employee experiences, Alight’s Benefits Operating System creates a single, intelligent platform. It leverages decades of domain expertise, serves tens of millions of users, and uses AI-powered guidance to turn benefits complexity into better outcomes — for both employers and employees. This operating system approach delivers: • True integration across the employee lifecycle • Scalability for complex, evolving organizations • Deeper insights and personalization • Reduced costs and administrative friction • A better, more engaging benefits experience that actually drives retention and wellbeing In today’s world, benefits aren’t just a cost center — they’re a strategic advantage. But you can’t achieve that with apps alone. You need the right operating system underneath them. That’s the Alight difference.

The biggest myth about AI in Talent Acquisition? That it replaces the human. Fact is AI is making the human element more critical than ever before. We are witnessing a profound transformation in HR department. AI is automating tasks that once demanded significant manual effort across sourcing, screening, scheduling and interviewing. Organizations now can turn TA into a science. But this doesn't diminish the recruiter; it elevates them. AI handles the routine workflows, allowing recruiters to focus on the high-value responsibilities that truly define success: 1. Relationship-Building: AI frees recruiters to prioritize relationship-building and strategic, human-centered decision-making. 2. Managing Exceptions: They step in to manage exceptions, solve complex problems, and handle nuanced negotiations that fall outside automated workflows. 3. Delivering Empathy: In this new era, Empathy & Human Connection are listed as critical capabilities. Recruiters are the "human glue", ensuring a seamless and emotional experience where technology alone cannot provide it. Companies that embrace AI see exponential gains, like Forvis Mazars Group who increased the hiring volume while reducing manual screening time by 60%! The future of TA isn't less human. It's more strategic human enabled by technology.

Today we are releasing our(Stello Inc.) AI Compensation Agent - purpose-built to perform complex compensation analysis and simulations in real time. Here’s the problem we're solving: Every compensation cycle, HR and Finance teams are forced into the same workflow: → Export data into Excel → Build fragile models to simulate merit and market adjustments and bonus calculations → Run scenario after scenario manually → Recalculate everything when one assumption changes Answering seemingly simple questions becomes computationally expensive: → What is the total cost of moving all engineers to midpoint? → Should high performers receive 6.0% or 5.5%… and what does that do to budget burn? → What market adjustment pool is required to bring employees to minimum? → What are the trade-offs between compa-ratio increases and retention? These are multi-variable optimization problems across workforce data, market benchmarks, performance signals, and financial constraints. Here’s what the AI Compensation Agent Does The Stello AI Compensation Agent performs this analysis instantly by combining: → Building a merit matrix under budget constraints → Scenario simulation across thousands of variables simultaneously → Explainable outputs so leaders understand why a recommendation exists → Real-time compa-ratio modeling Instead of static models, customers now operate a live compensation decision engine. Built for Computational Depth Our agent is powered by Anthropic's Claude Sonnet 4.5 - one of the fastest LLM architectures available for structured reasoning and complex calculations. This allows us to: → Execute compensation simulations across entire workforces in seconds → Maintain numerical precision across chained calculations → Move beyond generative AI → into analytical AI for enterprise decision-making The Impact for Customers Organizations using the AI Compensation Agent are now able to: → Reduce compensation planning cycles by 80%+ → Replace brittle Excel models with governed, repeatable analysis → Explore scenarios they previously avoided due to modeling complexity This is not automation. This is augmentation of compensation science. Compensation has always been one of the most data-dense, calculation-heavy processes in HR. Now it finally has infrastructure designed for that reality. *** Want to see how the AI Compensation Agent works for your next comp cycle? Book 20 minutes and I'll walk you through it: https://lnkd.in/eezQ9T73

8 in 10 recruiting teams missed hiring goals by 50% last year. We helped one client cut time-to-hire from 47 days to 18 days. Here's the exact system we used: The challenge was familiar. Dozens of open positions. Hundreds of resumes per role. Manual screening eating up entire days while top candidates accepted offers elsewhere. It wasn't a talent shortage. It was a systems problem. Interview scheduling became a nightmare. The team was burning out. Qualified candidates were getting overlooked because their experience didn't match exact keywords. They implemented The Hire Insight's AI screening paired with human oversight. Time-to-hire dropped from 47 days to 18 days. A 61% reduction. New-hire performance improved 22% based on 90-day reviews. Diverse candidates in final interviews increased from 28% to 42% in 6 months. Burnout dropped to near zero while each recruiter managed nearly twice the workload. The system addressed the real bottleneck: initial screening and coordination. The AI analyzed career progression patterns and evaluated skills in context, identifying candidates whose experience aligned with actual requirements even when job titles didn't match. Recruiters could review AI-surfaced finalists in 2.5 hours instead of 6. The biggest time-saver? Eliminating interview scheduling back-and-forth. Automated scheduling cut coordination emails by 90% and saved recruiters up to 12 hours weekly. Real-time analytics showed where bottlenecks emerged so teams could intervene immediately. Human judgment remained central to every decision. Recruiters made final calls using structured scorecards for cultural fit, communication style, and team dynamics. Zero compliance breaches across 24 months. Bias monitoring was embedded with audit trails in every step. After rollout, recruiters spent 65% more time on proactive relationship-building versus reactive admin. Building talent pipelines before roles opened. Strengthening hiring manager relationships. Improving candidate experience. Faster hiring cycles enabled expansion into 2 new regional markets within the same fiscal year. This is what modern talent acquisition looks like. AI handling volume and speed. Humans ensuring quality and fit. Systems designed for both efficiency and fairness. If you're a TA leader trying to move faster without sacrificing quality, or a staffing firm looking for infrastructure to scale, The Hire Insight powered by ROI is built for that. Follow me for insights on AI recruiting and people-first hiring, or reach out to explore what's possible for your team. Learn more: roiagency.us

I used to manage global compensation for 500+ employees across 20+ countries...all on spreadsheets 😅 Let’s just say...I have the scars to prove how manual and stressful this was 🤯 Fast forward to today and the world of compensation looks completely different. Modern tools and real-time market data have revolutionized how startups plan and manage pay. In Part 1 of the Scaling Startup Compensation guide, created in partnership with Paula Judge and Peter Clarke, from the Accel Talent Team, we built the foundation (link in comments!). Now in Part 2, we’re getting tactical. How do you operationalize your compensation strategy with the right tools, data, and expert support? You’ll learn how to: 🛠️ Utilize compensation planning tools to give HR, Finance, and Leaders real-time insights, ditch spreadsheets, while automating manual work 🛠️ Choose the right tools and market data sources, like ChartHop, Pave, Complete, Kamsa, CandorIQ, and others, balancing accuracy of data and stage-appropriate features 🛠️ Engage specialized consultants to help you navigate the complexities of compensation with confidence Features insights from Lola Han, Armina Behrouzi, Matt McFarlane, Ashish Raina, Tudor Havriliuc, and Brett Ungashick - trusted experts guiding early-stage startups through key compensation decisions. Next up in Part 3 → Designing equity programs that attract and retain top talent. But first — read Part 2: Planning with Precision👇 https://lnkd.in/eK9xQGJx

Real-world AI in Talent Acquisition: The Truth Behind 1,000+ Placements A reality check from our consulting with 50+ tech hiring managers and TA leaders across various clients last quarter: 📊 The Starting Point: • 72% were deeply skeptical of AI recruiting tools • 89% felt pressured to "implement AI somehow" • Top concerns: Missing great talent & damaging candidate experience After successfully placing over 1,000+ professionals across various Andiamo divisions and clients, here's what ACTUALLY works: 🚫 The Wrong Approach: Jumping straight to AI screening. Yes, there are countless tools promising to revolutionize screening to reject candidates - but the technology isn't there yet. Period. ✅ The Right Approach: Start where it matters most (today) - efficiency, accuracy, and speed of candidate engagement. Real Client Case Study #1: Fortune 500 client company implementing AI for: → Real-time ATS-driven status updates (24/7) → Intelligent scheduling automation → Instant FAQ response system The Results? 📈 Candidate satisfaction increased 89% in just 60 days The Numbers That Actually Matter: • 15 hours/week saved per recruiter • Candidate update response time slashed: 72 hours → 5 minutes • Interview no-show rates down 35% 🔑 Key Insight: Candidates actively prefer automated interactions for routine updates. Speed wins over human touch for *basic* communications. Real World Case Study #2: F100 Tech Division Challenge: High applicant volume Previous Approach: AI auto-rejection Audit Discovery: Lost 3 eventual top performers to AI screening Solution Implemented: • AI ranking without rejection power • Human review guaranteed on all ranked candidates  • AI-assisted prioritization Results: • Quality of hire: +22% • Time to hire: -30% The Bottom Line: AI's Role: ✅ Decision support (analyzing and ranking) ✅ Administrative efficiency ✅ Experience enhancement AI's Boundaries: ❌ No autonomous decisions ❌ No replacement of human judgment ❌ No unsupervised operations ❌ Never Use AI For: • Candidate elimination • Final hiring decisions • Cultural fit assessment Additional use cases are being tested now, with data to come in the coming quarter: 1. JD optimization & improvement 2. Enhanced smart resume-to-job matching (again, ranking but never rejecting) 3. Custom interview question generation 4. Automated notes & summary creation Implementation Framework: 1. Comprehensive recruiting touchpoint mapping 2. High-volume task identification 3. AI implementation for admin/engagement 4. Careful expansion to screening support 5. Maintained human oversight 6. Continuous measurement & optimization ⏱ Implementation Timeline: 6-8 weeks 🤔 Leading talent acquisition? Let's talk about implementing this framework as part of our dedicated recruiting TA consulting solutions for your team. #TalentAcquisition #AIRecruitment #TechHiring #RecruitingInnovation #TalentStrategy.

If you’ve read Aeqium’s 2024 Compensation Review Survey, you know: - Collaboration challenges between rewards teams, managers, and leadership slow decision-making for 80% of teams. - 65% of rewards leaders cite manual processes—like spreadsheets and disconnected systems—as the biggest frustration during comp reviews. - One-third of managers lack the data needed for confident, data-driven decisions. The result? Missed opportunities, frustrated teams, and comp cycles that feel like a mad dash. If you’ve felt this, you’re not alone. The Compensation Review Problem ❌ Rewards leaders told us their current approaches are strikingly similar: - Spreadsheets: Prone to errors, time-consuming, and not collaborative. - HRIS Systems: Good for data storage but lack the flexibility for dynamic comp strategies. The outcome? Too much time chasing data and not enough time for meaningful decisions.  💡A Better Way to Manage Compensation Cycles 💡 The solution isn’t a better spreadsheet or tweaking old tools—it’s a purpose-built compensation planning tool. What it does: ▶ Integrates all comp data in one place: Salary, equity, variable pay, benchmarks, and performance metrics in a systems as flexible as excel. ▶ Empowers confident decisions: Managers get the context, data, and guidance they need. ▶ Fosters collaboration: Rewards leaders, managers, and leadership align in real-time. ▶ Tracks every decision: Full transparency and auditability. Why Not Adapt Existing Systems? Because we’ve all tried and seen the poor results. Spreadsheets buckle under complexity, and HRIS tools lack context, flexibility, and collaboration. Compensation Cycle Management tools enable: ▶ Seamless integration of data. ▶ Transparent modeling and recommendations. ▶ Real-time stakeholder alignment without endless emails. ▶ Clear, confident communication of changes. The Bottom Line You’re not alone in these struggles. By rethinking your tools and processes, you’re taking a critical step toward fairer, faster, and more impactful decision-making. Check out our comp planning product deep dive in the comments to see how we can help 👇

Compensation design has changed more in the last few years than in the decade before it. And the way people model it has completely shifted too. When we first launched the Levels.fyi calculator page, it became one of the go-to tools for breaking down offers and visualizing equity growth. Many talent teams would even use it to send out offers, which led us to build Interactive Offers (https://lnkd.in/g7guUvif). But the landscape evolved. Front-loaded vesting schedules replaced the old four-year curves. Refreshers grew into a major part of expected total pay. And people stopped thinking in static numbers they started modeling trajectories. So we went back to the drawing board. The new Levels.fyi calculator lets you see your comp as it really unfolds. Equity that front-loads heavily, stacked refresher grants that kick in, signing bonuses that shape your early years. And of course, model growth into all of that using our equity slider. All in one clear, interactive view. It’s how hiring teams and comp analysts model offers internally, now in your hands. More realistic. More transparent. Built for the modern offer. Check it out, play with the inputs, and see what your actual total comp could look like: https://lnkd.in/gWSqKwCx Thank you to all our users who pushed us in this direction, allowing us to build better tools to serve you.