Thank you to everyone who joined @Vijay_Swami's session, Deploy Skills at the Speed of AI, at the Gartner HR Symposium/Xpo in London. It was great to share what we've learned from helping organizations redesign work across industries. In case you missed it, here are three key takeaways: * Build the skills your competitors don't have: Companies with a higher share of unique skills are growing up to 8x faster than their peers. * Put experience at the center of your AI talent strategy: Since generative AI went mainstream, mid-career employment has grown 11.4%, while entry-level employment has fallen 2.4%. * Hire and develop for judgment, because execution skills are becoming abundant: There are 21 data analysts with SQL skills for every one skilled in data storytelling, so the real advantage now lies in judgment-led skills. All three point back to one core idea: every enterprise that wants to win in the AI era needs a workforce ready to deliver. Etter for AI workforce transformation helps CHROs build that workforce with the value, speed, and capacity this year's event has centered on. We're at the Draup booth through tomorrow, so there's still time to stop by, say hello, and see how AI will reshape your workforce! #GartnerHR #HRSymposium #CHRO #WorkforceTransformation #FutureOfWork #AI #HumanResources

Is AI decreasing demand for knowledge workers? The short answer is no. But it is fundamentally changing what employers pay for. Draup's new research paper, The Execution Premium: How AI Has Redefined Professional Value Across Six Knowledge Work Functions, analyzed compensation benchmarks, profile data and job postings to understand what has changed. Here is what we found: -> Execution skills are now table stakes. Routine ETL and legacy BI tooling are falling out of job descriptions. The skills that defined the role have become the entry requirement. -> Judgment commands the premium. For every data analyst who can tell a story with data, 21 can query it. Employers have started asking for the first kind. -> The divergence opens in mid-career. Pay stays flat early, then climbs sharply when professionals move from execution to judgment. The top 10% of earners make 2.0 to 2.3 times the bottom 10%. The report covers six enterprise roles, with the saturated skills, the emerging ones and the compensation evidence behind each. Read here: draup.com/talent/blogs/the-e…

Most AI sovereignty debates start from the wrong question: how much of the stack can we own? The Ethical AI Governance Group and Draup have released The AI Sovereignty Paradox: Why AI Advantage Depends on Interdependence. The report introduces the Global AI Adoption Scorecard, a framework that evaluates national AI readiness across talent, workforce readiness, infrastructure, data ecosystems, innovation, capital formation, and policy. The inaugural study covers five ecosystems: the United States, Israel, France, India, and Japan. The argument is straightforward. Sovereignty is less about total control and more about strategic control and optionality: enough choice across models, infrastructure, talent, data, and partnerships that no single provider, supply chain, or geopolitical shift can dictate a country's AI future. The sharper finding is the deployment gap, the distance between AI capability and AI actually running at scale. Chips and models are not the bottleneck. Talent, institutions, and organizational readiness are. #AISovereignty #AIAdoption #TalentIntelligence #AIGovernance #Draup #EAIGG

Tier-2 city talent hubs are growing headcount ~2x faster than metros. Is the "hire where the office is" model quietly dying? Same skills, 20-35% lower cost, 10-12% lower attrition. Talent is decentralizing faster than enterprise workforce models can adapt. The org chart is mapped to a talent geography that no longer exists. #GCC #TalentIntelligence

76% of organizations now have a Chief AI Officer, up from 26% a year ago (IBM study, 2,000 CEOs). The title scaled faster than the org design behind it. The decision that matters is not who you hire. It is whether the seat reports to the CEO or the CTO. #AILeadership #OrgDesign

"Which roles will we struggle to hire in 12 months?" If you can't answer that today, your workforce plan breaks quietly over the next four quarters. The insight you're looking for is buried deep in demand trends, supply ratios, competitor hiring, cost curves, and a dozen other signals. That changes today. Introducing Deep Research mode on Curie, for your highest-stakes workforce decisions. Ask it the questions you'd normally commission a study for: → Which roles will we struggle to hire next year, and where should we source them now? → Bangalore or Warsaw for our next AI hub, and how does that fit our stated strategy? → Reskill our current analysts into AI roles, or hire from the market? Curie's Deep Research plans the investigation. Runs it across demand, supply, cost, and competitor signals. Verifies every figure. Hands you a decision-ready brief. Weeks of work, now in minutes. And when the brief raises your next question, you just ask. Follow up. Drill down. Redirect. What's the first decision you'd hand it? #AgenticAI #WorkforcePlanning #TalentIntelligence #HRTech #Draup #CHRO #LaborMarketIntelligence

"AI is transforming HR" is one of those phrases everyone says and almost no one proves. This week @Vijay_Swami tries to prove it, or disprove it, using Deep Research in Curie on the Draup platform. The question was simple: are we seeing genuinely new HR roles, or just old jobs with an AI label? The data was clear. Postings at the intersection of HR and AI grew 77% in eight months, and a cluster of roles has emerged that did not exist two years ago: → HR Automation Manager → AI Workforce Strategy & Transformation Leader → HR AI Enablement Leader → AI Learning & Workforce Capability Leader These aren't rebranded HR jobs. They carry new mandates, and they demand a rare mix of AI fluency, HR depth, and change management in one person. That scarcity is exactly why the window to build this capability is open right now, before titles standardize and pay premiums set in. What are you seeing inside your own org? Are these titles showing up in your reqs yet? Comment below for the full report. #HR #AITransformation #FutureOfWork #WorkforceStrategy #PeopleAnalytics

65% of shared services orgs now rank agentic AI as their top investment priority. 56% are still only 25-50% automated. The ambition layer is outrunning the execution layer. Draup's research across Finance, HR, and Procurement shows why: audit any shared services tech stack and 30-40% of proposed AI use cases turn out to be already partially solved by automation the org forgot it owns. Broken RPA bots. Unadopted rule engines. Pipelines that fail silently every Tuesday. Two rules separate compounders from stallers: ->Go upstream. AI at intent classification removes cost. AI at post-call summarization only inspects it. ->Don't fund AI on any process whose prior automation is under 80% functional. No model fixes a broken bot. The cheapest capacity you'll buy this year is the automation you already paid for. More insights in our report; comment below to access. #SharedServices #GBS #AgenticAI #IntelligentAutomation #FutureofWork #TalentIntelligence

Bayer collapsed 12 management layers into 6 and cut roughly half of its management positions. It is not alone. Gartner projects that through 2026, 1 in 5 organizations will use AI to flatten their structure, removing more than half of current middle management roles. But most companies are only doing half the redesign. They delete the layer without asking what the layer was actually doing. Middle management did more than approve things. It coordinated work, routed information, and checked that output was correct before it moved forward. AI can absorb the first two. It cannot yet own the third. So as structures flatten, the bottleneck moves to a new place: verification. Cursor crossed $1B ARR with a lean team, agents now writing over a third of the pull requests in its own codebase, because verification lives in disciplined review. Haier split into 4,000+ micro-enterprises, then had to build new platforms to coordinate them. Same lesson: flattening doesn't delete the coordination and checking. It forces you to rebuild it in the open. So the mandate for HR and org design leaders is not to cut the middle. It is to rebuild what the middle was quietly holding together: • Name verification as a function, and put someone accountable for it on the chart • Keep teams small enough that ownership is obvious and nobody hides • Write the job family your org design forgot: the people who decide what is correct, complete, and safe before it ships We put our org design research on this into a full report, including how to structure the verification layer and staff it. Comment below and we'll send it over. #ArtificialIntelligence #FutureOfWork #Leadership #HumanResources #OrgDesign #WorkforcePlanning #AIWorkforceTransformation

Big Tech just spent ~$9 billion admitting that AI's bottleneck isn't the model. It's deployment. In about eight weeks: 1. Microsoft launched Frontier Company, a $2.5B unit with 6,000 engineers and specialists embedded inside enterprise customers. 2. OpenAI stood up a deployment venture with $4B+ led by TPG. Anthropic formed a $1.5B unit with Goldman Sachs and Blackstone. 3. AWS committed $1B. All to place forward-deployed engineers inside customer organizations, a role whose postings grew over 1,000% year over year (Live Data Technologies). Our Five Skill Bets research called Forward Deployed Engineers the #1 capability bet enterprises can safely make: durable, scarce, and compounding regardless of which model or vendor wins. Comment below to access the report. But there's a trap in copying Big Tech's move headcount-first. MIT's Project NANDA research found roughly 95% of AI pilots fail to deliver measurable P&L impact, for organizational reasons rather than technical ones. The FDE is the anchor. The crew around them (adoption and change managers, process owners, enablement specialists) is what keeps the work alive after launch. An FDE who ships a flawless integration nobody adopts has still failed. For talent leaders: the market just repriced deployment talent. If your workforce plan doesn't have a build-vs-buy answer for this capability, and the non-engineering crew around it, you're now competing with $500K+ frontier-lab comp for the same people. Bet on the anchor. Build the crew. #WorkforcePlanning #TalentIntelligence #AITransformation #SkillsStrategy #FutureOfWork

Which skills survive when AI can do the task? Here's what we found: We did a durability analysis across nine core engineering and data/ML roles. 2.85 million job descriptions. 12 months. AI is not reducing demand for technical talent. It is changing which skills command a premium. The market is expanding, but the internal composition of every role is shifting faster than most hiring frameworks have caught up with. Three findings held across all nine roles. >The seniority premium is structural. The market is buying the harder, more specialized version of each job. >AI fluency is now baseline, not bonus. Employers no longer ask if you use AI tools. They ask if you can direct them. >Pay maps directly onto durability. This is the market pricing judgment and system-level reasoning in real time. The rule underneath all of it: a skill stays durable when it needs judgment. It shifts when it is routine. The full report has the durable-versus-exposed breakdown for every role, the durable skills pyramid, and the pay-durability map. Comment below and we'll send you the full report. #FutureOfWork #AI #SkillsStrategy #SkillsArchitecture #reskilling #WorkforcePlanning #AITransformation #WorkforcePlanning #CHRO

AI is not reducing demand for technical talent. It is changing which skills command a premium. This week, @vijaydraup dives into the durability of skills across nine core engineering, data, and AI roles, based on an analysis of approximately 2.85 million active job descriptions. There's one thing common to every role. Value is moving away from routine coding, testing, reporting, and data processing, and toward judgment, architecture, problem framing, governance, and AI orchestration. The market is definitely expanding, not contracting. What is shifting is the internal composition of each role, faster than most hiring frameworks have kept pace. Read the full article here: linkedin.com/pulse/durabilit… #futureofwork #talentintelligence #chro #workforceplanning #aiandwork

Most enterprises are still hiring for AI job titles that will not exist a year from now. This week, @vijaydraup dives into the five capability bets an enterprise can safely make and the scarce roles that stay valuable no matter which model or vendor wins. The throughline is that none of them is a single hire. Each bet is a small crew built around a single anchor role, because most AI initiatives stall for organizational rather than technical reasons. Check out the full article here - linkedin.com/pulse/critical-… #chro #futureofwork #talentintelligence #aitransformation #strategicworkforceplanning

Two board preparations in four weeks, and not one led with headcount cuts. This week, @vijaydraup explores why the AI conversation in boardrooms has become a workforce conversation. Three questions now dominate the room: ➡️ Is the workforce ready for the skill shifts AI will create? ➡️ Can we grow, organically or through M&A, without growing headcount at the same rate? ➡️ Can we redeploy people toward harder customer problems? The new floor for a credible answer is to redesign your workforce before reduction. Two meetings are not a trend, but these questions are outpacing most annual workforce plans. Read more here: linkedin.com/pulse/transform… Comment "report" for the full brief. #chro #futureofwork #workforceplanning #aitransformation #boardgovernance

Algorithmic problem-solving now accounts for 8% of engineering time. Most interview loops still weigh it at 50%+. This week, @vijaydraup presents the case that enterprise hiring has a structural misalignment problem and a 90-day window to fix it. The nature of work has shifted toward AI collaboration, systems integration, and judgment under uncertainty, while most screening processes are still designed for a world that no longer exists. This paper reframes the core hiring question from "Can this candidate solve a textbook puzzle?" to "What can this person accomplish with an AI agent sitting next to them?" and argues this single reorientation, applied at the interview loop level, is the highest-leverage talent action available to CHROs this quarter. Want the full playbook? Comment "playbook" and we'll DM to you #talentacquisition #hiringinnovation #aiworkforce #workforcestrategy #chro linkedin.com/pulse/90-day-pl…

Skilling budgets are growing, but skilling outcomes are not keeping pace. This week, @vijaydraup examines the reasons why the challenge of enterprise skilling is more intricate than it seems. What are the most advanced organizations actually doing differently? The key is to look at it as an operating model problem. The companies pulling ahead have embedded workforce transformation into their technology roadmaps, redesigned work at the task level between humans and machines, and built skill architectures that function as live infrastructure. Ten approaches emerge consistently across mature programs, and they interlock. A gap in any one weakens the whole. To know more, check out: linkedin.com/pulse/how-compa… #workforcestrategy #talentintelligence #reskilling #futureofwork #chro

For three decades, every capability decision was reduced to three boxes. Build it, buy it, or borrow it. AI has quietly added a fourth pillar that breaks the model. Bots, the digital workers now executing discrete tasks across systems end-to-end, have turned a clean sourcing choice into a stack of decisions. The more consequential shift is that Build itself has split into Foundation, Agents, and Skills, each with its own sourcing logic and its own pace of change. This is where most organizations are quietly making the same mistake. They are over-investing in bots, where productivity is most visible, and starving the agent layer, where institutional knowledge actually gets encoded. Our latest working paper introduces the Build-Buy-Borrow-Bots model, a reference architecture for sourcing capability in the AI era. #talentintelligence #workforceplanning #futureofwork #aitransformation #chro

Org design has quietly become the most consequential decision leaders make this decade. This week, @vijaydraup expands on the principles of modern org design, building on a short paper that drew significant interest from talent and transformation leaders. The extended edition delves deeper into how enterprises should structure roles, teams, and decision rights as AI reshapes the nature of work. It is a practical read for anyone redesigning their organization for the next era of productivity. Read the full article here: linkedin.com/pulse/revolutio… #orgdesign #futureofwork #talentstrategy #workforceplanning #aitransformation