AI-Powered Talent Acquisition

New research joint with Maxim Massenkoff: How is AI affecting the US labor market? In this research brief, we introduce a new measure of AI displacement risk to spot disruption, then test it against employment data. We find limited evidence AI has increased unemployment to date. Our measure, "observed exposure," compares the tasks LLMs are theoretically capable of to the tasks people actually use Claude for at work. We find that actual usage is far from reaching theoretical capability. This measure tracks with independent forecasts. Jobs with higher observed exposure to AI are projected by the BLS to grow more slowly over the next decade. We find limited evidence, however, that AI is playing a role in the broader labor market today. The top 25% of workers most exposed to AI automation have similar trends in unemployment rates to workers with no exposure at all. Hiring of younger workers in the most exposed occupations appears to have slowed faster than for non-exposed roles, but our estimates are imprecise and other non-AI factors may be playing a role. This research is a first step. Our goal is to establish an approach for measuring how AI is affecting employment, and to build on these analyses periodically as more data becomes available.

Emerging Departments: How AI is Transforming Organizations Transformation in light of AI isn't just about digital change—it's strategic, cultural, and organizational. Early results of organizational optimization with AI reveal that traditional structures are evolving into new, combined departments that break down silos and enhance collaboration. Here are some emerging trends: 1. Human Experience Department (Led by the CXO) Combines marketing, HR, and customer service to create a unified experience approach. Focuses on customer and employee experience as a seamless continuum. Example: Airbnb and Starbucks blending internal and external engagement for holistic experience design. 2. The Intelligence Function (Led by Chief Data & Intelligence Officer (CDIO)) Merges IT, data analytics, and AI strategy into a unified intelligence function. Enhances decision-making with data-driven insights and technology integration. Example: Microsoft and Amazon use intelligence functions to support strategy and innovation. 3. Integrated Growth Department (Led by the CGO) Combines Marketing, Sales, and Customer Success to create cohesive client journeys. Prioritizes growth by aligning customer interactions across all touchpoints. Example: HubSpot and Salesforce driving client experience continuity. 4. Strategic Innovation & Transformation Office (Led by Chief Strategy Officer or Chief Transformation Officer) Combines strategy, innovation, and transformation initiatives for continuous evolution. Fosters agility by integrating foresight and innovation into long-term strategy. Example: Tesla blending innovation with strategic growth planning. 5. Technology and Digital Transformation Department (Led by the Chief Technology & Transformation Officer) Integrates IT, digital transformation, and cybersecurity under one strategic role. Embeds technology into workflows while ensuring security and compliance. Example: Cisco and IBM streamlining their digital transformation efforts. 6. Resilience and Continuity Department (Led by the Chief Risk Officer) Oversees Risk Management, Business Continuity, and Strategic Foresight. Ensures organizational resilience in an increasingly FLUX world. Example: JP Morgan building resilience to mitigate risks and ensure continuity. 7. Ethics and Responsible AI Office (Led by the CEAO) Ensures ethical AI use and compliance with regulatory standards. Maintains trust and integrity as AI becomes central to business strategy. Example: Microsoft and IBM proactively building ethics frameworks for responsible AI. In sum, AI is driving fundamental shifts in how we structure our organizations. To thrive, leaders must think beyond digital transformation and focus on strategic, cultural, and organizational evolution. The companies that succeed will be those that break down silos, integrate their functions, and embrace transformation as a continuous journey.

The 2026 Microsoft Work Trend Index points to a shift every leader should be paying attention to—AI is changing how work itself is designed at an organizational level, going beyond individual tasks.   For the past couple of years, many organizations have focused on AI adoption. Pilots. Use cases. Productivity gains. Now we’re entering the next phase: building the operating model for people and agents to work together in ways that create measurable business impact.   A few themes stood out to me:   ➡️ AI is expanding what people can do The report shows that 49% of Microsoft 365 Copilot conversations support cognitive work, including analysis, problem-solving, evaluation, and creative thinking. AI is not just helping people move faster. It is helping more people participate in higher-value work.   ➡️ Human judgment becomes even more important As agents take on more execution, people play a bigger role in setting direction, defining quality, and evaluating outcomes. Workers recognize this too: 50% of AI users say quality control of AI output is becoming more important, and 46% point to critical thinking.   ➡️ Copilot Cowork and Dynamics 365 help move AI from insight to action The latest innovations shared by Jared Spataro and Bryan Goode show how Microsoft is helping organizations connect people, agents, and systems in the flow of work. With Copilot Cowork, Dynamics 365 plugins, and connected data across business processes, AI can move beyond generating outputs to helping teams coordinate work, reduce friction, and drive real outcomes.   What’s becoming clear is that this next chapter is about reimagining how work moves, where human judgment matters most, and how teams come together to turn intelligence into meaningful action.   The organizations that pull ahead will be the ones that embrace AI as a foundational part of their operating model for how work gets done.

When anyone in an organisation can use AI to build a working system in minutes, the focus shifts from how to build to what should exist at all. The implications of this are counterintuitive because this isn’t just about democratisation, it’s about the not-so-subtle removal of a governing mechanism most organisations never realised they relied on. When building took weeks and required many departments, it was the scarcity that enforced selection. Now departments produce overlapping versions of the same capability because nobody is asking permission, and because each system works perfectly in isolation. Replit recognises both the opportunities and the difficulties and asked me to explore these dynamics and share what I found here on LinkedIn. In analysing how instant creation reshapes organisational structure, it's clear that traditional governance was never built for this condition. It is not trivial to govern what no longer depends on permission. The ability to create an application by describing it is remarkable, but it tells only half the story. Replit’s enterprise direction reveals the other half. Its security and production infrastructure are not simple features. They are designed for a landscape defined by abundance rather than scarcity. The platform assumes proliferation as the natural state and builds around it. What I consider may work, or may have to work, inside organisations moving at the speed of AI is an inversion of governance. Instead of controlling who builds, they will manage what persists. They accept that creation will happen and focus on what deserves to remain, identifying which solutions warrant institutional support, which local innovations contain enterprise value, which experiments should become standards. This inversion calls for different organisational abilities. Most firms currently lack the structures to develop this kind of judgment. Their systems assume that control occurs at the point of creation, not selection. That assumption is defunct. Organisations that recognise this inversion early will build the capacity to manage abundance. This area will remain a focus of my research. I’m grateful to partners like Replit, whose support enables the sustained analysis this kind of work demands. #Leadership #Strategy #AI #OrganisationalDesign #Governance #FutureOfWork

✨ ‎ Let's be honest — recruiters spend way too much time perfecting search criteria when sourcing or reviewing candidates. Hours tweaking boolean strings, second-guessing qualifications, wondering if we're casting too wide (or too narrow) of a net... To save recruiters time, we’ve added AI-generated criteria and new presets. In Gem’s AI Sourcing and AI-powered App Review, you can now: → Build comprehensive qualification lists in seconds → Generate targeted search criteria based on your job post → Use popular presets to spot rising talent quickly → Review and edit AI-suggested criteria with ease Here's what makes this really special... We didn't just build this feature and ship it. We tested it extensively against user-generated search criteria. The results? Our AI-generated criteria performed as well or better than manually created searches across the board. Think about that for a second. The same (or better) quality candidates, without the hours spent perfecting search strings. This is what excites me about AI in recruiting — it's not about replacing human judgment. It's about giving recruiters back their time so they can focus on what matters most: building meaningful connections with candidates.

𝗘𝘃𝗲𝗿𝘆 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗡𝗲𝗲𝗱𝘀 𝗮 𝗡𝗲𝘄 𝗠𝗲𝘁𝗿𝗶𝗰: Industrial era → Output per hour (measured physical productivity) Internet era → Digital economy accounts (captured online service value) Intelligence era → Skills-centered measure (reveals AI-human skill overlap) “The Iceberg Index measures where AI technical capabilities overlap with human occupational skills across 151 million workers, providing forward-looking intelligence to complement traditional workforce metrics that track employment outcomes after disruption occurs. The analysis reveals a substantial measurement gap. Current AI adoption concentrates in technology occupations representing 2.2% of labor market wage value. Yet AI technical capability extends to cognitive and administrative tasks spanning 11.7% of the labor market—approximately $1.2 trillion in wage value across finance, healthcare, and professional services. This fivefold exposure difference is geographically distributed nationwide … The Index measures technical exposure—where AI can perform occupational tasks—not displacement outcomes. … Project Iceberg enables states to test interventions before committing resources, transforming workforce planning from reactive crisis management to strategic foresight. … The Iceberg Index provides measurable intelligence for critical workforce decisions: where to invest in training, which skills to prioritize, how to balance infrastructure with human capital. It reveals not only visible disruption in technology sectors but the larger transformation beneath the surface. By measuring exposure before adoption reshapes work, the Index enables states to prepare rather than react—turning AI into a navigable transition.”

AI is fundamentally reshaping our workforce, but the impacts are nuanced. The latest report, “Potential Labor Market Impacts of Artificial Intelligence: An Empirical Analysis,” by The White House Council of Economic Advisers, provides critical insights for leaders that will impact everyone's future.. 📊 Key Findings: ✅ 𝐆𝐫𝐨𝐰𝐭𝐡 𝐢𝐧 𝐇𝐢𝐠𝐡-𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲, 𝐀𝐈-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐑𝐨𝐥𝐞𝐬 Roles requiring advanced AI skills have increased by 30% over the last five years. Positions such as AI ethics officers and data scientists are on the rise, indicating a shift toward more complex, creative work. Occupations that integrate AI effectively are growing twice as fast as average, suggesting AI's role in complementing human skills rather than replacing them. ❌ 𝐇𝐢𝐠𝐡 𝐑𝐢𝐬𝐤 𝐨𝐟 𝐉𝐨𝐛 𝐃𝐢𝐬𝐩𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐋𝐨𝐰-𝐒𝐤𝐢𝐥𝐥 𝐑𝐨𝐥𝐞𝐬 40% of current jobs are at risk due to high AI exposure but low skill requirements, particularly in administrative and routine manual tasks. These jobs are declining at a rate of 2% annually. Sectors like customer service and data entry are vulnerable, raising concerns about job security and economic stability in these fields. 📍 Regional Disparities: ✅ 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 𝐢𝐧 𝐓𝐞𝐜𝐡 𝐇𝐮𝐛𝐬 Tech-centric regions like Silicon Valley show a high concentration of new, AI-driven job creation, reflecting significant economic opportunities for those regions. Urban centers with strong tech clusters are emerging as key players in AI employment, driving innovation and growth. ❌ 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐟𝐨𝐫 𝐑𝐮𝐫𝐚𝐥 𝐚𝐧𝐝 𝐒𝐦𝐚𝐥𝐥𝐞𝐫 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐢𝐞𝐬 Rural areas and smaller towns are facing increased risks of job losses due to AI, without comparable opportunities for new AI-driven roles. This geographic imbalance could exacerbate regional economic disparities. 👉 Here are my questions for Leaders: 1️⃣ Are we ready to leverage AI’s potential while minimizing risks? How are we preparing our teams for a future where AI enhances human capability? 2️⃣ What is our reskilling strategy? With 40% of jobs potentially vulnerable, how are we investing in upskilling our workforce to transition into growth-oriented roles? 3️⃣ How can we balance geographic and economic disparities? Are we focusing enough on regional strategies to ensure inclusive growth? As leaders, our role is to harness AI's potential to foster a resilient, inclusive, and dynamic workforce. Are we ready to lead this change and shape the future of work?

A new Stanford study has put hard data behind what many early-career professionals have been feeling: generative AI is disproportionately reducing entry-level job opportunities in fields like software engineering and customer support. The data is striking: 😢 Employment for workers aged 22–25 in the most AI-exposed roles has dropped by 13% since late 2022. 😄 Older workers in the same roles saw employment rise. ⭐ The biggest declines appear in jobs where AI is used to automate, not augment. ⭐ Salaries stayed flat — firms are cutting roles, not pay. This points to a deeper structural shift. AI appears to be replacing “codified” knowledge — the kind learned in school or bootcamps — faster than it can replace tacit, experience-driven skills. In other words: if your job can be learned from a textbook, it’s more replaceable. The result? The bottom rung of the career ladder is being sawed off. Without that first job, how does anyone gain the experience to climb? For leaders, this raises hard questions: ❓ How do we preserve pathways into high-skill careers? ❓Are we investing enough in human-AI complementarity, not just substitution? ❓What happens to organizations when new talent pipelines dry up? AI’s impact on work won’t be evenly distributed — and this may be one of the earliest, clearest fault lines. #AIWorkforce #EntryLevelJobs #FutureOfWork #AIEconomy #TalentPipeline #GenAI #Automation #AIImpact #LaborMarket #StanfordResearch

Stop drawing boxes around people. Start mapping the work that creates value. The traditional org chart is dying. In the AI Age, hierarchical lines and boxes don't just slow you down, they actually obscure where the work is happening. If you try to retrofit AI onto your existing structure, you’re just paving the cow path. As I discuss in my new article with Jonathan Brill for the leading HR site TalentCulture (link in Comments), the future belongs to Octopus Organizations. Like an octopus that has a brain in every arm, AI-ready businesses use distributed intelligence. They don’t organize by jobs; they organize by tasks. This requires a shift from the Org Chart to the Work Chart. A Work Chart isn't about who reports to whom. It’s a dynamic map of what needs to happen to deliver value. It’s about workflows, outcomes, and the blended human-AI teams that make them a reality. Ready to build one? Here's how to start: 1) Deconstruct the Function: Pick a priority area and be brutally honest. What work actually happens? Focus on tasks, not titles. 2) Apply the AI Filter: For every task, ask: - Can this be automated? - Can AI enhance the human doing it? - If you started this business today from scratch, who (or what) would do it? 3) Define the Jobs to Be Done: Move past "Manager reviews X." Define the underlying motivation, like "Optimize pricing given customer capex/opex preferences." This reveals where AI can crunch data and where humans provide the strategic "last mile." (Yes, this is a new application of our 20-year track record with JTBD, and it really works) The goal isn't to replace people; it’s to liberate them from the drudge work that fills the boxes of an old-school org chart. AI should enable people to focus on the most human elements of their jobs. Is your organization a rigid hierarchy, or can it be an agile octopus? Work charts will loosen you up!

This is a very revealing role because it quietly shows where large consulting firms now believe the future of HR is heading. What stands out is that this is no longer an “HR transformation” role in the traditional sense. It is increasingly: ▪ AI platform orchestration ▪ workflow architecture ▪ employee-experience engineering ▪ operational redesign ▪ AI-enabled service delivery ▪ ecosystem integration ▪ governance through technology stacks The language itself tells the story. The role sits across: ▪ Microsoft Copilot ▪ Copilot Studio ▪ ServiceNow HRSD ▪ Google Gemini ▪ workflow automation ▪ EX platforms ▪ enterprise operating models ▪ AI-enabled delivery ▪ business development tied to AI transformation revenue That is a major signal shift. What is fascinating is that many organisations still talk about AI in HR as “tools for HR teams”. But the large consulting firms are already redesigning HR as an AI-mediated operational environment built around platforms, workflow systems, orchestration layers, and integrated decision flows. The role also quietly reveals another important shift: The centre of gravity for organisational design is moving toward technology-led operational architecture. That matters because once workflow, AI agents, copilots, knowledge systems and employee interactions become interconnected, the real challenge is no longer simply HR process improvement. The challenge becomes: ❌Who designs the decision architecture? ❌Who governs it? ❌Who owns accountability when decisions emerge across systems, AI, workflows and human judgement simultaneously? That is exactly the structural gap many organisations are now entering and it is one of the reasons I created the PXB Ecosystem™. Because what roles like this reveal is that organisations are already evolving into AI-mediated operating environments often before governance, accountability, leadership capability and decision transparency have caught up. The interesting thing is Capgemini is not positioning this as experimental anymore. You can also see the wider ecosystem convergence: ▪ Microsoft positioning Copilot as the future HR operating layer ▪ ServiceNow positioning workflow orchestration as enterprise infrastructure ▪ Consulting firms building AI-enabled workforce transformation practices around them simultaneously. This is why I keep saying: ✨The future organisation will not be defined by AI tools alone. It will be defined by how decisions, workflows, accountability and human judgement are designed across interconnected AI-mediated systems. This is one of the core reasons behind the development of the PXB Ecosystem™ an AI-mediated operating architecture focused on connecting People, Experience and Business through decision architecture, governance visibility and accountable execution. 🔗https://pxb-ecosystem.com/ #AI #AIGovernance #Leadership #FutureOfWork #DigitalTransformation #DecisionArchitecture #PXB #AITransformation