AI's Impact on Jobs

Gartner just surveyed 350 large enterprises deploying AI. 80% cut jobs. Some by as much as 20%. The result? The companies that cut the most showed nearly identical financial returns to the ones that cut the least. In several cases, the ones that cut less performed better. No correlation between AI-driven layoffs and improved ROI. None. Gartner's Helen Poitevin was direct: "Workforce reductions may create budget room, but they do not create return." Cutting people frees up cash. It does not generate value. Most leadership teams are conflating the two. So what actually works? Upskilling staff to work alongside AI. Redesigning roles around what humans do well vs. what AI does well. Building operating models where people guide autonomous systems instead of getting replaced by them. There's a real difference between using AI to do the same work with fewer people and using AI to unlock work that was previously impossible. The first saves money on paper. The second compounds over time. We've already seen the pattern. Klarna cut 700 CS roles, watched quality decline, and started rehiring. IBM automated HR functions and reversed course. The Commonwealth Bank of Australia reversed 45 AI-driven layoffs after realizing those roles were never redundant. Gartner predicts half of companies that attributed headcount cuts to AI will rehire under new titles by 2027. If someone in your org is building an AI business case around headcount reduction, share this data. The assumption that fewer people equals better margins equals better returns is not supported by the evidence. AI is not leading to a jobs apocalypse. It's changing the shape of what people do. The companies that understand that difference will be the ones worth working for, and buying from, three years from now. Read the full piece on State of Brand here: https://lnkd.in/ggH-NXyM

For decades, career growth followed a familiar formula: More headcount. More budget. More scope.  That model is changing. In the AI era, careers won’t be built on span of control, they’ll be built on innovation density. Today, anyone - from ICs to execs - can scale their impact without more headcount, more budget, or more time. The playing field is flatter. The differentiator? How fast you can learn, apply, and compound innovation with AI. If you’re thinking about career growth, stop asking: “How can I get more?” Start asking: “How can I innovate more with AI?” The people who rise fast will: See problems through an AI-first lens. Move from manual to scalable. Iterate faster than the rest. Your team size won’t define your trajectory. Your creativity will. Your budget won’t signal your value. Your innovation density will.

Leaders, if you're going ahead with mass layoffs, you can't seriously be thinking that your #diversity, #equity, or #inclusion work will have any credibility left after the fact. Fundamentally, DEI work is about showing people that they matter by building a workplace where they can thrive. And fundamentally, mass layoffs communicate the exact opposite: that no matter a person's skill, experience, productivity, contribution, passion, or loyalty, they ultimately are just another cost to be cut. That people mean nothing in the face of short-term profit. The consequences of mass layoffs on your people, your biggest assets, are immediate and catastrophic. 📉 One study found a 41% decline in job satisfaction among survivors of a layoff, leading to a 36% decline in their desire to stay with the workplace. 📉 Another study found that a 1% workforce layoff resulted in a 31% increase in voluntary turnover. 📉 One study found a 20% decline in job performance, with another finding that 77% of layoff survivors see more errors and mistakes made. 📉 Another study found that layoffs tanked the quality of products, the safety of the workplace, and the quality of layoff survivor mental health and wellbeing. 📉 A bevy of other studies find a cascading set of issues triggered by layoffs that create a vicious cycle: worse morale and wellbeing leads to poorer job performance, overwork and forced productivity drives mass exoduses of skilled workers; reputational damage and loss of trust dampens the ability to hire fresh talent. Trying to achieve any sort of DEI impact amid this kind of avoidable chaos is like trying to renovate your house after setting it on fire. It's downright offensive to employees, especially those with marginalized identities, to be asked to continue their unpaid, voluntary efforts to benefit the business after you've destroyed any reason for them to undertake this extra work. It's a moot point—they're far too busy applying to your competitors, anyways. This is the point in time when those workplaces and leaders with empty promises and performative actions will be weeded out from those that get ahead by doing right by their people, their customers, and the world. There are many ways for a workplace to earn a spot in the latter group, but in case it wasn't clear? Mass layoffs aren't one of them.

Every customer and government leader I meet is asking, “How can we make AI a force for good for our people, and not a threat?” 92% of jobs are expected to undergo some level of transformation due to advancements in AI. The work begins with identifying and enabling the new skills and training needed for AI preparedness. That’s why I’m honored to share the insights from the AI-Enabled ICT Workforce Consortium's inaugural report, “The Transformational Opportunity of AI on ICT Jobs.” This report examines the impact of AI on 47 ICT job roles and offers tailored training recommendations. It's a unique guide to the skills needed for the AI future, with recommendations that couldn't be clearer, timelier, or more urgent. Here are some of the top takeaways: - 92% of ICT jobs will undergo high or moderate transformation due to AI. - 40% of mid-level and 37% of entry-level ICT positions will see high levels of transformation. - Skills like AI ethics, responsible AI, prompt engineering, and AI literacy will become crucial. - Foundational skills such as AI literacy and data analytics are essential across all ICT roles. Read the full report here: https://lnkd.in/gWfPc8WT The risks associated with an under-skilled, unprepared workforce are global in scale, ranging from economic wage gaps to trade imbalances, technological stagnation, social and ethical issues, and national security threats. This creates a pressing need for a coordinated effort to reskill and upskill employees around the world. By investing in a long-term roadmap for an inclusive and skilled workforce, we can help all populations participate and thrive in the era of AI. Led by Cisco and joined by industry giants like Accenture, Eightfold, Google, IBM, Indeed, Intel Corporation, Microsoft, and SAP the Consortium will train and upskill 95 million people over the next 10 years through their individual organizations' commitments.

Will #AI be a bloodbath for white-collar jobs? #Anthropic CEO Dario Amodei seems to think so—he made headlines warning that AI could wipe out up to 50% of all entry-level white-collar roles within the next 5 years. While we can debate the exact figure, I won’t quibble: a lot of work is about to be automated. AI isn’t just a helper anymore—it’s becoming a full-blown replacement for the repetitive, digitized, “thunking” tasks that fill so many junior roles. If you’re not paying attention, you’re at risk of missing the train entirely. Here’s the uncomfortable truth: What you see from AI today isn’t the ceiling—it’s the floor. The cutting-edge research is far ahead of what’s in your hands. Even the most notoriously janky AI products—like #OpenAI’s Operator or #Google’s Project Mariner—could get a massive capability boost almost overnight, just by cranking up the compute (and, with it, the costs). The real bottleneck? Companies and customers aren’t ready to pay for what’s already possible. We’re stuck in an awkward moment where the tech is ready, but the market—and the culture—aren’t. That gap won’t last forever. AI isn’t some far-off fantasy—it’s the next wave of automation, and it’s already reshaping industries. The problem isn’t that AI is “coming for your job”—it’s that the tasks we once thought were too complex to automate are suddenly on the table. Copying, pasting, filling out forms, writing first drafts of emails—those are the tasks AI is best at. And that means the entry-level training grounds we’ve relied on for generations—where people cut their teeth and build their skills—are vanishing fast. Where will the next generation of talent come from if we don’t rethink our pipelines? Let’s be clear: the next few years will be rough, especially for junior employees. AI is far less of a threat to those with industry experience, deep domain expertise, or strong networks. But if you’re doing work that “anyone can do,” AI will soon be able to do it too. I won’t sugarcoat this, so let me say it again for the folks in the back: ⚠️ If anyone can do it, AI will soon be able to do it too. ⚠️ If you’re a student or just entering the workforce, now is the time to build relationships, seek out mentors, and cultivate a love of learning—because the treadmill is real, and it’s only speeding up. The future belongs to those who can adapt quickly and learn the new rules of new games. If you’re a leader, this is your moment to lead with compassion. Not everyone loves a constant challenge, and some implicit promises—about stable career paths, about learning your trade and coasting—are about to be broken. AI can empower us to aim higher, but only if we stay nimble. Your job is to build safe learning spaces, empower your teams to experiment with AI tools, and create clear pathways for growth beyond the tasks AI will automate. Let’s not just brace for impact—let’s get ready to lead through it. Subscribe and read on: decision.substack.com

So much has been said and written about how AI is changing the job market. Time for some myth busting. You’ll be surprised by some of the findings. The latest PwC AI Jobs Barometer paints a much more complex picture than the headlines suggest. The biggest misconception? That AI adoption means mass job losses, wage suppression, and a deskilled workforce. 𝟭. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Industries most exposed to AI are seeing productivity grow 3x faster than those with low exposure. AI isn’t just replacing tasks - it’s enabling output at scale. 𝟮. 𝗪𝗮𝗴𝗲𝘀:  In high-AI exposure sectors, wages are rising 2x as fast as in less-exposed ones. Even automatable roles see strong wage growth, debunking fears of a universal race to the bottom. 𝟯. 𝗝𝗼𝗯 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻:   AI-exposed roles are growing, not shrinking. While the nature of tasks is changing, demand remains strong - especially in augmentable jobs that combine human skills with AI. 𝟰. 𝗗𝗲𝗴𝗿𝗲𝗲 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀:   Job ads in AI-heavy sectors are dropping degree requirements faster, opening up access and reducing formal barriers to entry. 𝟱. 𝗦𝗸𝗶𝗹𝗹𝘀:   Rather than deskilling, AI is increasing the complexity and decision-making nature of many roles - requiring more strategic, not mechanical, input. 𝟲. 𝗜𝗻𝗲𝗾𝘂𝗮𝗹𝗶𝘁𝘆:   While benefits are evident, the report flags a risk of polarisation: between companies that adopt AI fast - and those that lag. Gaps could widen in pay, productivity, and talent attraction. 𝟳.𝗗𝗲𝗺𝗮𝗻𝗱:   Employers aren’t just hiring engineers. There’s rising demand for data-literate business talent: managers, analysts, marketers - all needing fluency in AI tools. 𝟴. 𝗚𝗲𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝘀𝗽𝗿𝗲𝗮𝗱:   The pace of AI impact differs by country - but labour markets are adjusting, not collapsing.   AI isn’t simply replacing jobs. It’s reshaping them - and redefining what skills, education, and value look like in the workplace.   Source: PwC’s 2025 Global AI Jobs Barometer 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

Three major developments in the last week should have every HR leader, employer, and AI vendor paying attention: 1. The AI Civil Rights Act was reintroduced in the US Congress Led by Senator Ed Markey and Representative Yvette D. Clarke, this legislation places hard guardrails around AI and algorithmic systems used in decisions related to hiring, housing, healthcare and beyond. It demands transparency, bias testing, and accountability. Think of it as GDPR for bias, but with broader implications across HR, tech, and operations. “We will not allow AI to stand for Accelerating Injustice.” – Senator Ed Markey for U.S. Senate 2. California’s new workplace AI discrimination laws are now in effect. The new rule governing companies' use of automated decision-making technology will likely create a situation where companies are liable for hiring practices if a system violates anti-discrimination laws. As other U.S. states also implement laws and regulations containing similar ADMT protections, companies deploying the technology will need to be proactive in their record keeping and vetting of third-parties while auditing their own tools to understand how the software functions. It’s no longer enough to trust your tools and vendors, you must prove they’re fair. 3. Insurers are backing away from covering AI risks AIG, Great American, and WR Berkley are asking regulators to exclude AI-related liabilities from their policies. Why? Because the risks (from chatbots hallucinating to algorithmic bias in hiring) are seen as “too opaque, too unpredictable.” When insurers are pulling cover, it’s a warning sign: you own the risk. 👁 What this means for HR and recruitment business leaders: We’ve officially entered the age of AI Accountability. That means: ✅ You need visibility into how your AI systems work, especially if they’re used for hiring, performance management, or workforce planning. ✅ You must audit your HR tech stack (yes, that includes Workday, ATS platforms, and even AI resume screeners). ✅ You need to document fairness, not just assume it. ✅ You must rethink your contracts with AI vendors. If the tech goes wrong, insurers may not have your back. 🛡 If you haven’t already, it’s time to start building your AI Governance Playbook. 📌 Audit all AI tools in use 📌 Build an internal AI ethics committee 📌 Ensure legal, DEI and HR alignment on tool deployment 📌 Partner only with vendors offering bias mitigation, auditability, and indemnification

Sam Altman, the co-founder and CEO of OpenAI, made a provocative statement at a JP Morgan conference earlier this year. He believes a solo founder will soon reach a billion-dollar valuation without hiring a single employee. This one-person company would instead be powered by AI and “employ” dozens of AI agents to do the work. Not only do I believe this is entirely possible, but I think when it does happen, the company will be one of the fastest-growing unicorns ever. As I invest in AI-powered startups and teach my students how to use AI in their businesses, I have identified 5 general AI use cases that align with critical phases of the startup journey: 1. Research-Driven Ideation: The genesis of any successful startup is a deep understanding of market needs, pain points, and the competitive landscape. My colleague Scott Brady of Stanford calls this process Research-Driven Ideation (RDI). There are now AI-based tools for competitive analysts, automating competitive monitoring for senior managers—effectively Google Alerts on steroids, tracking personnel changes, marketing launches, traffic, and other publicly available data. 2. Customer Persona Development and Market Research: Understanding your target customer is crucial. Gen AI helps founders create multiple hyper-specific customer personas by analyzing customer data and building hyper-realistic, "living" customer personas to test key hypotheses quickly. 3. Experimentation and Validation: Gen AI facilitates rapid experimentation to validate key hypotheses such as CVP, GTM, and PF by enabling deeper business data insights and rapid prototyping. I have a founder friend who lost his technical cofounder and has been using ChatGPT to build his MVP. By learning to be more effective at writing prompts to generate the desired code output, he has been able to continue building as a solo founder. He told me, “The result is that my burn rate is incredibly low, and velocity has shot through the roof.” 4. Marketing and Customer Engagement: Founders will see major productivity boosts in marketing, community building, and sales prospecting. Flybridge has a portfolio company that builds super smart AI agents that can be used for just about anything. One of their customers trained their agent to automatically generate customized sales collateral and follow-up materials based on customer needs that a sales representative inputs into the system after a prospect call—and then the AI agent sends that tailored material to the customer. 5. Continuous Learning and Iteration: The path to PMF is iterative. Gen AI supports continuous learning by analyzing customer feedback and product usage data to improve their product, GTM, and onboarding processes quickly. How are you using AI to build your startup?

Think AI will cut contact center staff? If you’ve been reading the headlines, you’ve seen plenty of predictions that AI will reduce contact center—and overall customer service—staffing. The logic seems simple: AI can handle customer interactions directly, so organizations won’t need as many people. Add to that AI tools that help agents retrieve information, document cases, and shorten handling time, and the argument looks even stronger. But the assumption that contact center work will decline across the board? That’s misleading. I am first to put my hand up when there are opportunities to improve efficiency and effectiveness—and there almost always are. But there are also many factors adding to contact center workload: Unmet demand. In too many cases, customers can’t even get through quickly. As organizations improve experiences, that suppressed demand surfaces. Product and service complexity. Think connected devices, customized financial advice, challenges in the insurance sector, changes in healthcare ... you get the gist, this list could go on and on. More channels. These can include phone, text, email, chat, messaging apps, social media, video, et al. As any experienced contact center manager will tell you, adding channels rarely replaces old ones—it just adds to the mix. The self-service paradox. The more you automate the more defined interactions, the tougher ones land with your team. Proactive outreach. Organizations are starting to use AI to reach out—wellness checks, customer retention, and others. That’s more contacts overall, not fewer. Regulation and compliance. Especially in healthcare, finance, and utilities, oversight is increasing, adding to review work, including of decisions and summaries made by AI. Security and fraud. Scams are escalating in sophistication—often using AI. Detecting deepfakes, verifying identity, and resolving disputes are high-stakes responsibilities that require experienced humans. Business change. New products, subscription models, mergers—these always generate customer questions. Differentiating on experience. Customer experience is one of the most powerful (and few remaining) ways to stand out. Think concierge service, retention specialists, account advisors—roles that depend on skilled people. AI will play a powerful role in service delivery. But the real story is a redefinition and rebalancing of work. Don’t assume AI will magically erase demand. The headlines may scream “AI is cutting contact center jobs,”—don’t buy it. Customer expectations are only going up. Meeting them will require both the best of AI and the best of us.

AI will change professional work. But in high-stakes professions, adoption will not be driven by novelty. It will be driven by trust. If the output affects a legal judgment, a filing, an audit, or client advice, “almost right” is not good enough. The systems that matter will be those grounded in authoritative content, shaped by experts, and built to produce transparent, verifiable results. That is the case for Fiduciary-Grade AI™. As AI advances, accountability still remains human. Which means the real test is not whether a system can generate an answer, but whether a professional can examine it, defend it, and stand behind it. That is the future of AI in the professions, and that is the standard we build to at Thomson Reuters.