Impact of Generative AI

GenAI Isn’t Taking Your Job—It’s Changing It. Here’s How to Stay in the Game. The GenAI panic is loudest at the entry level, but let’s be real: this shift is coming for everyone. From first jobs to leadership roles, AI is rewriting what “value” looks like. Not eliminating roles. Recalibrating them. Here’s how it breaks down Entry-Level = From Doers to Reviewers AI is automating basic tasks—content drafts, research, reporting. But junior hires aren’t obsolete—they're evolving. Needed: AI fluency, editing skills, contextual awareness, critical thinking. Be the one who sharpens AI, not just watches it work. Mid-Level = From Managers to Translators This layer is getting thinner—unless you add value by translating business goals into AI-powered execution. Needed: Strategic prompting, workflow design, ethical oversight, cross-functional leadership. Be the bridge, not the bottleneck. Experienced Pros = From Leaders to Visionaries You’ve got the judgment, now combine it with AI’s speed. Needed: Org-wide AI strategy, talent transformation, innovation guardrails, data + storytelling. Lead the charge by reimagining what’s possible. No matter your level, your ability to adapt is the new superpower. It’s not “AI vs. you”—it’s “AI + you, reimagined.” Are you recalibrating? #GenerativeAI #FutureOfWork #Careers #AIMindset #Leadership #Reskilling #EntryLevel #MarketingAI #WorkforceTransformation

Just a thought before the weekend. With the introduction of new technologies certain tasks traditionally performed by junior staff are being automated. In many cases the junior position is eliminated, and residual task is redistributed to more senior employees, actually increasing their workload. Historically, the roles of secretaries and accounting clerks exemplify this transition. With the advent of personal computers and advanced software, routine tasks like typing, scheduling, and basic correspondence management, once the domain of secretaries, have been automated. Consequently, these tasks have increasingly been incorporated into the responsibilities of professionals themselves, including managers and executives. In accounting, sophisticated software has made the data entry and basic bookkeeping roles of accounting clerks redundant. These tasks are now often handled directly by accountants and finance managers, adding to their comprehensive role. In creative and technical fields, such as graphic design and engineering, advanced tools have automated tasks that were typically handled by junior staff. Senior professionals in these areas now directly engage with tools like CAD software, reducing the need for junior drafting roles. The future, shaped by GAI, will likely see an expansion of these trends. In industries like marketing and advertising, AI’s capacity to generate basic creative content might reduce the need for certain junior roles. Instead, senior marketing professionals might oversee the refinement and strategic integration of AI-generated materials. Likewise, legal services might witness AI automating document drafting and basic research, once the remit of junior staff, shifting oversight and strategic refinement to senior lawyers. Moreover, GAI is expected to make complex business platforms more accessible to a broader range of employees. This will enable senior employees without deep technical expertise to perform tasks that were previously the preserve of specialists. Consequently, the skill requirements for senior roles may grow. The result is that many professionals and managers will be responsible for a long list of simple and quick tasks, that once took much longer to perform, and were the responsibility of more junior workers. #generativeai #ai #tasks #automation

Early-career software engineers face an existential job market and it's no longer a debate. Since ChatGPT released two years ago, employment for software developers under 25 has dropped nearly 20%, yet headcount for those over 30 has held steady or climbed. A new paper out of Stanford by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen suggests this is a structural and lasting shift. Generative AI automates much of what used to be done by entry-level programmers. New grads who used to fill junior developer roles are now competing for a fast-shrinking number of jobs. Interesting, too, is just how insulated older, more experience engineers have been through the same period. AI has put a premium on "on-the-job" wisdom. Companies are hiring for the tacit knowledge — judgement, management, people skills — that comes from experience rather than credentials or raw technical ability. It's no coincidence that this divergence started when ChatGPT arrived. It was the first domino to mass adoption of AI, and all evidence points to an acceleration rather than a reversal. To be sure, this opens up new opportunities for the young people who are most fluent in AI. Plenty of 19-year-olds have launched products and startups that have pushed real innovation and led to massive paydays. Still, that doesn't change the takeaway from the data. Junior developer roles are disappearing.

Harvard just dropped a study on AI and the workforce: "Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Resume and Job Posting Data." It perfectly complements Stanford’s report, published only a a few days earlier. Together, these are the clearest signals yet of how Generative AI is not just changing productivity; it’s reshaping the very architecture of careers. Stanford (ADP payroll data): Since late 2022, employment among 22–25 year-olds in AI-exposed jobs has fallen ~13%, while 35–49 year-olds in the same roles have grown ~9%. Automation-heavy AI uses cut junior jobs; augmentation-heavy ones sustain or even expand them. Harvard (62M workers, 285K firms): At firms that adopt AI (measured via “AI integrator” hires), junior headcount falls 7.7% within six quarters. Hiring slows by ~10% per quarter, even as promotions rise 5%. In Wholesale & Retail, junior hiring contracts by nearly 40%. And graduates from mid-tier universities are the hardest hit. The message is clear: AI is shrinking the base of the career ladder; fewer entry roles, faster promotions for those already inside, and a premium on tacit, senior-level capabilities. The opportunity is differentiation. Companies that design AI-augmented apprenticeships, run talent impact diagnostics, and adopt augmentation-first operating models will not only protect their pipelines but also build the next generation of leaders faster. It seems like AI isn’t just an efficiency story. It’s a career architecture story. Those who act intentionally now will set the tone for an AI-powered workforce that is leaner, smarter, and more resilient. 🔗 Link to Harvard's report ("Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Resume and Job Posting Data"): http://bit.ly/47SyfTC 🔗 Link to Stanford's report ("Canaries in the Coal Mine?"): http://bit.ly/45Ttgzo

McKinsey & Company: "𝗧𝗵𝗮𝘁'𝘀 𝗛𝗼𝘄 𝗖𝗜𝗢𝘀 𝗮𝗻𝗱 𝗖𝗧𝗢𝘀 𝗖𝗮𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗜𝗺𝗽𝗮𝗰𝘁" This McKinsey & Co report highlights how #GenAI, when deeply integrated, can revolutionize business operations. I took a stab at CPG eCommerce use case below, and thriving with generative #AI isn’t about just deploying a model; it demands a deep integration into your enterprise stack. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗣𝗚⬇️ 𝟭. 𝗖𝘂𝘁𝗼𝗺𝗲𝗿 𝗟𝗮𝘆𝗲𝗿: → The user logs in, browses personalized product recommendations, and either finalizes a purchase or escalates to a support agent—all seamlessly without grasping the backend processes. This layer prioritizes trust, rapid responses, and tailored suggestions like skincare routines based on user preferences. 📍Business Impact: Boosts customer satisfaction and loyalty, increasing conversion rates by up to 40% through hyper-personalized interactions that drive repeat purchases. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Oversees user engagement: - Chatbot launches and steers the dialogue, suggesting complementary products - Escalation to a human agent activates if AI can't fully address complex queries, like ingredient allergies 📍Business Impact: Enhances efficiency in consumer support, reducing resolution times and operational costs while minimizing cart abandonment in #eCommerce flows. 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿: → Performs smart actions using context: - Retrieves user profile data - Validates promotions and inventory - Creates customized options, such as virtual try-ons - Advances the process, like adding to the cart 📍Business Impact: Accelerates innovation in product discovery, lifting marketing productivity by 10-40% and enabling dynamic pricing that optimizes revenue in competitive #FMCG markets. 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗽𝗽 𝗟𝗮𝘆𝗲𝗿 → Links AI to essential enterprise platforms: - User verification and access management - Promotion rules and order processing - Support agent routing algorithms 📍Business Impact: Streamlines supply chain and sales workflows, cutting technical debt by 20-40% and improving inventory accuracy to reduce stockouts and overstock costs. 𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 → Delivers instant contextual details: - Consumer profiles - Purchase records - Promotion guidelines - Support team directories 📍Business Impact: Powers precise AI insights, enhancing demand forecasting and personalization to minimize waste in perishable goods while boosting overall data-driven decision-making. 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿 → Supports scalability, efficiency, and oversight: - Cloud or hybrid setups - AI model coordination - High-speed response handling - Privacy and compliance controls 📍Business Impact: Ensures robust, secure operations at scale, unlocking value by optimizing resource use, slashing IT ops costs.

The biggest AI impacts won’t be borne out in a calculus of jobs but rather in seismic shifts in the level of expertise required to do them. In our article in Harvard Business Review, Joseph Fuller, Michael Fenlon, and I explore how AI will bend learning curves and change job requirements as a result. It’s a simple concept with profound implications. In some jobs, it doesn’t take long to get up to speed. But in a wide array of jobs, from sales to software engineering, significant gaps exist between what a newbie and an experienced incumbent know. In many jobs with steep learning curves, our analysis indicates that entry-level skills are more exposed to GenAI automation than those of higher-level roles. In these roles, representing 1 in 8 jobs, entry-level opportunity could evaporate. Conversely, about 19% of workers are in fields where GenAI is likely to take on tasks that demand technical knowledge today, thereby opening up more opportunities to those without hard skills.   Our analysis suggests that, in the next few years, the better part of 50 million jobs will be affected one way or the other. The extent of those changes will compel companies to reshape their organizational structures and rethink their talent-management strategies in profound ways. The implications will be far reaching, not only for industries but also for individuals and society. Firms that respond adroitly will be best positioned to harness GenAI’s productivity-boosting potential while mitigating the risk posed by talent shortages.   I hope you will take the time to explore this latest collaboration between the The Burning Glass Institute and the Harvard Business School Project on Managing the Future of Work. I am grateful to BGI colleagues Benjamin Francis, Erik Leiden, Nik Dawson, Harin Contractor, Gad Levanon, and Gwynn Guilford for their work on this project. https://lnkd.in/ekattaQA #ai #artificialintelligence #humanresources #careers #management #futureofwork

🧠 Is Generative AI Just Cool, or Does It Really Have an Impact? That's the big debate in tech circles these days. A study led by researchers from Stanford University, MIT, and the National Bureau of Economic Research (NBER) sheds light on this question by examining the real-world impact of deploying generative AI in a customer support environment. Their analysis offers empirical evidence on how AI tools, specifically those based on OpenAI's GPT models, are transforming customer service operations at a Fortune 500 software company. The researchers employed a mix of methodologies: a randomized control trial (RCT) and a staggered rollout, encompassing around 5,000 agents over several months. By analyzing 3 million customer-agent interactions, the study assessed metrics such as resolutions per hour, handle time, resolution rates, and customer satisfaction (Net Promoter Score). To understand the AI's impact over time, dynamic difference-in-differences regression models were used. Here is what they found: 1. 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐁𝐨𝐨𝐬𝐭 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲: The AI tool led to a 13.8% increase in the number of customer queries resolved per hour, particularly benefiting less experienced agents. 2. 𝐍𝐚𝐫𝐫𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐆𝐚𝐩: AI tools accelerated the learning curve for newer agents, allowing them to reach the performance levels of seasoned employees more quickly. 3. 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧: The AI deployment resulted in higher customer satisfaction scores (as shown by improved Net Promoter Scores) while maintaining stable employee sentiment. 4. 𝐋𝐨𝐰𝐞𝐫 𝐀𝐭𝐭𝐫𝐢𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐞𝐬: Interestingly, the AI support led to reduced attrition rates, especially among new hires with less than six months of experience. 5. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: The AI system reduced the need for escalations to managers, improving vertical efficiency. However, its impact on horizontal workflows, like transfers between agents, showed mixed results, suggesting more refinement is needed in AI integration. 6. 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐞𝐝 𝐀𝐈 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: The software wasn’t off-the-shelf; it was a custom-built solution tailored to the company’s needs using the GPT family of language models. This emphasizes the importance of context-specific AI applications for effective outcomes. For leaders, managers, and AI practitioners, these insights are invaluable—highlighting not just the potential of AI, but also the nuanced ways it reshapes workflows, impacts employee dynamics, and transforms customer experiences.So, does generative AI really make a difference? According to this study, the answer is a resounding yes—but it depends on how thoughtfully it is deployed. Link 🔗 to the paper: https://lnkd.in/ejhUfufz

A new piece with my colleagues The Burning Glass Institute, Matt Sigelman, and a long-standing collaborator and new member of the Harvard University community, Michael Fenlon. Commentators have been speculating on the impact of #generativeAI on specific jobs. The impact will be significant and widespread. But, the impact on career paths has been ignored. Our analysis validates a oft-asserted proposition that AI will both replace some workers and create entirely new occupations as it relates to career paths. The advent of generative AI will make it hard for people to get on career pathways where AI will replace many of the tasks in entry level occupations for on-the-job learning is key to achieving full productivity. The 'top of the funnel' will be pinched for such jobs. But, at the other end of the spectrum, there will be jobs were the technical skills requirements placed on applicants will be reduce as generative AI assumes responsibilities for more of the work. Hence, barriers to being hired into those positions will be reduced. However, the rate of income growth may be low, since the qualifications for those positions are limited and the premium awarded to those with experience less in these positions. What's the conclusion? The impact of generative AI will be extensive and complex, but the impact on upward mobility may be positive. #generativeAI #careerpaths https://lnkd.in/eGaSgjpZ

As we all look to understand the business value of generative AI, we commissioned Forrester to conduct a Total Economic Impact study for financial services organizations deploying GenAI solutions on Microsoft Azure OpenAI Service over a three-year period. Projected quantified benefits by Year 3 include: ✅ Increase in average revenue per client by 3% to 7%. ✅  Content generation time savings of 30% to 60%. ✅  Better engagement with current and existing service users driven by a 20% to 30% reduction in churn due to better user experience. ✅  Better engagement with prospective service users driven by a 10% to 20% increase in top-of-funnel prospects and 20% to 40% improvement in conversion rate. ✅ Improved deflection rates of 20% to 50% in contact center calls requiring a human support agent. You can read the full study here: https://lnkd.in/ePsQ_88s