Productivity Gains Offered by AI

Just out: Quantifying the impact of #genAI on job performance, by Erik Brynjolfsson & team: "Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different agents. Less experienced and lower-skilled workers improve both the speed and quality of their output, while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for moderately rare problems, where human agents have less baseline experience but the system still has adequate training data. Finally, we provide evidence that AI assistance improves the experience of work along several dimensions: customers are more polite and less likely to ask to speak to a manager." Open access: https://lnkd.in/d4UecpnQ

I came across a fascinating paper about AI’s impact on the productivity gains and job satisfaction. In conclusion, AI improved productivity for over 1,000 researchers* working on material discovery. However, researcher identified two unique insights: Let’s start with productivity - Papers on other jobs like manufacturing, finance, and consulting present that AI increases the productivity of low performers and does not impact top performers as much. - In this paper, the opposite is said: “While the bottom third of scientists see little benefit, the output of top researchers nearly doubles.” Why? “Top scientists leverage their domain knowledge to prioritize promising Al suggestions, while others waste significant resources testing false positives.” And now, let’s move on to Job satisfaction. There is a general view that as AI automates routine tasks, and humans will enjoy what they work on—in this paper, it’s the opposite. The paper found that the part that got automated, creating ideas for new materials, was the researchers' most satisfactory part of the work. Their job satisfaction declined by 82%. Researchers had some concerns over credit allocation since they were using an AI tool (20%) and the complexity of the AI tool(20%). Still, most of their dissatisfaction was caused by underutilization of their skills (73%) and less creative/more repetitive work (55%). So, why are these results different from previous papers researching AI-assisted manufacturing, finance, and consulting jobs? Finding new materials requires educated guesses, intuition, creativity, perseverance, and knowledge, some of which are only human qualities. Also a task could be repetitive but might be what people enjoy working on. Something to think about. Source: *The results are from a large US-based research lab where 1,018 researchers work with AI (deep learning models) for material discovery. “Al-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation.” *Paper: Artificial Intelligence, Scientific Discovery, and Product Innovation* Aidan Toner-Rodgers, Massachusetts Institute of Technology. https://lnkd.in/gVnxpBin #artificialintelligence #innovation #technology

AI isn't just a tool; it's becoming a teammate. A major field experiment with 776 professionals at Procter & Gamble, led by researchers from Harvard, Wharton, and Warwick, revealed something remarkable: Generative AI can replicate and even outperform human teamwork. Read the recently published paper here: In a real-world new product development challenge, professionals were assigned to one of four conditions: 1. Control Individuals without AI 2. Human Team R&D + Commercial without AI (+0.24 SD) 3. Individual + AI Working alone with GPT-4 (+0.37 SD) 4. AI-Augmented Team Human team + GPT-4 (+0.39 SD) Key findings: ⭐ Individuals with AI matched the output quality of traditional teams, with 16% less time spent. ⭐ AI helped non-experts perform like seasoned product developers. ⭐ It flattened functional silos: R&D and Commercial employees produced more balanced, cross-functional solutions. ⭐ It made work feel better: AI users reported higher excitement and energy and lower anxiety, even more so than many working in human-only teams. What does this mean for organizations? 💡 Rethink team structures. One AI-empowered individual can do the work of two and do it faster. 💡 Democratize expertise. AI is a boundary-spanning engine that reduces reliance on deep specialization. 💡 Invest in AI fluency. Prompting and AI collaboration skills are the new competitive edge. 💡 Double down on innovation. AI + team = highest chance of top-tier breakthrough ideas. This is not just productivity software. This is a redefinition of how work happens. AI is no longer the intern or the assistant. It’s showing up as a cybernetic teammate, enhancing performance, dissolving silos, and lifting morale. The future of work isn’t human vs. AI. The next step is human + AI + new ways of collaborating. Are you ready?

5 AI frameworks that save top performers 10+ hours every week I watched a CEO friend close his laptop at 2pm on Thursday. "Done for the day," he said. Six months ago? 8pm was normal. The difference? Not harder work. Smarter AI systems. PwC research shows: AI users see 4x productivity gains and earn 56% more. Here's what winners do differently: 1. The Brain Offload System - Turn repeating thoughts into AI prompts - Let AI handle small decisions - Save your focus for what matters most 2. The Batch Processing Method - Group similar AI tasks together - Use one knowledge base for all tools - Create prompts once, use them everywhere 3. The Tomorrow Prep Framework - Set AI to prepare what tomorrow-you needs - Train it on your best past work - Automate the setup tasks you hate 4. The Results-First Approach - Focus on outcomes, not process - Tell AI the goal, not just the steps - Review only the final product 5. The Connection System - Link your AI tools to work together - Build feedback loops between you and machines - Set up once, benefit daily 92% of businesses see real results from AI. But most use it randomly, not strategically. The difference isn't having AI tools. It's having an AI system. Which framework will you try first? ↓ Share below ♻️ Repost if this shifted your perspective 🔔 Follow Kabir Sehgal for more productivity insights

In our new paper we ran an experiment at Procter and Gamble with 776 experienced professionals solving real business problems. We found that individuals randomly assiged to use AI did as well as a team of two without AI. And AI-augmented teams produced more exceptional solutions. The teams using AI were happier as well. Even more interesting: AI broke down professional silos. R&D people with AI produced more commercial work and commercial people with AI had more technical solutions. The standard model of "AI as productivity tool" may be too limiting. Today’s AI can function as a kind of teammate, offering better performance, expertise sharing, and even positive emotional experiences. This was a massive team effort with work led by Fabrizio Dell'Acqua, Charles Ayoubi, and Karim Lakhani along with Hila Lifshitz, Raffaella Sadun, Lilach M., me and our partners at P&G: Yi Han, Jeff Goldman, Hari Nair and Stewart Taub Subatack about the work here: https://lnkd.in/ehJr8CxM Paper: https://lnkd.in/e-ZGZmW9

It's well understood that AI has the ability to impact individual productivity. But most critical work is done in teams. What's AI role within a team? A new HBS paper studies how AI acting as a Teammate impacts knowledge work. The study tracked hundreds of professionals (business & technical) at P&G and analyzed the impact of using AI on individuals and teams measured by time savings and output. (Link to paper in comments) * Big Takeaway: AI often functions as more of a teammate than a tool, democratizing expertise, improving quality of output, and even improving emotional experiences. * Big Productivity Gains:  Individuals and Teams using GPT-4 completed tasks 12-16% faster and produced work 0.37-0.39 standard deviations higher in quality.   * Blurring Expertise Boundaries: AI helped both R&D and Business specialists produce balanced technical and commercial solutions, erasing traditional knowledge silos.    * AI as a Teammate Equivalent: Individuals using AI performed on par with two-person teams without AI, demonstrating the AI as a teammate concept is real. * AI Teammates + Human Teammates Work Best: Teams using AI were significantly more likely to produce top-tier solutions, suggesting that there is extra value in having human teams working on a problem + AI. * Enhanced Emotional Experience: Participants using AI reported significantly more positive emotions (excitement, energy) and fewer negative emotions (anxiety, frustration). The author (Ethan Mollick) provides prescient guidance to companies:  “To successfully use AI, organizations will need to change their analogies. Our findings suggest AI sometimes functions more like a teammate than a tool. While not human, it replicates core benefits of teamwork—improved performance, expertise sharing, and positive emotional experiences.” AI founders would do well to remember AI should be more than a tool and seek to be a teammate.

When applied strategically, AI doesn’t just enhance productivity—it transforms the employee experience.    My latest AI at Work video explores a great example from the Bank of Queensland. To improve loan processing, their team used Copilot to uncover the root cause of delays—helping analysts identify issues 50% faster and drive better outcomes for customers and employees.    The impact? Equipping 1,000 employees with Copilot could boost productivity equivalent to adding 120 new employees—without increasing headcount. Watch the full video below and learn more about Bank of Queensland's story in our latest #WorkLab: https://lnkd.in/gGz-fndd

Two recent research reports examining AI's impact on teamwork and organizational structures at US companies provide some interesting insights that can also inform our approach to GenAI adoption in K12 and HE. A recent article by Ethan Mollick breaks down findings from a randomized controlled trial at Procter & Gamble examining how AI affects team performance. Key points from the study: • AI significantly boosted performance - individuals with AI performed as well as two-person teams without AI, and AI-enabled groups worked 12-16% faster while producing longer, more detailed solutions • Teams using AI performed best overall and were more likely to produce exceptional solutions • Workers using AI reported higher positive emotions and lower negative emotions compared to non-AI groups McKinsey's latest Global Survey on AI examines how organizations are structuring their AI deployment and creating value from it. Key points from the study:   • 78% of organizations now use AI in at least one business function • Only 21% of organizations have fundamentally redesigned workflows for AI, though this drives the biggest impact on revenue • Larger companies lead in implementing best practices like establishing dedicated AI teams, organization-wide adoption road maps, and AI literacy programs • 61-70% report revenue increases and cost reductions in business units using AI, though enterprise-wide impact remains limited Both studies point to the same conclusion: when GenAI is intentionally adopted there can be large positive impacts that go beyond productivity gains. We are particularly excited about how GenAI can act as a teammate for those that do not have access to a support network and its potential applications in high school and college classrooms. Links to the studies in the comments. AI for Education #GenAI #AI #teamwork

The numbers have started coming in about the impact of Generative AI on work productivity and quality - 40% increase in productivity and 18% better quality for professional writing tasks. Here is the abstract from a paper published in the prestigious journal Science by Shakked Noy and Whitney Zhang of MIT. We examined the productivity effects of a generative artificial intelligence (AI) technology, the assistive chatbot ChatGPT, in the context of midlevel professional writing tasks. In a preregistered online experiment, we assigned occupation-specific, incentivized writing tasks to 453 college-educated professionals and randomly exposed half of them to ChatGPT. Our results show that ChatGPT substantially raised productivity: The average time taken decreased by 40% and output quality rose by 18%. Inequality between workers decreased, and concern and excitement about AI temporarily rose. Workers exposed to ChatGPT during the experiment were 2 times as likely to report using it in their real job 2 weeks after the experiment and 1.6 times as likely 2 months after the experiment. #AI #artificialintelligence #chatgpt https://lnkd.in/gQiQ2Dek