The Impact of AI on Industry Productivity

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.

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?

🧠 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

"We seek to build upon research on #AI and productivity to better understand how #GenAI changes how people do work. We look at how the release of a GenAI coding tool (GitHub Copilot) changed how developers allocate their efforts to different types of tasks. We find that GenAI leads workers to spend more time on core work activities and less time on managerial tasks. We show two mechanisms drive this effect - workers with GenAI allocate more of their work efforts to things they can do by themselves (and less to collaborative work) and also do more exploration (new projects, new languages, etc.) and less exploitation (existing projects). Further we find the effects are greater for workers with lower ability. Finally, we do a back-of-the-envelope calculation and show that using GenAI allows developers to start coding in languages that have higher wages, leading to a labor market value impact of nearly $500 million (this would likely diminish in the long run). Though our empirical setting is open source software #OSS, we argue, and find evidence, that the results generalize to private work settings as well." Great work from Manuel Hoffmann, Sam Boysel, Frank Nagle, Sida Peng and Kevin Xu. Thanks to Frank for coming to present it at a recent MIT FutureTech lab meeting where I learned about it.

AI dramatically accelerates knowledge work because this work has historically been rate limited by the time it takes to research, write, review, or generate something just to move on to the thing that *actually* creates the value. Almost all of our work looks like this in some fashion. Writing a product design document, generating a marketing plan, reviewing a contract, connecting dots in market research trends, and so on. All this work *must* get done before you actually can produce the thing of value that you wanted: shipping new software for a customer, launching a marketing campaign, closing a deal, planning a new product, etc. And certainly there’s inherent value in parts of the work such that you may not want to automate 100% of it - you learn important insights along the way of doing this work or you can connect dots better in the future. But a large fraction of the work is not slow and costly because we’re thinking so much more about it in a useful way, but because we have to do it manually today. There’s been no other way. The Deep Research paradigm shows what this will look like for knowledge work. AI Agents let us parallelize this work in a way that was never possible before. You can just create as many “threads” of work as you want, and the AI Agent(s) will use its reasoning capabilities, external internet sources, and tap into different internal software tools to generate results. Then, in the final step you’ll review the output, grab the best ideas, collate, and move on with your next tasks. This will take tasks that took days and shrink them into hours or minutes. This is going to forever alter what work looks in the future, and the way many jobs look today will be different in even 5 years from now as a result of this acceleration. Best of all, we’ll do far more than we could’ve before.

Our recent research on #AI adoption in the workplace provides valuable insights into the practical benefits and challenges of integrating AI across various job functions. We surveyed 1,300 early Microsoft #Copilot users, exploring how AI tools are impacting time savings and identified which roles are gaining the most advantage.     Some key findings: - 𝐇𝐢𝐠𝐡𝐞𝐬𝐭 𝐭𝐢𝐦𝐞 𝐬𝐚𝐯𝐢𝐧𝐠𝐬: Cybersecurity, product development, and sales roles.  - 𝐋𝐨𝐰𝐞𝐬𝐭 𝐭𝐢𝐦𝐞 𝐬𝐚𝐯𝐢𝐧𝐠𝐬: Procurement, legal, and supply chain roles.  - 𝐀𝐈 𝐯𝐚𝐥𝐮𝐞 𝐩𝐞𝐫𝐜𝐞𝐩𝐭𝐢𝐨𝐧: Just 11 minutes of daily time savings is enough for most users to see AI as valuable.    Influencing Factors: - 𝐍𝐚𝐭𝐮𝐫𝐞 𝐨𝐟 𝐰𝐨𝐫𝐤: Roles with clear AI use cases, like sales, benefit more. Legal roles face challenges due to confidentiality requirements.  - 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐝𝐲𝐧𝐚𝐦𝐢𝐜𝐬: Rapidly evolving fields like cybersecurity adopt AI faster, while supply chain roles, dealing with legacy systems, show slower adoption.    The broader impact is that 75% of global knowledge workers use AI, highlighting its growing importance. Understanding how different functions use AI can help guide effective AI integration and drive business transformation. Read more in our latest #WorkLab article:

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 is going to do to knowledge work what Lean did to manufacturing." – Satya Nadella This quote has been stuck in my head since I first heard it. The more I think about it, the clearer it becomes: Lean eliminated waste, optimized workflows, and empowered workers to operate at a higher level. AI is doing the same for knowledge work, not by replacing people, but by shifting their focus to higher-impact tasks. Here’s where the parallels stand out to me—and why I think you should pay attention: ✅ Eliminating Waste - Lean cut unnecessary inventory and idle time. AI is removing repetitive knowledge tasks that were once too unstructured to automate. ➡️ Instant meeting summaries ➡️ Automated data entry ➡️ Seamless report generation 🤔 What changes? Roles shift. Companies will need to redefine job responsibilities, redeploy talent, and rethink required skills. ✅ Just-in-Time Insights - Lean production meant the right materials, at the right time. AI can deliver insights exactly when needed. ➡️ No more waiting for monthly reports—benchmarking happens instantly. ➡️ Marketing teams can approve AI-recommended campaign updates in real time. 🤔 What changes? Decision-making accelerates. Companies will need flatter org structures and leaders who are comfortable with continuous iteration instead of rigid planning cycles. ✅ Continuous Improvement - Lean championed small, ongoing improvements. AI now enables continuous, real-time enhancements. ➡️ Writers get instant clarity recommendations. ➡️ Sales teams receive AI-driven coaching on the fly. ➡️ Customer interactions improve through proactive suggestions. 🤔 What changes? A culture of experimentation becomes essential. Companies that reward iteration and learning will move faster than those that don’t. ✅ Empowered Workers- Lean gave factory workers more control over processes. AI is doing the same for knowledge workers by equipping them with expert-level insights and decision-making capability. ➡️ Customer support reps can resolve complex issues without escalation. ➡️ Employees make better, faster decisions without waiting for approvals. 🤔 What changes? Employee expectations shift. More autonomy means leaders must focus on coaching over command-and-control management. We’re at the start of a major transformation. At Dropbox, we’re building Dash to help knowledge workers focus on high-impact work, not busywork. And tomorrow, I'm giving a talk at the Gartner symposium in Dallas to share what we've learned tackling these challenges head on. Which Lean principles feel most relevant to how AI is changing your work? Let me know down below (and if you're in Dallas, come say hi!).

Most people assume the best way to use AI at work is to outsource tasks. But the biggest impact comes when we use AI to challenge us. We default to using AI for efficiency, like by delegating routine tasks. While this has its place, the real power of AI emerges when we use it as a thought partner, forcing us to think more critically, ask better questions, and elevate our decision-making. For example, I might need to have a difficult conversation with a vendor. I could tell ChatGPT what I really want to say, have it clean up my low-EQ draft, and simply send it. Alternatively, I could tell AI what I’m thinking and have it refine and guide me—offering suggestions for things to consider, such as starting the conversation by acknowledging aspects of the vendor’s work I appreciate, posing questions instead of making demands, and asking for the vendor’s help rather than assuming bad intent. When I use AI in this second mode, I might not save a few seconds right now, but I level up my game in the long run. Before AI, we had to do all the work ourselves, so we focused primarily on execution and meeting deadlines. Now, as we share the work with AI, we must take on new roles—question-asker, director/producer, critical thinker, and emotional actor—making us more curious, creative, and insightful about how things really work, both in the external world and in our own minds. AI doesn’t just make things easier; it makes us smarter. AI can introduce complexity and then explain it, expose us to new concepts and data, highlight where we may be wrong, push us to practice critical thinking and curiosity, and help us explore our own beliefs, behaviors, and theories of mind. As AI reshapes knowledge work, the real competitive edge will belong to those who embrace it as a partner in thinking—not just a shortcut for execution.

My AI tools finish in seconds what used to take hours. Yet somehow my workday has gotten longer. The time savings materialized immediately. Content creation, data analysis, and customer research cycles shortened dramatically. But something unexpected happened. For every hour saved, we discovered three hours of new work that suddenly became possible—and valuable. Market segments we couldn't previously analyze became accessible. Customer personalization we couldn't scale became feasible. Product improvements we couldn't resource became attainable. Our capacity expanded, but so did our ambitions. This pattern isn't unique to us. Every founder I've spoken with who's meaningfully implemented AI tools has experienced the same counterintuitive reality. The time saved doesn't translate to shorter workdays. Instead, it unlocks entirely new categories of high-value work that were previously impractical. The founders seeing the greatest returns aren't those using AI to reduce headcount or cut costs. They're the ones using AI to dramatically expand their capabilities while maintaining their team size. One SaaS founder in our network used AI to analyze customer conversations at a scale previously impossible. This revealed three new market segments they're now successfully targeting. Another used generative AI to create personalized outreach at 50x their previous capacity, transforming their entire go-to-market motion. The true value of AI isn't in doing the same things faster, but in doing entirely new things that create disproportionate value. This expansion of possibility is challenging. It requires constant prioritization and focus. The constraint is no longer technical capability but human judgment about what's worth doing. Productivity is fundamentally about maximizing impact, not minimizing effort. And in that light, having more high-value work to do than hours in the day isn't a failure of the technology. It's a sign you're using it correctly. #startups #growth #founders #ai