AI's Impact on Work Hours and Productivity

What is the true impact AI will have on your work? You can read all the research you want attempting to prognosticate the impact on jobs, but the best way to find out is to drill into specific tasks, projects and campaigns, and find out for yourself. Here’s an example of what I mean: I’m in the midst of planning the MAICON 2024 agenda, so Speaker and Agenda Planning is top of mind. To expedite things for this demonstration, I asked ChatGPT to build a list of what’s involved in the process. Prompt: you are an event planner responsible for creating the event agenda, researching speakers, and selecting speakers. build a task list of all the activities involved. [It gave me a solid list including Phase, Task and Details. But, for the purposes of this post, I wanted to be able to customize the data.] Prompt: can you give me this is a csv format for download It did. I then uploaded that CSV to Google Sheets and added the other columns you see in the image—Est. Time (All Human), Est. Time (With AI), Est. Time Saved, Sample AI Tools, and Notes. You can review the example spreadsheet for yourself, but here are a few notes and highlights: Assumptions (Not actual MAICON data, just for this demonstration) * 2 day, in-person event. * 40 Speakers. 30 from submissions, and 10 main stage who are recruited. * 100 speaker submissions to review. Highlights * 16 tasks * Est. 220 hours without AI * Est. 178 hours with AI * Total time savings of 42 hours (19%) * 9 of 16 tasks (56%) are able to use AI to increase efficiency and creativity This is rough (I put it together in about 30 minutes, so forgive any typos or errors), but hopefully gives you a practical approach you can use to cut through the hype and analyze AI’s impact on your workflows moving forward. P.S. No one actually knows what will happen to knowledge work jobs and careers when we have GPT-5 level AI and beyond. All we can do right now is analyze specific roles, tasks, projects and campaigns to the best of our ability, and then try to project out the impact over the next 12 - 18 months. Anything beyond that is educated guesswork at best.

We analyzed data from over 10,000 developers across 1,255 teams to answer a question we kept hearing from engineering leaders:     “If everyone’s using AI coding assistants… where are the business results?” This rigorous Faros AI longitudinal study of individual and company productivity exposes the gap between the two. On an individual level, AI tools are doing what they promised: - Developers using AI complete 98% more code changes - They finish 21% more tasks - They parallelize work more effectively But those gains don’t translate into measurable improvements at the organizational level. No lift in speed. No lift in throughput. No reduction in time-to-deliver. Correlations between AI adoption and organization-wide delivery metrics evaporate at the organization level. We’re calling this the AI Productivity Paradox—and it’s the software industry’s version of the Solow paradox:     “AI is everywhere—except in the productivity stats.” Our two-year study examined the change in metrics as teams move from low to high AI adoption. - Developers using coding assistants have higher task throughput (21%) and PR merge rate (98%) and are parallelizing more work. - Code review times increased by 91%, indicating that human review remains a bottleneck. - AI adoption also leads to much larger code changes (154%) and more bugs per developer (9%). Why is there no trace of impact on key engineering metrics at the organizational level? Uneven adoption, workflow bottlenecks, and the lack of coordinated enablement strategies help explain this paradox. Our data shows that in most companies, AI adoption is still a patchwork. And, because software delivery is inherently cross-functional, accelerating one team in isolation rarely translates to meaningful gains at the organizational level. Most developers using coding assistants rely on basic autocomplete functions, with relatively low usage of advanced features such as chat, context-aware code review, or autonomous task execution. AI usage is highest among newer hires, who rely on it to navigate unfamiliar codebases, while lower adoption among senior engineers suggests limited trust in AI for more complex, context-heavy tasks. We also find that individual returns are being wiped out by bottlenecks further down the pipeline, in code reviews, testing, and deployments that simply can't keep up. AI isn't a magic bullet, and it can't outrun a broken process. Velocity at the keyboard doesn't automatically mean velocity in the boardroom. If you want AI to transform your business, you can't just distribute licenses—you need to overhaul the system around them. This report might help guide the way. https://lnkd.in/gPb4j8kf #AI #Productivity #Engineering #AIParadox #FarosAI

New research shows AI's uneven impact on work 🤖🔍 Turns out collaborating with AI is not much different than working with a (human) team. Link to the paper in comments. I would recommend a read A study with 700+ consultants found that large language models like ChatGPT can greatly boost productivity on many tasks, but hurt performance on others. 📈📉 Key findings: AI created a "jagged frontier" where some complex tasks were easily automated while others were not. 🎢 With AI, consultants completed 12% more tasks 25% faster and had 40% higher quality results. 💪 But consultants were 19% less likely to be correct when AI was applied to tasks beyond its capabilities. ❌ Both higher and lower performing consultants benefited from AI, but lower performers saw bigger gains. 📊 Implications for business intelligence: 💡 Carefully evaluate which analytic tasks AI can augment vs. those needing human judgment. Redesign workflows to leverage AI's strengths while keeping humans focused on high judgement tasks. Provide guidance on appropriate AI usage as capabilities rapidly advance. 🗺 Implications for personal assistants: 💬 Can now help with complex analytical tasks but critical to convey limitations. 🤖 Risk of over-reliance - need transparency on capabilities. 👀 Likely to blend asking for AI help where it excels with reserving judgement tasks for humans. ⚖️ The key is learning to navigate AI's uneven capabilities! If you know, you know 🧭 What do you think? How will you navigate the #JaggedFrontier of AI? 🤔💭

Boston Consulting Group (BCG) consultants completed 12.2% more tasks, 25.1% faster with 40% higher quality when using AI, according to a new report from Ethan Mollick. This is one of the best analyses I've seen on the impact of AI on professional work and insights on how to best collaborate with AI. In addition to the improved performance with AI, Ethan shared: ► AI improved lower performing workers by 43% more than higher performers in the BCG experiment, reducing skill gaps between employees. But over-relying can make people "fall asleep at the wheel" and miss AI mistakes. Staying alert is key. ► There is an unpredictable "jagged frontier" to what AI can and can't do well. Knowing where AI excels and falls short is crucial. ► To best collaborate with AI, be "Centaurs" to strategically divide work or "Cyborgs" to closely intertwine work with AI. This combines the benefits of both humans and AI. Ethan's paper provides valuable insights into effectively leveraging AI to enhance productivity and performance. I highly recommend reading the report (link in comments) to learn more about optimizing human-AI collaboration. What has been your experience working with #AI so far? I'd love to hear your thoughts in the comments! #FutureOfWork #AIAdoption #AIProductivity #WorkforceProductivity #WorkPerformance

The PwC 2024 Global AI Jobs Barometer report provides a comprehensive analysis of the future of work, where Artificial Intelligence (AI) is poised to play a significant role. By examining a vast dataset of job postings, the report identifies trends in job creation and displacement across various sectors. The findings suggest a surge in productivity, postulating a potential labor productivity increase by 8-10% within three years (2027). However, this may be accompanied by evolution of job roles and re-imagined functions that underpins those roles pretty comprehensively. On the other hand, the report also forecasts new job creation at a rate of 5-7% within the same timeframe. These new positions will likely be in areas requiring collaboration with AI and human-machine interaction. The report highlights the imperative need for reskilling and upskilling initiatives, as approximately 20-25% of workers may require new skills within three years to adapt to the transforming job market demands of the AI-driven economy. This underscores the critical need for educational systems and training programs to evolve rapidly, equipping workers with the necessary skills to thrive in this new environment. The report suggests a continued trend of automation, with a growing demand for skills complementary to AI capabilities. The future of work will not be a competition between humans and AI, but rather a collaborative effort between the two to achieve greater efficiency and innovation. By understanding and leveraging AI's capabilities, workers can position themselves for success in the coming years. Extrapolating the trend lines beyond the report's three-year scope, we can anticipate a significant growth trajectory, potentially yielding labor productivity increases of 19.4% and 43.1% in five and ten years, respectively, as modeled by the exponential growth equation. In conclusion, AI is poised to drive a substantial increase in labor productivity. The key takeaway is that the future of work requires embracing change and preparing for a world where humans and AI collaborate. By equipping the workforce with the necessary skills to thrive in this AI-driven economy, fostering a culture of lifelong learning, and implementing robust reskilling programs, we can navigate the challenges and create a future that benefits all. #pwc #jobs #ai

Get ready for the future of work. It's a world where AI isn't just a buzzword but a catalyst for change, Microsoft's 2023 Future of Work Report is a goldmine of insights. The report unpacks the ways that AI and large language models are already revolutionizing our approach to work. What stands out to me is the emphasis on human-AI collaboration, not just as a technological advance but as a new frontier in workplace dynamics. It's not just about AI doing the work (and therefore replacing jobs); it's about AI augmenting human ingenuity and creativity across many sectors and disciplines. A few tidbits from the report: 1. 𝗦𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗔𝗜: Knowledge workers using ChatGPT show a significant boost in speed (37%) and quality (40%), though accuracy dips slightly (about 20%). This underscores the potential for AI to enhance productivity and work quality: as long as the user is still the editor-in-chief and not just delegating the work to AI. 2. 𝗔𝗜'𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝗦𝗸𝗶𝗹𝗹 𝗟𝗲𝘃𝗲𝗹𝘀: Lower-skilled workers see a substantial improvement (43%) with LLMs compared to more skilled workers (17%). This insight highlights how AI can democratize skills and elevate overall workforce capabilities. 3. 𝗧𝗵𝗲 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: LLMs are set to revolutionize social science research by analyzing human data and generating synthetic data, suggesting a significant shift in research methodologies and outcomes. This report is a must-read for anyone navigating the evolving landscape of digital workspaces, offering a nuanced perspective on the synergy between human creativity and AI efficiency. See the full report at: https://lnkd.in/eHa32z2X