AI's Impact on Knowledge Worker Productivity

AI's hype is everywhere, but its practical application is what truly matters. !! Unlike the self-driving car hype of a decade ago, AI's implementation in the real world is uniquely different. Over the past year, I've witnessed firsthand how AI can augment our capabilities at SJ Innovation. It may not replace our jobs, but it does serve as a powerful assistant, handling numerous tasks efficiently. Since OpenAI introduced the "OpenAI Assistant," we've created over 250 specialized assistants within our organization. Upon reviewing these AI assistants, I've come to realize they haven't replaced any jobs. Instead, they're akin to having a team of interns, each adept at performing specific tasks, saving us 10-15 minutes each time. If you're leveraging 5-10 such assistants, that's a savings of 1-2 hours per day — a significant boost to productivity that will only improve over time. Here are some unusual and small assistant example: 1) Attendance Analysis: Develop AI solutions to analyze attendance data across multiple files, generating comprehensive reports to identify patterns and optimize team schedules. Create and Used by: Admin/Hr department 2) Quality Assurance Report Review: Assist QA teams Assistant manager by tracking project hours versus contracted hours to prevent burnout and ensure optimal productivity. 3) QA/Test cases for Client Project: Upload client project data, past test cases and input new requirements. Result new cases 4) Convert my code to old Version of Cakephp: Client running an application with old version, write code and it convert to old version of cakephp 5) RFP helper: Upload All document about project and old RFP document and now it can help write based on client requirements and our past RFP My advice? Get involved. Sign up for ChatGPT premium, create your own GPT, or if you're leading a team, develop your own assistants using the API. These digital helpers could become your next competitive edge, much like an diligent interns, ready to streamline your daily tasks and workflows. #AIAssistants #ProductivityTools #Innovation #OpenAI #Teamwork #SJInnovation

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

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

Given knowledge workers' discomfort with AI, I find this survey fascinating: it shows that generative AI makes software developers happy 😮 Check out the provocative findings from Begum Karaci Deniz, Chandra Gnanasambandam, Martin Harrysson, Alharith Hussin, and Shivam S. at McKinsey & Company here: https://lnkd.in/ejCbcn_i My take is that by overcoming fear and embracing tools such as ChatGPT from OpenAI, Claude from Anthropic, or GitHub's copilot, knowledge workers can boost productivity and free up brainspace to do better work. That improves mental focus, performance, and job satisfaction. These numbers apply to developers but IMO have implications about the future for knowledge workers across the board. Data and AI leaders, what do you think? What anecdotal results have you seen with your teams so far? Report excerpts: "Our latest empirical research finds generative AI–based tools delivering impressive speed gains for many common developer tasks. "Documenting code functionality for maintainability (which considers how easily code can be improved) can be completed in half the time, writing new code in nearly half the time, and optimizing existing code (called code refactoring) in nearly two-thirds the time. "With the right upskilling and enterprise enablers, these speed gains can be translated into an increase in productivity that outperforms past advances in engineering productivity, driven by both new tooling and processes." "The research finds that equipping developers to be their most productive also significantly improves the developer experience, which in turn can help companies retain and excite their best talent. "Developers using generative AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow. "They attributed this to the tools’ ability to automate grunt work that kept them from more satisfying tasks and to put information at their fingertips faster than a search for solutions across different online platforms." And yet... software development needs humans for tricky tasks: "Generative AI technology can do a lot, but our research suggests that the tools are only as good as the skills of the engineers using them. Participant feedback signaled three areas where human oversight and involvement were crucial... > "Examining code for bugs and errors" > "Contributing organizational context" > "Navigating tricky coding requirements" What do you think? Chime in here. Wayne Eckerson Eckerson Group Jay Piscioneri Jeff Smith #artificialintelligence #ai #generativeai Bill Schmarzo

In 2023, Generative AI (GenAI) entered the workplace at a staggering rate.   Because it's all happening so fast, here are 10 quotes from various industry leaders and experts that summarize GenAI's role in the workplace this year.   ➡ First, we saw mass adoption of GenAI tools, especially among jobs with 'high exposure' to automation or augmentation…   "70% [of software developers] are already using or plan to use AI tools in their development process: 44% use AI tools now and 26% plan to soon." - StackOverflow Labs' finding from a May, 2023 survey of ~90k software developers   "79% percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work." - McKinsey finding from an April survey of ~1,700 respondents across industries, regions, company sizes, and functional areas    ➡ …but do GenAI tools increase productivity? Vendors and early adopters: Yup. Academics: So far so good.   “Our people are seeing immediate productivity improvements with [Microsoft's] Copilot.”  - Kate Johnson, CEO, Lumen Technologies, quoted in Microsoft's special report   "Access to [a GenAI] tool increases productivity…by 14 percent on average, with the greatest impact on novice and low-skilled workers…" - Stanford and MIT researchers in an April study of GenAI's impact on over 5,000 customer success agents   ➡ Still, businesses around the world dealt with concerns about hallucinations, errors, bias, and data security (some created workarounds).   “One of the worst AI blunders we saw this year was the case where lawyers used ChatGPT to create legal briefs without checking any of its work.” - Stanley Seibert, senior director of community innovation at data science platform Anaconda   “The company is reviewing measures to create a secure environment for safely using generative AI to enhance employees’ productivity and efficiency.” - Samsung spokesperson after banning employees from using ChatGPT due to a leak of confidential source code   ➡ Here's the bull case for AI-assisted work: less income inequality and more meaningful work.   "The positive case is that AI brings a lot more people into higher-paid expert work...” - David Autor, MIT, quoted in the Economist   "The worst parts of your job go to AI so that you get to focus on the good stuff." - Ethan Mollick, Wharton School of the University of Pennsylvania   ➡ Yet, we'll see work destruction and creation along the 'jagged frontier' for the next few years.   "GPT4 and other systems like it are good at doing tasks, not jobs." - Sam Altman, May 2023 testimony to the U.S. Senate's AI hearing   "We suggest that the capabilities of AI create a “jagged technological frontier” where some tasks are easily done by AI, while others, though seemingly similar in difficulty level, are outside the current capability of AI."  - HBS researchers summarizing their study of AI's impact on 800 BCG consultants

Across many industries, there is no doubt that #AI will be a major influence on the future of the workforce. Though the goal is to augment human capabilities or foster more efficiency and effectiveness, many companies may struggle with facing disruption as they integrate AI. However, the #knowledgemanagement (KM) industry should welcome these advancements in AI as a real opportunity to force-multiply the impact the right information and insight can have on the growth trajectory of a modern company.   This future workforce will benefit from the integration of AI technologies and KM systems. At the center of that integration is the ability to efficiently store, retrieve, and socialize data. AI supplements the KM storage systems with automation of tasks like tagging & categorization, improvement of search capabilities and answers, and socialization of that relevant knowledge across teams and departments. It adds layers of integration with the other software tools that employees use every day, and will be able to surface knowledge from legacy systems across the enterprise. Lastly, AI will help personalize knowledge to the user, whether that be by role, geography, or unique use case. Ultimately as a leader at a Knowledge Management-focused company like #Bloomfire, it’s the outcomes for our customers that matter most. And from what I have witnessed across our customer base is that when our new Generative AI features are implemented, not only are users’ day to day tasks more efficient but more opportunities for learning and collaboration occur. That learning and collaboration is building a resilient, future-ready workforce for their company, focused on innovation and growth. If you are interested in diving in deeper, I explored this topic as part of an ongoing series on the future of work in our latest article with Wired. Read more here: https://lnkd.in/dwwNZkM9 and then add your thoughts below.

I have been thinking about the possible impacts of generative AI on tech-enabled services for schools. I am particularly interested in the applications in places where talent shortages are a barrier to scale quality services - i.e., tutoring, coaching, career counseling, etc. and I came across this insightful paper by MIT Sloan associate professor Danielle Li, MIT Sloan PhD candidate Lindsey Raymond, and Stanford University professor Erik Brynjolfsson, titled "Generative AI at Work." There are so many nuggets worth exploring in the paper, but below are a few that stand out. Summary: The paper studied the impact of a chat-based generative AI support tool across 5K+ customer support agents and 3M+ chat-based conversations. The AI support tool was meant to augment and not outright replace the contact center employees. The model was trained using historical data from the company's highest-performing workers, and it only offered prompts if it was "sufficiently confident" in its answers, which reduced the number of incorrect responses. In addition, workers weren't required to use the recommendations. Key Takeaways -The customer support workers in the "treatment" group only followed the AI recommendations ~30-40% of the time, which is consistent with the industry average for generative AI tools -Overall, workers using the generative AI model increased the number of customer chats resolved per hour by 13.8%, and requests to speak to a manager declined by 25%. Additionally, transfers to other departments tended to happen earlier in the conversation, which suggests that the AI model was able to help workers better match a customer's problem to the right business unit for a solution -Productivity gains were highest among workers with the least experience, who resolved 35% more chats per hour when they used the generative model. Productivity was flat for workers with the most skills and experience. -New workers using the AI tool were able to reach the same level of productivity in 2 months that typically took 8-10 months for workers not using the tool - showing solid signs of the ability to use AI to progress up the learning/experience curve rapidly -The use of the AI tool leads to reduced turnover rates. The strongest reductions in attrition were seen among newer agents, those with less than 6 months of experience. https://lnkd.in/gvfFmv-w  #k12 #edtech #k12design #k12schools #k12education #edtechchat

Our recent research on the impact of generative AI–based tools on developer productivity found that a massive surge in productivity is possible. But there are two main mitigating factors: Task complexity and developer experience. We also found that developers using gen AI–based tools were more than twice as likely to report overall happiness, fulfillment, and a state of flow. This promises to help employers retain talent amid a persistent talent squeeze. To conduct this research, we set up what is now a permanent lab for ongoing analysis. Our lab has more than 40 McKinsey developers located across the U.S. and Asia who have different amounts of software-development experience. So stay tuned. In the meantime, here’s the full report from our first exercise: https://mck.co/46ttJaX #GenerativeAI #TechTalent #DeveloperTools #ArtificialIntelligence

The exact scale of AI's eventual impact on productivity is uncertain, and economists have wide-ranging estimates. What we do know is that the impact will depend upon how quickly AI spreads, how rapidly the models advance, how people and organizations choose to use it, and the policies that shape its deployment. With 2.5 billion messages sent to ChatGPT globally each day—including 330 million in the U.S.—OpenAI has a unique lens into how people are actually using AI today. In this new economic analysis, OpenAI explores early signals from ChatGPT usage data and what they might mean for productivity, economic growth, and the future of work. Key insights: - Top personal productivity use cases include Learning & Upskilling (20%), Writing & Communication (18%), and Programming, Data Science & Math (7%). - AI natives are emerging: One quarter (24%) of US users are between the ages of 18 and 24, and one third (32%) are between ages 25 and 34. This means that many students and workers in the early stages of their careers are becoming AI natives who will bring this expertise to their careers for years to come. - Small business adoption: ~40% of small U.S. businesses currently use AI; many rank it among the top tools for future success. - Early economic benefits are strongest in sectors with language- and pattern-heavy work: Think legal services, education, government, customer support, consulting, and marketing—where tasks like summarizing documents, generating content, and answering questions dominate. Bottom line: We're seeing real data on how AI is already transforming work across industries. The question isn't whether AI will boost productivity—it's how quickly these gains will scale. How has AI changed your daily work? What productivity gains have you experienced? #AI #Productivity #FutureOfWork #DigitalTransformation