My 17 Minute AI Workflow To Stand Out At Work

Vicky Zhao [BEEAMP]
9 Jan 202517:30

Summary

TLDRIn this video, the speaker explores the evolving nature of knowledge work in 2025, emphasizing the importance of improving input quality rather than relying solely on AI for output. By integrating academic research into workflows, using tools like Elicit and Notebook LM, individuals can gain valuable insights to solve practical challenges. The speaker illustrates this through an example of improving team performance, showcasing how leveraging high-quality, peer-reviewed research and AI tools can generate specific, actionable plans. The video highlights the potential of AI to enhance critical thinking, creativity, and productivity in modern knowledge work.

Takeaways

  • 😀 In 2025, it will take longer to read and comprehend something than it took to create it due to the overwhelming amount of AI-generated content.
  • 😀 The key to improving knowledge work lies in moving up the value chain and enhancing inputs, rather than focusing solely on processing or output.
  • 😀 Large language models (LLMs) have average input quality, so relying solely on AI tools like ChatGPT might not yield fantastic results.
  • 😀 To improve outputs, AI should be used to improve inputs by incorporating high-quality, academic research into decision-making processes.
  • 😀 Leveraging resources like Harvard Business Review, McKinsey Quarterly, and MIT Sloan Review can help professionals turn academic insights into actionable knowledge.
  • 😀 Instead of being passive readers or writers, aim to become a valuable resource for your team, summarizing complex ideas and delivering them in a digestible format.
  • 😀 Modern AI tools can make academic research more accessible, allowing individuals to extract practical and relevant insights from complex sources.
  • 😀 When facing challenges like team underperformance, use academic research to guide your approach, instead of relying on vague and broad recommendations from general search results.
  • 😀 Tools like Elicit and Notebook LM can help you discover academic papers, analyze their findings, and generate actionable insights without having to sift through the entire paper.
  • 😀 With AI assistance, you can generate clear, detailed action plans based on well-cited research, which can significantly improve decision-making and team performance.
  • 😀 The key to efficient knowledge work in the AI era is focusing on high-quality input and using AI to help distill complex ideas into practical, actionable strategies.

Q & A

  • Why is it important to focus on improving inputs when using AI for knowledge work?

    -Improving inputs ensures that the data or information fed into AI is of higher quality. This prevents the 'garbage in, garbage out' problem, resulting in more valuable and accurate outputs.

  • What are the four steps of knowledge work as described in the video?

    -The four steps of knowledge work are: 1) Input – gathering data, knowledge, and information, 2) Process – analyzing and interpreting this input, 3) Output – producing results, and 4) Feedback – using the output to refine and improve the input.

  • How does the speaker suggest overcoming the noise and inefficiency in AI-generated content?

    -The speaker suggests moving up the knowledge work value chain by improving the quality of inputs, particularly by incorporating academic research, to ensure that the AI-generated outputs are more actionable and valuable.

  • Why does the speaker emphasize the use of academic papers in improving knowledge work?

    -Academic papers often contain high-quality, peer-reviewed, and well-cited insights that can provide a strong foundation for solving practical problems, such as improving team performance, compared to average or generic advice found in online sources.

  • What is the value of tools like Elicit and Notebook LM in improving knowledge work?

    -Elicit helps find relevant academic papers on a specific question, while Notebook LM helps process and extract insights from these papers without the risk of AI hallucinations, allowing users to generate accurate and relevant answers based on high-quality sources.

  • How does the process of using Notebook LM improve team performance analysis?

    -By uploading academic papers into Notebook LM, users can automatically generate a table of contents and ask the AI to extract and summarize key insights. This allows the user to focus on the most relevant information for improving team performance.

  • What is the issue with relying on average AI results from platforms like ChatGPT and Google search?

    -AI-generated results from platforms like ChatGPT and Google often provide broad and vague advice, such as 'focus on goals and open communication,' which are not specific enough to be actionable or solve real-world problems effectively.

  • Why does the speaker recommend creating frameworks from academic research rather than just reading research papers?

    -Creating frameworks from academic research allows the knowledge to be translated into practical, actionable steps that are tailored to a specific context, making it easier to implement and understand for busy professionals.

  • How does combining academic insights with AI tools like Claude improve decision-making in team management?

    -By combining academic insights with AI tools like Claude, users can create detailed action plans based on research, which can then be further refined to generate targeted leadership actions that lead to tangible improvements in team performance.

  • What impact does improving input quality have on the efficiency of knowledge work in an AI-driven environment?

    -Improving input quality in an AI-driven environment significantly enhances the efficiency and effectiveness of knowledge work, as it reduces the reliance on average, generic advice and enables more precise, actionable, and tailored outcomes.

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Related Tags
AI in WorkKnowledge WorkTeam PerformanceAI ToolsAcademic ResearchProductivityBusiness StrategyLeadershipAI EfficiencyTeam Management