3/5, 2024 - AI and Strategic Development

Jonathan Kahan

How will generative AI will transform problem-solving professions like consulting, strategy and design? Problem-solving means abstracting real world things into models made of data, variables and frameworks, and manipulating these models to shape the world into a desired future state. The first part requires analytical skills, the second requires creative ones. With just a little playing around with ChatGTP, you can easily see that, while LLMs don’t think in the way humans think, they can truly approximate both analysis and creativity skills. For example, LLMs can…

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Draw inferences, from causes to conclusions (”Here is a situation: […]. What could happen next? Create four scenarios”) 🖼️ Select the appropriate framework to analyze a problem (”Here is a problem: […]. What are the key drivers and tensions? What is a good framework to think about the problem”) 📊 Apply a framework to a given dataset (”Create a SWOT for this company […]”) 💡 Generate ideas and variations of ideas and concepts (”Here is a problem. Generate one hundred diverse and originial ideas to solve it”) LLMs do all of the above pretty well. Sure, not always at the level an expert human problem-solver would. On the other hand: ⏭ They are much faster and cheaper 🔆 They can be always on 📶 They can parallelize workflows Gen-AI powered problem solving is about leveraging the best of both worlds: identifying the phases of problem solving that it make sense to automate, where instead you should keep a human in the loop. Don’t have to take my word for it. Much research have been coming out demonstrating LLMs problem solving abilities. Here is a selection: 👩‍🎓 Improving the speed and quality consultant work overall: an experiment with BCG showed that LLMs can substantially improve productivity up to 40% on common information work tasks, with some qualifiers (Dell’Acqua et al. 2023) 🧠 Critical thinking: LLM-based tools can be effective as “provocateurs: to challenge our thinking (Sun et al. 2017; Lee et al. 2023) 💡 Ideation: when appropriately prompted, LLMs can provide as much variety in their ideation as humans (Mollick et al. 2023) 👨‍💻 Coding: GenAI can shorten time by 50%+ (Peng et al. 2022) 🔀 Breaking down processes: GenAI can identify sub-tasks out of a main task and forecast properties of the sub-tasks like duration (White et al. 2021) 🙋‍♀️ Task assignment: AI can help to allocate team member roles based on their present work schedules and their skill sets, attitudes, and actions (Sowa et al. 2020) --- 👉 The group "Generative AI for Problem Solving and Innovation" was created to discuss how genAI is impacting the way problem solvers and knowledge workers get their work done. If you have any interesting paper, case study, GTP, etc. feel free to join and share!

Microsoft New Future of Work Report 2023

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https://www.microsoft.com/en-us/research/publication/microsoft-new-future-of-work-report-2023/⁠

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Assigning AI: Seven Approaches for Students, with Prompts

This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student, each with distinct pedagogical benefits and risks. Prompts are included for each of these approaches. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI’s output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementarity of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop," the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4475995⁠

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If you’re not using ChatGPT for your writing, you’re probably making a mistake

How Ethan and Lilach Mollick learned to stop worrying and start using AI in their jobs.

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https://www.vox.com/future-perfect/2023/3/14/23637633/chatgpt-bing-generative-ai-ethan-lilach-mollick⁠

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