In-Ear Insights from Trust Insights

In-Ear Insights: Generative AI Reasoning Models For Scenario Planning


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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how reasoning models, a new type of AI, can revolutionize your scenario planning. You’ll discover how these advanced AI models can help you anticipate unforeseen challenges and opportunities for your business. Learn to move beyond reactive panic planning and create robust strategies for any future scenario. You will explore how to prepare your business knowledge for AI and scale your scenario planning efforts effectively across your organization. Tune in to learn how to leverage reasoning models to build a resilient and future-proof business today!

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    Machine-Generated Transcript

    What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

    Christopher S. Penn – 00:00

    In this week’s In Ear Insights, life is full of change and unpredictable, unforeseen circumstances of all kinds. And we’ve talked in the past, many times on this show about scenario planning, about coming up with what do we do if this happens, if this happens. However, a new family, a new genre of AI models has come out in the last two months really called reasoning models. First with OpenAI’s 01 and then with Deep Seek and its R1 and now OpenAI’s O3 and Google Gemini 2. Flash thinking. I hate their product naming and so many other models within this class which give us as marketers and as business professionals the ability to have a model that can really think things through. So Katie, when you hear about reasoning models and think about the need for good scenario planning, what comes to mind?

    Katie Robbert – 01:05

    Well, I guess the first thing is, admittedly, you’ve been talking about reasoning models since they hit the market. I don’t know that I really understand what a reasoning model is versus, you know, an existing open generative AI model. I don’t even know the terminology to be quite honest. When I hear “reason model,” it’s like, “oh, is it sentient now?” What does that mean?

    Christopher S. Penn – 01:36

    That’s a really good question. There’s three techniques in prompt engineering that we’ve talked about and which you can learn about in our prompt engineering course, Mastering Prompt Engineering at Trust Insights. Those three techniques are called chain of thought, reward functions, and reflection. And when we would do this by hand in the old days of AI three months ago, we would say things in a prompt. Our prompts would say, “think this through step by step. Show me your work, show me this, show me this. Explain this” in our prompts to say, “I want you to explain this.” So for example, I was

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