The Experimental Edge: Where Scientific Thinking Meets AI

When you explore AI collaborations, you open a world of possibilities. Photo of a golden sky.

10/14/25

LEARNING

Most people use GenAI only for search, missing its real potential. The difference between basic AI use and better results isn’t the technology—it’s your approach. When you combine scientific thinking with a clear vision of what you want to achieve, you can fundamentally change how you work, create, and solve problems.

Why This Matters

Every assumption we make about AI is actually a hypothesis waiting to be tested. As Adam Grant observes in Think Again, “the ability to rethink and unlearn” is crucial in a volatile world. By making our assumptions explicit, we can test them systematically—turning perceived limitations into opportunities for discovery. This scientific approach helps us build a learning mindset where mistakes become data points and uncertainty drives curiosity.

Your AI-Enhanced Future Self

How Models Think is AI Model Interpretability - How People Think Is Meta Learning Strategies

Before diving into specific practices, let’s set up your first experiment. Take a moment to envision a clear goal: 

  • What specific problem do you want to solve with AI in the next week? 
  • What would success look like? 

This vision becomes your north star—guiding which assumptions to test first and helping you measure real progress against your expectations.

Three Core Practices for Rapid Learning

1. Start With What You Know Deeply

Your decades of professional expertise are your greatest asset in mastering AI. Begin by asking AI to produce results in areas where your judgment is already razor-sharp. Your deep knowledge lets you instantly spot what’s brilliant, what’s mediocre, and what’s flat-out wrong in AI outputs. When you start with unfamiliar territory, you’re flying blind—but in your area of expertise, you can write better prompts, evaluate results more accurately, and refine your approach based on genuine understanding rather than guesswork.

Inside the AI's Mind is Model Interpretability in Action
Start with what you know deeply

2. Develop a Portfolio Approach

In an uncertain economy, you wouldn’t put all your investments in a single stock—the same principle applies to AI. Different models excel in different areas, and like markets, these strengths shift over time. Building a portfolio of AI tools gives you broader capabilities and reduces your dependence on any single model.

Try This Simple Workflow:

  • Start with your go-to model (e.g., ChatGPT)
  • Run the same exact prompt through Claude, Copilot, and Gemini
  • Note where outputs differ from what you expected
  • Ask the AI why it provided that specific response

3. Build Your Documentation System

Think of your documentation as a lab notebook for AI experiments. Keep track of what works, what doesn’t, and most importantly—what surprises you.

Start a Basic System:

Create a dedicated folder for your AI experiments. For each significant prompt or response:

  • Record your original prompt and what you expected
  • Save the outputs that differed across AI models
  • Note which AI model gave which response
  • Track any “why” explanations that shifted your understanding
  • Notice which AI models you prefer for different types of tasks (e.g., brainstorming vs. analysis)

Making It Stick: Your Learning System

After a week to explore AI collaborations and experiment with different models in your area of expertise:

  1. Match Tools to Tasks: Which AI models worked best for specific types of work?
  2. Note the Surprises: Where did an AI model give you something unexpected but useful?
  3. Trust Your Judgment: Where did your professional expertise help you spot the difference between good and mediocre AI outputs?

Reflection Prompts

Reflection is one of the most essential meta learning strategies. Character on swing hanging from sky, thinking.

Now that you’ve explored an experimental approach with multiple AI models, take 15 minutes to synthesize what you’ve learned and consider going deeper:

  1. What would you tell a colleague or loved one about what you’re learning through AI experimentation?
  2. What features would you like to explore in your preferred AI model tomorrow? (For example: privacy settings, custom instructions, or ways to save and organize your chats)

Your Next Step

Start with what you know best. Choose one task where your expertise runs deep, then explore how AI can support that work. Document what you learn. Begin building your understanding of these tools through direct experience. Real-world practice will teach you more than any amount of reading about AI.

👉 In Part 2 next week, we’ll explore how these individual practices can scale across organizations. But first, master these basics in your own domain.