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Let’s have feedbackloops. Just to give us a bit more of an idea of what we’re working with here today, I thought it’d be nice if we did a quick exercise. Discovery is about: How do we build fast feedbackloops to support our decisions? It’s: How do I build fast feedback?
It is also good exercise when you live near a mountain. When I returned to it later, I started with good old mental gymnastics rather than turning to Google, Bing, CHATGPT, or some other magical AI tool. Work through an exercise I like to call ‘what would change if…’. Create feedbackloops.
Here are some ways I leverage AI as a solo in my day-to-day work: Obviously, ChatGPT still remains an invaluable personal assistant for a creative, whenever you need to create a copy, or ideate on a user interview script. QoQo plugin for FigJam helps a lot with ideating on user needs and journey map.
And while my team of AI agents are busy doing their chain of thought magic and working through tasks, perhaps Ill have more free time to chill, think, spend time with my kids, sit in the sun, meditate, exercise, look after my brain and my body, travel the world, hangout with my friends, work on side projects for fun rather thanprofit.
Maybe your team played around with ChatGPT Pro or built a quick internal chatbot. Most companies still treat AI like an R&D exercise, something to experiment with, not something to operationalize. And that creates friction at every step: from model deployment to user access to feedbackloops.
Creates its own feedbackloop. For this exercise, we’ll focus on opportunities that pre-AI automations couldn’t address. For batch tasks, consider working directly from an AI system: Export the data and manually upload to Claude, Gemini, or ChatGPT. Takes real-world action. by planning our second AI agent.
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