An Untangled System: How to Use Low-Stakes Experiments Build Real AI Adoption

Maybe Just Start with a Roast: How Low-Stakes Experiments Build Real AI Adoption

Yesterday started with me asking ChatGPT: “Give me the laziest way to cook this roast.” Could I have looked at a few recipes? Used my intuition? Cooked to temperature? Sure. But I use low stakes activities like this to see where AI is at as it is evolving quickly.

It suggested low and slow in the oven: specific temperature, timeline, the works. Perfect. I tossed it in and went about my day. Four hours later, the roast wasn’t quite done yet, and I realized I miscalculated my timing. Back to ChatGPT: “I need to leave for 2+ hours but this needs another 1.5 hours of cooking. What do I do?”

It walked me through a safe plan. The roast turned out great.

More importantly, I’d just demonstrated something every leader trying to enable AI adoption should understand: low-stakes experimentation is how people actually start using these tools.

Why Your Roast Matters More Than Your Roadmap

I spend a lot of time thinking about how organizations can utilize current improvements in AI to help with their processes. I’ve spent the last year helping my team adopt AI tools from automating moderation to building out pricing models. The biggest barrier is often taking the first step developing intuition about how to talk to and use AI.

You can’t build that model in high-stakes work situations. You build it by asking ChatGPT how to cook a roast.

My colleagues that are using AI most successfully started by using it in low stakes ways, usually for personal tasks. Then they ramped up to more complex scenarios.

Most organizations aren’t ready to be AI-first. But there are meaningful improvements that can happen right now. The question is: how do we get started?

Three Filters for Your First LLM Experiment

Pick something that meets all three criteria:

1. Low stakes: failure is inconsequential

If the roast overcooks, you order pizza. In work terms, start with tasks where a wrong or mediocre answer is easily fixable.

2. Immediate feedback: you’ll know quickly if it worked

Did the roast cook properly? You’ll find out in a few hours. This is different from “Will this AI-suggested strategy work in six months?” Start with things where you can evaluate right away.

3. Something you’d normally skip or delay: friction reducers

The web searches you keep meaning to do, the email you draft in your head but never send, the calculation you estimate instead of compute. These are perfect: you’re not replacing something that already works, you’re enabling something that wasn’t happening at all.

Where to Start

Personal tasks:

• Cooking questions when recipes feel too complicated
• Gift ideas based on someone’s interests
• How to fix a household thing you’d normally spend 20 minutes Googling
• Planning a weekend activity with constraints (weather, budget, kids’ interests)

Work tasks:

• Pull statistics out of a chart when you don’t have the raw data
• Improve an email’s tone: more assertive, more diplomatic, or just clearer
• Have the LLM summarize web sources, then you verify them
• Draft a meeting agenda from scattered notes
• Generate variations on messaging you’re testing

Start with things where you’d normally say, “I’ll just need to sit down and focus on this for 20 minutes.” That’s where the real leverage begins.

The Pattern That Emerges

After doing this regularly, something shifts:

You stop thinking “Should I ask AI about this?” and start instinctively reaching for it at the right moments.

You learn how to phrase better questions. You recognize its limits. You stop feeling weird about it.

That’s when it becomes genuinely useful and that’s when you’re ready to apply it to higher-stakes work. Not because someone mandated AI adoption, but because you’ve already internalized when and how it fits.

This kind of personal experimentation is the foundation for organizational change. You can’t train or mandate people into comfort with new tools. But you can lower the barriers to trying them and let people discover value on their own terms.

Your Next Step

If you tried AI a year ago and gave up because it felt clunky or wrong, you picked the wrong moment in a fast-moving field. The tools have gotten much better.

Try again now but pick something truly low-stakes. T

his week, pick one household question or one easy work task. Ask an LLM. See what happens. Don’t overthink it.

The roast in your freezer might just be your best teacher.

What’s your roast this week? Pick one small, safe experiment. That’s where change actually starts

-Kate