How to drive your own AI adoption?
AI Adoption (Part 2)
In Part 1, we looked at how organizations can drive adoption in a way that’s more than just installing a tool. It’s about culture, leadership, communication, and creating safe spaces for people to experiment.
This second part is about us as individuals.
Because adoption doesn’t start with a tool, it starts with a mindset.
A Story From My Kitchen Table
Our dining table used to double as my mom’s office for accounting during my childhood. One day, a computer landed there.
I wanted nothing to do with it. I was seven, and playing outside felt far more interesting than sitting in front of a screen. The computer seemed too complicated and quite pointless.
But eventually, my curiosity won. I tried a few games, then discovered I could write, and later, I could access the internet. Suddenly, the tool became more than a box on the table. It became a way to learn, play, and feed my love for research.
That’s exactly where we are with AI right now. At first, it feels too complex, too fast, too much. But the moment you stop resisting and start tinkering, it changes.
The tool only becomes meaningful when you use it.
The Cost of “Waiting This Out”
Resistance doesn’t help. What helps is building a deep, critical level of understanding.
The tool is only as good as the person using it. If you tried ChatGPT six months ago, dismissed it, and walked away, you are operating on dangerously outdated information. The version you used is practically no longer available.
We must accept that these tools are changing at a pace we have never experienced, and it is not slowing down.
This is hard. Change fatigue is real. We are overwhelmed.
We can’t control the speed of the technology. But we can control our own mindset.
Mindset Before Models
Before you worry about prompts, automations, or which platform is “best”, start with the mindset that makes adoption possible.
1. Choose Growth Over Comfort = Growth Mindset
New tools feel uncomfortable. That’s normal. A growth mindset means getting used to that discomfort and choosing to learn anyway.
It’s a very uncomfortable feeling, and it’s no wonder we try to avoid it as much as possible. We don’t like not knowing, so we need to adopt a mindset of being ready to learn, especially now with all of the new tools.
It almost feels like learning is the work.
How to practice it:
Reframe failure as data: Treat mistakes as feedback. Ask: What is this teaching me? Each stumble becomes data for iteration, rather than evidence of inadequacy.
Practice the Stretch: Seek challenges just beyond your comfort zone, hard enough to spark growth, not so hard they paralyze you. The sweet spot is where effort feels like exploration.
Language of Yet: Add “yet” to your self-talk: I don’t understand this…yet. It rewires the brain toward possibility instead of permanence.
Effort Over Outcome: Praise process, persistence, and strategies rather than fixed results. Focusing on the work you put in cultivates resilience.
Stay Curious*: Actively seek novelty, ask questions, and try small experiments. Curiosity is the way to make a growth mindset sustainable and joyful.
2. Question Everything (Especially AI) = Critical Thinking
Critical thinking is the skill that keeps us sharp. AI can give you an answer instantly, but is it accurate?
We need to focus on developing a sense of questioning, not accepting any answer at face value. Try to poke around and ask, “Is this actually true? What are the resources? Do I believe in this? Is it accurate?”
This will become even more critical with the growing number of deepfakes and AI-generated images and videos.
How to practice it:
Bias spotting: Train yourself to recognize your own cognitive biases, including confirmation bias, anchoring, and recency. Name them in the moment so that you create space between impulse and judgment.
Triangulation: Never trust a single feed or tool. Always cross-check information through at least two different lenses, sources, disciplines, or lived experiences.
Devil’s advocate. Once you believe something, argue the opposite with full force. Stress trusts your own ideas so you build humility and resilience, keeping you from falling into echo chambers or AI’s default smoothness.
Fallacy & Model Awareness: Learn the classics—logical fallacies (straw man, false dichotomy, slippery slope) and mental models (Occam’s Razor, Bayesian updating). Use them like lenses. Spotting flawed reasoning or applying the right model sharpens your intellectual edge.
Intellectual empathy: Step inside another person’s worldview—even one you disagree with. Understanding the internal logic doesn’t mean accepting it; it means strengthening your ability to discern weak arguments from simply unfamiliar ones.
3. Stay in the Pilot Seat = Sense of Agency
The tool is only as good as the person using it. Agency means you’re in control, not the algorithm.
Building a sense of agency means training yourself to act with awareness, choice, and influence, rather than simply drifting along with what’s happening.
How to practice it:
Metacognition: First, you need to notice that you are actually making choices. You need to build a habit of stepping outside of your own thinking and ask: “Is this decision mine or does it just come from the environment or the algorithm?” Think about: “What are the decisions that you are making each day? Why are you making these? What actually shapes your thinking?”
Asking better questions. AI is only as powerful as the questions you’re asking it. Our agency grows when we are refining the questions we’re asking. Start with simple requests and then expand by adding layers of depth.
Deliberate friction: Sometimes, stopping, even just for 10 seconds, before a decision, gives you a chance to think it through. Is this really what I am doing? Is this what I want to do? Why am I doing something?
Reclaim authorship: Find activities, choices, or even just tasks that you want to do yourself without giving them over to machines. Keep some work for yourself. Don’t outsource everything. Authorship matters.
4. Train for Speed, Not Certainty = Adaptability
Adaptability is about staying steady while the ground shifts beneath you. Change isn’t slowing down, so practice adapting quickly.
How to practice it:
Run small experiments: Use AI to plan dinner, pick your next movie, and then draft an email. Small tests build comfort. These small experiments train your nervous system to perceive novelty as stimulating rather than threatening.
Learning Agility: Pick up new skills quickly, even ones outside your domain. For instance, learn a bit of coding, dabble in design, or practice public speaking. The skill doesn’t need to be mastered; what matters is training your brain to adapt to new learning curves without dread.
Emotional Regulation: Change stirs up anxiety. Develop grounding habits, such as breathwork, journaling, and mindfulness, to help your nervous system stay balanced. The more you can remain calm in uncertainty, the easier it is to pivot with clarity rather than panic.
Feedback Loops: Seek feedback often and act on it. Adaptability thrives when you shorten the cycle between action, feedback, and adjustment. Treat it like an iterative process: try → learn → adjust.
5. Build AI Fluency = Go Beyond Prompting
AI Fluency is about more than “knowing how to prompt.”
It’s the deeper ability to understand, question, and use AI tools responsibly.
How to practice it:
Peek Under the Hood: Learn the basics of how AI models work: training data, tokens, reasoning, bias, probability. You don’t need to be a data scientist, but knowing why AI outputs what it does helps you spot limits and illusions.
Source Skepticism: Always ask: Where did this information come from? Practice verifying outputs against trusted sources, especially when using AI for factual information or decision-making. AI fluency is half trust, half verification.
Prompt Crafting and Iteration: Treat prompting like a dialogue, not a one-shot command. Experiment with context, tone, constraints, and “stretch” questions. Notice how different inputs shape outputs, and refine until you get what you need.
Bias and Ethics Awareness: AI systems inherit bias from their training data. Develop the habit of asking: Who benefits from this output? Who might be harmed? Ethical fluency is as important as technical fluency when adopting these tools.
Electricity paradigm: We don’t wake up and say, “I have to find some good ways to use electricity today.” Instead of asking “What can AI do?”, ask “What do I need to achieve? And find the best tool for that.
Tool Comparison and Experimentation: Don’t marry to one platform. Try multiple AI tools (chatbots, image generators, summarizers) and notice their differences. Understanding strengths and blind spots across systems enables you to be a discerning user, rather than a passive consumer.
6. Don’t Outpace Your Values = Ethical Reasoning
Ethical reasoning is the compass for AI adoption: without it, the tools race ahead while our values lag behind.
How to practice it:
Know Your North Star: Define your own guiding values around fairness, inclusion, transparency, and accountability. When faced with an AI-driven decision, check: Does this align with my principles? Anchoring choices in pre-declared values builds consistency.
Circle of Impact: Always ask: Who benefits, who is excluded, who might be harmed? Train yourself to expand the circle of consideration beyond the immediate user.
Collective Debate: Don’t reason alone. Join discussions, forums, or circles where ethical questions are explored collectively. Multiple perspectives reveal hidden assumptions and prevent ethical blind spots.
7. Leverage Social and Experiential Learning = Learning Works Better Together
Experiential Learning: Engage in trial and error and self-directed exploration. Direct experience is crucial for understanding the capabilities and limitations of AI. Tinker, build, try it out, iterate, make it better.
Social Learning: Learn by interacting with, observing, and imitating others. This includes peer-to-peer learning, discussions with colleagues, and shared problem-solving. Find communities of practice where learning happens in a circle, with other people.
Share Your Discoveries: When you learn what AI is good or bad at through direct experience, share these insights with your network. Your experiences can catalyze collective learning within your team or organization.
Where to Start (Low-Stakes Experiments)
Pick a playful problem. Use AI to plan your weekend trip, design a new recipe, or create a workout plan.
Return to basics. Read the tool’s own guide (yep, read the manual).
Fix the least-loved task in your job. Reports, summaries, scheduling: hand it over to AI and see what happens.
Run a pilot. Try one task with and without AI. Compare the results.
Guardrails You Set for Yourself
Define what you will use AI for, and what you won’t.
I encourage you to stay curious about bias, limitations, usage, and ethics.
You don’t need to know everything about AI.
You just need to keep choosing learning over resistance.
Adoption isn’t about knowing everything. It’s about staying in learning mode.
👉 What’s one low-stakes experiment you’re willing to try with AI this week?
Share it with me, I’d love to hear how it goes.