A Challenge That Sparked Something Bigger

In January, leadership at my workplace challenged the team to learn more about artificial intelligence. The request was simple enough, but I felt an immediate pull to go deeper. Rather than treat it as a side assignment, I made AI my central project. Over the first five months of the year, I invested most of my work hours in this effort and carried it into many evenings and weekends.

Building a Strong Starting Point

Before this deep dive, I had used ChatGPT off and on for about a year, mostly to kickstart documents and help troubleshoot code.

I started with LinkedIn Learning, working through courses on artificial intelligence, neural networks, and deep learning. These gave me the vocabulary and basic mental models I needed. But it became clear that simply watching videos would not give me the fluency I wanted.

Then I turned to ChatGPT as my personal tutor. Each week, I blocked time in my calendar for “AI Training” and “AI Review.” At first I used text conversations to think out loud and work through new ideas. Once voice became available, I began using that too. I would define terms, answer quiz questions, and take on practical exercises. Often we would break apart a scenario and talk through it: what problem is being solved, which algorithm fits best, and what model could handle it. That interactive style made complex concepts easier to grasp and let me test and strengthen my understanding in real time.

Moving Beyond Basics

As the weeks passed, I moved deeper into technical ground. I studied supervised, unsupervised, and reinforcement learning and explored the algorithms behind them. I examined how neural networks and transformers work and what makes generative AI possible. I also looked at real world applications across industries and thought about how they could translate into my own work. These were not just academic exercises; each conversation helped me think strategically about AI and how to use it responsibly.

By this point, ChatGPT had become part of my daily routine. I was using it every day to discuss all kinds of topics and explore ideas beyond AI while also testing concepts and clarifying questions.

Measuring Progress

By April, I felt ready to measure my progress in a tangible way. I set my sights on the AWS Certified AI Practitioner exam and spent two intense weeks preparing. To make the material stick, I used creative, game based examples and practical use cases. This kept study focused and fun while also pushing me to understand the tools beyond theory. Passing the exam in May felt like an important milestone. It showed that my study had turned into real working fluency.

Thinking Ahead and Exploring Possibilities

Looking back, those months were an unusually focused season of growth. AI was not just something I skimmed; it became something I lived with until the concepts felt natural and useful. My natural strengths in thinking ahead, strategy, and love of ideas gave me energy for the work and shaped how I learned. The potential of AI sparked my imagination about what could be built and how we might work differently.

Since then, AI has become part of my everyday thought process. It is no longer something we occasionally talk about; it is something I consider daily. I know it will dramatically change not just my own work but our department, our university, and the world.

That deep dive changed how I approach any complex subject. I now know how powerful it is to combine structured resources with interactive, conversational exploration. More than anything, it reinforced how forward thinking and curiosity about big ideas guide the way I learn.

Looking Forward

This post is the first in what I hope will become an ongoing conversation about AI. I plan to reflect on how AI has evolved since early 2023, how it is changing the way we work today, and where it may be heading next. My goal is to share what I am learning and thinking as this technology continues to shape our work, our lives, and the world.