The Journey to Becoming AI-Enabled

AI is Not Replacing My Expertise—It’s Extending It

I still use the same critical thinking, problem-solving, and architectural knowledge. The difference? I can now apply it faster, across more complex problems.

The Ability to Translate Prior Knowledge into New Tech Stacks is a Key Advantage

If you understand patterns and frameworks, learning AI isn’t about starting over—it’s about mapping what you already know to a new domain.

The Tech Landscape Always Evolves

  • Early innovations are complex.
  • Over time, abstraction layers emerge (think cloud computing, APIs, low-code tools).
  • AI will follow the same trajectory—we’re still in the “complex and experimental” phase.

The Oddly Polarized AI Adoption Spectrum

I’ve noticed two extremes:

  • “Black Box Users”: People who use AI tools but don’t understand how they work—either amazed or skeptical.
  • “AI Research Scientists”: Deep technical experts who talk in terms of model architectures and latent space.

🚦 Where is the Middle Ground?

Most tech adoption follows a curve—there’s always an in-between phase. But in AI, it feels weirdly binary. You either “get it” or you’re seen as outdated.

🚀 What Does it Really Mean to Be “AI-Enabled”?

It’s not just about using ChatGPT or writing prompts—it’s about integrating AI into workflows, decision-making, and systems thinking. That’s where real transformation happens.

🚀 The Real Problem? People Don’t Know Where to Start

I see so many smart, talented people staring at the AI landscape and feeling completely lost because:

  • It looks too vast—LLMs, embeddings, vector databases, fine-tuning, agents, RAG… where do you even begin?
  • It feels chaotic—every week, new models, new breakthroughs, and new jargon flood the space.
  • It lacks clear entry points—unlike cloud computing (which had certifications, bootcamps, and clear job pathways), AI feels more like the Wild West.

For those who prefer a more structured learning approach, I highly recommend checking out Salesforce’s AI Associate Certification. Kudos to Salesforce for creating this! It’s excellent—I love the bite-sized chunks, the constant feedback, and the gamification elements. It makes learning AI really fun and engaging.

📌 How Do You Actually Jump In?

The biggest barrier to AI isn’t the tech itself—it’s figuring out where to start when everything feels overwhelming.

Here’s what I’ve learned:

  • Start ridiculously small—don’t try to “learn AI” as a whole. Instead, just talk to ChatGPT, Gemini, or Claude and ask it to do something simple for you.
    Example:
    • “Write me a Python program that prints Hello World.”
    • Then ask: “I have a MacOS, how would I run this?”
    • Watch the world crack open with possibility.
  • Interact, experiment, and see what happens.
    • Ask it to design something for you.
    • Ask it how to do something you’ve always wanted to learn.
    • Marvel at how it never says no—it just tries.
    • Laugh when it drifts (it gets bored easily with repetitive tasks).
    • Shake your head when it hallucinates while debugging (it’s wildly overconfident).

This first step isn’t about AI frameworks, embeddings, or fine-tuning—it’s about experiencing AI as a tool that will teach you things in minutes that once took years to learn.

🚀 Once you’ve had that moment of realization, the rest follows naturally.
Along the way, you’ll start asking about prompt engineering, embeddings, RAG, agents, and all the deeper concepts—but that’s not where the journey starts.

💡 AI-Enablement is About Confidence, Not Expertise

You don’t need to be an AI scientist.
You do need to be comfortable navigating AI, asking the right questions, and applying it to your field.

📌 And one more thing:
💡 Learn Python. It’s foundational for AI. Every open-source AI library connects via Python, and you can’t really be AI-enabled without at least a basic understanding of it.

🌟 What This All Means

  • AI isn’t magic—it’s another wave of technological change, like the cloud, mobile, or the web.
  • The hardest part is getting started—once you take the first step, it stops being overwhelming.
  • The middle ground needs to be built—not everyone can (or should) be a research scientist, but we also shouldn’t leave everyone else behind.

This is why AI-enablement matters—it’s not just about keeping up, it’s about making sure smart people don’t get left behind just because they didn’t have a clear roadmap.