The first time I truly understood what generative AI could do, I had the same feeling I’d experienced years earlier when I first picked up a camera. That moment when you realize you’re holding something that’s both deeply technical and profoundly creative; a tool that rewards understanding its mechanics while opening entirely new possibilities for expression and problem-solving.
It happened close to a year ago, testing ChatGPT with the kind of skeptical curiosity that comes naturally to me. Within minutes, I understood I was witnessing something that would fundamentally change how we work, think, and create. Not because the technology was perfect – it wasn’t – but because it represented a shift as significant as the transition from film to digital photography, or from typewriters to word processors.
And the more I used this new technology, privately and for work, I recognized that adaptation would be the defining skill of this new era.
The AI Disruption We’re All Navigating
Let’s be honest about what’s happening. The generative AI disruption isn’t coming- it’s already here. Probably every profession, every industry, every individual who works with information, creates content, or solves problems is facing the same fundamental question: how do I make it in this new world?
For me, the answer became clear: I need to adapt, and I need to do it systematically. But adaptation is different from simply learning to use new tools. It’s about understanding what this technology means for human work, for organizational structures, for the very nature of expertise itself. But this adaptation is not just an individual, or even corporate, endeavor. It’s a societal requirement, something we need to do together, if we want to ensure a positive outcome.
That’s why I’m documenting this journey openly. Not because I have all the answers – I emphatically don’t – but because I believe the process of adaptation itself has value, and sharing my experiences can be of help, even a little. In sharing how I’m building knowledge, grappling with challenges, and discovering applications, I hope to demonstrate that transformation is possible while helping others find their own path forward.
The Photography-AI Parallel
Almost a decade ago I fell in love with photography. A coincidental photo opened my eyes to the possibilities of creative expression that this format allows us to do. But you need to understand what you’re doing, in order to get the best result. How to use the camera properly. And I see similarities between this and AI. And of course, using a camera and AI might seem like a shallow comparison to some, but the camera analogy isn’t superficial. Just as photography requires both technical proficiency and creative vision, effective AI use demands understanding both the mechanics and the possibilities.
In photography, you need to grasp the exposure triangle; how aperture, shutter speed, and ISO interact to create not just proper exposure, but artistic effect. A wide aperture doesn’t just let in more light; it creates shallow depth of field that can isolate your subject. A slow shutter speed doesn’t just brighten your image; it can capture motion in ways that transform your composition.
AI follows the same pattern. Understanding prompt techniques isn’t just about getting better outputs – it’s about unlocking creative possibilities. Knowing when to use a short, direct prompt versus a detailed, role-based prompt isn’t just technical knowledge; it’s the foundation for using AI as a true collaborative tool rather than a fancy search engine.
This technical-creative intersection fascinated me then, and it fascinates me now. I’m drawn to tools that reward both analytical understanding and imaginative application. AI, like a camera in skilled hands, becomes an extension of human capability rather than a replacement for human judgment.
My Learning Approach
I’m approaching this systematically through what I’ve decided to call “a knowledge domain triangle”: theoretical understanding, technical competency, and practical execution. Each part informs and strengthens the others.
Theoretical knowledge means understanding not just how AI works, but why it works the way it does. What are the principles behind large language models? How do different architectures influence capabilities? What are the fundamental limitations we’re working within? This isn’t academic exercise; theory guides better practical application.
Technical competency involves mastering the tools and techniques that make AI useful. Prompt engineering, obviously, but also understanding different AI models’ strengths and weaknesses, knowing when to use Claude versus ChatGPT (and the different models they offer) versus specialized tools, developing workflows that integrate AI seamlessly into existing processes.
Practical execution bridges theory and technique with real-world application. This is where subjects like for example change management, organizational psychology, and project management converge. How do you actually implement AI solutions that work? How do you help people adapt to new ways of working? How do you measure success and iterate effectively?
None of these dimensions stands alone. Theory without execution is academic; execution without theory is often inefficient; technical skills without practical application remain abstract.
The Human-Centered Approach
Here’s a crucial factor I’ve come to understand from the existing research: most AI implementations fail not because of technical limitations, but because of other factors, such as human factors. Research suggests that less than 20% of AI implementations succeed, and the primary culprit isn’t the technology – it’s the people side of the equation.
Consider customer support as an example. AI can absolutely handle routine inquiries; password resets, basic troubleshooting, simple procedural questions. These are perfect AI tasks: high volume, clearly defined parameters, predictable solutions.
But this doesn’t mean AI should replace customer support agents. It means it should free them to do what humans do exceptionally well: build relationships, solve complex problems, provide nuanced guidance that requires empathy and contextual understanding.
Instead of telling a customer support team, “AI will handle the easy stuff, so we need fewer of you,” effective adaptation says, “AI will handle the routine work so you can focus on the meaningful interactions that actually matter to our customers.” One approach creates fear and resistance; the other creates opportunity and engagement.
This human-centered perspective extends beyond individual roles to organizational transformation. When companies lead with “AI will replace people,” they generate exactly the resistance that kills implementation efforts. When they lead with “AI will augment human capabilities,” they create conditions for successful adoption.
Why Share This (AI) Journey?
I mentioned this previously, but I want to add some more thoughts to it. I’m documenting this process for several interconnected reasons.
First, frankly, it’s strategic. I want to demonstrate that I can learn, adapt, and navigate significant technological shifts. In a rapidly changing landscape, the ability to acquire new competencies and integrate them effectively becomes more valuable than any specific skill set.
But there’s more to it than professional positioning. I see too many people who are genuinely freaked out by these changes. They don’t know what AI means for their work, their skills, their future relevance. They’re paralyzed by uncertainty or overwhelmed by the pace of change.
I can’t provide a universal template – everyone’s adaptation will look different based on their background, goals, and context. But I can show that adaptation is achievable. That there are systematic ways to approach learning. That you don’t need to become a technical expert to use these tools effectively.
The more people share their learning processes, the easier it becomes for everyone to find their place in this new reality. We’re all figuring this out together, and collaboration beats isolation every time.
Individual and Organizational Applications
My focus operates on two levels: individual empowerment and organizational transformation.
For individuals, I’m exploring practical applications that make AI personally valuable. How can you use AI for deep research without falling into the trap of treating it as an infallible authority? How do you manage privacy concerns while benefiting from AI capabilities? How do you integrate AI into your workflow without losing your critical thinking skills or creative agency?
These aren’t just technical questions – they’re about developing judgment. AI is like that random person on the street who might give you useful directions: helpful for finding information, terrible as an authoritative source. Learning to verify, cross-reference, and think critically about AI outputs is as important as learning to prompt effectively.
On the organizational side, I’m particularly interested in change management and implementation strategy. How do you prepare teams to work with AI rather than against it? How do you identify which processes should be augmented versus automated? How do you maintain human agency and job satisfaction while leveraging AI capabilities?
I’m working with concepts like AI-assisted collaboration tools that enhance human decision-making rather than replacing it. Imagine managers using AI to surface patterns across their individual observations. Not to automate management decisions, but to provide better information for human judgment. These are the kinds of applications that actually work because they respect what humans do well while leveraging what AI does well.
What You Can Expect
This website will document my learning in real-time. I’ll share discoveries, challenges, failed experiments, and evolving insights. Some posts will be technical deep-dives; others will focus on practical applications; still others will explore the broader implications of AI adoption for work and society.
I’m not promising to have all the answers – I don’t. But I am committing to honest exploration. I’ll show my work, acknowledge uncertainties, and adapt my thinking as I learn more. If I get something wrong, I’ll correct it. If I change my mind about something, I’ll explain why.
Most importantly, I’ll maintain focus on the human element. Technology serves people, not the other way around. Any AI application that makes work less meaningful, relationships more superficial, or thinking more passive is moving in the wrong direction.
The Adaptation Imperative
Here’s what I believe: we’re in the early stages of a transformation that will reshape how we think about expertise, creativity, and work itself. The people who thrive won’t necessarily be those who master specific AI tools – those tools will continue evolving rapidly. The winners will be those who develop the meta-skill of adaptation itself.
That means learning how to learn in this new environment. It means building comfort with uncertainty while maintaining high standards for quality and ethics. It means staying curious about possibilities while remaining grounded in human values.
I invite you to follow along as I navigate this territory. Whether you’re an individual trying to figure out how AI fits into your work, an organizational leader planning implementation strategies, or simply someone curious about how we adapt to technological change, I hope this journey offers useful insights.
The future isn’t something that happens to us—it’s something we actively create through the choices we make and the skills we develop. Let’s build it thoughtfully, together.