Author Story

I didn't set out to become an advocate for Human First AI. Like most entrepreneurs, I started with a problem I wanted to solve.

The problem was me.

I'm neurodivergent. My brain works differently than most people's, which has been both my greatest strength and my biggest challenge throughout my career. I see patterns others miss, I think in systems and connections, and I can hyperfocus on complex problems for hours. But I also struggle with traditional learning methods, have difficulty with certain types of communication, and often feel like I'm speaking a different language than everyone else in the room.

For years, I tried to fit into conventional corporate structures. I worked at consulting firms, tech companies, and startups, always feeling like I was performing a version of myself rather than being myself. I was successful by most measures — I advanced in my career, led teams, delivered results — but I was exhausted by the constant effort to mask who I really was.

The breaking point came during a leadership role at a Fortune 500 company. I was managing a team of brilliant engineers, but our communication was breaking down. They needed mentorship and guidance, but the traditional approaches — formal training programs, documentation, scheduled check-ins — weren't working. The knowledge transfer was too slow, too rigid, and too removed from the actual work.

I found myself spending hours each day answering the same questions, explaining the same concepts, and walking people through the same problem-solving processes. Not because my team wasn't capable, but because the gap between expert knowledge and practical application was too wide to bridge with conventional methods.

That's when I had my first glimpse of what would become Human First AI.

I started experimenting with voice-based AI tools that could capture my thought process as I worked through problems. Instead of trying to document everything after the fact, I could think out loud while I was actually solving real issues. The AI could learn from my reasoning, my questions, my mistakes, and my breakthroughs in real time.

The results were remarkable. My team members could access not just my answers, but my thinking process. They could understand not just what to do, but why and how to think about similar problems in the future. Most importantly, they could do this without interrupting their flow or waiting for my availability.

For the first time in my career, my neurodivergent thinking style became a superpower rather than a liability. The same pattern recognition and systems thinking that made me feel different in traditional settings made me uniquely effective at training AI systems to think like an expert.

But the real breakthrough came when I realized this wasn't just about me or my team. This was about fundamentally changing how knowledge flows through organizations.

Traditional training and knowledge management systems are built on the assumption that expertise can be extracted, codified, and transferred like data. But that's not how expertise actually works. Real expertise is contextual, adaptive, and deeply personal. It's not just what you know — it's how you think.

Human First AI doesn't try to replace that thinking. It amplifies it. It makes the expert's reasoning process available to others without diminishing the expert's value. In fact, it makes the expert more valuable by extending their impact beyond the limits of their time and physical presence.

This realization led me to found Practical AI and develop Pocket Mentor, our flagship product. But more importantly, it led me to understand that we're at a critical juncture in how we think about AI and human potential.

The dominant narrative around AI focuses on replacement: which jobs will be automated, which skills will become obsolete, which humans will be rendered unnecessary. This narrative is not just wrong — it's dangerous. It leads to AI implementations that diminish human capability rather than amplifying it.

My experience taught me that the most powerful applications of AI are those that make humans better at being human. They don't replace judgment with algorithms; they enhance judgment with better information. They don't eliminate the need for expertise; they democratize access to it. They don't reduce the value of human connection; they make it more meaningful by removing the barriers that prevent it.

This is why I wrote this book. Not because I have all the answers, but because I've seen what's possible when we choose augmentation over automation, amplification over elimination.

The future of work isn't about humans versus machines. It's about humans with machines — and the organizations that understand this distinction will be the ones that thrive.

My neurodivergent brain taught me to see patterns others miss. Now I see a pattern in how we're implementing AI that could determine the future of human potential in the workplace.

We can choose to build AI that makes us more human, not less.

The choice is ours. Let's make it together.