Analytics to AI: A Leader’s Guide toDeveloping an AI Mindset

In the early stages of my career I made inroads into the world of analytics. Back then AI was a distant dream not the powerhouse it is today. My job involved untangling business mysteries by sifting through data from various sources. From figuring out why our platform had fewer users one day to fine-tuning social media campaigns and website conversion rates I was knee-deep in intriguing problem-solving. As I trekked through the domain of analytics I could not help but wonder what the future held. Should I stick with what I knew or venture into uncharted territory for a more thrilling challenge? Then in 2016 the buzz around Machine Learning and AI caught my attention. Although I was not immediately sold on the idea I found myself drawn to the possibilities as I tackled a few problems using AI. I even share one of these eye-opening experiences in my book. By 2017-18 AI had taken the world by storm becoming the talk of the town. It was crystal clear that AI was here to stay but I found myself at a crossroads unsure of where this new path would lead me. AI appeared to be a natural progression from Analytics so I decided to dip my toes into the world of artificial intelligence. However I quickly found myself drowning in a sea of technical jargon when I tried to dive into books on Machine Learning and Deep Learning.It was like trying to read a foreign language without a translator! I attempted to enroll in a few courses but they were clearly not designed for beginners like me. It was as if they were written by aliens for other aliens. So I threw in the towel back in 2017 and focused on using AI applications rather than creating them. But AI just would not leave me alone. It kept popping up in the news and various applications taunting me to give it another shot. So in 2019-20 I decided to give it a second chance. This time I knew what I wanted and what I did not want. Instead of drowning in complex math and core concepts I focused on grasping key ideas first before diving deeper. As I cut through the waters of AI I realized I was a bit of a Python programming dum-dum. I had been cruising along with R for basic stats and programming but Python was a whole new beast. So I took a breather and dedicated some time to mastering this new language which surprisingly helped me understand AI concepts better. This time around the sail was smoother and I finally felt like I was getting the hang of things. Who knew that a little perseverance and a lot of Python could make all the difference in the world of AI? In 2021 I decided to give AI a third shot getting to the roots of algorithms and the mathematical side of things. I was fortunate enough to be selected for Stanford’s Machine Learning course which was no walk in the park. This math-heavy course covered everything from algorithms to derivatives to backpropagation giving me a whole new appreciation for the field. Despite my background in programming transitioning to AI was no cakewalk. I had to navigate the same hurdles as any non-tech person would. Let’s face it just because you are a software engineer doesn’t mean you can seamlessly become an AI engineer. The only thing they have in common is the programming language they use but the way they use it to achieve results is worlds apart. As a seasoned professional in the middle of my career I have come to realize that different strokes work for different folks when it comes to learning AI. Those already familiar with the field have a different experience than newcomers like me. Not everyone wants to dive headfirst into statistics or coding. Non-coders or programming newbies need a different approach to grasp AI. The level of depth one delves into should be a personal choice but there’s no need to follow the beaten path.