Everyone has AI on their roadmap these days. Bottom-tier innovation verticals like HR, multi-level marketing, entertainment, fashion, medical, supply chain are even starting to talk about it. Despite the hype and excitement, the majority of companies that commit to tackling AI projects will fail. Even that $1M+ hire won't save you from failure. Here are some of the main reasons why your AI project failed or will fail. These have seemed to resonate well with others so I figured I would share.
Most people who attempt to get hired as a data scientist fail. This article is to help clarify what is happening and increase the chances of landing your first data science job. Everyone looks the same, literally. How do you expect a hiring manager to put you in their top 5-10 list for the final round if you look just like everyone else in the applicant pool? Once you admit that your fancy resume is actually boring and you look just like everyone else, you can start making some meaningful changes.
Hard work has always been an important competency for aspiring students to become data scientists. Despite having studied there was still a noticeable gap between what they had studied and what industry wanted. You can be a great data scientist, but you can't if you stay in a silo. So going to Meet-ups, reading Kaggle forums, reading recommended data science books, following technical thought leaders, can help ensure you are at least heading in the right direction. Finding an industry mentor can also be very helpful. Lastly, fall in love with it. Passion for the topic and intrinsic motivation will help you stand out from the school of fish in the market.
The most common questions asked is how do I become a data scientist? It is a fair question for those that are deciding to pivot that direction because they want to eliminate the learning waste that traditional educations are full of. The number one reason I think you will never be a data scientist is your lack of passion. Honestly, passion fixes everything. The biggest flaw in most people attempting to break into data science is their lack of breadth. People who are passionate have great breadth.
If you have a single data scientist and you already think they should be delivering more to your bottom line than they are news flash: "They suck" and you hired the wrong caliber individual for the job. You may still be able to keep them if they are good, but you need to bring in a type-E rockstar to cement your data arm and redirect the unstoppable ship. A type-E individual doesn't settle anywhere. If you ask an individual where do you see yourself in 5 years and they respond "Not working here" you have found a real winner.
We can use AI to do things we don't want to. We can automate human processes, and allow us to focus on difficult problems instead of mundane tasks. How can you design AI that we can control? If you want to find a bug in your code when it comes to goal optimization run it through a genetic algorithm. The AI we design will always be focused on maximizing a goal.