Your AI Projects Don’t Succeed for These 5 Reasons
AI projects are notoriously difficult to implement and bring into production. However, there are certain key things that most artificial intelligence projects do that cause them to succeed. The more awareness organizations have when it comes to creating successful AI projects, the better they will be at ensuring they are one of those rare successes. There are certain AI projects for beginners many organizations can adopt, but these are usually skipped, leading to many problems when it comes to implementation.
This simple lifecycle is difficult to complete, but if you know what you’re doing, you can come out ahead and be successful in AI adoption.
Your AI Project Doesn’t Solve a Business Need
The main problem with AI projects is that they are seen as magic that you cannot understand. However, this could not be further from the truth. The reality is that starting an AI project needs to be something you do with your stakeholders. You want to ensure that you have a purpose for starting this project in the first place. Why are you here? This is what should drive whether you will follow through with AI adoption or not.
This is usually the first step where organizations fail. The first thing they do is to start a project without thinking about what business objectives they have. When that happens, it creates a massive problem for the organization. They usually end up floundering around with no goal or purpose inside.
You Don’t Have the People to Execute Your Vision
If you can have a vision that goes along with what your AI project is supposed to represent, the next pitfall is not having the people to execute that vision. AI specialists are some of the rarest people on earth. It is quite difficult to find one, and they command some of the highest salaries in the industry. Because of that, you might not have the people needed to bring your AI project to fruition. It is a massive problem for most companies, and it could keep you from making your AI projects successful.
Your Data Sucks
The foundation of every AI model is good data. Your AI projects won’t be successful if your models are built on horrible data. Data quality and quantity can be a massive problem for AI adoption. Because of that, it becomes a huge issue when it comes to developing solutions your organization will like. Unfortunately, it is almost impossible to overcome bad data when developing models. You also don’t want to build your models using the wrong type of data.
Employees Don’t Understand AI
Many people fear AI projects because they think it is about replacing them. However, the reality is that AI projects exist to make their jobs easier. You want to improve AI literacy in your organization as much as possible. If you don’t, you might end up with a workforce that is hostile to your endeavors. By promoting AI technology as something to augment what they are doing, you will end up winning them to your way of thinking.
You Suck At Maintaining Your AI Model In Production
Putting an AI model into production is only the first step in a long cyclical process. In fact, you arguably begin the hardest step once you have successfully done that. In this step, you have to monitor and ensure your model has proper governance. For example, your business needs might change, or you might find that your model starts drifting. These issues come up relatively often, and you have to be able to solve all of them.