One of the most frequently asked questions in the data world is why most AI projects fail and only a few succeed. Failed AI projects can cost companies millions of dollars. 70% of executives whose companies made investments in AI projects said they had seen minimal or no impact from them. Why do people fail, and how can it be prevented? In this article, we will look at some issues that a lot of companies have with AI projects, and how they can be avoided.
5 Reasons Why AI Projects Fail
There are many reasons why AI projects fail, definitely not only 5 reasons, but for today’s article, we will focus on the 5 top reasons why AI projects fail. Hopefully, these reasons will help set you up for success when tackling your next AI project.
Having a Technology-First Approach
A lot of companies that decide they want to start an AI project, will immediately focus on the “cool” technology aspect of artificial intelligence. The “capabilities” the AI will be able to perform should not be your first thought. The solution to this problem is easy. The first thing your team can do when they decide they want to start an AI project is to focus on the data. Focus on what problems your AI project will fix in the world.
Your Team Has Insufficient Data Training
When it comes to AI solutions, it is important to keep in mind that they require meaningful, training on data. Your team needs to understand the data, in order to achieve the desired outcome. If your team lacks data training, you are set up to fail. The problem is that a lot of companies planning to start an AI project underestimate the importance of quality data in enabling AI implementation success. The solution to the problem is to hire the right people from the start. Make sure you have top data scientists as part of your team!
Lack of Collaboration Between Your Team Members
If you have a team working on your AI project, there needs to be great collaboration inside the team. A lot of the time you will have a team of data scientists working remotely on AI projects, especially in these times we are living in. Remote working can become a big disaster in this case. Building a successful AI project requires collaboration between all members of your team, this includes all your data scientists, data engineers, IT professionals, and designers. The stay clear of a problem where you get a team that does not work together, you can do the following:
- Plan ahead, and have a set team plan with a leader or project manager.
- Standardize your AI development process.
- Connect with your team and share each other’s learnings and experiences. Choose what works best.
- Choose the right team from the beginning.
Your AI Goals Has Nothing to Do With What Your Business Does
The basic concept of any AI project is to address certain problems in life and come up with solutions that will make life easier. But if your AI Project is designed to address problems that do not align with your company or business services, you might as well stop the project as it will be a failure from the start. Hire people like IT leaders that will address and identify meaningful problems in your business industry that are backed by data, and research. If your start an AI Project, make sure to have the research to back it up and the right objectives.
Lack of Governance and Monitoring After Launched
When building and deploying an AI project, you need to keep in mind that it has to be maintained well after deployment. Failing to keep your AI maintained will lead to project failure.
Remember, artificial intelligence makes decisions based on the data that are fed to them. If your AI arrives at a particular decision and it is unclear how that decision was made, you are sitting with problems, especially when you have an AI that suggests things like medical treatments.
To make sure you do not run into this problem, you need to make sure you consistently monitor your AI projects. Update the AI regularly because data is not static but ever-changing.
Is Your Business Ready for AI?
There are many advantages to AI, that includes, effective business growth, less staff, and better turnaround times. Before you decide to take on any big AI project, you need to make sure that your business is ready for it. If you take the problems we mentioned above into consideration and plan well in advance, you might just form part of the AI project success rate. Still unsure about finding a well-trained team for your next AI project? Take a look at some of our RandomTrees solutions, you might just find your answer!