Artificial Intelligence (AI) and Machine Learning (ML), a subset of AI, are buzzwords that are used extremely loosely. Most companies,worldwide, have at least one initiative having to do with AI/ML, or at least they think they do. In my last article, I discussed 5 steps that will make your AI project more effective. In this article, I am going to discuss a few of the main reasons that AI/ML projects fail.
1. Failing to clearly define your business problem
It is highly important to have a clear business problem prior to starting an ML/AI project. A common saying is “begin with the end in mind.” In other words, make sure to understand what your desired outcomes are. Any machine learning problem you would like to solve should be largely driven by the impact it will have on your organization. Clearly defining your business problem helps evaluate if AI/ML is the best solution to solve your business need(s) and will help measure success and ROI throughout the life cycle of the project.
2. Failure to understand the process
As I mentioned above, AI & ML are buzzwords and organizations often times do not understand the process, time investment, and cost of the projects that they are exploring. For an AI project to be successful, it is highly important to have a defined, organized data set.Without this, the chances of your project succeeding are extremely low. Many organizations are unaware as to how much time/work needs to be put in to organizing data before “starting” the actual AI/ML project. The typical rule of thumb is most data scientists spend only 20 percent of their time on actual data analysis and 80 percent of their time finding,cleaning, and reorganizing the data that will be used for the model. AI/ML consultants can be extremely valuable in helping you develop a well-rounded plan in order to provide a clear picture of what the entire scope of the project will look like.
3. There is no measurement for success
There are generally several iterations within machine learning projects. Without clearly identifying what your success measures are, there is no way to identify whether your project is successful, what changes need to be made, if the model is effectively solving your business needs, and finally, if it’s worth additional investment or if you should explore other options.
4. Buy in from leadership
Commitment from leadership and those that are involved with the project is critical. ML/AI projects have many moving parts that require both time and financial investments. Without commitment from all members that are involved, the chances of success are slim to none.
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