One of the many problems with AI being so common today is that most businesses want to find a way to integrate AI into their projects. It seems like it doesn’t matter if AI will be the right fit or solution to their business need. While AI and machine learning are both cool and useful, businesses must ensure that they require these technologies. Without this need, you might find that your business is wasting time and energy implementing AI solutions that will not need any KPI goals you have set for your business. Ultimately, the first thing you have to decide when moving into artificial intelligence is figuring out the KPIs you have set and whether you can make them. Once you have decided, you are then in a much better place to move forward with your project. Before everything, you have to ask the important question. Is implementing AI going to solve the problem I currently have? If the answer is yes, you will need to figure out the next step of ensuring that the model will meet your business KPI goals.
Making Sure the Model Will Meet the Business KPI
One of the many problems that businesses have with artificial intelligence is not having the data scientists in the room when making big business decisions. They will eventually go to the data scientist and asked them what they can do to help the business. Instead, business leaders should be stating the problem and then figuring out whether there is an AI solution to solve that problem. When you do things this way, you will know instantly whether artificial intelligence and machine learning will be helpful and useful for your business. It also helps data scientists understand the business in more detail, and they will be able to build the right models to meet your business needs. When you do things the other way around, the data scientist doesn’t have a seat at the table, and they end up trying to meet the model KPI instead of the business KPI. The specific tasks they train the model for should be the means to solve some business need.
Getting Value from Your AI Project
You need to know the end goal for the business to get the most value out of your AI project. Your data scientists need to understand why this AI project exists. You need to clearly explain to them why they need to build this AI model and integrate it into the application. You need to figure out what exactly the AI project will do and what problems will be solved. It involves bringing data scientists to the table when doing business strategy meetings. The solutions you come up with also have to be viable and doable by your data science team. It doesn’t matter if you have solutions if the team cannot build what you need.
The reason why doing things this way instead of designing the AI algorithms and trying to fit them into the program is that you are not achieving a specific goal with that methodology. This methodology ensures that you never waste a moment of your time on a project that is doomed to fail.