The return on your investment in AI will be different from your peers.
It’s important to evaluate and judge your potential return from the beginning of a test. Capturing all of the potential costs and savings of any AI project should start with the first meeting.
Some of the key metrics involved in calculating ROI for any AI project include:
– Labor cost
– Cost of compute
– Data cleansing
– Data labeling
– Hardware and associated infrastructure
– Training costs, for the model and your employees
This is just a sample of a few of the key measures involved. Yours should be different and specific to your project.
As you develop more tests across the company, the ROI inputs may be different for each one.
It is important to measure, evaluate and update your costs on a timely and consistent basis. It can’t be a one and done effort.
Making sure that everyone involved in the project as well as senior management has a view into the results is important as well. Results good or bad. Humans try to minimize bad news, fight the urge.
The only way to understand the benefits and cost gaps is to track and measure.
A consistent and accurate accounting of everything will serve to support the efforts.