As you’re getting ready to pitch your first AI initiative, or if you’re looking at investing in a new initiative, it’s critical to consider the return on investment (ROI) of your efforts. Without ROI, pitching a new AI initiative can be a hard sell. With demonstrable ROI, a new AI initiative becomes a sensical investment of both time and money.
This article will explore how you can ensure the ROI of your AI initiative, making it easier to get the rest of your organization on board.
Develop a use case with ROI
AI efforts have more ROI than BI efforts do. This is because with BI you are reporting about the past and AI is about making predictions for the future. To put it simply, foresight is more valuable than hindsight. Additionally, it’s important to note that the data science process will naturally create unexpected insights as you move data from descriptive analytics to more advanced analytics, such as machine learning and AI.
The use case you chose for your initiative needs to have ROI associated with it. This means it can’t just be exploratory, there needs to be a reason. For example, if you’re building evidence to make good business decisions, consider what the ROI is of making better decisions.
When choosing your use case, the bottom line to consider is: Is it saving you money or helping you make money? If the answer is no, you should look for a different use case.
Calculating the ROI of your AI initiative
Calculating the ROI for data initiatives definitely seems to be easier said than done. Businesses invest in data teams, infrastructures, and tools for different reasons and utilize them at various stages of maturity, complicating the calculation greatly. Keep costs down by selecting tools and architecture that you can grow into.
The first step toward calculating ROI is to define “success” and consider the ways that the initiative (direct or indirectly) contributes. Since value can come in many different forms, part of the work of measuring ROI is considering all possible ways data can contribute to success.
According to Dataiku, these are the top five ways to measure data science ROI:
Effect on Costs. Is your data initiative resulting in cost savings?
Competitive Edge. Are your projects bringing something to the organization that helps differentiate from competitors?
Speed-to-Value. Can the ability of the data team to deliver more projects, faster be quantified?
Team Efficiency. What are you gaining (or saving in cost by making people more efficient at their jobs?
Effect on Revenue. Will this project affect the number of customers, time spent per customer, or other revenue impacting factors?
It’s easier to get buy-in on your data initiative when you can demonstrate the return to the organization as a result. Ensure you’re selecting a use-case with demonstrable ROI for greatest success.
Now that you ROI is attached to some potential use cases, how do you choose which one to select? That answer is easy: The easiest one. It’s important to get that quick win. With Data Science, as with any new competency, success breeds success. Each initiative makes the next one easier and helps you identify new business insights to explore.
Looking to get buy-in on your next AI initiative? Grab this checklist for help getting started.