Each day, more and more businesses continue to join the early adopters of AI, investing in data teams, infrastructure, and tools in hopes of improving or increasing the velocity and output of the team. However, many organizations claim they have not seen a return on investment (ROI) and are left unable to prove that any of it is really worth the resources — both time and money.
In fact, according to a report from MIT and BCG, only 10% of companies report significant financial benefits from their AI investments. Without hard numbers pointing to success, it is difficult for executives to continue to invest thousands or millions of dollars on the latest data efforts.
However, an ESI Thought Lab survey on the topic has a different take, “It is revealing that 79% of companies that report negative or no ROI, and 56% of those showing ROI of just above 0% to 5%, do not have systems in place to measure returns.” This begs the question, is it a matter of organizations actually not driving ROI from their data science, machine learning, and AI projects or simply not knowing how to properly measure it?
In this ebook, data science experts from Dataiku explore key challenges and nuances on the road to calculating ROI for data efforts as well as provide a helpful value framework and other best practices for the future. DOWNLOAD now to take a deep dive into:
- 5 key considerations for effectively tracking ROI for data science, machine learning, and AI initiatives
- The key challenges surrounding quantifying the value of AI and how to overcome them
- Step-by-step guidance on how to build a model for measuring efficiency across the data science lifecycle (rather than pure cost/return analysis)
Plus! At the end of the document, Dataiku has provided an interactive spreadsheet to help you calculate and quantify the value AI could potentially bring to your organization.