Assessing and tracking AI maturity is pivotal for any organization interested in accelerating their journey toward Enterprise AI and extracting more value out of their investments.
This guidebook introduces the five-step AI maturity model, helps organizations evaluate their placement, and identifies steps for acceleration.
How to move past “low-hanging fruit” projects onto less obvious use cases that may be potentially more costly to implement and deploy
A multitude of ways that a collaborative data science tool can help reduce costs associated with data projects
Challenges that persist even after data science tools are implemented (and how data leaders can help their teams move toward pervasive AI)
How concepts and strategies like reuse, capitalization, and MLOps play a pivotal role in helping organizations accelerate their AI maturity