Originally published on CIO.com .
Organizational leaders have widely recognized the promise of AI as the cornerstone of digital transformation and it’s no surprise that many are now attempting to accelerate its deployment and adoption.
However, most of these same organizations are still struggling to increase adoption and interest in analytics. Even with the emergence of business intelligence (BI) platforms, promises of better decision-making can go unfulfilled without widespread adoption.
For an organization to have any chance at success with AI, it must first have a solid BI strategy rooted in the core pillars of people, process, and platform. In recent years, many organizations have moved beyond basic descriptive analytics and into more diagnostic analysis, but few have created a true self-service environment capable of embracing the benefits and risks of AI.
Without this foundation in place, efforts to fast track an AI deployment could ultimately lead to negative outcomes like incorrect decisions resulting in lost revenue opportunities, penalties, or even long-term damage to an organization’s reputation. To avoid common pitfalls, organizations seeking to bolster investments in AI and accelerate its adoption should first assess the current state and foundational stability of their BI program.