5 ways to fast-track your next AI implementation

Preparing for and implementing AI projects can be a multi-year journey. According to the latest figures, only 28% of respondents reported getting past the AI planning stage in the first year. This is due to several factors including the relative maturity of the technology (at least in the ever-expanding set of industry use cases), the level of complexity involved such as extensive integration requirements, limited enterprise experience and lack of internal skill sets, concerns with AI bias as well as governance, risk and compliance concerns, extensive change management requirements and more. With so much emphasis on demonstrating quick wins, whether as part of corporate innovation programs or digital transformation initiatives, over-long AI projects can potentially impact the reputations of much larger initiatives than just their own. As CIOs move from "projects to products" in their approach to product management, these lengthy AI projects can delay innovative new internal or external product releases as well. One of the first decisions to make is whether to build or buy.