Chapter 4. Challenges to Delivering Value from Custom AI Development and Engineering Countermeasures
Deep learning can have a profound impact on the enterprise, but developing and implementing it is not easy, and organizations will face unique challenges that are unlike those that accompany the adoption of other technologies.
Despite amazing breakthroughs in artificial intelligence (AI) software and hardware, organizations must confront the poor interoperability of open source software components and the need to optimize highly specialized hardware, not to mention the challenge of first accessing and then harnessing both high-value and high-velocity data, working across multiple cloud environments, and doing all of this at scale. Further, deep learning methods are a radical departure from traditional statistical and machine learning techniques. As such, they can challenge even advanced data-driven organizations.
In terms of operationalizing, most organizations struggle in the transition from insight to action because of analytical systems that are incapable of reliably serving millions of decisions at the speed of real-time business. Many will also underestimate or discount the governance and risk management aspects of developing AI solutions, elements that must be considered for a successful strategy.
Figure 4-1 summarizes the barriers to AI adoption discussed in this chapter. We will also address some approaches that we can take to address them.
Figure 4-1. Five pillars of ...
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