Nearly Half of CIOs Are Planning to Deploy AI

Nearly Half of CIOs Are Planning to Deploy AI
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Meaningful artificial intelligence (AI) deployments are just beginning to take place, according to Gartner. Their 2018 CIO Agenda Survey shows that four percent of CIOs have implemented AI, while a further 46 percent have developed plans to do so.

As with most emerging or unfamiliar technologies, early adopters are facing many obstacles to the progress of AI in their organizations. Gartner analysts have identified the following four lessons that have emerged from these early AI projects.

1. Aim Low at First 
Expect AI projects to produce, at best, lessons that will help with subsequent, larger experiments, pilots and implementations. In some organizations, a financial target will be a requirement to start the project.

2. Focus on Augmenting People, Not Replacing Them
Big technological advances are often historically associated with a reduction in staff head count. While reducing labor costs is attractive to business executives, it is likely to create resistance from those whose jobs appear to be at risk. Gartner predicts that by 2020, 20 percent of organizations will dedicate workers to monitoring and guiding neural networks.

3. Plan for Knowledge Transfer
Conversations with Gartner clients reveal that most organizations aren't well-prepared for implementing AI. Specifically, they lack internal skills in data science and plan to rely to a high degree on external providers to fill the gap. Fifty-three percent of organizations in the CIO survey rated their own ability to mine and exploit data as "limited", the lowest level. Gartner predicts that through 2022, 85 percent of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them.

4. Choose Transparent AI Solutions
AI projects will often involve software or systems from external service providers. It’s important that some insight into how decisions are reached is built into any service agreement. Although it may not always be possible to explain all the details of an advanced analytical model, such as a deep neural network, it’s important to at least offer some kind of visualization of the potential choices. In fact, in situations where decisions are subject to regulation and auditing, it may be a legal requirement to provide this kind of transparency.