New research from CDW can help you build on your success and take the next step.
When it comes to innovative and emerging technology, everyone’s talking about artificial intelligence. There is palpable excitement around the efficiency and cost savings that many government IT leaders predict as a result of AI adoption. But there’s also a sense of uncertainty as many organizations struggle to identify best practices for full implementation.
In a recent survey conducted by CDW 68% of respondents indicated they already use AI in some form. There are many use cases for AI, including content creation with generative AI, chatbots and other contact center applications, and predictive analytics. But cloud may be critical for state and local governments seeking to adopt AI.
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According to CDW’s research, among respondents who use AI across various workloads, 48% run AI in a public cloud, 53% run it in a private cloud and 53% use a hybrid environment.
A hybrid cloud model is the most popular because it provides flexibility. You don't have to build out a lot of graphics processing unit farms on-premises; you can do the initial portion in the cloud and then bring proprietary data back onsite.
There are two stages for government agencies incorporating AI into their operations. There’s the upfront AI training, data analysis, data cleaning and similar activities. A lot of these are done in the cloud because it provides scalable access to GPUs and computing capacity. Once these are done, a model is built. Whether it’s a data analysis model or a generative AI model, these are a lot easier to use than officials often realize.
DIVE DEEPER: State CIOs discussed their top AI opportunities at NASCIO 2024.
Certain AI use cases rose to the top for CDW survey respondents who said they’re using AI in some capacity. Those include customer contact centers, data analysis and research and automating repetitive tasks.
All of these AI use cases may draw upon cloud’s capabilities to thread together multiple channels from various inputs. And cloud power makes workloads efficient by distributing them to available resources. All three use cases also benefit from the scalability and accessibility of cloud computing.
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