Next year’s federal budget will allocate approximately $1 billion for the pursuit of AI projects by non-Department of Defense agencies, which means the push is on within the federal government to adopt Artificial Intelligence (AI)-powered solutions across all agencies. The clear benefits of AI – from cost savings to speeding critical healthcare research and streamlining the management of innumerable forms – has agency business and IT leaders eager to deploy this technology.
The reality is, however, that $1 billion is just a drop in the bucket when it comes to successfully delivering on AI projects across all civilian agencies. Applications like facial recognition for threat detection to support the Department of Homeland Security or using AI-powered solutions to speed the reading of medical images at VA hospitals require a significant investment. To ensure success and fully realize the promise of the data they hold, agencies should have a strategy in place to invest not only in AI tools but the infrastructure, people and processes to support it.
From a robust data pipeline to a data management strategy that facilitates real-time access to information, while maintaining optimal security and privacy control in place, agency the right infrastructure will enable them to derive maximum value. In a recent study on AI readiness within the federal government, nearly two-thirds of respondents (61 percent) felt that their agency didn’t have the right resources to deploy AI solutions, and a further 36 percent were concerned about a lack of technology to support an AI initiative. And of course, there were the usual concerns about budget constraints and how to manage costs and impact of this new technology.
With a clear appetite among federal agencies to put AI-powered solutions to work, what’s the simplest and most effective way to get there?
At the heart of a successful AI deployment is a robust infrastructure that can manage data volumes, organize data warehousing, move data from multiple sources from the edge to the core and the cloud, and access sufficient compute power to process the data and generate real-time analytics. Not only should this capability be available on day one of an AI undertaking, but it must also be able to scale as demands increase.
If you think about the costs of building and maintaining a data environment this efficient, effective, and capable from the ground up, it could be a significant barrier to entry for an agency. However federal agencies can mitigate these challenges working with trusted industry partners, to provide the foundational storage, networking, and compute capability, as well as the AI infrastructure and expertise, delivered as-a-service. Moreover, the most complete and valuable partner solutions offer access to geographical processing units (GPU), access to data scientists, and the opportunity to test out new deployments in a DevOps lab environment before it is pushed live.
Thinking of AI as-a-Service (AIaaS) removes the burdens of both upfront and capital expenses and replaces it with what is essentially a subscription plan. It also allows agencies to begin with an investment in a smaller AI program and grow as they become more proficient. The subscription model comes with the flexibility to scale storage and compute up or down, no physical infrastructure to become outdated, and should also come with a team of 24×7 experts to help project manage and architect the infrastructure as data volume and expectations grow.
There’s no doubt that AI has finally exited the hype cycle and is clearly mission-ready. What agencies should understand as they put their plans in place is that they don’t need to have everything in place to get started with AIaaS. In fact, it can be best to start out with a smaller system and grow from there to ensure the best outcomes are achieved. While the billion dollars earmarked in the budget will cover some of the investments in infrastructure, technology, and expertise, thinking of AI as-a-service will help agencies maximize their investment and truly propel success.
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