Artificial intelligence (AI) is continuing to gather steam in the federal government with a top-down focus from the highest levels. A prime example took place during the recent ACT-IAC Imagine Nation conference in Philadelphia, PA, during a keynote presentation by Federal Chief Information Officer Suzette Kent. When discussing the administration’s technology priorities for fiscal year 2020, Kent noted an emphasis on the government’s data. Specifically, she called out financial management and geospatial data as “priority data sets,” and said the administration will encourage sharing of those data sets to “support artificial intelligence and other automated technologies.”
With this kind of support from the administration, agencies are sure to dedicate resources to increase their use of AI across the board. The question is, how do they do this in a strategic way? AI won’t show its true value by simply integrating it into a low-level function. Agencies need to identify real operational pain-points where AI can help increase efficiencies and/or effectiveness of internal programs. This starts with identifying specific use cases where AI can add significant value.
A use case approach
By determining a strong use case for AI, agencies can overcome one of the most common barriers: Good data governance. A use case provides a means of spreading good data governance across an agency at a manageable and impactful scale. It requires an agency to look at what data they need to support that use case, how they manage that data and make it accessible to the people or systems that need it.
Effective data governance will lead to data maturity, resulting in better data policies and practices around individual programs and then streamlining and scaling them for broader adoption across the organization. In July, I published a commentary article in GCN that focused on this very topic (it’s a great read, if I do say so myself). In that article, I called out five stages that agencies go through in the data maturity process, including:
- Zero: An organization does not consistently collect or store key data for analysis.
- One: Individual teams or offices begin collecting data, but with no shared definitions or processes.
- Two: The organization begins creating data hubs or data lakes with well-defined management and governance.
- Three: Power users have access to expanded data for exploration, while business users can run queries as needed.
- Four: The organization can rapidly deploy data platforms designed to solve specific problems.
- Five: Data-driven insights are ingrained in processes and accessible across the organization to inform decision-making.
A use case approach will help drive more value out of AI adoption, helping agencies generate more mature data practices, and subsequently leading to more advanced use cases that deliver even more value.
Learning more about use cases
Government agencies looking to learn more about appropriate use cases for AI deployment should attend the upcoming NVIDIA GPU Technology Conference (GTC), taking place November 4-6 in Washington, DC. This event offers an avenue for agencies to connect with experts to get hands-on technical training and insights into the latest AI and data science approaches, applications and breakthroughs, including 100+ talks, panels, posters and demos covering deep learning, machine learning, cybersecurity, autonomous machines, HPC, intelligent video analytics, healthcare, 5G, VR and more.
As a sponsor of the NVIDIA GTC event, WWT will be in attendance to discuss our approach, capabilities and success in deploying AI capabilities for the public sector in booth 402. If you’re interested in attending the event, please register here. And if you do attend, we look forward to seeing you at our session, Taking AI to the Next Level with an Impactful R&D Program, on Wednesday, November 06, 2019 | 05:00 PM - 05:25 PM | Oceanic Conference Room.