New working conditions have led agencies to adjust AI adoption methods and strengthen data analytics capabilities by leveraging commercially available solutions from industry and implementing other practices during the pandemic.
“As part of our mission set, our organization, the Technology Transformation Service (TTS), is really about leveraging private sector innovation, and government expertise in best practices for a holistic sustainable transformation,” Anil Chaudhry, Director of Federal AI Implementations at the General Services Administration’s AI Center of Excellence (CoE), said during ATARC’s 2021 AI Data Analytics Virtual Summit. “It’s about leveraging commercially available solutions and expertise from industry to deliver outcomes to programs.”
Chaudhry noted the AI CoE was focusing recently on fraud detection and combing through big data sets to help pinpoint fraudulent financial activity. Additionally, Chaudhry touched on the importance of AI in building resilient supply chains.
“We were recently involved with an organization … to help go through lots of big data sets, to see, whether we could pinpoint a fraud, especially as it related to the loans – in the loan guarantees,” Chaudhry said. “There’s suicide prevention, there’s natural disasters with the Department of Defense with firefighting and floods, and also with pandemic supply chain modeling so that what you’re not doing is sending truckloads of water somewhere where they need gloves. So how can you build resilience, resiliency in the supply chains so that you are pushing things that are needed the most at a particular phase and time dependent?”
Chakib Chraibi, Chief Data Scientist for the Office of Data Services at the National Technical Information Service within the Department of Commerce, added that Federal agencies need to think big.
“My message today is we – as Federal agencies or government agencies – we need to be bold and ambitious,” said Chraibi. “There is no time to waste; data never sleeps. The sooner we get the capabilities to actually processing the data, structure the data, enrich that data, the better.”