The Defense Logistics Agency’s deputy chief financial officer explores the power of artificial intelligence to accelerate the agency’s transition to clean financial auditing.
“Although we are still in the early stages of using AI in finance, we believe there is great potential. We have already seen success with technologies such as robots and robotic process automation. We plan to build on that with AI,” said Shawn Lennon.
AI can transform DLA advancements by loading policies, data, process documents and more into a large, searchable language model for easy and quick retrieval. The goal is to capture data from DLA’s business systems and use AI to detect errors, generate insights and provide solutions to improve data quality and financial reporting, Lennon added.
With AI, employees can approve an AI-proposed solution and execute the remediation through robotic process automation.
“We are also considering using AI to reconcile DLA inventory in the warehouse management system with our financial records,” Lennon continued. “Today, many people are manually reviewing erroneous transactions, trying to understand what went wrong, why, and the potential solution. It’s too much for us to track.”
The Department of Defense must obtain an unqualified audit opinion by FY 2028, consistent with the National Defense Authorization Act of 2024. A draft NDAA 2025 directs the Secretary of Defense to make report to Congress on the department’s use of AI to achieve this.
Lennon volunteered to lead the DOD Financial Management Task Force to determine how DLA, the military services and other defense agencies can integrate AI for auditing purposes while protecting DOD data. The group is studying use cases from industry and other federal agencies and learning from the experiences of DOD entities such as the Defense Finance and Accounting Service, which has already solidified some governance over the use of AI for efforts such as fraud detection.
The Government Accountability Office’s chief scientist recently briefed Lennon and participants in the Partnership for Public Service’s federal AI Leadership Program on how they are using a large language model to search a century of audit reports to learn from it.
“The DOD could do something similar. For example, if you want to know the history of the Joint Strike Fighter and every audit that has been carried out, ask it to present summaries and recommendations, as well as the implementation status, the AI can put all of that to your reach,” Lennon said.
Although DOD agencies will likely use AI for auditing in different ways, Lennon said the group helps everyone benefit from each other’s progress and setbacks. One agency’s ideas may also appeal to another, Navy Cdr. Johnathan Henson, Managing Director of DLA Finance.
“If there are solutions that could be helpful to different agencies, they could potentially pool their resources to fund something together.” And anything any one of us learns from a data tagging or security perspective, we can all learn from that,” he said.
DLA leaders also hope to learn from the experience of other federal agencies such as the State Department, which has spent the last year deploying AI across its internal network and will soon demonstrate its practices for DLA .
Using AI to store and sort data is risky, however, especially when it’s shared outside the agency, because the information can fall into the wrong hands, Lennon said. GAO mitigated this risk by disconnecting its environment from the Internet.
“It does, however, reduce some of your abilities; so these are the trade-offs that we have to decide on, even if the data we gather is unclassified,” he said.
Since most large AI language models are web-based and not specifically intended for DOD use, DLA Finance has only limited experience so far. When given temporary access to Microsoft Azure OpenAI, the team used it to load large documents such as the 1,500-page DOD Financial Management Regulation and perform data synthesis on specific topics.
The team is now working with DLA Information Operations to responsibly adopt AI tools and fund use cases.
“We need to figure out how we’re going to prioritize what we do with AI and establish processes for approving and testing AI – everything that comes with any new technology,” Lennon said.
Other considerations include whether DLA should create positions for employees specializing in AI or train existing employees in skills such as rapid engineering.
Although job security often comes to mind when employees talk about new technology, Henson said AI will allow employees to spend less time on manual tasks such as managing job sheets. calculation and more time on meaningful tasks such as data analysis and decision-making.
Lennon added that employees should start thinking about the benefits of AI and seek training to better understand the possibilities.
“I want to inspire staff to raise their expectations for how we can use AI to help us be more efficient and effective in our mission, not only in finance and audit, but in the across the agency, as we support the new strategic plan. and objectives,” he said.