Since the President’s Executive Order 13859 passed in February 2019, the federal government has been charged with promoting, developing, and leveraging AI across all agencies and offices. Specifically, the Executive Order promises that the U.S. is “actively leveraging AI to help the Federal government work smarter in its own processes and services.”
As offices move to prioritize existing funds toward AI implementation, cost savings and increased productivity have emerged as target concerns. According to the Federal News Network, “many public-sector routines are marked by convolution, rigid conformity and excessive administrative burden that can result in contractual delays or inaction.” It’s therefore crucial that we find ways to transform the procurement process with the power of AI.
Notably, a new report from the Partnership for Public Service points to a pilot program in the Air Force as a test case for making contracting quicker and more efficient. According to estimates, the strategic and widespread use of AI "could save the government up to 1.2 billion work hours and $41.1 billion annually.”
Additionally, Federal Computer Week foresees that, if successful, the pilot “would offer agencies a chance to repurpose the hours that would have been spent on the lengthy acquisition process.” Moreover, the pilot stands to offer an AI approach that “simplify and speed up what is now a mystifying government necessity."
AI can expedite several stages of the procurement process, including setting up the RFP, developing scope, and evaluating bids and proposals—arguably the most time-consuming component of the process. Using sophisticated machine learning that carries deep domain expertise, AI can allow an agency to configure and prioritize proposal criteria, then sort and rank bids. These capacities can virtually eliminate the need to manually review hundreds of pages of proposal text and ultimately expedite decision making.
And while AI certainly stands to improve processes of scoping, ranking, and evaluating vendors, one of the most exciting efficiency improvements can be seen in contract management. Monitoring contracts can often be costly and time-consuming. The manual work involved in comparing the contract to actual spend alone requires hours that most budgets can no longer sustain.
Not only can powerful AI tools eliminate time spent on manual reviews, they can also offer valuable insight analytics into understanding spend data, uncovering hidden savings, tracking payment discounts, or consolidating vendors. Additionally, using AI to review contractual language can assist with negotiation, risk mitigation, and compliance. These kinds of actionable data can result in faster, more cost-effective decisions.
In terms of data, it’s widely known that data transparency and maintenance at the federal level are lacking. Audits reveal discrepancies, inaccuracies, and a lack of commitment to open data. Alarming data erasures and disappearances are growing more common, and federal and civilian groups are advocating for clean and open data. Harnessing and leveraging our massive amounts of federal data would require sophisticated AI tools and security measures.
When it comes to choosing an AI partner with a proven track record, time to market is an essential consideration. Platforms with powerful and accurate machine learning that also have the ability to integrate and get up and running quickly will in turn uncover savings and value more quickly.
With projects increasing alongside budget cuts, procurement AI tools should be poised to deliver cost savings, both by increasing overall efficiencies and discovering hidden savings on current contracts. Not only will this allow funds to be reallocated to other critical projects, but it will move employees into more meaningful work with time saved via automation. The key is to find cost and time savings quickly so that budget and resource reallocation is immediate and seamless.
Deron Hurst is Senior Vice President of Federal Division at ElectrifAi, a global leader in the development of innovative Artificial Intelligence (Ai) and Machine Learning (ML) products.