A procurement paradigm for digital government

By contracting with emerging technology companies that can create and deploy data-driven solutions, agencies can cut down on the number of government contracts and the associated red tape.

knowledge management (Alexander Supertramp/Shutterstock.com)
 

If there’s one bright spot to come out of the COVID-19 pandemic, it’s that the global health crisis turbocharged the adoption of high-tech tools by government agencies that had long been inching their way to more digital operations but never quite getting there.

This transformation carries enormous potential to improve nearly every aspect of government, from automating time-intensive administrative tasks like processing health care claims to opening up exciting new possibilities for disease treatment and research. And it promises to save lives and American taxpayers billions of dollars over time.

But amid this tectonic shift in the way government operates, procurement officers in Washington and across the country face a daunting task: Where to start? And how to prioritize the solutions that will have an immediate impact rather than being distracted by shiny objects?

To meet this generational challenge that could shape the way our government operates for decades to come, procurement officers should consider revamping their approach to determining what their respective agencies need.

Currently, the government looks for general-purpose tools that could potentially solve a wide range of existing challenges as well as issues that have yet to arise. Then, agencies tap service firms to help implement those tools into the day-to-day work of government. This not only leaves room for tools to be misapplied to problems that they are not built to handle, but for communication breakdowns and latency. Further, it amounts to buying new gizmos before knowing exactly how they can be useful.

In addition to its operational shortcomings, this approach is also expensive. In 2018, the federal government alone spent $400 billion on contracts for goods and services -- on top of inefficiencies, as the government must not only purchase these tools but then hire and coordinate with contractors who know how to use them.

A better approach is to identify specific problems and then go looking for targeted, integrated solutions. In data-science parlance, that means understanding the domain space first. Instead of the current bifurcated approach being taken by numerous government agencies -- i.e., purchasing tools and then hiring a system integrator to implement those tools -- it would benefit both the federal government and taxpayers to find emerging technology companies that are both creating data-driven solutions to problems -- sometimes even before those problems exist -- and are able to supply their own manpower to harness the technology and manifest a positive outcome.

To their credit, some parts of the government are already taking this approach. In support of military and national security infrastructure, the Air Force contracted with AAR Corp., a company that provides total supply-chain management to support transport planes. AAR not only sources the parts and materials needed to keep these crucial airships running and landing smoothly, but also provides the mechanics and experts needed to repair and maintain parts of the planes. Before AAR, the government needed to source parts from Lockheed Martin if it had to repair landing gear on C-130s and then hire a contractor to perform the maintenance work. Approaches like this can improve government across the board.

Other federal agencies -- from the National Institutes of Health and the Centers for Disease Control and Prevention to the Environmental Protection Agency -- have an opportunity to reimagine their capabilities for a post-pandemic world.

By identifying a problem before it arises and then finding an integrated solution that cuts down on the number of government contracts and the associated regulatory red tape, the federal government can deliver better services while saving taxpayer dollars.

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