Government efficiency used to mean trimming budgets and cutting staff. Now, it increasingly means something else: getting smarter about how public institutions use data, automation, and, yes, even Doge-era meme thinking to question old assumptions about how work should be done.
In U.S. federal circles, DOGE is shorthand for digital optimization and government efficiency, a loose banner over efforts to modernize systems, simplify rules, and kill wasteful projects. Global Government Forum reports that DOGE-inspired teams have pushed agencies to cancel underperforming contracts, consolidate duplicative programs, and redirect funds toward digital services that measurably improve outcomes for citizens. Instead of chasing flashy pilots, they focus on hard metrics like processing times, error rates, and cost per transaction.
The question is whether we are DOGE‑ing it wrong by treating efficiency as a one‑time tech upgrade instead of a continuous discipline. Microsoft’s recent case study on the Ontario Public Service shows what getting it right looks like: service times cut in half, tens of thousands of hours saved each year on tasks like license plate renewals, and customer satisfaction above 80 percent, all by redesigning services end to end and tying AI to clear goals, not hype.
Carahsoft’s work with VisualVault highlights another lesson: records automation and AI‑driven document extraction only deliver real efficiency when agencies clean up data, remove duplicates, and redesign workflows around proactive insight instead of reactive paperwork. Government Executive’s coverage of “big and small AI” in agencies warns that defaulting to massive general‑purpose models for every task is wasteful; small, domain‑specific tools can be faster, cheaper, and more accurate for routine classification, eligibility checks, and citizen FAQs.
Transportation Department officials recently told FedScoop that modernization and AI are crucial in fighting fraud, but they stressed that the real gains come from stepping back, defining the problem clearly, and then picking the narrowest tool that works. At the policy level, new Office of Management and Budget guidance summarized by Ogletree Deakins is forcing agencies to pair aggressive AI adoption with risk assessments, human oversight, and transparency, turning efficiency into something that must also be explainable and trustworthy.
In other words, listeners, we are DOGE‑ing it wrong any time we chase tools instead of outcomes, pilots instead of platforms, and cuts instead of capability. Done right, efficiency is not about doing more with less; it is about doing less of the wrong things, and letting technology amplify what only public servants can do: deliver fair, fast, and dignified service.
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