The General Services Administration just released its 37-page Elimination, Optimization, and Automation (EOA) Playbook. Built on the back of the GSA’s internal push to save 1 million workload hours, it’s designed to help agencies streamline workflows and scale AI/automation tools.
But as with any massive government tech push, people are divided on what happens next. The community seems split into two clear camps:
Camp A: The Optimists (A Force Multiplier)
They see this as a massive win. Government agencies waste millions of hours on redundant, manual tasks. By providing a standardized blueprint, backed by cross-agency successes from NASA and the Department of Education, the GSA is preventing agencies from wasting budget to “start from go” every time. It’s a fast track to a smarter, leaner government.
Camp B: The Skeptics (The Implementation Chasm)
They point out that playbooks are easy to write, but execution in government is notoriously complex. Cultural resistance, legacy IT architectures, and complex compliance frameworks often stall these initiatives. Plus, pushing automation and tools like USAi too fast without deep institutional change risks creating fragmented, poorly managed automated systems.
Where do you stand?
Will this standardized approach successfully accelerate digital transformation across government?
Or will it stall at the agency level due to bureaucratic inertia and legacy infrastructure?
Great breakdown of the two sides. The reality is that both camps are right, and the success of the EOA Playbook hinges on bridging that “Implementation Chasm.”
Standardized blueprints are fantastic for preventing agencies from reinventing the wheel—sharing wins from NASA and the DoE is a huge step forward. However, a playbook can’t refactor legacy IT or fix a culture resistant to change. For this to actually scale, leadership needs to focus less on the technology itself and more on the unglamorous work of change management and compliance integration. Without that, we risk just automating bad, fragmented processes faster.
The risk of “automating bad processes faster” is exactly what keeps Camp B up at night. Standardizing the tech is the easy part; standardizing the human element and legacy infrastructure is where the real friction lies.
If change management is the secret sauce here, do you think the GSA’s playbook actually gives agency leaders the practical leverage they need to shift culture, or is it missing the teeth required to force compliance integration?
Good framework, but let’s look at the timing. With massive workforce downsizings happening across agencies, this playbook feels less like an innovation strategy and more like emergency triage. Can automation scale fast enough to bridge the massive institutional knowledge gap left behind by departing staff? That’s the real chasm.
I understand your point, and I agree that we need to be mindful of both current capacity and past performance. At the same time, I’d suggest we look at this holistically, balancing immediate availability with long-term team utilization and development.
If there’s an opportunity to re-engage or redeploy resources in a structured way (with clear expectations and accountability), it could still add value rather than keeping capacity idle. However, this should be aligned with performance safeguards and proper monitoring.
Well said, but ‘redeployment’ assumes there is still a workforce left to redeploy! When headcount shrinks drastically, the remaining staff end up wearing three different hats just to keep the lights on. They don’t have ‘idle capacity’ to spare for structured development. Automation can absolutely handle the routine tasks, but if we don’t fix the institutional knowledge drain first, we’re just building highly efficient systems that lack the human judgment required for complex federal service delivery.
We’re talking about redeployment in a reality where teams are already overstretched. No slack = no real upskilling.
Automation can handle tasks, but it can’t replace context, judgment, or institutional memory.
If we don’t fix the knowledge drain first, we’re just building faster systems with weaker decision-making behind them.
Efficiency without expertise is a risky trade-off.
