The Blind Spot
Artificial intelligence adoption often begins with promising pilots. A proof-of-concept runs on a limited dataset, costs appear manageable and leaders greenlight broader deployment. What’s missing from these early approvals is a true accounting of inference costs, the ongoing expense of running models at scale. (“Inference” is the ability of trained AI models to recognize patterns and draw conclusions from information that they haven’t seen before.)
As adoption expands from a dozen pilot users to tens of thousands of production transactions, inference costs balloon. Each API call, each chatbot interaction, each real-time decision consumes compute power and energy. Without planning, agencies discover too late that their “affordable pilot” has transformed into a budgetary sinkhole.
This is not just a financial issue. Inference sprawl also drives unmonitored energy demand. AI models are compute-hungry, and at scale, their carbon footprint becomes material. Without deliberate guardrails, agencies risk undermining federal climate goals, ESG reporting, and even local data center capacity.
Why It Matters
Budget impact. Inference sprawl can exceed pilot budgets by orders of magnitude. A model that costs $10,000 to test might require millions to sustain when scaled across mission workflows.
Sustainability. Energy demand from large-scale inference is growing faster than renewables can offset. AI-driven workloads contribute to grid strain and undermine agencies’ commitments under federal sustainability mandates.
Strategic waste. Agencies that fail to budget and control inference demand often lock themselves into expensive, inefficient models. Without usage policies, staff default to overpowered general-purpose models instead of lighter, task-specific options that deliver similar results at a fraction of the cost.
Executive Moves for the Next 90 Days
Agencies can act quickly to mitigate inference sprawl by embedding financial and environmental discipline into AI operations:
- Track inference costs. Require that every AI service reports cost and performance metrics per 1,000 requests. Dashboards should make visible where compute spend is concentrated, enabling leaders to balance mission value against operational cost.
- Set usage policies. Establish quotas and guardrails to ensure AI resources are used appropriately. For example, lightweight models can handle routine classification tasks, while large general-purpose models are reserved for high-complexity analysis.
- Integrate GreenOps. Extend FinOps beyond financial stewardship to include carbon and energy impact. AI services should be measured not just by cost per transaction, but by kilowatt-hours consumed and emissions intensity per inference.
- Engage consulting partners. External firms can create FinOps + GreenOps dashboards that integrate financial, operational, and sustainability data. These tools allow agencies to track inference demand in real time and make evidence-based decisions on model use, scaling, and retirement.
From Pilots to Production Discipline
AI pilots often escape rigorous scrutiny because of their small scale and experimental nature. Leaders, excited by innovation, sign off on projects without modeling lifecycle costs. But scaling without FinOps discipline is like building highways without budgeting for maintenance.
Inference demand is not static, it grows with adoption. Agencies that treat inference as a variable cost, subject to discipline and optimization, will prevent waste and align innovation with sustainability mandates. Agencies that ignore it risk being forced into abrupt service cuts when budgets collapse.
The Role of Standards and Oversight
The NIST AI Risk Management Framework (2023) highlights the importance of mapping risks across technical, organizational, and societal dimensions, including sustainability. Likewise, the OMB memoranda on AI adoption emphasize lifecycle accountability in federal AI deployments. Yet most implementations still underplay inference costs, treating them as secondary rather than core risks.
International bodies are also sounding alarms. The International Energy Agency (2023) has forecasted that data center electricity demand, much of it AI-driven, could grow by double digits annually through 2025, creating risks to climate commitments and energy resilience.
By embedding inference budgeting into AI governance, agencies can preempt both financial audits and climate scrutiny.
The Consulting Imperative
Consulting firms are well-positioned to help agencies close this gap. Emerging services might include:
- Inference cost modeling, projecting cloud bills across adoption scenarios.
- FinOps + GreenOps dashboards, combining cost-per-inference data with carbon-per-inference tracking.
- Model efficiency audits, identifying where lighter models can replace overpowered alternatives.
- Sustainability integration, aligning AI scaling plans with agency ESG commitments.
This is a space where consulting firms can differentiate. In a crowded AI market focused on innovation, firms that help agencies operationalize responsible scaling will stand apart as trusted advisors.
Conclusion
AI resilience is not only technical and cultural, it is financial and environmental. Agencies that budget for inference sprawl, enforce usage policies, and monitor carbon footprints will sustain innovation without undermining trust or fiscal responsibility.
The pilots may be cheap. But at production scale, inference demand is relentless. Leaders who bring FinOps and GreenOps discipline into AI governance today will ensure that tomorrow’s AI is not only powerful, but sustainable.
Dr. Rhonda Farrell is a transformation advisor with decades of experience driving impactful change and strategic growth for DoD, IC, Joint, and commercial agencies and organizations. She has a robust background in digital transformation, organizational development, and process improvement, offering a unique perspective that combines technical expertise with a deep understanding of business dynamics. As a strategy and innovation leader, she aligns with CIO, CTO, CDO, CISO, and Chief of Staff initiatives to identify strategic gaps, realign missions, and re-engineer organizations. Based in Baltimore and a proud US Marine Corps veteran, she brings a disciplined, resilient, and mission-focused approach to her work, enabling organizations to pivot and innovate successfully.
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