C. Smith, Bard College MBA in Sustainability
As both a graduate student and a full-time working professional, I have watched enterprise AI adoption accelerate over the past two years with a growing unease I struggled to name. This discomfort wasn’t just concern about job displacement, though that conversation is important, nor was it solely anxiety about model accuracy or hallucination in workplace output. Something quieter, more below-the-surface and more systemically pervasive, was driving discomfort: the realization that millions of people were being handed powerful AI tools with no structural guidance on how to use them well, and that this gap was costing organizations, workers, and the environment in ways that almost no one was measuring.
That realization became foundational to my thinking around the locus of control in an AI-First Workplace and its deployment: that prompt literacy, the ability to communicate clearly and efficiently with AI systems, is not just a technical skill of the modern worker.
Prompt literacy is a people operations imperative. The locus of responsibility for building that literacy at scale is not the AI platform provider, the IT department, or the individual employee, that belongs to HR.
The Invisible Environment Variable
Most conversations about AI and sustainability focus on what happens inside the model: energy-intensive model training runs, water-cooled data centers, carbon-heavy infrastructure buildouts. These are all legitimate and pressing concerns. However, there is a variable in the environmental impact equation that receives little or no attention: the structure of the user’s prompt.
AI inference, which is the computational process triggered every time a user submits a query, accounts for 80 to 90 percent of AI’s total energy consumption.1 The IEA projects global data center electricity demand could nearly double to 945 TWh by 2030.2 A single ChatGPT query already consumes up to ten times the energy of a standard Googlesearch.3 The research is clear: when users submit vague, unstructured, or ambiguous prompts, models are forced into costly iterative clarification cycles, generating more tokens, consuming more compute, and drawing more energy per resolution. Structured prompting, by contrast, has been shown to reduce inference overhead by up to 2.39 times.4 This is not a marginal technical finding. At the scale of hundreds of millions of
daily AI interactions across enterprise, the aggregate inefficiency of unstructured prompting is a material environmental variable. In addition, unlike data center siting or model architecture decisions, it is one that HR leaders can directly influence right now.
Why This is a People Operations Problem
Organizational behavior is shaped by training, culture, and the defaults people encounter at the point of work, and AI is no different. The habits workers are forming with AI tools today, how they frame a request, how much context they provide, and how many follow-up iterations they expect to need, will only solidify. Retraining millions of workers who have already internalized inefficient prompting behaviors is exponentially more costly than building good habits at the point of onboarding and integration. The window for this training is open now, and it won’t be open forever.
People Operations sits at exactly the right location in the organizational architecture to act on this. HR functions are the primary institutional throughline for AI deployment across the enterprise. Deloitte’s 2025 analysis of AI in talent acquisition confirms that HR is the first mover in AI adoption, and organizations are moving faster than their governance frameworks can accommodate.5 This is not a crisis; it is an opportunity. If CHROs and People Operations leaders choose to embed structured prompting standards into AI onboarding, workforce policy, and ongoing learning and development programs, they become the delivery mechanism for one of the most scalable environmental interventions available to organizations today.
The World Economic Forum has articulated this imperative clearly: AI agents should be onboarded with the same rigor applied to human employees, with well-defined roles, safeguards, and structured oversight.6 I would extend this logic: if we onboard AI agents with human-employee rigor, we must also train human employees with AI-partnership rigor. That means teaching people how to interact with AI systems clearly, efficiently, and intentionally.
What Human-in-the-Loop Means, Operationally
The phrase “human-in-the-loop” has become a compliance concept, something invoked to satisfy auditors and ethics committees and then, imposed prescriptively across the enterprise. I believe this term should be embraced as an operational standard. In an AI-first workplace, keeping humans meaningfully in the loop requires that humans know what they are doing when they engage AI. An employee who cannot construct a well-formed prompt is not in the loop, they are simply and passively along for the ride.
Structured prompting frameworks such as CIDI (Context, Instructions, Detail, Input) are learnable, teachable, and when embedded into organizational workflows, become adopted as second nature quickly.7 The research on choice architecture confirms this: smart defaults shape behavior at scale without removing individual agency.8 If organizations build structured prompting into their AI governance policies, and HR functions deliver that training at the point of workforce integration, the environmental and operational benefits compound.
This is what a genuinely human-in-the-loop, AI-first program looks like. It doesn’t look like AI tools handed to employees with a disclaimer, but a workforce equipped to direct AI clearly, evaluate its outputs critically, and iterate efficiently. The prompt is the point of contact between human judgment and machine capability. Treating it carelessly is not just an individual inefficiency, it is an organizational and environmental choice.
The Equity Dimension
There is an equity argument embedded in this thinking that should be named explicitly. Access to prompt literacy should not be a function of which organizations have invested in training budgets. Organizations that have built internal prompt engineering capability produce more efficient, lower-impact AI outputs. Those that have not generated excessive compute overhead through disjointed, iterative exchanges. This creates a two-tier system in which resources determine environmental impact. That asymmetry is neither just nor sustainable at scale. When People Operations leaders embed structured prompting into standard AI onboarding (as a baseline workforce training, not premium professional development) they close that gap. Prompt literacy becomes infrastructure, not advantage.
A Call to the People Operations Community
I am writing this for practitioners and leaders in People Operations because they have both the organizational access and the professional responsibility to act. The sustainability conversation in this field has rightly centered on equitable hiring, inclusive culture, and workforce well-being. Now it is important to expand that frame. How organizations deploy AI is a sustainability question, and how employees engage with AI is also a sustainability question. Ensuring that deployment is thoughtful and governed well is a People Operations question.
The prompt interface is not a neutral input field. It is a design choice with environmental, organizational, and human consequences. HR has the tools, the relationships, and the institutional position to make structured, sustainable AI interaction the organizational default. That work begins in onboarding, continues in policy, and compounds quietly and materially every time a well-trained employee sits down to direct an AI system with clarity and intention.
- James O’Donnell and Casey Crownhart, “AI Energy Usage and Climate Footprint,” MIT Technology
Review, May 20, 2025, https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-
footprint-big-tech/. ↩︎ - International Energy Agency, Energy and AI: Energy Demand from AI (IEA, 2024),
https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai. ↩︎ - Lekha Naik, “The Carbon Cost of Your AI Prompts,” CNN, June 22, 2025,
https://www.cnn.com/2025/06/22/climate/ai-prompt-carbon-emissions-environment-wellness. ↩︎ - Drishti Shah, “Optimizing Token Efficiency in Prompts,” Portkey.ai, March 21, 2025, https://portkey.ai/blog/optimize-token-efficiency-in-prompts/. ↩︎
- Deloitte, “AI in Talent Acquisition,” Deloitte Insights, 2025, https://www.deloitte.com/us/en/services/consulting/blogs/human-capital/ai-in-talent-acquisition.html. ↩︎
- World Economic Forum, “AI Agents Onboarding and Governance,” World Economic Forum, 2025, https://www.weforum.org/stories/2025/12/ai-agents-onboarding-governance/. ↩︎
- AI Academy, “CIDI Prompting Technique: A Beginner’s Guide to Mastering ChatGPT Prompts,” AI Academy Blog, https://www.ai-academy.com/blog/cidi-prompting-technique-a-beginners-guide-to-mastering-chatgpt-prompts. ↩︎
- Michael Schrage and David Kiron, “The Great Power Shift: How Intelligent Choice Architectures Rewrite Decision Rights,” MIT Sloan Management Review, January 28, 2025, https://sloanreview.mit.edu/article/the-great-power-shift-how-intelligent-choice-architectures-rewrite-decision-rights/. ↩︎