As HR leaders, we’ve spent the last few years encouraging our teams to “lean in” to Generative AI. We’ve supported employees through trial and error, we’ve run crisis communications when mistakes occur, and we’ve seen the productivity and efficiency gains touted by these tools. But there is a silent, possibly deadly assumption baked into our current strategy: that these tools will always be as accessible and affordable as they are today.
What happens when the “subsidized” era of AI ends? If we fast-forward a few years, we might find ourselves in a landscape where AI – touted as a cost-saving panacea – isn’t just a line item; it’s our largest operational expense.
The Shift from Subscription to Tax
Today, most companies pay a flat per-user fee for AI. It’s predictable. But as the computational power required for these models stays high and energy crunches continue to put pressure on costs, the industry is likely to shift toward consumption-based pricing.
Think of it like an “AI Tax.” Every email summarized, every line of code suggested, and every meeting transcribed will carry a micro-cost. For a company that has “gone all in,” those pennies will quickly add up to millions of dollars.
The Risks of Algorithmic Dependency
If we bake AI into the very DNA of our workflows without a strategy for responsible usage, we risk major organizational headaches:
Skill Atrophy: If a task becomes too expensive to perform via AI, but our humans have forgotten how to do it manually, we face a “competency debt” that is hard to repay.
Budgetary Friction: When every “Ask AI” click costs the department money, will managers start policing curiosity? We don’t want to create an environment where innovation is stifled by a meter.
The Efficiency Trap: We might find ourselves “super-charging” processes that weren’t even necessary in the first place, paying a premium to automate noise.
The Vibe Coding Catch22: Just because you were able to build something with AI doesn’t mean you’re able to maintain or debug an app or product with AI- and if the vibe coder leaves, the company is left with a brittle product that no one actually knows how to fix.
Building for Speed vs. Quality: There is a fundamental difference between building a product with static code (a one-time development cost) and one that calls an LLM for every interaction. In the latter scenario, your product’s margins are at the mercy of the AI provider. We must be careful not to build “rented” features that could become cost-prohibitive when API pricing pivots.
Leading with “Computational Mindfulness”
Responsible AI usage isn’t just about ethics and bias; it’s about sustainability. To prepare for a future where AI has a high price tag, HR needs to champion a few key shifts today:
Intentional Automation: We must ask, “Should we use AI for this?” instead of just “Can we?” High-value human reasoning should be the gold standard, not the fallback.
Critical Thinking as a Core Competency: We need to double down on training our people to be “Editors-in-Chief” of AI output. If we pay for an AI-generated draft, the human value-add must justify the cost.
Audit the Workflow, Not Just the Tool: Periodically review which AI-integrated processes are actually driving ROI and which are just digital “fidget spinning.”
The Bottom Line
The goal of going “all in” on AI shouldn’t be to replace human effort with a cheaper machine. It should be to elevate what our people can do. By treating AI as a finite, premium resource now, we ensure that when the bill finally comes due, our workflows—and our people—are worth every cent.
If you want to ensure you’re optimizing your AI use sustainably, our team of experts can help! Connect with Donna Medeiros, VP of AI and Data Advisory, to talk through how to support your workforce through this shift.
