AI token optimisation as a workforce strategy: The capability CEOs must build before the AI cost daemon takes over

By Shaakun Khanna, Senior Advisor, AI & Leadership Transformation, SHRM

For the past two years, organizations have been racing to integrate artificial intelligence into everyday work. Companies have invested heavily in AI tools, launched enterprise-wide training programs, encouraged experimentation, and celebrated rising adoption rates as a sign of digital transformation success. The assumption was simple: the more employees used AI, the more productive and future-ready the organisation would become.

But as AI adoption matures, a quieter challenge is beginning to surface inside boardrooms and finance teams. Every interaction with an AI system carries a cost. Every prompt, uploaded document, generated summary, automated workflow, and AI agent action consumes tokens. Individually, these costs seem negligible. Collectively, across thousands of employees and millions of interactions, they are becoming a significant operational expense.

This is pushing organisations into a new phase of AI transformation—one that is less about adoption and more about optimisation. The critical question is no longer whether employees are using AI. It is whether they are using it intelligently enough to create meaningful business value.

The New Economics of Enterprise AI
Every major technology wave has created its own hidden cost problem. The cloud era brought unexpected infrastructure bills. The SaaS revolution created software sprawl and subscription overload. The AI era is now introducing a similar phenomenon through uncontrolled token consumption.

Unlike traditional enterprise software, many AI systems operate on a consumption-based model. The more employees use them, the higher the cost. That changes the economics of digital transformation entirely. A tool that appears affordable during a pilot program can become expensive when scaled across an enterprise workforce.

This is why AI spending is beginning to attract the attention of CFOs and Boards. Leaders are realizing that AI cannot be treated as an unlimited utility. It needs the same discipline and governance applied to financial, human, and intellectual capital.

Why Token Consumption Is a Workforce Issue
Many organisations still treat token optimization as a technical problem for IT teams and AI engineers to solve. That is only part of the story. A large portion of AI waste is driven by human behavior.

Employees often use AI to answer questions they already know the answers to. Teams upload entire documents when only a few paragraphs are relevant. Managers generate lengthy AI-produced reports that no one reads. In some cases, AI becomes a substitute for unclear processes, weak knowledge management, or fragmented decision-making.

The result is a growing gap between AI activity and business value. High usage numbers may create the impression of innovation, but they do not automatically translate into productivity or profitability.

This is why organisations need to think beyond prompt engineering and model efficiency. They need a workforce strategy built around intelligent AI consumption.

The Rise of “Token Debt”
A useful way to understand this challenge is through the idea of Token Debt. Just as companies accumulate technical debt through poor technology decisions, they can accumulate Token Debt when AI is used to compensate for organizational inefficiencies.

Unclear ownership, poor documentation, fragmented workflows, and weak decision-making frameworks all increase unnecessary AI usage. Every avoidable prompt and repetitive interaction adds to this hidden liability.

Token Debt grows quietly. It often remains invisible until organizations begin questioning why AI costs are rising faster than business outcomes. By then, significant resources may already have been wasted.

Building Token-Wise Employees
The first generation of AI capability-building focused on literacy: teaching employees how to use AI tools. The next generation must focus on judgment.

Employees need to understand when AI should be used, when it should not be used, how much context is necessary, and how to achieve outcomes with fewer iterations. The goal is not to reduce AI usage altogether. The goal is to maximize value generated from every interaction.

In practical terms, this means organisations should start rewarding outcomes rather than activity. The most valuable employee of the future may not be the one who uses AI the most. It may be the one who combines strong human judgment with efficient AI utilization.

Governance Must Evolve
Current AI governance discussions are dominated by privacy, security, compliance, and risk management. These remain essential. But organisations now need a second layer of governance focused on value creation and cost discipline.

Leaders need visibility into AI consumption patterns. Business units may eventually receive AI budgets in the same way they receive financial budgets today. The conversation will shift from “How much AI did we use?” to “What value did we create with the AI we consumed?”

That shift matters because it reframes AI from a novelty tool into a strategic business asset that must deliver measurable returns.

Protecting Human Ingenuity
There is another risk that receives far less attention than rising token costs: declining human thinking.

As AI tools become more capable, organisations can unintentionally create workforces that depend on AI for every recommendation, summary, analysis, and idea.

That is not transformation. That is dependency.

The organisations that thrive in the AI era will continue strengthening uniquely human capabilities such as creativity, curiosity, critical thinking, judgment, and problem-solving. AI should amplify human ingenuity, not replace it.

This balance will become one of the defining leadership challenges of the next decade. Companies that rely entirely on AI-generated thinking may gain short-term efficiency but lose long-term innovation capacity.

The Next Competitive Advantage
The first wave of AI transformation was about access. The second wave is about efficiency. The third wave will be about competitive advantage.

Soon, every organisation will have access to powerful AI models and sophisticated agents. Access alone will no longer differentiate businesses. What will separate leaders from followers is their ability to generate superior returns from AI investments.

Organisations have long optimized financial capital, talent capital, and intellectual capital. AI is rapidly becoming another form of enterprise capital that requires disciplined management. Optimizing it demands more than technology. It requires a workforce strategy that aligns people, processes, governance, and culture around one objective: creating maximum value from every AI interaction.

The future will not belong to organisations that consume the most AI. It will belong to those that create the most value from every token they spend.