For the last two years, much of the conversation around AI has focused on jobs.

Which roles will change? Which tasks will be automated? How many people will organizations need in the future?

Those are important questions. Yet in conversations with enterprise customers, a different concern is rising to the top.

The conversation is shifting

We were all told to use AI, deploy AI tools and find ways to move faster. Boards pushed for adoption. Executive teams launched initiatives. Business units raced to identify new use cases before competitors did.

Then the bills started showing up.

If you're following technology news, you've seen growing concerns about organizations losing control of AI spend. Budget overruns, runaway token usage and unexpectedly large invoices have become common discussion points among technology leaders. AI adoption is accelerating across every line of business, and many organizations are discovering they have far less visibility into consumption than they thought.

That's because AI doesn't just innovate. It invoices.

Every AI decision and action has a price tag. AI doesn't just answer questions. It reasons, retries, plans, triggers calls, leverages data and compute just to complete one task. It’s now integrated into the business applications we are using everyday, from Microsoft Copilot and Google Gemini to SAP, Workday, and LinkedIn. Whether we know it or not, the meter is running.

For many organizations, that creates a challenge they weren't prepared for.

A familiar problem in a new form

A decade ago, companies faced similar issues with cloud adoption. Teams spun up services quickly, often without governance, visibility or accountability. Costs grew faster than expected, and organizations ultimately had to introduce new disciplines to understand what was being used, who was using it and whether the value justified the spend.

AI is creating a similar challenge at even greater scale.

The difference is that AI is not confined to one team or department. It exists across every part of the organization. Marketing teams use it. Sales teams use it. Product teams use it. Developers use it.

AI capabilities are increasingly embedded in business applications while organizations are also building custom agents and workflows of their own. In fact, agentic AI is expected to drive a 24-fold increase in token consumption by 2030 according to Goldman Sachs.

As a result, AI spend is showing up everywhere.

Many organizations still don't know exactly which models are being used, which agents are consuming resources, which teams are generating the highest costs or how much token consumption is tied to specific business outcomes, business value and real ROI. Without this visibility, you don’t have a foundation for governance, control, or accountability.

This lack of visibility becomes particularly important when organizations start making workforce decisions based on projected AI savings. Too often, the comparison begins with a salary and an AI license. The challenge is that the license is only one component of the cost.

The real cost includes token consumption, infrastructure, data platforms, cloud resources, failed attempts, retries and the human oversight required to validate outputs and manage exceptions. Those costs are frequently spread across different systems, teams and budgets, which makes them difficult to measure accurately.

A salary is relatively predictable. AI costs are not.

The same task can generate very different costs depending on which model is selected, how much context is provided, how many times the system retries a process and how much computing power is required to complete the work. That variability matters because a role removed from payroll does not automatically translate into savings. The expense may simply move somewhere else.

It may move into cloud consumption. It may move into AI services. It may move into infrastructure costs. It may move into additional work for employees responsible for reviewing outputs and managing exceptions.

Without visibility into those costs, organizations are not comparing AI to a salary. They're comparing a salary to an assumption.

Visibility before optimization

The answer is not to slow innovation or abandon AI initiatives. Instead, enterprise leaders must understand where AI creates value, what it costs to operate at scale and how to make informed decisions about adoption.

That starts with visibility.

Organizations need to see which AI applications, agents and models are being used across the business. They need to know where tokens are being consumed, where costs are accumulating and how usage maps to business units, products and outcomes.

Only then can they begin making informed decisions about optimisation. That may mean selecting different models for different use cases. It may mean identifying unnecessary consumption. It may mean understanding when lower-cost alternatives can achieve the same outcome. In some cases, it may simply mean discovering that a small number of users or workloads are responsible for a disproportionate percentage of spend.

These are manageable problems.

The bigger risk is making financial decisions before understanding the economics.

The rise of AI tokenomics

AI has moved into an inference economy where costs are driven by usage, tokens and real-time execution. Tokens are emerging as the atomic unit of AI spend, value and pricing. Organizations are going to need new ways to govern, measure and optimize that consumption if they want to run AI affordably at scale. This emerging discipline of AI tokenomics is becoming increasingly important as AI adoption expands across the enterprise.

What we're seeing today is the beginning of a new economic model that many companies are not equipped to manage yet. As AI becomes embedded across applications, agents and workflows, understanding consumption becomes just as important as understanding adoption.

What does AI return?

Ultimately, the question organizations should be asking is not simply what AI costs but what it returns. What business value does it create? What outcomes does it improve? What's it worth?

Organizations that can answer those questions with confidence will make better decisions about where AI belongs, where it delivers measurable results and where it doesn't.

Before counting AI-driven savings from job cuts, understand the full cost of the work being done. Because the organizations getting the most value from AI won't be the ones making the fastest assumptions about efficiency. They'll be the ones making decisions based on visibility, evidence, and a clear understanding of what the technology actually costs.

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