The Next AI Challenge Isn't Capability, It's Consumption

AI Is Beginning to Look More Like Electricity

Think about electricity.

You do not typically pay simply for access to an outlet.

You pay based on how much power you consume.

For years, business software has largely operated differently.

Organizations purchase licenses, assign seats, and pay predictable subscription fees regardless of whether employees use the software occasionally or throughout the day.

AI is beginning to change that model.

As organizations move from generative AI toward agentic AI, the conversation is shifting from what the technology can do to what it costs every time it does something.

That distinction matters.

The next major AI challenge may not be capability.

It may be consumption.

‍

From Software Licenses to AI Consumption

Traditional enterprise software has generally been relatively straightforward to budget.

A company purchases a certain number of licenses.

Each license has a defined cost.

The organization can estimate its monthly or annual spending based on the number of users.

AI introduces a different economic model.

While some AI platforms still offer subscription pricing, many enterprise AI services also use consumption-based pricing.

Costs may depend on:

• The number of tokens processed

• The model selected

• The volume of requests

• The amount of information analyzed

• The tools or services used

• The complexity and frequency of workflows

As AI becomes more deeply integrated into everyday business processes, understanding these costs becomes increasingly important.

Organizations can no longer evaluate AI investments based only on licenses purchased or employees enabled.

They also need visibility into how much AI is being consumed and what value that consumption produces.

‍

Understanding Tokens and Why They Matter

Much of AI consumption is measured in tokens.

A token is a unit of information processed by an AI model. For text, tokens often represent words or parts of words, although AI systems may also process images, audio, and other types of information.

When someone submits a request to an AI model, the system processes input tokens and generates output tokens.

Depending on the provider and pricing structure, organizations may be charged differently for each.

A simple request may consume relatively few tokens.

A more complex request involving lengthy documents, multiple instructions, extensive analysis, or large outputs may consume significantly more.

Individually, these differences may appear small.

But consumption becomes much more important when AI is used repeatedly across thousands of employees, applications, and automated workflows.

The cost of a single interaction may not be significant.

The cost of millions of interactions can be.

‍

Agentic AI Changes the Economics of Automation

Generative AI typically involves a relatively straightforward interaction.

An employee asks a question.

The model processes the request.

The employee receives a response.

Agentic AI can be considerably more complex.

An AI agent may be given an objective and then execute multiple steps to complete it.

For example, an agent might:

1. Retrieve information from several systems.

2. Analyze the information using an AI model.

3. Determine what additional information is needed.

4. Call external tools or applications.

5. Generate recommendations.

6. Check its own results.

7. Revise its approach.

8. Prepare a final output or request human approval.

To the employee, this may appear to be one task.

Behind the scenes, it may involve many model interactions, tool calls, and processing steps.

Each of those activities can contribute to the total cost.

This creates a different financial consideration for organizations deploying AI agents.

One business task does not necessarily equal one AI request.

And as workflows become more autonomous, the relationship between user activity and technology consumption becomes less obvious.

‍

The New Metric Is Cost per Completed Workflow

Organizations have traditionally evaluated software investments using metrics such as cost per user, cost per license, and total subscription expense.

With agentic AI, those measures may not tell the full story.

A more useful question becomes:

What does it cost to complete a business workflow using AI?

Consider an AI agent that prepares a customer briefing.

The workflow may involve gathering account information, reviewing previous interactions, analyzing open issues, and generating a summary.

The organization should understand:

• How much does each completed briefing cost?

• How frequently is the workflow executed?

• How much employee time does it save?

• Does the output meet quality expectations?

• Does it improve customer preparation or decision making?

• Is the business value greater than the total operating cost?

The goal is not simply to minimize AI spending.

It is to understand the relationship between consumption and outcomes.

Practical Enterprise Implementation Ideas

1. Establish workflow-level cost tracking. Measure AI consumption by business process rather than relying only on overall platform spending. Include model usage, tool costs, infrastructure, and any necessary human review.

2. Define measurable value for each workflow. Compare total operating costs against time saved, process improvements, error reduction, and other business outcomes.

These measures help organizations identify which AI investments are creating meaningful returns and which may need to be redesigned.

‍

Not Every Task Requires the Most Powerful AI Model

Another important consideration is matching the right level of AI capability to the work being performed.

Some activities require sophisticated reasoning, extensive analysis, or complex decision support.

Others are relatively simple.

A workflow that categorizes routine information may not require the same model capabilities as one analyzing a complex security incident or evaluating competing business strategies.

Using the most capable and expensive model for every activity may create unnecessary cost.

The opposite can also be true.

Using a less capable model for a complex or high-risk task may produce inaccurate results, additional rework, or costly errors.

The objective is to find the appropriate balance between capability, quality, speed, and cost.

Practical Enterprise Implementation Ideas

1. Match models to task complexity. Evaluate whether routine classification, summarization, and information extraction can be completed reliably using less expensive models.

2. Test cost against quality. Compare model performance using representative business tasks, measuring accuracy, latency, error rates, and total workflow cost before selecting a deployment approach.

The best model is not necessarily the most expensive.

It is the one that delivers the required outcome at an acceptable cost and risk level.

‍

AI Consumption Is Also a Security and Governance Issue

AI consumption is not only a financial concern.

It is also becoming an important part of enterprise security and governance.

When employees interact directly with AI tools, organizations can often associate activity with individual users.

Agentic AI introduces additional complexity.

Agents may execute multiple requests, interact with connected systems, and consume resources without requiring a person to initiate every individual action.

Without appropriate controls, an agent could continue making requests, repeatedly retry unsuccessful operations, or consume more resources than expected.

This can create operational, financial, and security risks.

Organizations need visibility into what their AI systems are doing, which resources they are consuming, and whether their activities remain within approved boundaries.

Practical Security Implementation Ideas

1. Establish consumption limits for AI agents.

Define appropriate controls such as:

• Maximum spending per workflow

• Limits on model requests or execution steps

• Timeouts for long-running operations

• Restrictions on repeated retries

• Approval requirements when consumption exceeds established thresholds

These controls can help prevent unexpected costs and limit the impact of malfunctioning or compromised workflows.

2. Monitor AI usage alongside security activity.

Security and IT teams should monitor agent identities, permissions, tool calls, data access, and unusual consumption patterns.

Unexpected increases in usage may indicate inefficient workflows, configuration problems, or potentially unauthorized activity.

Consumption monitoring should complement existing access controls, logging, and security oversight.

‍

Agentic AI Requires Stronger Operational Visibility

As organizations deploy more autonomous workflows, traditional usage dashboards may become insufficient.

Knowing how many employees accessed an AI platform does not necessarily explain how much work agents performed or how many resources they consumed.

Organizations may need greater visibility into:

• Which agents are running

• Which workflows they are executing

• Which models they are calling

• How many tokens they consume

• Which external tools they use

• How often they retry or fail

• What each completed workflow costs

• Whether the workflow produces the intended result

This is where AI observability becomes important.

Observability allows organizations to understand not only whether an AI system is functioning, but also how efficiently, reliably, and securely it operates.

Practical Security Implementation Ideas

1. Create centralized AI usage reporting. Consolidate consumption, workflow execution, errors, and agent activity into dashboards accessible to appropriate technology, security, and financial stakeholders.

2. Review abnormal consumption patterns. Establish alerts for unusual token usage, excessive tool calls, repeated failures, or unexpected increases in agent activity.

The objective is to identify inefficiencies and potential risks before they become significant problems.

‍

AI Cost Optimization Should Focus on Value, Not Just Savings

There is a danger in approaching AI consumption entirely as a cost reduction exercise.

Organizations could become so focused on minimizing tokens or selecting cheaper models that they undermine the value AI is supposed to create.

A workflow that costs slightly more but produces accurate results, reduces rework, and improves customer outcomes may be a better investment than one that is inexpensive but unreliable.

The same principle applies to security.

Reducing AI consumption should never come at the expense of necessary validation, monitoring, or human oversight.

The objective should be to eliminate waste while preserving quality and control.

That means measuring AI efficiency through a combination of:

• Total workflow cost

• Successful completion rates

• Output quality

• Employee time saved

• Business outcomes

• Security and compliance performance

Cost matters.

But cost without context tells an incomplete story.

‍

AI Economics Will Become a Leadership Responsibility

As AI consumption grows, managing it will require collaboration across several functions.

Finance teams will need to understand variable AI spending and forecast consumption.

IT teams will need to optimize infrastructure, model selection, and system performance.

Security teams will need to establish boundaries, monitor activity, and manage operational risks.

Business leaders will need to determine whether AI workflows are creating sufficient value to justify their cost.

This requires a shift in thinking.

AI cannot be managed exclusively as another software subscription.

It increasingly needs to be managed as an operational resource whose consumption changes with business activity.

Organizations that develop this discipline early will be better positioned to scale AI without losing visibility into costs, quality, or risk.

‍

Final Thoughts

Back to electricity.

The goal is not to stop using power because it costs money.

The goal is to understand how much power is being consumed, where it is being used, and whether that consumption creates value.

AI is moving in the same direction.

As generative AI evolves into more autonomous agentic workflows, organizations will need to look beyond what the technology is capable of doing.

They will need to understand what it costs to operate.

How efficiently it completes work.

Where consumption creates measurable business value.

And where unnecessary usage introduces cost or risk without improving outcomes.

The companies that gain the greatest advantage from AI will not necessarily be those deploying the most powerful models or the largest number of agents.

They will be the ones that understand their AI consumption and manage it intentionally.

Because as AI becomes more capable, the next competitive advantage may come from knowing how to use that capability efficiently.

‍

FAQs: AI Consumption, Agentic AI Costs, and Enterprise Governance

1. Why can agentic AI cost more than traditional generative AI?

Agentic AI workflows may involve multiple model requests, tool calls, information retrieval steps, and validation activities to complete a single objective. These additional operations can increase token consumption and other usage-based costs. Organizations should measure the total cost of completed workflows rather than assuming each task requires only one AI interaction.

2. How can security teams help control AI consumption?

Security teams can work with IT and finance to establish agent permissions, usage limits, monitoring, logging, and escalation thresholds. These controls help identify abnormal consumption, prevent unauthorized activity, and reduce the operational risks associated with autonomous AI workflows.

3. What metrics should organizations track to manage AI consumption effectively?

Organizations should monitor token usage, model costs, workflow execution frequency, cost per successful workflow, error and retry rates, output quality, and business outcomes. Combining financial, operational, and security metrics provides a more complete understanding of AI efficiency and return on investment.