AI adoption is moving quickly.

Governance? Not always.

Over the past few years, companies have rushed to add AI to sales, marketing, customer service, development, operations, and nearly every other part of the business. Employees have adopted their own AI tools. Software vendors have embedded AI into existing products. And businesses are experimenting with increasingly sophisticated AI agents and automated workflows.

But as AI moves from experimentation into everyday operations, a new set of questions is becoming much harder to ignore:

What is all of this AI actually costing us?

More importantly: Is it creating enough value to justify that cost?

That was the focus of a recent Pipeline to Profit conversation with Mike Danforth, founder and CEO of Cortave, about what his team calls the cost of intelligence.

And while token consumption is part of that conversation, AI governance goes much further than controlling a bill.

It is about understanding what AI your organization is using, where data is going, which workflows are delivering value, and how to scale AI without allowing cost, security, quality, and operational risk to scale alongside it.

AI Has a Visibility Problem

Traditional SaaS spending is relatively straightforward.

A company buys 100 licenses. Each license costs a known amount. Finance can forecast the expense, IT knows which application employees are using, and business leaders can evaluate whether those licenses are worth renewing.

AI introduces a very different operating model.

Usage can fluctuate dramatically based on prompts, models, workflows, agents, integrations, and individual user behavior. At the same time, employees can access free or individually purchased AI tools without going through a traditional software procurement process.

That creates a problem that extends beyond cost: companies may not actually know how AI is being used across the organization.

Danforth compares this to the shadow IT problem companies have dealt with for years. The difference is that shadow AI can be even more difficult to identify.

An employee doesn’t necessarily need to install enterprise software or submit a procurement request. They can open an AI tool in a browser and begin using it immediately.

That accessibility is one of AI’s greatest strengths.

It is also one of its biggest governance challenges.

Employees can unintentionally provide customer information, internal documentation, financial data, contact lists, or other sensitive information to AI systems before security or IT teams even know the application is being used.

The first step toward AI governance, therefore, isn’t necessarily restricting AI.

It’s observability.

Companies need to understand:

• Which AI tools are being used?
• Who is using them?
• What are they being used for?
• What information is being sent through them?
• How frequently are they being used?
• What does that usage cost?
• What business value is being created?

This kind of visibility is also central to frameworks like the NIST AI Risk Management Framework, which gives organizations a structured approach to identifying and managing AI Risk. Without that visibility, organizations are trying to govern something they cannot see.

The Real Question Isn’t “How Much Does AI Cost?”

It’s “Is it worth it?”

That’s how Danforth describes the idea behind the “cost of intelligence.”

The financial cost of an AI workflow is only one part of the equation.

Organizations also need to consider the time required to design, test, maintain, and monitor the workflow. They need to consider security and privacy exposure. They need to consider the resources required to manage the underlying systems.

And then they need to compare all of that against the outcome.

Consider an AI workflow that costs $10,000 per month but saves a team hundreds of hours of repetitive administrative work.

That could be an excellent investment.

Now consider another workflow that costs $2,000 per month but simply generates more sales emails that don’t improve response rates, pipeline, or revenue.

The second workflow may technically be cheaper, but its cost of intelligence is higher because the business value isn’t there.

This is where AI governance becomes particularly relevant for Revenue Operations.

RevOps teams already spend significant time connecting activity to outcomes.

AI should be evaluated the same way.

Instead of asking:

How many employees are using AI?

Ask:

Which AI workflows are improving the revenue engine?

Activity Isn’t the Same as Value

One of the most important points from the conversation was a simple question leaders should ask when evaluating an AI use case:

Does this actually save us time, or does it just give us something different to do?

That’s an important distinction.

AI can generate enormous amounts of activity.

More emails.

More content.

More research.

More workflows.

More automated actions.

But increased activity doesn’t automatically translate into better business outcomes.

Danforth shared an example of a sales organization that initially used AI to generate outbound emails at scale. The activity increased, but the organization didn’t see meaningful improvements from the approach.

The team then changed how it used AI.

Instead of asking AI to replace the human interaction, they used it to accelerate account research and planning.

Salespeople could understand their target accounts faster and then use that information to write personalized messages or call prospects directly.

According to Danforth, the organization saw a significant improvement in its pipeline trajectory within roughly 60 days.

The technology hadn’t changed.

The use case had.

That distinction should become a core part of any AI governance framework.

Governance shouldn’t only ask whether an AI workflow is permitted.

It should ask whether the workflow is worth running at all.

AI Costs Can Hide Inside Inefficient Workflows

Token pricing introduces another unusual dynamic into enterprise software economics.

With traditional software, inefficient usage doesn’t necessarily change the monthly bill.

With consumption-based AI, inefficiency can have a direct financial cost.

Poor prompt design can lead to repeated attempts.

The wrong model can be used for a relatively simple task.

Multiple employees may repeatedly ask AI systems essentially the same questions.

Agents can generate unnecessary actions.

Workflows can send more context to a model than the task actually requires.

Each individual interaction might appear inexpensive.

Multiply those interactions across hundreds or thousands of employees, applications, agents, and workflows, however, and the economics begin to change.

This is why AI cost optimization isn’t simply about negotiating a better token price.

Organizations need visibility at the workflow level.

Instead of only knowing that the company spent $500,000 on AI, leaders should eventually be able to understand something closer to:

This workflow consumed this amount of AI capacity, saved this amount of employee time, influenced this business process, and produced this measurable result.

That is a much more useful conversation for a CFO, CTO, CIO, or RevOps leader.

Getting the Answer Right the First Time Matters

One of the areas Cortave is focused on is reducing repeated AI requests through a centralized context layer.

The idea is straightforward.

Imagine two employees working in the same department.

Employee A asks an AI system a question.

The model processes the request, consumes tokens, and returns an answer.

The following day, Employee B asks essentially the same question.

Without shared organizational context, the model may process the entire request again.

The company has effectively paid twice to generate intelligence it already produced.

Cortave’s approach is designed to create a centralized context layer that can recognize when the organization has already encountered similar requests. When appropriate, existing knowledge can be reused rather than sending every request back through an underlying model.

The broader lesson applies regardless of which AI platform a company uses:

AI efficiency depends heavily on context management.

As AI deployments grow, companies will need to think about organizational knowledge differently.

Instead of every user, agent, and workflow independently rebuilding context, organizations should consider how trusted knowledge can be shared and reused.

Better context can mean better answers.

Better answers can mean fewer retries.

And fewer retries can mean lower costs.

Shadow AI Is the New Shadow IT

AI governance isn’t only a financial problem.

It is also a data governance problem.

Consider a marketer trying to improve campaign performance.

They export a customer list and upload it into an AI tool to analyze segmentation opportunities.

The intention isn’t malicious.

They’re trying to do their job better.

But the dataset may contain customer names, email addresses, phone numbers, purchase history, or other information that shouldn’t be shared with an external model.

The same scenario could happen in sales, customer success, finance, HR, engineering, or virtually any department.

That’s what makes shadow AI particularly challenging.

The risk often comes from employees trying to be productive.

A successful governance strategy therefore can’t rely entirely on telling employees what they aren’t allowed to do.

Organizations need technical guardrails that make responsible AI usage easier.

That can include automatically detecting or removing sensitive information before it reaches a model, defining which applications can access certain data, setting usage thresholds, establishing approved models, and monitoring where AI interactions are occurring.

The goal should be to make governance largely invisible to the average employee.

Good governance enables productive behavior while reducing unnecessary exposure behind the scenes.

Guardrails Shouldn’t Become Roadblocks

There is an important balance here.

If every AI experiment requires a lengthy approval process, employees will find ways around it.

But if organizations allow unrestricted experimentation, they can create significant cost, privacy, compliance, and security exposure.

The answer isn’t bureaucracy.

It’s lightweight, enforceable guardrails.

Start with obvious areas of risk.

Customer personally identifiable information shouldn’t be casually sent into unapproved models.

Credit card information shouldn’t reach an LLM.

Sensitive financial information may need additional controls.

Certain applications may need approved models or usage limits.

And unusually high consumption should trigger alerts before it turns into a significant bill.

These controls don’t have to prevent experimentation.

They create a safer environment for it.

Think Crawl, Walk, Run

One useful framework from the conversation is to approach AI governance based on an organization’s current maturity.

Crawl: Observe

Start by understanding what’s happening.

Which tools are being used?

Which teams are using them?

What workflows exist?

Where is the spend occurring?

What data is moving through those systems?

Many organizations may discover that simply answering those questions is harder than expected.

Walk: Govern

Once the organization understands its AI footprint, establish guardrails.

Define acceptable use.

Protect sensitive information.

Set thresholds.

Determine approved applications and models.

Establish ownership.

Begin connecting consumption to individual workflows and business functions.

Run: Optimize

Once AI usage is observable and governed, organizations can begin optimizing it.

That could include smarter model routing, shared organizational context, caching, better prompt design, workflow consolidation, or renegotiating consumption commitments based on actual usage.

At this stage, the organization isn’t simply trying to control AI.

It is trying to make AI economically efficient.

RevOps Has an Important Role to Play

AI governance may sound like a problem for IT, security, or finance.

Those teams absolutely need to be involved.

But Revenue Operations has an important role as well.

RevOps sits at the intersection of systems, processes, data, and business outcomes.

That’s exactly where many AI use cases live.

Sales teams want AI prospecting tools.

Marketing wants AI content and campaign automation.

Customer success wants AI assistants.

Operations teams want AI-powered workflows.

Leadership wants productivity gains.

Someone needs to connect those individual experiments into a coherent operating model.

RevOps can help answer questions like:

What problem are we solving?

What process does AI improve?

What does the workflow cost?

Who owns it?

Which systems and data does it touch?

How will we measure success?

What happens if usage increases 10x?

And what business outcome proves this was worth doing?

Those are not AI engineering questions.

They’re operational questions.

And they’re questions RevOps teams are already accustomed to answering.

AI Governance Isn’t About Using Less AI

Perhaps the biggest misconception about AI governance is that governance exists to slow adoption.

It shouldn’t.

The purpose is to create the visibility and controls required to adopt more AI responsibly.

Companies aren’t going to stop experimenting with AI.

Employees aren’t going to stop looking for faster ways to work.

Software vendors aren’t going to stop adding AI capabilities.

And agents and automated workflows are likely to become increasingly common across the technology stack.

Trying to prevent that shift isn’t realistic.

The better strategy is to build an operating model capable of supporting it.

That means understanding usage.

Protecting data.

Managing cost.

Measuring outcomes.

Reducing waste.

And determining which AI investments genuinely make the business better.

Because ultimately, the question isn’t whether your company can use AI.

It’s whether the intelligence you’re buying is actually worth what you’re paying for it.

Listen to Pipeline to Profit

In “The Cost of Intelligence: An Introduction to AI Governance,” Pipeline to Profit host Heather Davis Lam sits down with Mike Danforth, founder and CEO of Cortave, to discuss shadow AI, token waste, governance guardrails, AI cost attribution, centralized context, and what businesses should consider before scaling AI across their organizations.

For organizations experimenting with AI today, the message is simple:

Don’t wait until AI is everywhere to figure out how you’re going to govern it.

Build the visibility, measurement, and guardrails now so that when the right AI use cases emerge, you’re ready to scale them.