The CFO's perspective: ROI, risk, and scaling credit-based AI investments

That scenario is common. In a 2026 McKinsey survey of enterprise participants, 93% reported exceeding their AI budgets, and I believe confusion about credits is the issue.

Most guidance on credit-based AI is written for the people running the tools: operations leads, IT, and procurement. But this post covers the top-down view for CFOs: how to evaluate the investment, manage its financial risk, and decide when to scale it.

This is the ninth post in our AI credit-based pricing series. For the operational detail, start with our guides to budgeting for AI credits, forecasting consumption, managing credit spend, and setting alerts and limits.

Table of Contents

• Why Credit-based AI Is Different For Finance
• Making the Case For ROI
• Managing Financial Risk in a Variable-Cost Model
• Scaling From Pilot to Enterprise
• Questions CFOs Should Bring to Vendor Conversations
• Turn credit-based AI into a defendable financial investment.

Why Credit-based AI is Different for Finance

Credit-based pricing is a whole different way of thinking and planning for finance leaders. Seat-based software is usually a fixed cost. Headcount times price per seat gives you a number that holds until renewal. Simple.

Credit-based pricing, however, is a variable cost. The cost is tied to what the AI does, so spend rises with task volume, task complexity, and the number of workflows running.

Successful adoption raises cost almost by definition. A flat budget might signal disciplined use, or it might signal a tool nobody is using.

It is less predictable, creating a push-pull strategy that finance has to manage deliberately. As a finance leader, you want teams to adopt AI because unused capacity is wasted spend, but simultaneously, you also want costs to stay predictable, and adoption is what makes them less so.

Recent research suggests many organizations are struggling with this:

• Zylo’s 2026 SaaS Management Index found that 78% of IT leaders had unexpected charges tied to AI or consumption-based pricing in the past year, and 61% were forced to cut projects because of unplanned SaaS cost increases.
• Gartner reported in 2024 that more than 90% of CIOs said managing cost limits their ability to get value from AI. It is estimated that CIOs who don’t understand how generative AI costs scale could be off by 500% to 1,000% in their cost calculations.
• McKinsey found that only 20% to 25% of companies have mature practices for managing AI spend, a discipline it calls AI FinOps.
• Gartner also found that only 13% of organizations believe they have the right governance in place for AI agents.

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Where Organizations Typically Go Wrong

Three patterns show up repeatedly among companies having difficulty navigating credit-based AI investments.

Budgets ignore growth. A budget built on pilot usage describes the pilot, not the rollout. McKinsey’s survey found that AI spend rises nearly fourfold as organizations move from isolated use cases to enterprise-wide adoption.

The fix: Model base, best, and worst cases instead of a single number, and add a buffer and a quarterly growth assumption (See our budgeting guide for more). Revisit the base case every quarter using actual consumption.

Over-buying to stay safe. The opposite of disregarding growth is just as costly. Committing to a higher number of credits to avoid overages means paying for capacity you may never use, especially if credits expire.

Jennifer Lendler, founder of Alea Advisors, advises CFOs at private equity firms and their portfolio companies. She suggests asking vendors about their commitment structure. “Are there break points where the additional credits become cheaper?” she says. She also recommends a short initial contract where one is available, so you can adjust pricing once you have a track record.

The fix: Start your credit commitment at the low end of your forecast range and negotiate a short initial term so you can adjust based on real data later. Ask up front about rollover, true-up options, and per-credit price breaks (see our forecasting guide).

No visibility into consumption. If finance can’t see which workflows are using credits, it can’t tell what’s worth it and what’s not. Zylo’s index found that business units now control 81% of SaaS spend while IT directly manages 15%.

That means oversight is decreasing just as pricing gets harder to forecast. McKinsey adds that, in its experience, 20-30% of AI spend is often unaccounted for because it is fragmented across vendors, tools, and commercial models.

The fix: Name one owner for AI credit spend that reviews workflow-level usage before going live. Review actual versus forecast monthly (see our spend management guide).

Visibility tools only show where the credits are going. The next question is whether that spend is producing anything worth the cost, which is where return on investment (ROI) comes in.

Making the Case for ROI

Finance teams are used to judging software on cost and seat utilization. With credits, the more useful question is actually ROI, but that isn’t as straightforward with most AI as it is with other tools.

Here’s how to approach it.

Measure business impact in three categories.

In its early-2025 State of AI survey, McKinsey tested 12 adoption and scaling practices for generative AI. Tracking well-defined KPIs had the strongest link to the earnings before interest and taxes (EBIT) impact. In the 2026 edition, AI high performers were twice as likely as other respondents to have defined processes for measuring their AI initiatives.

The measurement gap is visible in the same 2026 data. 80% of respondents said AI improved their productivity, but only 37% reported an EBIT impact. Deliverable tracking is how credit spend becomes an investment case instead of a line item.

So what should you measure?

Most AI return falls into one of three buckets. Choose the ones that match your use, and define the metric before the spend starts so you know what you’re looking for in terms of performance.

The common thread is cost per outcome, not total credits consumed.

Set realistic expectations on timing.

AI investments often don’t pay off on a normal software timeline.

Technology investments are often expected to pay back in seven to 12 months. However, in Deloitte’s 2025 survey of 1,854 executives in Europe and the Middle East, most respondents reported reaching satisfactory ROI with AI within two to four years. Only 6% reported payback in under a year, and even among the most successful, just 13% saw returns within 12 months.

Early credit consumption also tends to sit below the steady state, as we cover in our forecasting guide. Much of the early value shows up as individual productivity before it reaches enterprise financial results, which is the gap McKinsey’s data describes.

What does this mean for your financial reporting? Build the timeline into the business case. Agree on a measurement window up front, and use it to judge the trend in cost per outcome, since full payback may take longer. Judging a pilot on its first month will likely overstate its cost per outcome. Instead, 2-3 quarters of data is a reasonable starting point.

Know what to track when attribution is blurry.

Not every AI use case ties neatly to a dollar figure. Faster first drafts, better-prepared sales calls, and quicker internal answers have value that doesn’t appear directly on a P&L. For these workflows, track leading indicators like:

• Adoption: how many people use the tool each week, and whether that number is growing
• Throughput: tasks completed per person before and after
• Cycle time: how long a process takes from start to finish
• Quality signals: revision, escalation, or error rates on AI-assisted work

For each indicator, also track how many credits a task costs. If your team gets more done and the cost per task stays the same or decreases, you have more support for ROI, even if you can’t tie it to an exact dollar amount.

Even a solid ROI case has to hold up when the bill changes from month to month. A workflow that pays off can still put pressure on your budget if usage spikes, so managing the financial risk of variable cost becomes essential.

For workflows tied to clear outputs, some AI agents, such as HubSpot’s Prospecting Agent, Content Agent, and Customer Agent, are designed to automate common, repeatable tasks, making ROI measurement more direct.

Source

Tracking the output of agents like HubSpot’s Prospecting Agent, Content Agent, and Customer Agent (tickets resolved, emails drafted, contacts enriched) gives you a direct line from credit spend to business result.

Source

Managing Financial Risk in a Variable-Cost Model

As with most strategies, cost is already limiting the use of AI for some organizations. About one in five respondents to McKinsey’s 2026 survey said operating costs, including token costs, had constrained their use of AI.

How Unpredictable Usage Creates Unexpected Costs

Variable cost in AI shows up in a few predictable ways:

• Volume spikes. Campaign periods, end-of-quarter pushes, and product launches can push consumption well above a flat monthly estimate.
• Concentration. One high-volume workflow can consume a disproportionate share of the budget. In our guide to managing credit spend, we illustrate how a hypothetical customer service agent handling 10,000 conversations a month could exhaust a 50,000-credit allotment on its own.
• Unintended scope. Thibault Garcia, founder of go-to-market agency Reachly, tested a revised prompt on 10 rows of a spreadsheet. It ran on all 28,000 companies in the table, and he says it “cost us a lot of money.” Test environments and confirmation steps exist to prevent exactly that.
• Always-on consumption. Oleksii Glib, CEO of software development company Acropolium, notes that some agent tools run on recurring schedules and consume tokens even when no one is actively using them. Usage can also climb during peak hours.
• Estimates that miss. A Stanford Digital Economy Lab paper found that the same task can cost as much as 30 times more in credits if run again. The AI models also guessed their own costs too low, and they did it consistently. The study examined coding tasks, but the lesson applies more broadly. When you budget for AI, give a range, not a single number.

With these in mind, spending responsibly on AI means treating credits like any other currency in business: set a plan, build in a cushion, assign a budget owner, and review actuals against forecast on a fixed cadence. Let’s unpack these steps.

How Much Cushion to Build In

Our AI budgeting guide recommends a buffer of 15-25% above the base-case monthly estimate, plus a 10-20% quarter-over-quarter growth assumption. (A majority of firms in McKinsey’s survey expect their AI spend to rise by at least 25% over the next 12 months.)

For example, on a 50,000-credit monthly base:

• A 15-25% buffer puts the monthly planning figure at 57,500 to 62,500 credits.
• Compounding growth of 10-20% per quarter takes the base from 50,000 to roughly 73,000-104,000 credits a month after four quarters.

That is roughly 1.5-2x the starting budget in a year. Showing finance that ranges like this early keeps it from arriving as a surprise.

Oversight That Keeps Finance Informed Without Slowing Teams Down

Controls work best when they are specific. A blanket cap on total monthly credits creates friction for every team at once. A smarter approach has three parts:

1. Feature-level limits cap the number of credits a given agent or workflow can consume, so a single use case can’t drain the entire allocation.
2. Approval gates let established workflows run uninterrupted while adding a check on new ones.
3. A fixed review cadence keeps monitoring proactive. Review actual versus forecast monthly and flag variances above 20%. Revisit base-case assumptions quarterly. Rebuild the model annually from actual data.

The payoff can be huge. McKinsey found that organizations with high forecasting maturity save 10% more on AI spend than their peers on average.

Ownership matters as much as the controls. One common structure is a hybrid model, where a central budget holder sets guardrails and approval thresholds, while functional leads manage day-to-day allocation within those guardrails. Whatever structure you pick, document four decisions before go-live:

• approval thresholds,
• escalation paths,
• reallocation rules, and
• overage policy.

Be careful not to tighten restrictions so much that teams look for ways to cheat the system. Gartner analyst Max Goss has warned that blocking or restricting AI agents often pushes employees toward shadow AI, which carries far greater risks than the original problem.

Teams avoiding certain workflows or asking to bypass credit limits are a sign that controls are too tight. Consumption that consistently exceeds forecast with no clear explanation is a sign they’re too loose.

With a buffer, controls, and an owner in place, you have the foundation to expand AI adoption. The harder question is how to scale from pilot to enterprise rollout without losing the cost discipline and measurement practices that made the initial investment work.

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Scaling From Pilot to Enterprise

A pilot is a contained experiment: a handful of users, one or two use cases, and instrumented tracking. Enterprise rollout is a whole new game. More teams use the platform, more workflows run, and the budget becomes a company-level line item that someone has to own. So, remove this incomplete sentence or complete it with a clear claim, such as ‘It’s difficult to distinguish between disciplined use and lack of adoption.‘ It’s important not to jump into it mindlessly.

Decide when and how much to expand based on performance data.

Let performance data, not enthusiasm, set the pace.

Look for these signals:

• Cost per outcome is improving. This is the strongest signal that a workflow is working. Expand investment where returns are strongest.
• New teams are asking for access. Demand signals growth that wasn’t in the original model. Expand the budget proactively rather than reactively.
• Spend is frequently hitting contingency reserves. Base-case assumptions may be too conservative for current adoption.

Hold steady if spend runs 30% or more below forecast, which may indicate the platform is underused or the budget was sized too conservatively.

Workflows whose cost per outcome isn’t improving after a full measurement window belong in the same group.

Tools like Agent Hub, which powers HubSpot’s agent capabilities across Marketing Hub, Sales Hub, and Service Hub, let you get started for free (ideal for a pilot) and provide usage dashboards that make it easy to track credit consumption at the task and workflow levels throughout the pilot phase.

The visibility provided by usage dashboards is key to gathering accurate consumption data before deciding whether to scale from pilot to enterprise rollout.

Tie budget growth to business results.

Tie each budget increase to a specific, documented result. Expansion requests should name the workflow, the current cost per outcome, and the volume the team expects to add. McKinsey recommends that AI business cases include projected consumption curves, sensitivity analyses, and expected cost-per-outcome metrics. Finance can then approve growth in the workflows that have earned it and hold the line on those that haven’t.

Scaling is also the right moment to revisit commercial terms. Steady usage patterns give you evidence to negotiate better rates at renewal, and volume data indicate whether a larger commitment is justified.

Scaling decisions also depend on the contract terms, which arise from conversations with vendors. Walking into them with the right questions makes the plans above easier to enforce.

Questions CFOs Should Bring to Vendor Conversations

Procurement and IT will cover the technical questions. The ones we share below, divided into common subcategories, belong firmly to finance.

For longer lists, see our guides to the five critical questions to ask AI vendors, comparing credit models across vendors, and the buyer’s guide to credit-based AI pricing.

Usage Spikes

• What happens when we exceed our allotment? Do overages auto-approve and bill, escalate for sign-off, or pause the feature?
• Is the overage rate higher than the committed rate?
• Can we set hard caps by feature or workflow, and pause credit-based features at any time? (HubSpot Credits, for example, supports feature-level limits, alerts, and pausing.)
• Are we locked into current rates for the contract term? Can prices change mid-contract, and what notice would we receive?

Unused Credits

• Do unused credits roll over, expire monthly, or expire at the end of the contract year? Is there a partial rollover on renewal?
• If we consistently use well below our commitment, can we right-size mid-term, or do we wait for renewal?
• Can we scale up at the same unit price if adoption outpaces forecast?

Audit and Reporting Rights

• Can we get usage broken down by workflow and feature, and how far back does the history go?
• Are invoices itemized to show what consumed credits?
• What forecasting and alerting tools come with the platform, and at what thresholds?

Warning Signs in Vendor Agreements

• Credit definitions that can’t be mapped to specific actions, or ranges like “one to ten credits depending on complexity,” with no table showing what that costs
• Charges for failed actions
• Rates that rise per credit as volume grows
• “Unlimited” claims without clear limits on rate, features, or model quality
• No spend visibility tools, which shifts the risk management to you
• Pricing that mostly reflects access to an underlying model. Glib’s test is to compare the vendor’s price to the cost of using the foundation model directly, then ask what the product adds on top, such as proprietary data, workflow design, or integrations.

Jennifer Lendler also suggests asking how a vendor evaluates changes to its underlying models, and whether the savings from cheaper, faster models are passed on to customers. As she puts it, these are “questions that not everybody asks, but I think Remove this incomplete sentence or complete it with a clear claim, such as ‘It’s difficult to distinguish between disciplined use and lack of adoption.‘ worth probing.”

Together, these questions turn the earlier sections into contract terms. The ROI case, the risk controls, and the scaling plan all depend on what the vendor agrees to in writing.

Turn credit-based AI into a defendable financial investment.

Credit-based AI requires finance leaders to manage costs that vendors and researchers are still studying and refining.

But Lendler describes the credit model as useful since it forces regular questions about whether the spending is paying off, and finance wants that answer anyway.

The tools for doing that are familiar: unit economics, scenario planning, ownership, and review cadences. Applying them early makes this easier by allowing an AI investment to compound rather than draining the budget one credit at a time.

Free AI Agents Playbook

This practical guide reveals where to start, which applications deliver real value, and how to implement agents that transform workflows without replacing jobs.

• Marketing Workflow Automation
• Sales Acceleration System
• Operational Excellence
• Implementation Blueprint

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