Tokens, tools and trade-offs: How finance should think about AI consumption

AI spending is starting to look a lot like cloud spending did a decade ago: fast-growing, business-critical and increasingly difficult to predict.

At first glance, it can seem familiar. Another software category. Another line item. Another tool for teams to adopt. But underneath, the economics are very different.

Traditional software tends to be relatively straightforward. You pay a fixed subscription fee, allocate licences and review renewals on a predictable cycle. AI doesn’t work like that. Costs can rise or fall dramatically depending on how often tools are used, which models are being accessed and how deeply AI is embedded into everyday workflows.

That creates a new challenge for finance teams. Because when spend is tied to consumption rather than seats, forecasting becomes more complex, oversight becomes more important and the risk of costs scaling faster than expected increases.

The opportunity, however, is just as significant. Managed well, AI can deliver productivity, speed and competitive advantage. But to capture that value, finance needs a new framework – one built around usage, visibility and intentional control.

Key takeaways:

• AI spend behaves differently. Unlike traditional SaaS, AI costs are driven by usage, not licences, making spend more variable, dynamic and harder to predict.
• Token economics changes the finance playbook. Forecasting AI spend requires a shift from fixed-cost budgeting to monitoring real-time consumption and adoption patterns.
• Without a clear view of usage across teams and tools, AI sprawl, duplicate spend and hidden costs can escalate quickly. Visibility is non-negotiable.
• The right controls enable innovation. Smart guardrails – like real-time monitoring, spend thresholds and automated approvals – help businesses scale AI responsibly without slowing experimentation.
• Value matters more than volume. The goal isn’t simply to manage AI costs, but to connect AI consumption to measurable business outcomes and maximise return on investment.

Understanding token economics

To manage AI spend effectively, finance first needs to understand what it’s actually paying for.

Unlike traditional SaaS, many AI tools operate on a usage-based pricing model. Instead of paying for access alone, businesses pay for consumption – often measured in tokens.

In simple terms, tokens are the units that AI models use to process information. Every prompt submitted, every response generated and every automated task completed consumes them. That might sound technical, but the financial implications are straightforward: the more your teams use AI, the more it costs.

And unlike a standard software licence, usage can change quickly. A team experimenting with AI for occasional tasks may generate only modest costs. But once those tools become embedded into daily workflows – drafting content, analysing data, automating support or generating code – consumption can scale rapidly.

This is what makes AI economics fundamentally different. Rather than fixed, spend is dynamic, variable and directly tied to behaviour.

That variability has important implications for budgeting and forecasting. Traditional annual software planning assumes relative stability. AI introduces a much more fluid cost curve. Usage can spike as adoption increases, as new teams come on board or as more sophisticated models are deployed.

For finance leaders, this means shifting from a licence mindset to a consumption mindset. The key questions are no longer simply: ‘How many seats do we need?’

Instead, they become:

• How quickly is usage growing?
• Which teams are driving consumption?
• What business value is that usage creating?
• Where might costs scale faster than expected?

Understanding token economics is about recognising that AI behaves more like a utility where demand can change in real time.

The visibility challenge

If AI spend is variable, visibility becomes essential – but achieving it is easier said than done.

In many organisations, AI adoption is happening quickly and often organically. Teams are trialling tools, experimenting with different models and integrating AI into workflows long before formal procurement processes catch up.

That speed is part of AI’s appeal. But it can also create a fragmented spend landscape.

Different teams may adopt overlapping tools for similar use cases. One department might use an AI writing assistant, whilst another subscribes to a separate platform offering much the same functionality. Developers may access model APIs directly. Marketing teams may experiment with multiple content-generation tools. Operations may layer in automation platforms independently.

Without central visibility, this can lead to a familiar problem: AI sprawl.

Unlike software sprawl, AI sprawl can be harder to detect. Consumption-based pricing often starts small. Individual subscriptions or API costs may seem insignificant in isolation. But across a business, those costs can accumulate quickly.

The bigger risk is hidden usage. Spend that sits outside approved systems. Experimentation that scales without oversight. Teams using tools that finance doesn’t know about until invoices arrive – or until budgets are exceeded.

This creates three key challenges:

• Duplicate spend across overlapping tools
• Unpredictable cost growth as usage scales
• Limited ability to assess ROI across AI investments

Without a clear view of consumption, finance risks losing control over one of the fastest-growing areas of business spend.

The question is how to achieve visibility and control without slowing AI down.

Building smarter controls

AI’s value lies in experimentation. Teams need space to test, learn and discover where these tools can create the greatest impact. Overly rigid controls risk stifling innovation before value has been proven.

Instead, finance should focus on building smarter controls that enable experimentation whilst maintaining visibility and accountability. There are three key elements to achieving this:

1. Real-time monitoring

Finance teams need to see usage as it happens. That means tracking consumption across tools, teams and use cases, with clear reporting on where spend is occurring and how it’s changing over time.

Real-time visibility makes it possible to spot trends early:

• Which teams are increasing usage fastest
• Where duplicate tools may exist
• Which applications are delivering meaningful adoption
• Where costs are rising without clear business value

That early visibility is what makes AI spend manageable. The sooner finance can see patterns forming, the sooner they can guide investment in the right direction.

2. Establishing practical guardrails

The best controls are often the least visible. They simply create boundaries that keep experimentation aligned with business priorities.

That might include:

• Spend thresholds for individual tools or teams
• Approval triggers when usage exceeds predefined limits
• Clear policies around approved vendors and models
• Regular reviews of adoption, overlap and outcomes

The goal is to ensure teams can move quickly within a framework that keeps spending intentional, scalable and aligned with company priorities.

3. Connect AI consumption to business impact

Usage alone isn’t a measure of success. High consumption may indicate value – but it may also indicate inefficiency, duplication or poor optimisation.

The real question is whether AI spend is delivering measurable outcomes. Is it saving time or improving productivity? Is it increasing revenue, enhancing decision-making or reducing operational friction?

When finance can connect consumption data to business results, AI investment becomes far easier to manage – and far easier to justify. It shifts the conversation from cost control to value creation, which is where the most strategic decisions get made.

From spend management to AI governance

AI is creating a new category of business spend. It’s dynamic, decentralised and deeply tied to how work gets done. That requires finance to evolve.

Managing AI consumption is about building the frameworks that allow innovation to scale responsibly. It’s about ensuring that experimentation happens within clear parameters. And it’s about turning usage data into strategic insight.

The organisations that succeed will be the ones that understand AI best. Because in the AI economy, competitive advantage will come from knowing how to manage consumption, optimise value and scale investment with confidence.

That’s where finance has a critical role to play: helping the business invest wisely, experiment safely and grow sustainably.