AI Unit Economics: Stop Counting Tokens, Start Counting Value
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As Generative AI has evolved from boardroom buzzword to indispensable operational tool, the conversation around cost must also evolve. For too long, organisations building AI Applications have focused on the technical details: model selection, token consumption, and input/output ratios. While these metrics are vital for engineers, they are often meaningless to the executive suite.
To bridge the gap between technical teams (Architects, Engineers) and business leadership (CEOs, CFOs), we need a universal language: Unit Economics. Shifting your reporting from raw technical units to measurable business outcomes is crucial to justify investment, demonstrate ROI, and ensure the sustained, profitable growth of your enterprise AI applications.
The Pitfall of Technical Metrics
When an architect reports that a new application uses 20% fewer tokens compared to the previous version, what does that actually mean? Although the information may be technically correct, it is strategically deficient. Why?
Lack of Context
A business leader cannot easily translate '20% fewer tokens' into business value. Does this saving justify the engineering time spent? More importantly, did the change in token usage improve the customer experience or speed up a core process?
Obscured ROI
Token counts hide the complexity of the underlying task. An application using expensive tokens might deliver high-value outcomes, while one using cheap tokens might run continuously with minimal business impact. Accurate ROI comparisons (human vs. AI or AI vs. AI) become impossible.
The core problem is simple: LLM providers charge you based on tokens, but your business generates revenue based on queries, customer interactions, or solved problems. The charging unit does not match the value unit.
Introducing Unit Economics for AI
Unit Economics is about defining the cost to deliver one instance of a quantifiable business outcome. In the context of AI, this means shifting your reporting from technical inputs (tokens) to business outputs (queries, tickets, reports).
This move is not just a reporting exercise; it is the difference between guessing your AI's profitability and proving it.
The Business Angle: Metrics That Drive Strategy
By adopting business-centric metrics, the return on investment (ROI) becomes immediately apparent, enabling decisive strategic action:
Business Metric | Description | Strategic Insight |
Cost per Query | The total cost (LLM API call, pre/post-processing, infrastructure) required to answer one user query. | Justifies investment in internal knowledge base AI tools versus traditional search methods. |
Cost per Solved Ticket | The total cost of using an AI agent (e.g., in ServiceNow) to successfully resolve a customer support ticket end-to-end. | Provides a clear ROI comparison against human agent salaries and operational costs. |
Cost per Report Generated | The cost to synthesise, draft, and finalise one complex report (e.g., quarterly financial summary). | Helps management decide whether to deploy a specific AI model for high-value synthesis tasks. |
The ROI is undeniable
If your Cost per Solved Ticket via an AI agent is £0.50, and the cost for a human agent to handle the same ticket is conservatively estimated at £5.00, the AI delivers a clear 10x ROI. This metric speaks directly to the business leadership team. Furthermore, comparing the Cost per Solved Ticket between two different LLM providers (Model A vs. Model B) provides the proper economic justification for model selection, moving beyond token pricing alone.
The Strategic Advantage
Adopting Unit Economics moves the enterprise AI discussion away from abstract technology and towards measurable business revenue and profit. It gives CIOs and architects the vocabulary to articulate strategic value, enables FinOps to optimise spending based on proven outcomes, and provides CEOs with the confidence to scale investments in AI, knowing the returns are quantified, clear, and justifiable.
To achieve sustained success with enterprise AI, you must not only build efficient models but also measure them correctly. The future of AI is not just about saving pennies per token; it's about delivering pounds of profit per unit of business value.
Derek Ho
Senior AI & Cloud Consultant