See what AI costs you, cut it, and keep it under control.

AI tokenomics is the practice of understanding, managing, and optimizing the consumption-based costs of enterprise AI. It examines what drives AI spend – tokens, API calls, models, agents, users, and workloads – and connects that consumption to business value.

Effective AI tokenomics helps organizations identify which AI services and use cases are driving costs, forecast future consumption, and find opportunities to reduce waste. This can include selecting more cost-effective models, improving how applications consume tokens, removing unused services, and introducing controls for high-cost or low-value usage.

Building an accurate AI inventory requires looking beyond centrally approved software, as AI can be purchased and deployed through many different channels. Organizations should examine procurement records, contracts, expense claims, cloud platforms, SaaS applications, browser-based tools, departmental purchases, and existing software products with embedded AI functionality.

This can reveal both approved AI and shadow AI tools or services being used outside established IT, procurement, or governance processes. The resulting inventory should identify what AI is being used, who owns it, how it is paid for, what data it accesses, and whether its use creates cost, security, compliance, or contractual risk.

Good AI governance should enable responsible AI adoption rather than slow it down. Clear policies, ownership, approval routes, and risk controls help employees understand which AI tools they can use, what data they can use with them, and when additional review is required.

Without these guardrails, organizations can end up responding reactively to shadow AI, security concerns, unexpected costs, and regulatory risk. A proportionate governance framework allows lower-risk AI use cases to move quickly while applying greater scrutiny to higher-risk applications.

An AI governance review assesses how effectively an organization identifies, approves, manages, and monitors AI across the business. It should cover areas such as AI inventory and ownership, policies, data use, procurement, security, risk management, regulatory requirements, cost control, decision-making, and accountability.

A review identifies gaps between current practices and the organization’s required governance framework, prioritizes risks, and defines practical remediation actions. Livingstone’s AI Governance & Policy Assessment evaluates policies, controls, and evidence across eight governance dimensions and maps the assessment to ISO/IEC 42001 and ISO/IEC 27001.

Yes. A strong AI business case should assess a proposed use case against expected business value, implementation and ongoing costs, technical feasibility, risk, adoption requirements, and measurable outcomes.

Livingstone’s AI Business Use Case Development service helps organizations identify and prioritize potential AI opportunities rather than investing in AI without a clear value case. The process considers existing cloud and AI costs, evaluates potential use cases, and creates an executive roadmap for implementation and investment decisions.

After the initial AI assessment, organizations receive a prioritized view of the actions needed to reduce costs, close governance gaps, and improve control over AI usage. Depending on the findings, organizations can implement these actions internally, address them through a targeted optimization program, or manage them through an ongoing AI cost and governance service.

Continuous management can then monitor AI usage, licenses, token consumption, and spend as adoption grows, helping prevent cost leakage and governance gaps from reappearing. Livingstone’s managed AI tokenomics service provides ongoing monitoring, controls, and optimization rather than treating AI cost management as a one-off exercise.