AI Economics Glossary: Definitions for AI Cost, Value, and Governance

Absorption capacity

The capacity of an organisation to decide what to do with AI output, adopt it, change the process around it and bank the benefit. It is limited by decision slots, change slots, training and implementation capacity.

Why it matters: Value is capped by what an organisation can absorb, not by how much AI can generate.

Agentic AI

AI systems that can plan, decide, and take multi-step actions with some degree of autonomy toward a goal.

Why it matters: Agentic AI matters because more autonomous systems can create more business value, but they also increase governance, observability, and economic risk if actions scale without control.

Agentic Orchestration Cost

The cost generated by the planning, tool-calling, error recovery, and verification logic of an agentic AI system - the overhead incurred in executing a task autonomously, beyond the direct inference cost of the final output.

Why it matters: In agentic systems, orchestration cost can account for 30-60% of total inference spend and is largely invisible in standard cost reporting. Organisations that track only direct inference cost will systematically underestimate the true cost of agentic workloads.

AI Adoption

A term used in AI governance to describe user engagement with an AI capability - but applied inconsistently to mean licence assignment, feature usage, workflow integration, or behavioural change, with different evidence implications for each.

Why it matters: Adoption as a metric can mean very different things. High licence deployment with low active usage is not the same as high usage with low workflow change, which is not the same as high workflow change with low value capture. Using adoption as a proxy for value without specifying which definition is in use produces misleading performance reports.

AI Baseline

The measured pre-AI state of the process, metric, or outcome against which AI-related improvement is claimed.

Why it matters: Without a credible baseline, an AI productivity or quality claim cannot be evaluated. A baseline constructed after the AI is deployed - or estimated rather than measured - is structurally biased toward the result the sponsor needs to show.

AI FOMO (Fear of Missing Out)

The dynamic by which competitive anxiety about AI adoption leads to investment decisions that bypass normal capital discipline, justified by strategic necessity rather than demonstrable economic return.

Why it matters: AI FOMO is one of the primary structural causes of the AI Value Gap. Investments approved on competitive anxiety rather than economic evidence enter portfolios with weak proof standards, diffuse ownership, and no defined exit criteria - all of which make value realisation harder, not easier.

AI Portfolio

The full set of an organisation's AI initiatives, tools and agents, each with an owner, a cost and an evidence level, managed together so that each can be scaled, held, redesigned or stopped.

Why it matters: Treating AI use cases as a portfolio is what lets a CFO or CIO move money from what is not working to what is. Without it, the portfolio accumulates rather than curates: investments are added faster than underperforming ones are removed.

AI Pressure Index

A heuristic that shows whether downstream human capacity is being consumed faster than it recovers. Load at a stage is the capacity required divided by the protected capacity available. Pressure is the highest of the verify, decision and change loads. Under 0.70 is flowing, 0.70 to 0.85 is tight, above 0.85 is overloaded, and above 1.00 is unsustainable.

Why it matters: Queues form before full load, and quality can slip before a backlog is visible, so the index prompts action while there is still slack.

AI ROI

The return created by AI initiatives relative to the cost of building, operating, and governing them.

Why it matters: AI ROI matters because leaders need a credible way to judge whether AI spending is translating into measurable gains in efficiency, revenue, risk reduction, or strategic capability.

AI Sprawl

The uncontrolled proliferation of AI tools, pilots, vendors, and workflows across an organisation.

Why it matters: AI sprawl matters because duplicated tools, inconsistent standards, and weak ownership make cost, risk, and value proof harder to govern at portfolio level.

AI TCO

The total cost of owning and operating an AI capability across model access, infrastructure, data, governance, integration, and support.

Why it matters: AI TCO matters because the model price alone rarely explains the real economic burden of enterprise AI, especially once production controls and operating overhead are included.

AI usage cap

A limit on how much AI a person, team or API key may consume in a period, set in money, tokens or credits, after which use stops, slows or needs approval. Also called a token budget or AI allowance. A cap controls the size of the bill; it does not say which part of the bill produced value.

Why it matters: Caps are now a common response to AI spend that has outrun its budget. Set per person, they bind only on the heaviest users, who may also be the people getting the most from the tool, while low-value use below the line carries on. Budgets set by use case, with exemptions earned by evidence, control cost without making that trade blind.

AI Value Data Layer

A draft proposal from the Tokenomics Foundation, published on 5 November 2026, to join AI cost to value in one data store. It would add workload, task and outcome identifiers to OpenTelemetry, and four datasets alongside FOCUS billing data: telemetry, tasks, outcomes and value. A playbook and reference implementation are targeted for June 2027.

Why it matters: No measure that spans cost and value, such as cost per successful outcome, can be calculated from records until the bill, the work and the result share keys. A common format would make the evidence for the first three of this site's five layers comparable across tools and organisations.

AI Value Density

How much useful business outcome is created per unit of AI cost, risk, human effort, and operational complexity.

Why it matters: Value density is a better frame than generic ROI because it applies to a workflow, a product, a model choice, or a portfolio decision, not only to the aggregate. It moves the conversation from AI spend to how much outcome each unit of spend and effort produces.

AI Value Gap

The difference between what an organisation spends on AI and the share of that spend it can connect to a measured outcome: the complement of attribution coverage.

Why it matters: The AI Value Gap matters because organisations can scale platform cost, vendor commitments, and operational complexity before they establish enough visibility, ownership, and proof to justify continued investment.

AI value ledger

A small operating record for a material AI workflow that treats value evidence like financial evidence. The minimum record is an owner, a workflow ID, a model route, a value hypothesis, a measurement method, the evidence confidence and a scale, redesign or stop decision.

Why it matters: It joins technical telemetry, workflow metrics and business outcomes in one place, so a value claim has an owner, a baseline and an evidence level.

AI Value Management

The discipline of managing AI cost, usage, risk, adoption, quality, and realised business value across the full enterprise portfolio.

Why it matters: AI Value Management matters because AI spend can scale across SaaS, cloud, data, labour, and agentic workflows before organisations can prove which investments deserve more funding and which should be stopped.

Attribution Coverage

The percentage of AI expenditure linked to a baseline, an owner, and evidence of change. The headline metric of AI value management, and the complement of the AI Value Gap.

Why it matters: Attribution coverage tells a CFO or CIO how much AI spend has evidence behind it. Cost is metered to the token, but value usually is not, so without this figure a leadership team cannot say which part of its AI budget is defended and which is, in audit terms, unevidenced.

Autonomy ladder

A five-rung scale for how much authority an AI system has in a workflow: assist, recommend, act with approval, act within policy, and self-optimise. Autonomy should follow impact, reversibility and evidence, not the novelty of the model.

Why it matters: It turns the question of how much AI can be left to do into a question about proof, with high-impact and hard-to-reverse decisions staying with humans.

C

14 terms

Capability ROI

Return created by building reusable organizational capability that improves the economics of future AI work.

Why it matters: Capability ROI matters because some AI investments do not pay back through one immediate use case but through lower marginal cost, faster delivery, and stronger governance across later deployments.

Chargeback

A mechanism for allocating and billing technology cost directly to the business unit, service owner, or consumer responsible for demand.

Why it matters: Chargeback matters because AI demand can scale quickly and expensively, and formal cost assignment can create stronger accountability for who is consuming shared capability and why.

Cloud Efficiency Posture Management (CEPM)

A discipline and tooling category focused on continuously detecting and remediating cloud waste, inefficiency, and misconfiguration.

Why it matters: CEPM matters for AI because many AI cost problems are still mediated through cloud infrastructure behaviour, especially around GPUs, clusters, and shared platform waste.

Coding Agent

An AI tool that plans and carries out multi-step software tasks, such as editing several files, running tests and fixing failures, rather than only suggesting the next line of code.

Why it matters: Coding agents consume far more tokens per task than code completion, and the main tools now bill that usage beyond an allowance included with each seat. Spend therefore depends on how each engineer uses the agent, and value has to be judged on delivered work rather than on usage.

Cohort Mode

A coarse starting point in the Tokenomics Foundation's draft AI value data layer (November 2026): each workload is treated as its own task and outcome over a monthly window, before the organisation instruments individual requests.

Why it matters: It lets an organisation compare cost and value for its main workloads within a quarter, without waiting for every gateway and agent to emit task-level identifiers.

Cost Allocation

The method used to distribute shared technology cost across services, teams, products, or business units.

Why it matters: Cost allocation matters because AI costs often begin as shared platform investment and later become demand-driven; without a sensible allocation model, prioritisation and ROI discussions become distorted.

Cost Avoidance vs. Cost Reduction (AI)

Cost avoidance is the prevention of future costs that would otherwise have been incurred; cost reduction is a decrease in current expenditure. In AI business cases, these are often conflated, with cost avoidance claims presented as if they were cost reduction.

Why it matters: Cost avoidance and cost reduction have different accounting treatments, different certainty levels, and different budget implications. A CFO approving an AI investment on cost avoidance grounds should apply higher scrutiny than one approving on demonstrated cost reduction, because cost avoidance depends on a counterfactual that cannot be directly observed.

Cost per Action

The cost incurred each time an AI-enabled workflow completes a defined action such as a summary, recommendation, classification, or customer response.

Why it matters: Cost per action matters because it gives leaders a concrete way to compare AI cost against process value, service cost, or unit margin at the workflow level.

Cost per Inference

The average cost incurred each time an AI system generates one response or prediction.

Why it matters: Cost per inference matters because it gives leaders a simple but powerful way to compare AI usage behaviour against adoption and business value.

Cost per Outcome

The cost of achieving a defined business result such as a resolved case, converted lead, or completed internal workflow.

Why it matters: Cost per outcome matters because it links AI spend to business performance rather than stopping at model or workload activity.

Cost Pools

Aggregated groups of cost that are collected before being allocated, mapped, or analyzed further.

Why it matters: Cost pools matter because AI cost is often fragmented across vendors, cloud services, labor, and governance functions, and pooled views are a first step toward useful attribution.

Cost to Serve

The full cost required to deliver a capability, product, service, or workflow to its end user or customer.

Why it matters: Cost to serve matters in AI because model usage is only one layer of the economics; integration, support, governance, and platform overhead often determine whether the service is truly viable.

Cost-exhaustion attack

An attack that drives up an organisation's AI usage on purpose, so that the AI bill rises or a budget limit shuts the service down. It typically targets public-facing chatbots, agents and APIs, using long or repeated prompts, prompts that trigger expensive reasoning or tool calls, or automated traffic. It is also called a denial-of-wallet attack.

Why it matters: Usage-based pricing turns a traffic problem into a cost problem. A public-facing AI service without per-user limits can run up a large bill before anyone notices, so token consumption needs to be watched as a security signal as well as a cost line.

Counterfactual evidence level

A five-step scale, E0 to E4, for how strong the evidence is that an outcome was caused by AI. E0 is anecdote or expert opinion, E1 a before and after baseline, E2 an adjusted baseline or matched cohort, E3 a holdout group or quasi-experiment, and E4 a controlled experiment that has been independently validated.

Why it matters: An improvement after AI arrives is not proof that AI caused it. The level sets how much money or autonomy a claim can support: no monetised claim below E2.

D

1 term

Delivery Alignment

Layer 4 of AI value management: whether AI effort is aligned to funded initiatives, product outcomes, and strategic portfolio commitments, with AI spend and effort mapped to the structures used to plan work.

Why it matters: It connects AI activity to what the organisation has chosen to fund. Without it, leaders cannot prioritise, sequence, remove duplication or decide whether an experiment should become delivery.

E

1 term

Exit Criteria

Pre-agreed conditions under which an AI investment will be stopped, paused, or redesigned rather than continued.

Why it matters: Most enterprises have formal entry criteria for AI investments (business case requirements) and almost no exit criteria. This asymmetry produces portfolio accumulation rather than portfolio curation - investments that should be stopped persist because there is no trigger to stop them.

F

6 terms

Fine-Tuning

Adapting a pre-trained model with additional domain data so it performs better on a narrower set of tasks. It pays when it lets a smaller, cheaper model clear the quality floor at high volume; otherwise retrieval and better prompts are usually cheaper.

Why it matters: Fine-tuning adds a one-off training cost and a recurring burden of evaluation, hosting and retraining. Whether that pays depends on volume and on what the alternative costs, not on whether the tuned model is better in a demo.

FinOps

An operating discipline for managing variable technology spend through shared accountability between engineering, finance, and business teams.

Why it matters: FinOps matters for AI because model usage, context size, orchestration choices, and infrastructure patterns can all create fast-moving cost dynamics that need continuous management rather than periodic budget review.

FinOps for AI

The application of FinOps practice to AI spend: making it visible, allocated and efficient, with the same aim of business value that FinOps has for cloud.

Why it matters: It is usually the first discipline to get hold of AI cost, and the one that makes usage transparency possible. Knowing where it stops matters too: billing data does not contain outcome evidence, which is where AI value management picks up.

FinOps Scope

A formal domain of technology spend governed within the FinOps framework, such as cloud, SaaS, or AI.

Why it matters: FinOps Scope matters because AI is now recognised as a formal FinOps area, which creates a stronger basis for dedicated reporting, forecasting, and accountability.

FOCUS

The FinOps Open Cost and Usage Specification, an open standard from the FinOps Foundation that normalises billing and usage data across cloud, SaaS, data centre and other technology vendors. Version 1.4 was ratified on 4 June 2026. FOCUS 1.5 is scoped to add AI model identity and token consumption.

Why it matters: FOCUS matters because AI cost visibility depends on consistent usage and billing data, and common standards make it easier to analyze consumption across platforms and providers.

Frontier Model

A large AI model at the leading edge of current capability. Frontier models launch at premium prices that fall more slowly than those of older capability levels, so the economic question is when the extra capability is worth paying for.

Why it matters: Defaulting every task to the most capable model is one of the commonest causes of avoidable AI cost. Many tasks clear the quality floor on a smaller model at a fraction of the price.

G

3 terms

Generate, Verify, Absorb

A three-stage model of an AI-enabled workflow. Generate produces drafts, code, analysis, options and agent actions. Verify establishes that the output is right, safe and compliant to the level the risk demands. Absorb decides, adopts, changes the process and banks the benefit.

Why it matters: It shows that one stage is the binding constraint at a time, and that the constraint moves downstream when generation gets cheaper.

Governance Theater

Governance processes that generate reports, reviews, and documentation without materially changing investment decisions or operating behaviour.

Why it matters: Governance theater is expensive without being useful. It consumes management time, creates a false sense of control, and can actually slow down effective decision-making by substituting process activity for genuine scrutiny.

GPU

A graphics processing unit, the most common processor for training and running AI models. TPUs and other ASICs are specialised alternatives. For a buyer, accelerator economics turn on utilisation, commitment and who carries the cost of idle capacity.

Why it matters: Accelerator capacity is the largest cost line in self-hosted and committed AI, and it is paid whether or not it is used. A cheap hourly rate on a lightly used cluster can cost more per successful outcome than a dearer API.

I

4 terms

Idle Capacity

Reserved or owned AI compute that is paid for but not doing productive work in a period, whether held as headroom, booked but unused, or never allocated.

Why it matters: Idle capacity is a large part of the cost of self-hosted or reserved AI. How it is allocated decides whether teams see a fair internal price. Spreading it silently across users inflates the rate and can push work to external providers, which lowers utilisation further.

Inference

The process of running a trained AI model to produce an output from a prompt, query, or input signal.

Why it matters: Inference matters because most enterprise AI cost is incurred not when a model is trained, but when it is repeatedly used in production at scale.

Inference Cost

The recurring cost of generating outputs from a deployed AI system in production.

Why it matters: Inference cost matters because AI spend often scales with usage, and a workflow that appears affordable in testing can become structurally expensive once it spreads across teams, customers, or transactions.

Inference Optimisation

The practice of reducing the cost, latency, or resource intensity of AI inference without unacceptable loss of quality.

Why it matters: Inference optimisation matters because recurring production cost, not one-off model development cost, often becomes the main economic bottleneck in enterprise AI.

J

1 term

Jevons Paradox

The tendency for total consumption to rise when efficiency improvements make a resource cheaper or easier to use.

Why it matters: In AI, Jevons Paradox matters because falling per-token or per-inference costs can still lead to higher total spend if usage expands faster than unit cost falls.

M

4 terms

Measurement Capture

The conflict of interest that arises when the team that builds or sponsors an AI system is also responsible for evaluating whether that system is creating economic value.

Why it matters: Measurement capture does not require dishonesty to produce biased results. Confirmation bias in evaluation design - choice of baseline, selection of metrics, attribution methodology, measurement timing - systematically favours positive findings when the evaluator has a stake in the outcome.

Model Hosting

The way an AI model is deployed and run in production, whether through a managed provider, dedicated service, or self-hosted infrastructure.

Why it matters: Model hosting matters because deployment choice has major consequences for cost structure, performance, governance, utilization, and operating complexity.

Model Risk

The risk of adverse outcomes arising from decisions made using incorrect, misused, or inappropriately deployed AI or analytical models.

Why it matters: Model risk is a regulated category in financial services and an increasingly important governance concept more broadly. It is also a TCO cost driver - model validation, ongoing monitoring, and compliance obligations are recurring expenses that standard AI cost models routinely underestimate.

Model Routing

The logic used to direct different requests to different models, providers, or configurations based on cost, quality, latency, or policy.

Why it matters: Model routing matters because it directly changes AI unit economics; a well-routed workflow can reserve the most expensive models for high-value tasks while using cheaper paths where appropriate.

N

1 term

Net AI value

A management equation: (incremental outcome × monetisation × attribution × persistence × confidence) minus total cost, expected loss and opportunity cost. The incremental outcome is the outcome with AI minus the estimated outcome without it.

Why it matters: It penalises weak evidence, short-lived benefit, hidden cost and downside, and keeps confidence explicit rather than hiding it in a precise ROI number.

O

2 terms

Observe, Net, Convert, Book

The four-step claim chain in the Tokenomics Foundation's value classification. Observe gathers the inputs in one unit. Net turns them into one number with one confidence mark. Convert carries the net number into another unit if the audience needs one, and can repeat. Book names where a dollar result lands in the ledger. A claim that stops at Net is complete.

Why it matters: It gives a value claim a visible structure, so each step can be questioned: what was counted, what was netted off, what conversion was assumed and where the money lands.

Output Quality

Layer 2 of AI value management: whether AI is producing work that can be trusted, used, and improved, judged by useful and discarded output, review burden, model fit and quality risk rather than call volume.

Why it matters: A productivity claim built on output nobody uses is not a claim. This layer is where leaders decide to route models, tune prompts, strengthen evaluation, set human review thresholds and stop low-quality use cases.

P

5 terms

Planned vs Realised Value

The value an AI workload was expected to produce, recorded before the work starts, compared later with the value it actually produced. The Tokenomics Foundation's draft AI value data layer (November 2026) tracks each value claim through three stages: planned, estimated and realised.

Why it matters: Writing down planned value first gives a baseline that cannot be revised after the event. It makes a missed plan visible without making it a surprise, which lets an organisation fund experiments without pretending every one will pay off.

Portfolio Strategy

Layer 5 of AI value management: whether the total AI portfolio justifies its cost, risk, dependency, and opportunity cost, comparing investments by strategic fit, value evidence, operating risk, vendor exposure and human capability impact.

Why it matters: This is where a CFO, CEO or board decides to scale, optimise, contain, replatform, renegotiate or stop. It is only as good as the evidence the four layers below it supply.

Position ledger

A dated record of the positions a publication has taken, with their current status, the last assessment and the evidence behind it, so each can be shown wrong.

Why it matters: It makes editorial claims falsifiable and shows how they have held up over time.

Productivity Value

Layer 3 of AI value management: whether AI is improving the workflow or creating new work around it, comparing usage with throughput, rework, cycle time, quality and the human effort needed to manage AI.

Why it matters: Business cases often claim this layer before the layers below can support it. It separates capacity actually released and put to use from activity growth, which is what a CFO needs before treating time saved as value.

Prompt Efficiency

Getting the required output, at the agreed quality, with fewer tokens, cheaper models or less rework. Prompt engineering, the craft of designing prompts and context, is the means; prompt efficiency is its economic measure.

Why it matters: Prompt and context design is one of the cheapest levers on AI cost, because every token in a prompt is paid for on every call. It is also easy to overdo, because a shorter prompt that lowers quality creates review and rework that cost more than the tokens saved.

Q

1 term

Quality floor

What makes an AI output count as finished at all, together with the rate at which outputs clear it. Only outputs that clear the floor enter the count of benefit. It is set per workload before the estimate is made.

Why it matters: It stops rejected or unusable output being counted as value. Automating a thousand tasks with a hundred rejected means counting eight hundred, less the effort of fixing the rejects.

R

6 terms

RAG

Retrieval-augmented generation, which supplies a model with documents retrieved at query time so its answers are grounded in the organisation's own information. It avoids retraining, but the retrieved text is billed as input tokens on every call.

Why it matters: RAG is the usual way to make a general model useful on company data. Its cost is easy to underestimate because it sits in input tokens, embeddings and search infrastructure rather than in an obvious line item.

Realised Productivity Gain

The economic value actually captured from AI-driven time savings, as distinct from the theoretical time freed by an AI capability.

Why it matters: Most AI productivity claims measure time freed, not value captured. Realised productivity gain only occurs when freed capacity is converted into lower cost, higher output, or redeployment to more valuable work - through an active management decision, not automatically.

Reasoning Tokens

Tokens a reasoning model generates internally before it gives its visible answer. They are billed at the output rate, usually not shown to the user, and often several times longer than the answer itself.

Why it matters: Reasoning tokens are one of the largest sources of error in AI cost forecasts. A workload that looks cheap from the length of its answers can cost several times more once the hidden reasoning is counted, and standard cost dashboards often show only the total.

Reported vs Booked Value

Two statuses for an AI value claim, from the Tokenomics Foundation's draft AI value data layer (November 2026). Reported value is notional capacity, such as hours returned to a team. Booked value is a hard impact that the finance team has verified on a profit and loss line, such as a reduced cost or a revenue increase.

Why it matters: Most AI value claims are reported, not booked. Marking every claim with its status stops capacity estimates being added to cash savings in one total, and tells a CFO which part of a claimed return will show up in the accounts.

Return on scarce capacity

Expected net AI value divided by the load a project places on the binding constraint, such as review hours, change slots, risk capacity, compute budget or executive attention.

Why it matters: Two projects with the same return on cost can differ tenfold in return on scarce capacity. It ranks work; it does not state a value.

Risk tiers: Glance, Check, Scrutinise

Three levels of human attention matched to risk. Glance is for low-stakes, reversible work, verified by automated checks and a random human sample. Check is for material but recoverable work, where a person checks the evidence pack and samples the work. Scrutinise is for high-stakes or hard-to-reverse work, with a named independent reviewer who can reject it.

Why it matters: Attention is scarce. Tiering decides how much of it each item earns, and should be set against published criteria rather than chosen by the sender.

S

7 terms

SaaS Token Aggregation

The practice of bundling and obscuring underlying token consumption within SaaS subscription pricing, creating a visibility gap between AI usage and cost.

Why it matters: SaaS token aggregation hides the relationship between token consumption and business value, making it difficult for organizations to optimize AI usage, measure productivity, or make informed procurement decisions.

Scrap Rate

The share of AI spend that went into work that failed its quality check, was abandoned partway, or could not be tied to a task at all. A term from the Tokenomics Foundation's draft AI value data layer (November 2026), which counts such spend as scrap rather than dropping it.

Why it matters: Failed and abandoned runs are paid for like successful ones. Leaving them out flatters every unit cost. Scrap rate makes that waste visible, and keeping scrap in the numerator is what makes cost per successful outcome honest.

Shadow AI

AI tools, models, or workflows adopted outside formal enterprise governance, budgeting, or architecture processes.

Why it matters: Shadow AI matters because it weakens visibility, creates unmanaged spend, and increases the risk that AI demand scales before accountability and proof are in place.

Shared AI Platform

A common internal platform that provides reusable AI services, tooling, guardrails, and infrastructure to multiple teams.

Why it matters: Shared AI platforms matter because they can lower marginal delivery cost and improve control, but they also create allocation, chargeback, and governance questions that are easy to ignore early on.

Showback

A mechanism for reporting technology cost back to consumers without formally charging them for it.

Why it matters: Showback matters because it creates cost visibility and accountability signals before an organization is ready to move to formal chargeback for AI services, shared platforms, or model consumption.

Sovereign AI Infrastructure

AI infrastructure operated in a way that preserves jurisdictional control over data, models, compute, and service operation.

Why it matters: Sovereign AI infrastructure matters because control requirements can materially change the economics of hosting, support, compliance, and platform design.

Stage Gate (AI Investment)

A defined decision point in an AI investment lifecycle at which continuation of investment, and release of the next tranche of resources, requires demonstrating specific evidence of value or technical readiness.

Why it matters: Stage gates are the primary mechanism for raising proof standards as investment deepens. Without them, the evidence required to scale from pilot to production is no higher than the evidence required to begin a pilot - which produces portfolios where scale decisions are made on narrative rather than on evidence.

T

11 terms

TBM Framework 2.0

An updated TBM framework used to structure how technology cost is translated into business-facing management decisions.

Why it matters: TBM Framework 2.0 matters because AI governance needs more than cost visibility; it needs a management framework that connects services, capabilities, and portfolio choices.

TBM Taxonomy 5.0

A recent evolution of the TBM taxonomy that more explicitly supports AI-related resource, solution, and consumer modelling.

Why it matters: TBM Taxonomy 5.0 matters because it gives TBM leaders a more concrete structure for representing AI types, GPU-heavy resources, model choices, and AI-specific labour.

Technology Business Management

A management discipline that connects technology cost, consumption, and investment to business capabilities, services, and outcomes.

Why it matters: TBM matters in AI because it gives leaders a way to understand whether platform cost, model usage, and shared services are supporting business capabilities in an economically coherent way.

Technology Consumers

The business units, products, functions, or capabilities that consume a technology service or solution in a TBM model.

Why it matters: Technology Consumers matter because AI cost only becomes decision-useful when leaders can see who benefits from and who drives demand for shared AI capability.

Technology Resource Towers

A TBM structure that groups technology resources into broad categories such as compute, storage, network, software, or labor.

Why it matters: Technology Resource Towers matter because AI cost often spans several resource categories at once, and leaders need a structured way to see how those layers combine.

Technology Solutions

A TBM layer used to group technology capabilities into solutions that can be mapped to business demand and service consumption.

Why it matters: Technology Solutions matter because AI often needs to be represented as a shared platform capability rather than only as infrastructure or software spend.

Token

The unit in which AI models read and write text, and the unit in which most AI usage is metered and priced. A measure of consumption, not of value.

Why it matters: Tokens set the model bill and drive capacity planning, so they are the natural unit for managing AI spend. They do not say whether anything valuable happened, so they cannot stand in for outcomes, productivity or return.

Token Productivity

The business value generated per token consumed-a measure of how efficiently AI workflows convert token consumption into meaningful outcomes.

Why it matters: Token productivity reveals that not all tokens are equal. Two workflows consuming the same token volume can deliver vastly different business value, making token productivity a critical metric for AI optimization and investment decisions.

Total cost of AI (TCA)

In the Tokenomics Foundation's value classification, the costs that cannot be written in the unit of the baseline, such as tokens, GPU hours, power, licences and the labour to build and operate the platform. Together with a value claim it would be used to determine ROI. The Foundation describes it as work in progress.

Why it matters: It marks the boundary between a value claim and a return. It also overlaps with, but is not the same as, the seven-layer AI TCO framework used on this site.

Total Cost of Ownership

The full cost of building, operating, governing, and sustaining a technology capability over time.

Why it matters: TCO matters in AI because model price alone rarely reflects the real economic burden; infrastructure, data, governance, support, and portfolio overhead often shape whether an initiative remains viable.

Training

The process of teaching an AI model patterns from data so it can perform a task. For most enterprises, training is a cost the model provider carries and recovers through its prices; the enterprise's own costs sit in inference, adaptation and operation.

Why it matters: Training dominates headlines about AI cost, but few enterprises train large models themselves. Mistaking the provider's cost structure for your own leads to business cases that miss the recurring costs of running AI in production.

U

3 terms

Unit Economics

The economics of delivering one unit of output, service, transaction, user interaction, or business outcome.

Why it matters: Unit economics matters because it translates AI from abstract platform cost into a form leaders can compare against margin, service cost, or business value at operational scale.

Usage Transparency

Layer 1 of AI value management: knowing what the organisation spends on AI, where demand is appearing, and who is consuming AI capability.

Why it matters: Every later layer rests on it. Until AI cost can be normalised across SaaS, APIs, cloud platforms, private infrastructure and shared services, nobody can allocate, forecast, charge back, cap or investigate it, or separate pilots from run-rate demand.

Utilization

The degree to which a technology asset, platform, model capacity, or workflow is actually being used relative to what it could support.

Why it matters: Utilization matters because low usage can make AI platforms look expensive even when the architecture is sound, while high usage without governance can magnify cost and control problems.

V

5 terms

Value Capture Failure

The outcome when an AI system performs as designed technically but the organisation fails to convert its technical performance into measurable economic benefit.

Why it matters: Value capture failure is distinct from technology failure. The AI worked - the value was not absorbed. This distinction matters because the remedies are entirely different: technology failure requires technical intervention; value capture failure requires operating model and management intervention.

Value claim

A defensible statement about the incremental benefit of an outcome, measured or estimated against a baseline. In the Tokenomics Foundation's value classification it is the product of the method, and it is distinct from a return or ROI, which also needs cost.

Why it matters: It keeps a benefit estimate from being presented as a return. A claim can be complete without being an ROI.

Value Realisation

The process of converting expected benefits from an investment into measurable business outcomes.

Why it matters: Value realisation matters because AI usefulness does not automatically become business impact, and leaders need proof that promised gains are actually being delivered.

Vendor Lock-in (AI)

The accumulation of switching costs - technical, contractual, and operational - that make it economically difficult or practically infeasible to change AI providers, platforms, or model sources after initial deployment.

Why it matters: AI vendor lock-in is more pervasive and harder to reverse than traditional software lock-in because it operates at multiple levels simultaneously - model API, platform architecture, data format, workflow design, and contractual terms. Organisations that do not manage lock-in risk actively will find their pricing and capability choices constrained over time.

Verification cost

The cost of establishing that AI output is right, safe and compliant to the level the risk demands: reviewer time, triage, rework, escalation and the tooling for automated checks.

Why it matters: It is a real and growing line of AI total cost of ownership that is often buried in people or governance costs, and it rises as generation gets cheaper.

W

1 term