A
13 terms
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 Return on Investment
A measure of the return created by AI initiatives relative to the cost of building, operating, and governing them.
Why it matters: AI ROI matters because enterprise AI often creates multiple forms of value at different speeds, and weak ROI models can lead leaders to overstate benefits or scale spend before proof is credible.
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 Value Gap
The difference between the value organisations expect from AI and the value they can actually verify in operating performance.
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 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.
Allocation
The process of assigning shared technology costs to services, teams, products, or business units.
Why it matters: Allocation matters because AI often begins as a shared platform cost, and weak allocation models can make local use cases appear cheaper or more expensive than they really are.
ASIC
An application-specific integrated circuit designed to perform a narrow set of computing tasks very efficiently.
Why it matters: ASICs matter because specialised hardware choices influence the economics of AI training and inference, especially in large-scale or sovereignty-focused deployments.
C
10 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.
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.
E
1 term
F
7 terms
Fine-Tuning
The process of adapting a pre-trained model using additional domain-specific data so it performs better on a narrower set of tasks.
Why it matters: Fine-tuning matters because it can improve relevance or accuracy for enterprise use cases, but it also introduces extra cost, governance overhead, and lifecycle complexity.
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 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, a standard for normalizing cloud cost and usage data across providers.
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.
FOCUS Specification
The formal FinOps Open Cost and Usage Specification used to standardise technology cost and usage data across providers.
Why it matters: The FOCUS Specification matters because AI cost governance depends on better-structured usage and billing data than many native provider exports currently provide.
FOCUS Standard
A common shorthand for the FOCUS specification, used to describe the emerging FinOps standard for cost and usage data normalisation.
Why it matters: The FOCUS Standard matters because AI cost data needs more consistent structures if leaders want to compare workloads and providers intelligently.
Frontier Model
A highly capable large-scale AI model operating near the leading edge of current performance.
Why it matters: Frontier models matter because they often set the benchmark for capability, but they may also come with higher cost, higher governance requirements, and less predictable economics than smaller alternatives.
G
2 terms
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 commonly used to train and run AI models because it handles parallel computation efficiently.
Why it matters: GPU matters because access to GPU capacity is often a major cost driver in model training, inference, and self-hosted AI infrastructure.
I
3 terms
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
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.
P
2 terms
Prompt Efficiency
The practice of achieving the desired model output using fewer tokens, lower-cost models, or simpler prompt structures.
Why it matters: Prompt efficiency matters because prompt design directly affects token usage, latency, model choice, and therefore the recurring cost of AI workflows.
Prompt Engineering
The practice of designing prompts, instructions, and context structures so an AI system produces better outputs more efficiently.
Why it matters: Prompt engineering matters because it affects quality, latency, and cost at the same time; better prompts can reduce waste as well as improve outcomes.
R
3 terms
RAG
Retrieval-Augmented Generation, an approach that combines a model with external information retrieval to improve relevance and grounding.
Why it matters: RAG matters because it can improve enterprise usefulness without full model retraining, but it also adds cost through retrieval pipelines, vector infrastructure, and orchestration.
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.
Retrieval-Augmented Generation
A pattern in which a model uses retrieved external context at runtime to generate more grounded answers.
Why it matters: Retrieval-augmented generation matters economically because it can improve output relevance without always requiring fine-tuning, but it also adds context, data, and infrastructure cost that must be included in TCO.
S
5 terms
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
10 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
A unit of text that AI models use to process prompts and generate outputs.
Why it matters: Token matters because many commercial AI models are priced according to the number of input and output tokens consumed, making token volume a core driver of AI operating cost.
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.
TPU
A tensor processing unit, a specialized processor designed to accelerate machine learning workloads.
Why it matters: TPU matters because specialized hardware can change the performance and cost profile of AI workloads, influencing where and how models are economically hosted.
Training
The process of teaching an AI model patterns from data so it can perform a task or generalise across tasks.
Why it matters: Training matters because it is often the most visible part of frontier-model economics, even though many enterprises will find that inference and operations dominate long-run cost.
U
2 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.
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
3 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 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.