Glossary

Source: Ibbaka Glossary for Value Packaging Pricing and Billing of AI, shared under a Creative Commons licence. valueIQ-specific entries have been added.

A

Agent

AI Packaging Pattern An AI agent is a software program designed to autonomously perform specific tasks or solve problems by using artificial intelligence. These agents can interact with users, systems, or environments to achieve predefined objectives. They often rely on techniques such as machine learning and natural language processing to adapt and improve their performance over time.

Agent Family

AI Packaging Pattern A collection of Agents performing related functions or tasks. Agents in a family can often be sequenced to perform a more elaborate business process or in some cases substituted for each other.

Agentic AI

AI Foundation Agentic AI refers to artificial intelligence systems capable of autonomous action and decision-making. These systems, often called AI agents, can pursue goals independently, make decisions, handle complex situations, and adapt to changing environments without direct human intervention. They leverage advanced techniques such as reinforcement learning and evolutionary algorithms to optimize their behavior and achieve specific objectives set by their human creators.

AI-as-a-Service (AIaaS)

AI Packaging Pattern A cloud-based service model that provides artificial intelligence capabilities to businesses without the need for extensive in-house AI development. This allows companies to leverage AI technologies through APIs or pre-built models.

AI Bias

AI Foundation AI Bias refers to systematic errors in AI systems that can lead to unfair or discriminatory outcomes. These biases can stem from training data, algorithm design, or human prejudices inadvertently incorporated into the AI system. AI used in pricing models must be carefully tested for bias.

AI-Driven Dynamic Pricing

Pricing Methodology A pricing strategy that uses artificial intelligence algorithms to automatically adjust prices in real-time based on market demand, competitor pricing, and other relevant factors.

AI Pricing Optimization

Pricing Methodology The use of AI and machine learning techniques to determine optimal pricing strategies that maximize revenue or profit.

Algorithmic Pricing

Pricing Methodology A method of using algorithms to set prices for products or services, often incorporating AI to analyze market data and customer behavior.

Ambient AI

AI Foundation AI that is available anytime, anywhere, often through devices.

Anchor Price

Pricing An anchor price is a published price used as a reference point to make subsequent prices appear reasonable. It is used to anchor buyer expectations about pricing in a market and is part of behavioral pricing.

Application Credits

Pricing Methodology Fungible credits that can be used to pay for a variety of different functions and are transferable from one function or product to another.

Artificial Intelligence (AI)

Discipline Artificial intelligence (AI) is a branch of computer science that aims to build machines capable of performing tasks that typically require human intelligence. AI enables machines to simulate human abilities, such as learning, problem-solving, decision-making, and comprehension.

Attribution

Customer Value Management A standard variable in a value driver equation that indicates what part of an outcome can be attributed to a solution.

B

Behavioral Pricing

Pricing Behavioral pricing involves setting prices based on an understanding of how customers behave and make purchasing decisions. It leverages psychological factors, such as cognitive biases, to optimize pricing strategies and increase revenue, anchor price, price framing and the order in which pricing information is provided are used in behavioral pricing.

Bundled AI Services

Pricing Methodology A pricing strategy where multiple AI-based features or services are combined into a single package offered at a unified price.

Bundling

Pricing A pricing strategy where multiple products or services are sold together as a package, often at a discount compared to buying them separately. Bundles are often less formal than packages and may be constructed for specific marketing campaigns.

Business case

Customer Value Management The executive-ready output valueIQ generates for a deal: a cited, structured account of the economic value a product delivers to a specific customer, built on the value model and the deal's confirmed variables. In the app it is generated from Value Stories in Deal Studio, where it can be previewed, versioned, shared and exported to PowerPoint or PDF. It is distinct from an ROI calculator or a slide deck because it is built on a value model, so every number traces back to an equation and a source.

C

Category Value Map

Customer Value Management A visual tool used to analyze and communicate how a product or service creates value for customers. It typically consists of a Value Map (describing the products and services offered) and a Customer Profile (outlining the customer's jobs-to-be-done, pains, and desired gains).

CausalML

AI Foundation CausalML is a subfield of machine learning that focuses on understanding and estimating causal relationships between variables.

Chain of Thought

Prompting Strategy Chain of Thought (CoT) is an AI reasoning technique that enhances the problem-solving capabilities of large language models by breaking down complex tasks into smaller, logical steps.

Co-Pilot

AI Packaging Pattern An AI Co-Pilot is a smart assistant powered by artificial intelligence that collaborates with users to boost productivity, decision-making, and creativity through real-time suggestions, automation, and insights.

Cognitive Pricing

Pricing Methodology A pricing approach that leverages AI to understand and predict customer behavior, preferences, and willingness to pay.

Computational Intelligence

AI Foundation An alternative to the transformer-based approach used by Wolfram Alpha to compute answers to questions. Especially powerful in math-related applications.

Compute Costs

AI Cost Factor The financial expenses associated with the computational resources required for AI systems to perform tasks such as processing data, training machine learning models, and making predictions. These resources can include hardware like GPUs and TPUs, as well as cloud computing services.

Consumption Pricing

Pricing Methodology An approach to pricing in which the pricing metric is the amount of a service actually consumed.

Context Based Pricing

Pricing Methodology Pricing methodology proposed by Mark Stiving where the business context is used to determine the best approach to packaging and pricing.

Cost Plus

Pricing Methodology A pricing methodology where a fixed percentage or amount of profit is added to the cost of a product or service. This approach ensures that all costs are covered and a profit is made on every sale.

Credit-Based Pricing

Pricing Model A monetization model in which customers purchase a fixed allotment of credits upfront or on a recurring basis, then spend those credits as they consume individual features, actions, or outputs. Rather than paying per seat or per subscription tier, usage is metered at the activity level: each action draws down a credit balance, and customers replenish when depleted. Credits typically abstract over variable-cost outputs: a short task might cost 1 credit while a complex one costs 5, letting vendors align revenue to the actual work performed. This makes it popular in AI tools, API platforms, and analytics products where workload intensity varies significantly across customers.

Cross Price Elasticity

Pricing A measure of how likely one is to switch vendors or brands in response to a price difference (often triggered by a price change).

Customer Lifetime Value (CLV)

Customer Value Management The total worth of a customer to a business over the entire duration of their relationship, used to inform pricing and customer retention strategies.

D

Data Monetization

Pricing Methodology The process of generating revenue from data assets, often involving AI for data analysis and insights generation.

Deep Learning (DL)

AI Foundation A subset of machine learning that uses artificial neural networks with multiple layers to learn from large amounts of data. It enables computers to process information in ways similar to the human brain.

Design Structure Matrix (DSM)

Customer Value Management A square matrix used to represent and analyze the structure of complex systems or processes. It lists system elements along both rows and columns, with off-diagonal cells indicating relationships or dependencies between elements.

Diffusion Model

AI Foundation A type of generative AI model that progressively adds random noise to data and then reverses the process to generate high-quality outputs. Widely used in image generation, video synthesis, and sound design.

Disruptive Innovation

Innovation Approach A process where a smaller company with fewer resources successfully challenges established incumbent businesses by introducing innovations that disrupt the market. Term coined by Clayton M. Christensen.

Distillation

AI Foundation A process for transferring a bigger model's capabilities to a smaller one.

Dynamic Pricing

Pricing Methodology An AI-driven strategy that allows prices to fluctuate based on real-time factors such as market demand, customer behavior, and competitor pricing.

E

Economic Value

Customer Value Management The worth of a good or service determined by people's preferences and the trade-offs they choose given their scarce resources.

Elastic Access Model

Pricing Model A flexible monetization approach that allows for dynamic adjustment of prices and packaging, enabling businesses to adapt quickly to market changes and customer needs.

Embedding

AI Foundation A means of representing objects like text, images, and audio as points in a continuous vector space where the locations of those points are semantically meaningful to machine learning algorithms.

Estimated value

Part of Value Model The unadjusted total of a deal's enabled value drivers: the sum of each selected, valid driver's base value before attribution and execution risk are applied. It is the sticker number, what the drivers add up to before anyone discounts for realism. Show it alongside expected value, never on its own, because an unadjusted total presented by itself reads as an inflated claim.

Expected value

Part of Value Model The risk- and attribution-adjusted total of a deal's enabled value drivers. For each driver, impact equals base value multiplied by the attribution percentage and by one minus the execution risk; expected value is the sum of those impacts. It is the defensible number to present to a buyer and the denominator of Value Capture. In views where it sits directly beside Price it may be labelled simply Value.

Explainable AI (xAI)

AI Foundation A set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. It aims to make AI systems more transparent and interpretable.

F

Federated Learning

AI Foundation A machine learning technique that enables training models on distributed datasets without centralizing the data. It allows multiple parties to collaboratively train a model while keeping their data locally.

Few-Shot Learning

AI Foundation An AI approach where models are trained to recognize new classes or perform new tasks with very few examples, typically 1 to 5 samples per class.

Foundation Model

AI Foundation A large language model, generally with more than one trillion parameters, that powers many AI applications.

Freemium AI Model

Pricing Model A business model where basic AI features are offered for free, with advanced capabilities available for a premium.

Freemium-to-Premium Conversion Rate

Packaging The percentage of free users who upgrade to a paid version of a product or service, a crucial metric for freemium business models.

Frontier Model

AI Foundation A model that is at the leading edge of development, based on its size (number of parameters) or on some performance criteria.

Fungible Token or Credit

Pricing Methodology A type of digital asset that is designed to be identical in value and interchangeable with other tokens of the same type.

G

Generated Value Model

Customer Value Management A value model generated using generative AI by a value model generation process (VMG).

Generative Pricing

Pricing Methodology Uses generative AI to create new pricing models and strategies. It leverages AI's ability to analyze vast amounts of data to develop innovative pricing approaches, including dynamic pricing, personalized pricing, and value-based pricing.

Generator

AI Packaging Pattern A product or function using a model capable of producing new data examples that resemble the training data it was provided.

Good Better Best (GBB)

Packaging Pattern See Tiered Pricing (GBB).

Growth Motion

Go-to-Market Approach A Go-to-Market tactic: Product Led Growth, Sales Led Growth, Service Led Growth, AI Led Growth, Community Led Growth, Partner Led Growth, Relationship Led Growth.

H

Hybrid Pricing Models

Pricing Model Pricing strategies that combine multiple approaches, such as blending subscription-based pricing with usage-based components to offer more flexibility and predictability for both customers and vendors.

I

Input Token

AI Pricing The pieces of text provided to a language model as input. These tokens can be individual characters, words, or sub-words and are used by the model to understand meaning and context.

Input:Output Ratio

AI Pricing The ratio of the price of an input token to the price of an output token. In most foundation model pricing, the output token is priced higher than the input token.

J

Jevons Paradox

Innovation Approach An observation by 19th-century economist William Stanley Jevons: even when we develop technology that more efficiently uses a commodity, it may increase, rather than decrease, demand for that commodity.

Job Based Pricing

Pricing Methodology Pricing methodology proposed by Gary Bailey for pricing agents. Leverages Clayton Christensen's Jobs-to-be-Done model.

K

K-Means Clustering

AI Approach An unsupervised learning algorithm used to group unlabeled data into clusters based on similarity. It partitions data into a predefined number of clusters (K) by minimizing the variance within each cluster.

L

Large Action Models (LAM)

AI Foundation Designed to learn and execute complex sequences of actions, often used in robotics and autonomous systems.

Large Audio Models (LAM)

AI Foundation Focused on processing and generating audio data, including speech recognition and music generation.

Large Knowledge Models (LKM)

AI Foundation Focused on storing and retrieving vast amounts of factual information.

Large Language Model (LLM)

AI Foundation A type of AI model designed for natural language processing tasks. These models utilize deep learning and are trained on vast datasets to predict and generate text. They are foundational models in AI, often based on Transformer architectures.

Large Multimodal Models (LMM)

AI Foundation Models that can process and generate content across multiple modalities, such as text, images, and audio.

Large Reasoning Models (LRM)

AI Foundation Models that emphasize logical reasoning and problem-solving capabilities, implementing approaches such as Chain of Thought or Self-Discover.

Large Simulation Models (LSM)

AI Foundation Used for creating detailed simulations of complex systems, such as climate models or economic forecasts.

Large Video Models (LVVM)

AI Foundation Designed to understand and generate video content, combining aspects of vision and temporal processing.

Large Vision Models (LVM)

AI Foundation Specialized in processing and understanding visual information, crucial for computer vision tasks.

Leiden Clustering

AI Approach A community detection algorithm used in network analysis. It identifies groups of nodes that are more densely connected to each other than to the rest of the network, improving upon the Louvain algorithm.

M

Machine Learning as a Service (MLaaS)

Pricing Model A cloud-based offering that provides machine learning tools and services on a subscription or pay-per-use basis.

MCP (Model Context Protocol)

valueIQ Platform An open protocol that lets an AI assistant call tools exposed by an external system. valueIQ's MCP Server at mcp.valueiq.ai connects Claude, Cursor, VS Code Copilot and ChatGPT to your workspaces, value models, deals and market data. You sign in through OAuth 2.1 the first time you use a tool, and every call runs under your own identity and workspace permissions, so the assistant sees exactly what you see in the app. Tools and session checkpointing are live.

Menu Options

Packaging Pattern A packaging pattern where the buyer selects from a set of options for each category, much like a restaurant menu.

Mixture of Experts

AI Foundation A model in which only some specialized parts (experts) are active at a time, making the model cheaper to use.

Monetization Flywheel

Pricing A self-reinforcing cycle where improvements in one area of monetization (e.g., pricing, packaging, or customer acquisition) lead to improvements in others, creating a virtuous cycle of growth.

O

Open Source Model (OSM)

AI Foundation A machine learning system that grants users the freedom to use, study, modify, and share its components without restriction. This includes access to the model's weights, source code, and training data information.

Open Weights (OW)

AI Foundation AI models whose parameters or 'weights' are publicly accessible and can be used or modified without restriction, offering more transparency and control than closed or proprietary models.

Outcome Based Pricing

Pricing Methodology A pricing model in which the cost of a product or service is based on the value or outcomes it delivers to the customer, rather than a fixed agreed cost.

Output Token

AI Pricing Units of text that a language model generates as a response to the input tokens. These are the building blocks that allow the model to interpret and generate language.

P

Packaging

Packaging Pattern Standard approaches to combining functions, data, and services so that they can be priced and sold.

Parameters

AI Foundation The billions of small numbers that make up an AI model and determine its behavior.

Pay-Per-Prediction Pricing

Pricing Model A pricing model where customers are charged based on the number of predictions or inferences made by an AI model.

Platform + Extensions

Packaging Pattern The integration of additional functionalities or capabilities into an existing platform. Extensions can include new algorithms, APIs, or tools that enable real-time data processing, task automation, or other advanced functionalities.

Predictive Pricing

Pricing Methodology The use of AI and predictive analytics to forecast future pricing trends and optimize pricing strategies accordingly.

Price Elasticity AI

Pricing Methodology AI systems designed to analyze and predict how changes in price will affect demand for products or services.

Price Elasticity Dynamics

Pricing The interactions between price elasticity of demand and cross price elasticity. Different market dynamics and different pricing strategies arise in each of the four quadrants: high price elasticity of demand x high cross price elasticity, high price elasticity of demand x low cross price elasticity, low price elasticity of demand x high cross price elasticity and low price elasticity of demand x low cross price elasticity.

Price Framing

Pricing Price framing refers to the way prices are presented to influence customer perceptions. It involves highlighting the benefits, outcomes, and overall value of a product to justify its price, shifting the focus from cost to investment.

Pricing Metric

Pricing Model The unit of consumption for which a buyer pays. See also Value Metric.

Prompt Engineering

AI Foundation The practice of designing and refining input prompts to optimize the output of generative AI models, essential for effective use of AI in various applications including pricing and value analysis.

Prompt Orchestration

AI Foundation The practice of sequencing a series of prompts, often with feedforward and feedback loops, to implement a business process through generative AI. Value Model Generation (VMG) is an example of prompt orchestration.

Provenance

Part of Value Model The recorded origin of every variable value in a business case. Each variable carries a provenance badge in one of six states: market default, derived, seller-set, seller-confirmed, customer-explored and customer-confirmed. The badge is projected live from the variable's value history, and a business case set to resolve to confirmed values always shows the latest confirmed entry beside a customer-confirmed badge, never a later unconfirmed exploration. Hover a badge in the app for a plain-language explanation of its state.

R

Realisation rate

Customer Value Management The proportion of the attributed value projected to land in year one given implementation timeline and adoption realities. A high rate reflects a straightforward rollout; a low rate reflects complex change management or a long adoption ramp. Paired with the attribution estimate (the share of the KPI movement the platform specifically causes), it produces the projected annual value for a value driver. This is a timing and adoption projection, not a probability-of-failure estimate.

Reasoning Model

AI Foundation AI systems designed to perform logical inferences, problem-solving, and decision-making tasks. These models often employ techniques like chain-of-thought reasoning to break down complex problems.

Reasoning Token

AI Pricing Metric A token generated in OpenAI o1 and o3 models and used as a pricing metric.

Reference Price

Pricing A reference price is a type of anchor price. It is a baseline price that customers use to evaluate the fairness or attractiveness of a product's price. It can be an internal benchmark (e.g., a previous purchase) or an external comparison (e.g., competitor prices).

Reinforcement Learning From Human Feedback (RLHF)

AI Foundation A machine learning technique that combines reinforcement learning with human evaluation to train and optimize AI models. It uses human feedback to create a reward model that guides the AI's learning process.

S

Scaling Laws

AI Foundation An observation that a model's performance tends to reliably improve given more parameters, training data and compute.

Self-Discover

Prompting Pattern A prompting pattern where the LLM chooses the best reasoning structure to tackle complex reasoning problems. An alternative to chain of thought.

Service as Software

AI Packaging Pattern A business model that involves automating tasks previously performed by humans while maintaining the same user interface. The backend processes are replaced with algorithms or robotics.

Session checkpointing

valueIQ Platform A capability of the valueIQ MCP Server that lets an AI agent save its working state and pick it up again later. A save checkpoint tool stores the state and a get checkpoint tool retrieves it, so a coaching agent can resume where it left off across turns and processes rather than starting from scratch. Session checkpointing is live.

Subscription-Based AI

Pricing Model A recurring revenue model where customers pay a regular fee to access AI-based tools and services.

Sustaining Innovation

Innovation Approach Incremental improvements to existing products, services, or processes to maintain competitive advantage and meet customer needs. From Clayton Christensen's work on innovation.

Synthetic Data

AI Foundation Artificially generated data that mimics real-world data. Created using algorithms and particularly useful for training AI models when real data is scarce, sensitive, or expensive to obtain.

T

Tiered Pricing (GBB)

Packaging Pattern The Good-Better-Best pricing strategy involves creating three price bands or tiers for different product or service bundles. Each level includes better features or functionality than the one below, encouraging consumers to upgrade.

Token

AI Pricing Metric See Input Token, Output Token, and Reasoning Token.

Token-based Pricing

Pricing Methodology A monetization model where users purchase tokens or credits that can be exchanged to access various AI functionalities. This approach allows for flexible pricing and usage metering.

Tokenization

AI Foundation The process of breaking down data into smaller units called tokens. Essential for machine learning models to process and understand the data.

Training Data

AI Foundation The data (text, images, videos etc.) used to develop an AI model. For LLMs, training data comes in two stages: pre-training (learning to predict text from trillions of words) and post-training (more refined data including RLHF).

Transformer

AI Foundation A type of neural network architecture that processes sequential data using mechanisms like self-attention to understand context and relationships within the data. Widely used in NLP and other AI applications. "All you need is attention."

U

Usage-based Pricing

Pricing Model A pricing model where customers are charged based on their actual usage of the AI service, such as the number of API calls, data processed, or computational resources consumed.

V

Value

Pricing Foundation There are three types of value: Economic Value, Emotional Value, and Community Value. All are relevant to pricing but Economic Value plays the leading role.

Value Based Pricing

Pricing Methodology A strategy of setting prices primarily based on a consumer's perceived value of a product or service. Companies base their pricing on how much the customer believes a product is worth.

Value Based Segmentation

Customer Value Management A method of dividing customers into groups based on the value they derive from a product or service, allowing for more targeted pricing and marketing strategies.

Value-Based AI Pricing

Pricing Methodology A pricing strategy that sets the price of AI services based on the perceived or measured value they provide to customers.

Value Capture

Customer Value Management Price divided by expected value: the share of the value created for the customer that the seller is charging for. It is the inverse of return on investment (value divided by price) and is used to target pricing levels in value-based pricing and, within a deal, to judge pricing strength and negotiation leverage. A Value Capture of 20 percent means the customer keeps 80 percent of the value created, a five times return on their spend. Some views in the app still label this figure "VCR" (Value Capture Ratio) or "vIQ Score"; both mean Value Capture. It has also been called the Value Ratio.

Value confirmation

Customer Value Management The act of attesting that a variable's current value is correct, which is a separate gesture from editing it. A seller can confirm a value from the deal page or from within a business case, and an invited customer can confirm it from the shared business case, which records a customer-confirmed state. Confirmations are supersession-only: a new confirmation replaces the previous one and the full history is kept, so there is no way to unconfirm a value to nothing. Confirmed values feed the provenance badge and the business case's resolution policy.

Value Cycle

Customer Value Management A management framework that defines how organizations create, communicate, deliver, document, and capture value. It is an iterative process used to sustain superior performance.

Value Driver

Part of Value Model A factor determining the value of a solution to a customer. There are six main types: Increase Revenues, Reduce Operating Costs, Reduce Operating Capital, Reduce or Defer Capital Investment, Reduce Risk, and Increase Options.

Value Driver Equation

Part of Value Model An equation used to quantify a value driver.

Value Driver Variable

Part of Value Model A variable in a value driver equation, usually specific to a customer/configuration combination. There are four types: Solution Variable, Customer Variable, External Variable, and Improvement Claim.

Value Metric

Part of Value Model The unit of consumption by which a buyer gets value. See also Pricing Metric.

Value Model

Customer Value Management A system of equations that estimates the economic value (dollar value) a solution provides to the buyer. It is foundational to value-based pricing and market segmentation.

Value Model Generation (VMG)

Customer Value Management The use of generative AI and related technologies to generate a value model and keep it updated. Generative AI can also be used to customize a value model for a specific customer.

Value to Customer (V2C)

Customer Value Management The value that a solution provides to a customer. Can be cumulative over the life of the solution or for a specific time interval.

W

Willingness to Pay (WTP)

Pricing Methodology The maximum price a customer is willing to pay for a product or service. It reflects the perceived value of the product or service.

Related

• FAQ
• Value drivers and equations
• Customer variables and provenance