Words that reduce Friction.

Behavioral Economics⁵

The Center of Gravity of AInomics™: the study and practical application of how people and organizations actually make decisions and change behavior. AInomics™ applies Behavioral Economics as an organizational choice architecture that nudges the repeated production of informed—not optimal—decisions, exposes them to Reality, and supports learning and adaptation.

Attribution: Behavioral Economics is grounded in the study of actual decision behavior, including bounded rationality and systematic judgment under uncertainty. Daniel Kahneman’s Nobel lecture.

Behavior

Observable choices and actions that shape how work is performed.

Reality

Actual conditions, evidence, constraints, behavior, and consequences. Reality is the Final Reviewer™.

Conversation

The exchange through which questions, distinctions, knowledge, decisions, commitments, and work develop.

Capture

The deliberate preservation of useful decisions, definitions, constraints, learning, risks, and next actions.

Friction

Unnecessary effort, delay, search, ambiguity, misunderstanding, handoff, approval, or rework.

Transaction Costs⁴

The time, effort, delay, search, negotiation, coordination, and rework required to move from intention to action.

Attribution: The transaction-cost tradition begins with Ronald Coase and was extended through Oliver Williamson’s work on economic governance. Ronald Coase, “The Nature of the Firm”.

Decision

A commitment to a course of action, including the authority, rationale, constraints, and consequences attached to it.

Decisions in Progress (DIPs)

Important organizational decisions made visible while questions, evidence, alternatives, constraints, consequences, ownership, and next actions are still developing. AInomics™ treats progress as the relentless pursuit of informed—not perfect—decisions. Each informed decision constrains the remaining Decision Space, imposes greater order on Organizational Entropy, and allows Reality to produce evidence for the next decision.

Shared Vocabulary

A common set of operational definitions that reduces communication Transaction Costs, creates Shared Understanding, and codifies desired organizational behavior.

Operating Artifact

A reusable artifact designed to make a useful organizational behavior easier to perform consistently.

Decision Space

The range of unresolved alternatives, questions, constraints, and possible actions surrounding a decision. Each informed decision constrains the remaining Decision Space without requiring perfect certainty.

Organizational Entropy

A practical AInomics™ term for the ambiguity, unresolved questions, conflicting assumptions, fragmented knowledge, and uncoordinated activity that produce organizational chaos. Informed decisions impose greater order on this Entropy.

Enterprise AI

The AInomics™ Subject Matter Domain. Enterprise AI (EAI) is AI embedded in organizational work together with the People, Process, Platform, data, access, governance, security, and measurement required to create and deliver Customer Value. An LLM may be one component of EAI; it is not the whole system. EAI earns its place by increasing the utility of organizational behaviors that improve how the organization creates and delivers Customer Value.

Large Language Model (LLM)

A model trained on large amounts of language data to generate and transform language from learned patterns and the Context made available during the work. An LLM is one component that may be used within Enterprise AI. Fluent output is not verified truth.

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Context

The information, definitions, constraints, evidence, history, examples, and current objective made available to an LLM during the work. Better organizational Capture makes useful Context easier to assemble and reuse.

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Verification

The proportionate process of checking AI-assisted output against evidence, authoritative sources, rules, or human judgment before relying on it.

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Tool

A bounded capability an AI system can use to perform a function such as calculation, search, file generation, or database lookup.

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Connector

A governed bridge between an AI system and an application or repository, such as SharePoint, SAP, a document-management system, email, or a customer platform.

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System of Record

The authoritative source for a defined class of business data. AI may interpret or act through the system but should not silently replace its authority.

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Agent

An AI-enabled system that pursues a bounded objective through multiple steps using Context, Tools, and a defined degree of authority. For example, an Email Agent may classify messages, retrieve customer information, draft responses, schedule follow-up, and escalate exceptions. Executive design must specify what the Agent may perceive, decide, and do without additional human approval.

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Calibration

The organizational decision about how much confidence, Verification, human review, authority, and control are required before relying on an LLM-assisted recommendation or action. The required level rises with uncertainty and with the probability and consequences of being wrong.

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Compute

The computing resources available to execute software workloads. For AI, Compute is the Grid.

Hallucination

An AI output confidently presented but fabricated, unsupported, or incorrect.

Personal AI (PAI)

AI used primarily to augment an individual’s work, judgment, learning, or productivity.

On-Prem

AI systems or computing resources operated within infrastructure controlled directly by the organization.

Off-Prem

AI systems or computing resources operated outside the organization’s premises, commonly through a cloud or hosted provider.

People

The individuals, teams, roles, authority, judgment, incentives, and behaviors through which organizational work is performed.

Process

The repeatable sequence of conversations, decisions, actions, controls, and learning through which work moves.

Platform

The technology, data, access, integration, security, governance, and Compute capabilities that enable Enterprise AI.

Customer Value

The useful outcomes an organization creates and delivers for customers, including the differentiators that sustain its value proposition.

Three Ps

People, Process, and Platform—the interdependent elements that must align for Enterprise AI to create and deliver Customer Value.

Conceptual Vocabulary

Shared terms that give participants a common understanding of important AI and organizational concepts.

Behavioral Vocabulary

Shared phrases and principles that encode and reinforce desired organizational behavior.

Human-in-the-Loop

A governance checkpoint requiring human judgment, review, approval, or intervention before an AI-assisted decision or action proceeds.

Shared Understanding

Enough common meaning across participants to reduce ambiguity, improve coordination, and support consistent action.

AI Software/Hardware Stack

The layered combination of hardware, Compute infrastructure, models, data, software, tools, connectors, security, and applications required to deliver an AI capability.

Network Effect

A condition in which the value of a shared capability increases as more participants use it. For Shared Vocabulary, each additional participant creates more opportunities for lower-friction communication.

Organizational Critical Mass

The level of adoption at which a Shared Vocabulary becomes broadly useful across organizational conversations, decisions, and work.

AInomics™ Unlimited Organizational License

The AInomics™ organizational license designed to remove seat-count barriers so the Shared Vocabulary and Framework can reach Organizational Critical Mass.

Framework

The integrated AInomics™ structure that connects education, Shared Vocabulary, organizational behavior, execution, measurement, and learning around Customer Value.

Operating Asset

A reusable organizational resource that improves how work is performed, coordinated, or learned over time.

Execution

The conversion of decisions into coordinated action that produces evidence from Reality.

Learning

The disciplined use of evidence from Reality to update understanding, decisions, and future action.

Mission Critical Conversations™ (MCCs)

Conversations with enough leverage, uncertainty, consequence, or cross-functional dependency that their quality can materially affect organizational outcomes. An MCC typically advances a consequential Decision, changes commitments or resource allocation, creates an Operating Artifact, or materially changes what the organization does next.

Decision in Progress (DIP)

A consequential decision being advanced through uncertainty by making what is known, assumed, unresolved, and next required evidence visible.

Wicked Problem

A problem with no perfect solution. Understanding develops through attempts to solve it; every intervention changes the problem; proposed solutions are judged relatively as better, worse, or good enough for the current circumstances; and there is no natural stopping rule.

Bounded Rationality

Herbert A. Simon’s account of how people and organizations actually make decisions when rationality is constrained by limited information, time, attention, knowledge, foresight, and computational capacity. Decision-makers cannot identify or compare every possible alternative or consequence. They therefore use simplified models, routines, experience, judgment, and satisfactory thresholds to make responsible progress under uncertainty. Bounded Rationality does not imply irrationality; it explains why optimization is often unavailable in real organizational work.

Satisficing

Herbert A. Simon’s decision rule of ending search when a satisfactory solution has been found. For Wicked Problems, “Good Enough” is a disciplined and relative standard because no perfect solution exists.

Minimum Viable Product (MVP)

A sufficiently complete product, service, workflow, or intervention that Reality can evaluate, allowing the organization to test utility and generate meaningful learning.

Learning Velocity

The speed at which an organization converts encounters with Reality into improved judgment, reusable Operating Artifacts, and action.

Organizational Learning

Learning that is captured, made reusable, and converted into improved organizational Decisions, behavior, execution, or Customer Value.

Pruning

Rejecting a path made implausible by Reality while preserving the learning that reduces the Decision Space.

Fail Forward Fast (FFF)

A disciplined approach to organizational learning under uncertainty: bound the downside, make an informed Decision, move rapidly to Embodiment, capture Visible Demonstrable Evidence, and allow Reality to revise the problem and the next Decision. FFF is not permission to be careless.

Call to Action (CTA)

The specific action the course asks executives to take after learning. The Call to Action of AInomics™ for Executives is a bounded Customer Value pilot.

Sprint

A bounded execution and learning cycle tied to a specific Decision in Progress, Customer Value hypothesis, and evidence requirement.

Attention Cycle

A recurring organizational rhythm that concentrates scarce attention on evidence, Mission Critical Conversations, unresolved decisions, and the next execution cycle.

Mission Critical Conversation

A focused conversation required to resolve a material Decision in Progress, revise assumptions, surface conflict, or assign decision authority.

Reality Brief

An Operating Artifact that records what happened, what Reality revealed, and how the problem definition or next decision changed.

Personal Notes

A learner-created Operating Artifact capturing evolving assumptions, decisions, risks, evidence, and unresolved questions.

Pilot Portfolio

The managed collection of Enterprise AI pilots through which an organization allocates attention and resources, compares evidence, preserves learning, and progressively improves its ability to create Customer Value. In AInomics™, the portfolio functions as an enterprise learning loop without requiring a new executive label.

Embodiment

Definition: The practical expression of an idea, principle, design, or Decision in a tangible form that people can use, observe, test, and improve. Within AInomics™, concepts become embodied through Operating Artifacts, pilot work, workflows, products, services, executive Decisions, and organizational action.

Why it Matters: Organizations learn through execution rather than abstraction alone. Embodiment makes a Decision sufficiently real for Reality to test and for Visible Demonstrable Evidence to emerge.

See Also: Decision; Operating Artifact; Reality; Visible Demonstrable Evidence (VDE).

Executive Gates

Definition: Intentional evidence-based decision points at which executive leadership determines whether an Enterprise AI initiative should proceed, pivot, pause, scale, or stop. Executive Gates are governance events for improving Decisions under uncertainty—not bureaucratic approval checkpoints.

Why it Matters: Executive Gates prevent unresolved Decisions in Progress and sunk costs from carrying work forward without sufficient evidence, authority, or Customer Value.

See Also: Executive Decision Log; Pilot Scorecard; Reality Brief; Pilot Portfolio.

Pilot Operating Model (POM)

Definition: The governance and coordination framework that defines how an organization organizes authority, executes, learns from, and continuously improves Enterprise AI pilots. The POM establishes Decision Rights, operating rhythms, Handoffs, metrics, governance practices, the Pilot Portfolio, and the Operating Artifacts that support disciplined organizational learning.

Why it Matters: Enterprise AI success depends on organizational capability as well as technology. The POM provides the repeatable management system that converts bounded pilot experience into reusable organizational capability and Customer Value.

See Also: Pilot Project Roadmap (PPR); Operating Artifact; Pilot Portfolio; Enterprise AI Coordination Function; Handoff.