A common language reduces ambiguity and coordination friction.
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.
The AInomics™ organizational license designed to remove seat-count barriers so the Shared Vocabulary and Framework can reach Organizational Critical Mass.
The layered combination of hardware, Compute infrastructure, models, data, software, tools, connectors, security, and applications required to deliver an AI capability.
A recurring organizational rhythm that concentrates scarce attention on evidence, Mission Critical Conversations, unresolved decisions, and the next execution cycle.
Observable choices and actions that shape how work is performed.
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.
Shared phrases and principles that encode and reinforce desired organizational behavior.
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.
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.
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.
The deliberate preservation of useful decisions, definitions, constraints, learning, risks, and next actions.
The computing resources available to execute software workloads. For AI, Compute is the Grid.
Shared terms that give participants a common understanding of important AI and organizational concepts.
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.
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.
The exchange through which questions, distinctions, knowledge, decisions, commitments, and work develop.
Any person, team, organization, or external party that receives value from the outcome of a process. A Customer may be internal or external.
The useful outcomes an organization creates and delivers for customers, including the differentiators that sustain its value proposition.
A commitment to a course of action, including the authority, rationale, constraints, and consequences attached to it.
A consequential decision being advanced through uncertainty by making what is known, assumed, unresolved, and next required evidence visible.
A current course of action selected to advance a Decision in Progress. A Decision Path remains provisional and should continue only while evidence supports its plausibility.
Explicit authority defining who may recommend, decide, execute, verify, escalate, override, or stop work.
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.
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.
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. 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. Decision; Operating Artifact; Reality; Visible Demonstrable Evidence (VDE).
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.
The conversion of decisions into coordinated action that produces evidence from Reality.
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. Executive Gates prevent unresolved Decisions in Progress and sunk costs from carrying work forward without sufficient evidence, authority, or Customer Value. Executive Decision Log; Pilot Scorecard; Reality Brief; Pilot Portfolio.
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.
The integrated AInomics™ structure that connects education, Shared Vocabulary, organizational behavior, execution, measurement, and learning around Customer Value.
Unnecessary effort, delay, search, ambiguity, misunderstanding, handoff, approval, or rework.
An AI output confidently presented but fabricated, unsupported, or incorrect.
The bidirectional transfer of learning under uncertainty between people, teams, organizational functions, AI systems, tools, or external parties. A Handoff is not merely a transfer of responsibility; material learning must return immediately when it changes the problem definition, invalidates assumptions, creates organizational conflict, or exceeds delegated decision authority.
The intentionally designed allocation of tasks, judgment, authority, verification, and accountability between people and AI systems.
A governance checkpoint requiring human judgment, review, approval, or intervention before an AI-assisted decision or action proceeds.
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.
The disciplined use of evidence from Reality to update understanding, decisions, and future action.
The speed at which an organization converts encounters with Reality into improved judgment, reusable Operating Artifacts, and action.
A sufficiently complete product, service, workflow, or intervention that Reality can evaluate, allowing the organization to test utility and generate meaningful learning.
A focused conversation required to resolve a material Decision in Progress, revise assumptions, surface conflict, or assign decision authority.
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.
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.
AI systems or computing resources operated outside the organization’s premises, commonly through a cloud or hosted provider.
AI systems or computing resources operated within infrastructure controlled directly by the organization.
A reusable artifact designed to make a useful organizational behavior easier to perform consistently.
A reusable organizational resource that improves how work is performed, coordinated, or learned over time.
The level of adoption at which a Shared Vocabulary becomes broadly useful across organizational conversations, decisions, and work.
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.
Learning that is captured, made reusable, and converted into improved organizational Decisions, behavior, execution, or Customer Value.
The individuals, teams, roles, authority, judgment, incentives, and behaviors through which organizational work is performed.
AI used primarily to augment an individual’s work, judgment, learning, or productivity.
A learner-created Operating Artifact capturing evolving assumptions, decisions, risks, evidence, and unresolved questions.
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. 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. Pilot Project Roadmap (PPR); Operating Artifact; Pilot Portfolio; Enterprise AI Coordination Function; Handoff.
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.
The repeatable sequence through which a bounded Customer Value opportunity moves from definition through execution, evidence, learning, and the next Decision.
The technology, data, access, integration, security, governance, and Compute capabilities that enable Enterprise AI.
The repeatable sequence of conversations, decisions, actions, controls, and learning through which work moves.
The operating plan that defines the pilot’s scope, responsibilities, dependencies, milestones, evidence requirements, and Executive Gates.
Rejecting a path made implausible by Reality while preserving the learning that reduces the Decision Space.
Actual conditions, evidence, constraints, behavior, and consequences. Reality is the Final Reviewer™.
An Operating Artifact that records what happened, what Reality revealed, and how the problem definition or next decision changed.
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.
Enough common meaning across participants to reduce ambiguity, improve coordination, and support consistent action.
A common set of operational definitions that reduces communication Transaction Costs, creates Shared Understanding, and codifies desired organizational behavior.
A bounded execution and learning cycle tied to a specific Decision in Progress, Customer Value hypothesis, and evidence requirement.
A bounded short-cycle plan that identifies the work, learning objective, expected evidence, and responsibilities for the next period of execution.
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.
People, Process, and Platform—the interdependent elements that must align for Enterprise AI to create and deliver Customer Value.
A unit an AI system uses to process text and other information. AI usage and cost are often measured in tokens. Token consumption can therefore matter economically, particularly as AI use scales, but the number of tokens consumed is not by itself a measure of productivity or Value.
A bounded capability an AI system can use to perform a function such as calculation, search, file generation, or database lookup.
The time, effort, delay, search, negotiation, coordination, and rework required to move from intention to action.
Calibrated organizational reliance earned through bounded performance, verification, legibility, evidence, and recourse.
The proportionate process of checking AI-assisted output against evidence, authoritative sources, rules, or human judgment before relying on it.
Observable, reviewable evidence produced when Reality tests an embodied Decision in Progress. VDE informs whether a Decision Path remains plausible and supports learning, pruning, continuation, or pivoting.
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.