AIFC Standard Draft: English Language Baseline

Defines the core principles of AIFC for communities that want to use AI as an accelerator while preserving human ownership of purpose, values, decisions, know-how, responsibility, and direction.

Defines the core concepts of the AIFC standard so they are human-readable, agent-actionable, and software-verifiable.

Describes the basic AIFC community model, including purpose, values, knowledge, decisions, work, learning, feedback, AI involvement, and interfaces with other communities.

Describes the role of values and purpose in an AIFC community and their connection to strategy, work, AI involvement, and feedback.

Defines how an AIFC community receives signals, converts them into change proposals, evaluates them, and reflects approved changes back into the Source of Truth, strategy, workflows, skills, or values.

Defines the structure of an AIFC community knowledge base and explains how documentation becomes a Source of Truth usable by people, AI agents, and validators.

Defines Operational DNA as the critical part of a community knowledge base, including its content, value, risks, protection, and relationship to AI, the Source of Truth, and the Human Cockpit Layer.

Defines principles for writing an AIFC knowledge base in a human-readable, agent-actionable, and software-verifiable form using Markdown, metadata, and standardized structure.

Defines principles for creating content that is understandable to people, usable by AI agents, and verifiable by software.

Defines Human-Managed AI: AI may accelerate and support the community, but it must not own purpose, values, accountability, critical decisions, or operational capability.

Defines AI as external expert capacity governed by purpose, scope, confidentiality, budget, owner, audit, knowledge return, and exit strategy.

Defines the AI-NDA Boundary as the confidentiality boundary for using AI with non-public community know-how, including data, purpose, processing, audit, incidents, and lock-in risks.

Defines AI as a governed team member with role, scope, permissions, limits, human owner, audit, approval rules, fallback, and shutdown capability.

Defines Human Capability Reserve as the deliberately maintained ability of people and the community to understand, perform, review, recover, and transfer critical work even without AI.

Defines AI Capacity Planning as a governance mechanism for planning, allocating, measuring, and evaluating AI capacity according to purpose, values, risk, cost, and human review capacity.

Defines the difference between AI Autonomy and AI Intensity and how to set them according to risk, values, data sensitivity, review capacity, governance maturity, and community impact.

Defines AI Operating Modes as named modes of AI involvement in a community and their relationship to intensity, autonomy, capacity, AI-NDA Boundary, fallback, risk, and budget.

Defines rules for governing AI cost, budget, thresholds, cost visibility, cost ownership, cost-value measurement, and budget incidents.

Defines AI lock-in as the risk of losing independence from an AI vendor, model, agent, tool, memory, skill store, workflow, or integration, and describes exit strategy.

Defines AI Retrospective as the mechanism by which a community evaluates the impact of AI on value, cost, risk, human capability, dependency, knowledge base, workflows, skills, and governance.

Defines the AI Waste Backlog as a structured place for recording, classifying, prioritizing, and resolving repeated or low-value AI capacity use.

Defines Workflow Conversion as the conversion of repeated AI work, AI waste, unclear routines, or manual repetition into stable workflows, templates, validators, scripts, skills, or stop-work decisions.

Defines Skill Evolution as the mechanism by which a community converts experience from work, AI outputs, retrospectives, reviews, waste, incidents, and good patterns into human skills and AI skills.

Defines the difference and relationship between human skills and AI skills and explains why critical know-how must remain human-readable and agent-usable.

Defines the Community Interface as a governed interface through which a community communicates with members, other communities, customers, suppliers, AI agents, and external experts.

Defines the Enterprise Interface as a governed system of interfaces inside and around a company, connecting values, strategy, teams, products, processes, customer voice, AI agents, and the Source of Truth.

Defines the Shared Values Layer as a values layer for cooperation between communities, teams, companies, AI agents, suppliers, customers, and ecosystems.

Defines Multi-Community Governance as a mechanism for cooperation, decision-making, escalation, values alignment, risk sharing, AI involvement, and conflict resolution between multiple communities.

Defines Knowledge Security as protection of the community's knowledge, operational, and decision-making capability, including classification, audit, AI boundaries, integrity, backup, recovery, and exit readiness.

Defines Access Control as a governed access mechanism for people, AI agents, systems, vendors, and communities interacting with the knowledge base, Source of Truth, Operational DNA, skills, decisions, and workflows.

Defines Agent Permissions as a governed permission model for AI agents working with the knowledge base, tools, workflows, data, decisions, and community interfaces.

Defines Auditability as the ability to trace, explain, and verify meaningful actions by people, AI agents, systems, vendors, and communities over the knowledge base, AI workflows, access, and changes.

Defines Data Classification as a security and governance mechanism for the knowledge base, Operational DNA, AI inputs and outputs, derived knowledge, audit logs, and sharing.

Defines Company as a System as the application of AIFC principles to a company described as an intentionally designed, knowledge-grounded, human-managed, and AI-accelerable system.

Defines Company as Product as a company operating model structured and protected so that it can be replicated, licensed, deployed, audited, or operated as a digital product.

Defines Company Generation as the process through which AI and people jointly design, validate, create, and prepare a new company or community as an AIFC-compliant system.

Defines Digital Company and Ghost AI Company Risk: the risk of a company facade without a responsible community, human ownership of purpose, values, governance, fallback, and transparency.

Defines AIFC Compliance Levels as maturity and conformance levels from basic conscious AI use to a human-managed, agent-actionable system.

Defines the minimum requirements a community must meet to be considered minimally AIFC-compliant.

Defines the Certification Model for demonstrating conformance of a community, company, product, workflow, AI agent, or Company as Product with the AIFC standard.

Defines AIFC as a standard that is not only human-readable, but also agent-usable and partially software-verifiable through rules, schemas, templates, workflows, and skills.

Defines Schemas and Metadata Registry as the central catalogue of structured artefacts, metadata, fields, types, statuses, relationships, and validation rules in the AIFC knowledge base.