Project Ops

AI Readiness and Risk Assessment Support — Historical Track Audit

Project operations source: AI Readiness and Risk Assessment Support — Historical Track Audit.

This is historical evidence, preserved on 2026-09-09 from source checkpoint 9ff6eec. The matching Astro checkpoint is 5467ba9. The original inventory, July ideation, candidate sequence, and reserved decisions below are planning evidence, not current curriculum readiness or commitments to build every item.

The current Track Guide and shared Track Map carry the current composition: Friction Mapping as the signature, three developing investigation routes, shared support, and optional depth. The Track starts with understanding work; AI use is not a completion requirement.

Next Artifact Pass — Evidence and Unresolved Questions

  • Friction Mapping remains Sketch / Planned, owner Unassigned. Its current canonical module offers a 20–30-minute in-page practice and estimates about 60 minutes for the full lesson; these are not measured pilot results. The Track remains Draft / Drafting and Unassigned.
  • AI Is a Tool, Not the Organizing Frame remains Seed / Planned and is Friction Mapping’s stated dependency. It is shared preparation, not a second signature or a finished orientation. No dependency is removed or added by the Track pass.
  • Identifying the Squeaky Wheel is an internal module-local concept, not a canonical shared Lab. It names a future package location that does not exist. Its Lab package and standalone Notebook remain missing. The draft concept link stays visible for feedback without creating an available-Lab record.
  • The concept asks for Mermaid maps, eight retained outputs, and toolkit-ready artifacts. The current module permits paper, whiteboard, or discussion and says no formal record is required. Reconcile learner scope and completion before building the Lab; preserve a low-friction, nontechnical entry and make its Notebook self-contained.
  • Information-Flow Discovery Before Automation lacks a canonical module source. The old seven-module foundation path is a candidate sequence, not a ready Core. Friction vs Pain, Working but Person-Dependent, Preserve What Already Works, Decision Ownership Before Delegation, and Resilience and Outage Planning remain Seed / Planned.
  • The Decision Ownership Map and Delegation Ladder and Control Plan remain candidate artifacts, not a Lab or worksheet package. The Track’s broad readiness loop does not settle the reserved five-versus-six-stage ladder question or approve broader AI authority.
  • Consumer Reports for Emerging Technology remains Alpha, primary to Becoming an Informed Technology Consumer (Sketch / Planned). Product cards, a worksheet, scenarios, an answer guide, and facilitator material exist; its Notebook and learner guide remain Coming Soon, and timing/format are undetermined.
  • Generative Image Modification Lab remains Alpha, primary to Brand Images in the Age of Generative Editing (Sketch / Planned). Prompt cards, a worksheet, and facilitator material exist; its Notebook is Coming Soon. It is an optional paper prototype, not a full guided entry into readiness.
  • Conversational Privacy and Relationships with Conversational AI remain Sketch / Planned, primary to AI Literacy and related to AI Readiness. What Does the Tool Need? remains Beta, primary to Conversational Privacy, with its own related Track still AI Literacy. Its featured use here is a candidate handoff, not a reciprocal metadata change.
  • Safety and Reality Check remain Draft / Drafting, primary to AI Literacy and related to Curiosity. Their shared-support role here is a candidate relationship. Module ownership, lab ownership, and dependencies remain unchanged throughout.
  • How Technology Changes the Ecology of Communication remains Sketch / Planned and related to AI Readiness. Customer Communication Before AI Drafting, Secure Photo-Based Triage, and What Happens After Intake remain Seed / Planned.
  • When AI Starts Arguing Instead of Thinking and Designing Systems That Can Admit They Are Wrong remain Seed / Deferred. AI Story Reality Check remains an exercise idea rather than a standalone module or published Lab.
  • The old generic names (including What Is a Friction Map?, Business Continuity, and Safe AI Use for Small Businesses and Organizations) need selection or mapping, not invented source links. Reusable explainers should not become duplicate modules by default.
  • Beginner facilitation, observation-versus-hypothesis habits, privacy, accessibility, participant correction, and actual pilot timing need review. Demonstrations remain Coming Soon. Real client assessment and operational changes are separate decisions.
  • Preserve the sibling AI Readiness Kit boundary. Do not copy proprietary assessment sources here or settle the reserved vocabulary, tacit-context, and proprietary-artifact questions through a Track rewrite.

Original Track Guide Snapshot

AI Readiness and Risk Assessment Support — Original Outline

Status: Working track outline with July 14 ideation additions, not finished curriculum.

This track helps small businesses, nonprofits, teams, and advisors understand the work before choosing tools. It starts from respect: most organizations are not broken, they are operating inside real constraints. AI may help, but only after the team can see the workflow, information flow, communication patterns, decision ownership, operational memory, and resilience needs.

The invitation is practical. Learners do not need to become technologists. They need to learn how to ask better discovery questions, preserve what already works, identify friction without blame, and decide where AI should wait.

Audience: Small-business owners, managers, staff leads, consultants, facilitators, and future RS advisors.

Purpose: Support the AI Readiness and Risk Assessment offer without making AI the organizing frame.

Responsible AI is not only about protecting data and preventing misuse. It is also about understanding how AI systems reason, where they can become biased by prior conversations, and how humans can recognize when a conversation has shifted from exploration toward persuasion.

July 14 Ideation Additions

These additions capture a proposed direction for the next Track Guide. The two initial module ideas are now integrated into existing modules rather than shown as duplicate standalone sketches. This does not change the active July 13 through July 24 plan, establish a required sequence, or make any activity runnable or learner-available.

Proposed Track-Introduction Refinement

Learn to see the work system clearly enough to decide what AI may assist, what requires controls, and what should remain human.

AI readiness is often framed as a question of tools, data, budget, or willingness. This track starts with the work itself. A small organization is a living work system: people, routines, records, relationships, physical spaces, external services, and informal habits that change over time.

A job title is not an AI use case. A manager, for example, may be handling customer service, inventory, staffing, purchasing, compliance, troubleshooting, and emotional support—often using context that never appears on a dashboard. The useful unit of assessment is a decision loop: its trigger, context, remembered state, judgment, action, feedback, repair path, and consequence owner.

AI may help observe, organize, draft, compare, forecast, recommend, or narrow options. Durable records, authorization, limits, and transactions need systems that can maintain state and enforce boundaries. Human ownership remains especially important where context is tacit, action is hard to reverse, authority is consequential, or trust is at stake.

Readiness is therefore a delegation question, not a verdict on whether an organization is “ready for AI.” Learners map which decision loops are observable, controllable, testable, reversible, and accountable; some may be assisted now, some need better information or clearer ownership first, and some should remain human-led.

Integrated Module Direction

The integrated material adds two distinct readiness artifacts: a role-to-decision-loop map and a delegation ladder/control plan. It prepares the existing friction, information-flow, decision-ownership, resilience, and preservation work without creating duplicate modules.

Track Quick Start Seed: The AI Story Reality Check

This is a seed activity, not a standalone module or published lab. A learner chooses a public AI success or failure story and records:

  1. the claim and system boundary;
  2. what the system could observe and what context was informal or unavailable;
  3. where state, money, identity, commitments, authorization, and limits actually lived;
  4. the authority level, reversibility, feedback timing, human scaffolding, and cleanup work; and
  5. the evidence present, evidence missing, and narrower conclusion justified for their organization.

The retained artifact is an AI Story Reality Check with five sections: claim, system boundary, evidence present, evidence missing, and decision relevance. The goal is a more useful conclusion than either “AI works” or “AI does not work.” Use primary or originating sources where possible; do not teach a headline or a single case as a universal result.

Post-Beginner Ideation: Trustworthy AI Conversations

These deferred seeds belong after learners have basic AI-literacy experience. They help learners recognize when a conversation itself is beginning to shape judgment. They do not change the current foundation path or make a new class available.

Candidate Foundation Path

This is a suggested path, not a required sequence unless a future module explicitly states a prerequisite.

  1. AI Is a Tool, Not the Organizing Frame
  2. Friction Mapping – Understanding Work Before Recommending Solutions, including the role-to-decision-loop lens
  3. Friction vs Pain
  4. Information-Flow Discovery Before Automation
  5. Decision Ownership Before Delegation, including staged bounded delegation
  6. Preserve What Already Works
  7. Resilience and Outage Planning

Likely Modules

Starter Lab Ideas

  • Map one intake-to-outcome workflow.
  • Create a friction map from a realistic small-business scenario.
  • Sort candidate improvements into practical fixes, AI candidates, and AI-should-wait.
  • Role-play an outage or service interruption.
  • Rewrite a messy customer communication process before drafting with AI.
  • Review a fictional subscription, connected device, or AI-enabled service before purchase or renewal.
  • Rewrite an over-disclosing chatbot prompt into a useful but less identifying version.
  • Run a trust-by-task exercise for an AI tool a team has started relying on.

Additional Reading

No additional reading is listed yet.

Dependencies

  • AI is a tool, not the organizing frame.
  • Friction vs pain.
  • Workflow and information-flow discovery.
  • Decision ownership.
  • Safe data handling.
  • Basic technology consumer judgment.
  • Conversational privacy and account/tool boundary awareness.
  • Task-specific trust and human decision-ownership boundaries.

MVP Holes

  • Define the minimum assessment-support path for a beginner facilitator.
  • Decide how much of the sibling AI Readiness Kit should be referenced without duplicating it.
  • Define a reusable friction-map explainer that can appear inside other modules.
  • Keep functional decomposition and deterministic versus probabilistic processes as reusable explainers, not additional modules, unless repeated use proves otherwise.
  • Review possible glossary follow-up: decision loop, partial observability, tacit context, weak signal, error budget, bounded delegation, correlated failure, anchoring, confirmation bias, evidence selection, independent verification, adversarial review, red teaming, epistemic humility, calibration, and confidence versus correctness. Do not create definitions until the vocabulary pass confirms a shared need.
  • Clarify how much business-continuity content belongs in this track versus a deeper resilience path.
  • Decide whether technology-consumer renewal review becomes a required readiness step or remains a supporting module.

Decisions Made on July 14

  • Work Is a System, Not a Job is now part of Friction Mapping rather than a standalone module.
  • From Shadowing to Bounded Delegation is now part of Decision Ownership Before Delegation rather than a standalone module.

Decisions Reserved for Mike

  • Whether “grace gap” is useful language for the reliability and trust asymmetry, and whether the delegation ladder should have five or six stages.
  • Which tacit-context examples can be used without normalizing surveillance, and which assessment artifacts should remain proprietary to the sibling AI Readiness Kit.

Source: ops/track-audits/ai-readiness-and-risk-assessment-support.md