AI Readiness and Risk Assessment Support
- Track lifecycle
- Draft
- Learner availability
- Drafting
- Owner
- Unassigned
Understand how work actually happens, preserve what works, and decide where AI can help, what needs better controls, and what should remain human-led.
Track Promise
Understand how work actually happens, preserve what works, and decide where AI can help, what needs better controls, and what should remain human-led.
Start with the work, not a tool. Most organizations are not broken; people are working within constraints, maintaining relationships, and finding ways to get things done. Understanding those practices comes before recommending AI, automation, a new system, or a process change.
This Track is for small-business owners, nonprofit leaders, managers, staff leads, advisors, and facilitators. You do not need to become a technologist. Learn to ask useful discovery questions, describe friction without blame, and distinguish evidence from assumptions. A job title is not an AI use case, and an organization does not need one blanket verdict of “ready” or “not ready.”
Start Here
Begin with Friction Mapping — Understanding Work Before Recommending Solutions — Sketch / Planned. It is this Track’s signature module.
Try its existing in-page practice: trace one fictional or low-risk process from beginning to end, mark a handoff or delay, separate an observation from a hypothesis, and name something the evidence does not yet justify recommending. Paper, a whiteboard, or discussion is enough; no AI or diagramming tool is required.
Stated preparation: AI Is a Tool, Not the Organizing Frame — Seed / Planned. This is the signature module’s existing dependency and the Track’s framing, not a second signature or a finished orientation package. The Track does not remove that dependency or turn it into a new mandatory module sequence.
Still to build: Identifying the Squeaky Wheel. Its internal planning concept is open for feedback, but the canonical Lab package and standalone Notebook do not exist yet. Use the module’s in-page practice for a limited review; do not mistake the concept for a complete Lab.
The Readiness Loop
Scope → Map → Notice → Investigate → Decide
- Scope one ordinary process. Name its beginning, end, purpose, participants, and information boundary. Agree what is outside this inquiry.
- Map how the work happens now: inputs, steps, decisions, outputs, handoffs, exceptions, and repair. Include the people doing the work; a diagram is a draft they can correct.
- Notice waits, repeated work, missing context, conflicting expectations, and useful workarounds. Record what works as well as what causes difficulty. Friction is information, not a judgment about a person.
- Investigate before explaining or recommending. Separate observations from hypotheses, ask whose perspective is missing, and trace the information and decision ownership. A role may contain several different decision loops.
- Decide what the evidence supports next. Gather more information, improve something without AI, explore bounded AI assistance, preserve a useful practice, or leave the work human-led. State what should wait.
AI may help organize notes, compare possibilities, or draft a map. Compare its output with the source material and participant corrections. It must not invent observations, resolve disagreements on people’s behalf, or turn a plausible story into evidence. Readiness includes the possibility that AI does not belong in the task.
Choose Your Next Investigation
Follow the question your map raises. These are developing routes, not a finished seven-module core or a required course sequence. Linked modules keep their lifecycle and learner-availability labels; labs have separate release labels. A source file can be present while its package is still unfinished.
Developing route
Understand the work
Find out whether a difficulty is wasted effort, a necessary control, a useful adaptation, or a sign that important context is missing.
- Friction vs Pain Seed / Planned
- Working but Person-Dependent Seed / Planned
- Preserve What Already Works Seed / Planned
- Information-Flow Discovery Before Automation Proposed · no module source
Missing: the information-flow module and guided Lab packages for these developing directions. Mapping work is not permission to collect private records or monitor employees.
Facilitator route demonstration: Coming Soon
Developing route
Decide what to delegate
Choose one decision, name its human owner, and ask what help could be allowed without handing over more authority than the evidence supports.
- Decision Ownership Before Delegation Seed / Planned
Observation, drafting, recommendation, and execution are different permissions. Broader autonomy is not the assumed destination. Keep approval, limits, exceptions, recovery, and stopping conditions visible.
Missing: a guided Decision Ownership Map and Delegation Ladder and Control Plan. These remain candidate artifacts, not a runnable Lab or authority to automate a real workflow.
Facilitator route demonstration: Coming Soon
Developing route
Prepare for dependence
Before relying on a tool, ask what it can do today, what happens when it changes or fails, and how people can recover or leave.
- Becoming an Informed Technology Consumer Sketch / Planned
- Its lab: Consumer Reports for Emerging Technology Alpha — fictional product cards, a decision worksheet, and facilitator material.
- Resilience and Outage Planning Seed / Planned
The consumer lab’s Notebook and learner guide are Coming Soon; its format is not yet set. A guided outage/recovery Lab is also missing. No purchase, subscription, or live business-system change is required for this planning route.
Facilitator route demonstration: Coming Soon
The earlier seven-module foundation sequence remains historical planning evidence. The guide now starts with Friction Mapping and offers questions to pursue from it; individual module dependencies remain unchanged.
Strengthen Your Practice
Choose support when the inquiry needs it. These are not extra requirements before you can draw a first map.
- Conversational Privacy — Sketch / Planned, primary to AI Literacy and related to AI Readiness. Its What Does the Tool Need? lab is Beta, with a standalone Lab Notebook. Use its fictional task packet to practice sharing only the context a tool needs; it is privacy practice, not a substitute for the missing Friction Mapping Lab.
- Safety: Share Less, Notice More — Draft / Drafting. Shared-support candidate from AI Literacy for information boundaries and limited tool access.
- Reality Check for AI — Draft / Drafting. Shared-support candidate from AI Literacy when an AI claim or output needs checking.
- Relationships with Conversational AI — Sketch / Planned, primary to AI Literacy and related to AI Readiness. Notice when fluent conversation starts carrying decision authority or more trust than a task warrants.
Module-owned labs stay with their modules. These selections do not add reciprocal Track metadata or change ownership. What Does the Tool Need? remains assigned to Conversational Privacy and related to AI Literacy in its own metadata; its use here is a proposed cross-Track handoff.
Optional: Go Deeper
Public communication and image decisions can become useful branches when they arise from the work:
- Customer Communication Before AI Drafting — Seed / Planned. Set voice, approval, and escalation expectations before drafting on an organization’s behalf.
- How Technology Changes the Ecology of Communication — Sketch / Planned, primary to AI Literacy and related to AI Readiness.
- Brand Images in the Age of Generative Editing — Sketch / Planned, with its Generative Image Modification Lab, Alpha. The lab is a paper prototype with prompt cards and a worksheet; its Notebook is Coming Soon. Use fictional training images, not client or employee material.
Developing applications: Secure Photo-Based Triage and What Happens After Intake remain Seed / Planned. They are planning sources, not ready assessment workflows.
Deferred depth: When AI Starts Arguing Instead of Thinking and Designing Systems That Can Admit They Are Wrong remain Seed / Deferred. Their discussion of persuasion, challenge, uncertainty, and correction belongs after basic AI-literacy experience.
The AI Story Reality Check remains a developing exercise idea for examining the evidence and limits in an AI success or failure story. It is not a standalone module, published Lab, or replacement for the signature entry. Its original notes are preserved in the historical audit linked from the shared Track Map.
Participation and Access
Paper, whiteboard, discussion, paired work, and facilitated participation are valid ways to complete the current in-page practice. No AI account, paid tool, diagramming software, or public presentation is required. A learner can describe a process while a partner records it; the people represented should have a chance to correct the picture.
Use fictional, public, role-based, or suitable sanitized examples. Do not bring confidential business processes, customer records, employee performance details, credentials, or private communications into a classroom exercise or chatbot. Missing context calls for a follow-up question within the agreed boundary, not broader surveillance.
If AI helps organize or redraw the work, record its contribution and check the result. A spoken explanation, written note, or short demonstration can show what changed in your understanding. A polished diagram is not the goal, and no live business change is required.
For Facilitators
Begin with the signature module’s working facilitator notes. For a limited first review, use its in-page practice with one fictional process, room for corrections, and a clear point where recommendations must wait. Signature walkthrough demonstration: Coming Soon.
Gather actual timing, language and setup friction, accessibility needs, and evidence that learners distinguish observations from hypotheses. Notice whether they preserve useful workarounds and can explain what they still need to know. Use that evidence to select the next route.
The full signature Lab and self-contained Notebook still need design before a Lab pilot. The internal concept asks for Mermaid maps and toolkit-ready outputs, while the current module permits paper and does not require a formal record. Resolve that difference in the later artifact pass; do not make Mermaid or client-ready deliverables a hidden requirement.
This is learning and assessment preparation, not a completed client assessment, professional qualification, or authorization to change a business system. Keep the sibling AI Readiness Kit’s source and proprietary assessment materials in that project; this Track does not copy or replace them.
Additional Reading
No additional reading is listed yet.
Status and Feedback
This Track remains Draft / Drafting, with ownership Unassigned. Friction Mapping remains Sketch / Planned, owner Unassigned. Its internal Lab concept is not a canonical Lab package. Existing Alpha and Beta labs retain their own release labels and open reviews; source presence does not establish readiness.
Use this page’s Track feedback and review links for the promise, starting point, preparation, route roles, status language, and missing or misplaced modules and labs. Does the entry start with the work? Do the routes help learners choose a next investigation without rushing toward AI? Are any support choices unnecessary or missing? Teaching details belong in the separate Module and Lab reviews.
The shared Track Map records this organization, open relationships, and the historical audit. This Track pass creates no new Lab, Notebook, assessment instrument, or demonstration, and promotes no curriculum status.
Team review information. You are viewing the team review site. The contributor review tools below are separate from the learner activity and learner feedback.
Review status
- Package lifecycle: Draft
- Learner availability: Drafting
- Review needs: Needs track-level composition, AI Readiness alignment, accessibility, and pilot review.