AI & Digital Quality School · Learning track
Use AI where it improves work—and keep people accountable for the result.
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01 Learning plan
Public foundation 2026.10.08-learning-paths-1 · published 2026-10-08. Independent subject-matter review is pending; paid delivery and credential issuance remain closed. This track is organized around inspectable work, not attendance alone.
Workload
No validated completion time is published. Read the six modules, then complete the six case assignments and self-review across as many sessions as needed. This release has not yet been timed with learners; no facilitated capstone is included.
Prerequisites
Outputs
Working vocabulary
02 Public foundation curriculum
Each module states what to understand, what to practice, what evidence to retain, and the criteria for moving forward. The case lab turns those standards into six realistic assignments.
Module 1
Objective: Decide whether and how AI may assist a task by evaluating harm, data sensitivity, reversibility, expertise, scale, and available controls.
Open Lesson 1: examples, worksheet & feedback →Begin with the task, not the tool. Describe the current input, output, user, decision, frequency, owner, and failure consequence. Separate assistance from authority: drafting a meeting summary is different from approving a loan, diagnosing a condition, selecting a candidate, or changing a supplier. Score potential impact on people, finances, safety, rights, reputation, security, and operations. Then consider data sensitivity, reversibility, need for licensed or specialist judgment, likelihood that users will overtrust the output, and scale. A frequent low-severity error can create more harm than an obvious one-off failure.
Choose a treatment only after classification. Low-risk tasks may use AI with ordinary review; moderate tasks need stronger sourcing, testing, access, and sampling; high-impact or regulated decisions may require a qualified specialist, formal impact assessment, or no AI use. Record prohibited uses as clearly as approved uses. The classification should name the accountable process owner and the person authorized to accept residual risk. Reassess when the model, vendor, data, audience, jurisdiction, or decision changes. This course supports operational risk thinking but does not determine legal compliance or approve high-impact systems.
Practice
Classify eight fictional tasks, including summarization, marketing copy, invoice coding, hiring screening, safety instructions, customer refunds, translation, and vulnerability triage.
Evidence to keep
Submit a task inventory with impact, sensitivity, reversibility, required expertise, AI role, human owner, controls, and approve-restrict-prohibit decision.
Ready to move on when
Module provenance
External authorities, common professional practice, and Academy-selected controls are identified separately. A source supports only the stated scope; it does not convert this lesson into legal, licensed, accredited, or regulatory advice.
Module 2
Objective: Create a versioned instruction package that defines the task, authorized context, evidence rules, output contract, uncertainty behavior, and review handoff.
Open Lesson 2: examples, worksheet & feedback →A dependable instruction package describes purpose, audience, authorized sources, definitions, constraints, required output, prohibited content, and what to do when evidence is missing. Give the model the minimum context needed and label source boundaries explicitly. If the answer must come from a policy set, require source identifiers and quotations short enough for a reviewer to verify, while respecting copyright and confidentiality. Do not ask the system to invent citations, legal certainty, numerical inputs, customer facts, or current information it cannot access. Examples should demonstrate the rule, not secretly replace it.
Treat instructions as controlled process documentation. Record the model or service, instruction version, source version, key settings where available, output schema, and reviewer role. Test ambiguous language and conflicting sources before release. Define how the system should express uncertainty, abstain, and request clarification. Structured outputs can improve consistency but do not guarantee truth; a well-formed JSON object can still contain fabricated facts. A reviewer should be able to trace material claims to authorized sources and distinguish quoted evidence from model-generated interpretation. Changes to prompts or sources require targeted regression testing, not silent production edits.
Practice
Rewrite a vague request to 'summarize our policy and recommend what to do' as a controlled instruction for a synthetic policy set with conflicting versions.
Evidence to keep
Submit the instruction, source manifest, output contract, abstention rule, two test outputs, and a reviewer traceability check.
Ready to move on when
Module provenance
External authorities, common professional practice, and Academy-selected controls are identified separately. A source supports only the stated scope; it does not convert this lesson into legal, licensed, accredited, or regulatory advice.
Module 3
Objective: Map data entering and leaving an AI workflow and enforce approved-purpose, access, retention, vendor, and deletion boundaries.
Open Lesson 3: examples, worksheet & feedback →Inventory the information a workflow receives, retrieves, generates, logs, exports, and shares. Classify each element under the organization's data rules and ask whether it is needed. Names, contact details, financial records, health information, credentials, source code, contracts, trade secrets, client files, export-controlled material, and regulated records can create distinct obligations. Redaction is not automatically anonymization; combinations of details may re-identify a person or reveal a business. Use synthetic or de-identified test data when possible, and never paste sensitive material into a personal or unapproved service.
Tool approval should cover more than a brand name. Record the specific service and plan, administrator, authentication, access roles, data-use terms, training-use settings, region, retention, connectors, subprocessors, logging, deletion method, incident path, and exit plan. Confirm which outputs may be stored or reused. A browser extension or integrated assistant can move data even when the user does not intend it, so approved workflows should specify where the tool may operate. Privacy, security, procurement, legal, and records specialists must review questions within their authority. The operator documents and follows the boundary; the operator does not invent one.
Practice
Create a data-flow and approval record for a fictional AI-assisted customer-case summary, then redesign it to remove unnecessary personal data and disable an unneeded connector.
Evidence to keep
Submit the before-and-after data map, data inventory, approved tool profile, retention and deletion rule, access roles, and unresolved specialist questions.
Ready to move on when
Module provenance
External authorities, common professional practice, and Academy-selected controls are identified separately. A source supports only the stated scope; it does not convert this lesson into legal, licensed, accredited, or regulatory advice.
Module 4
Objective: Design review that is capable of detecting material failure and assign authority for approval, action, exception, and shutdown.
Open Lesson 4: examples, worksheet & feedback →Human review is a control only when the reviewer has time, competence, context, authority, and evidence. Clicking approve after scanning a fluent answer is not meaningful oversight. Define what must be checked, against which sources or rules, at what sampling rate, and before which action. High-consequence outputs may require review of every case or separation of duties. Give reviewers an accessible view of inputs, relevant sources, model output, uncertainty, prior changes, and known limitations. Measure whether reviewers actually detect seeded defects; otherwise the workflow may create automation bias rather than safety.
A decision-rights matrix should name who prepares, reviews, approves, publishes, acts, investigates, and can pause the workflow. Avoid shared accountability that leaves nobody responsible. Reviewers must be able to reject output without penalty for slowing automation and must know when specialist expertise is required. Consider impacts on people who cannot challenge a result and provide a redress route where appropriate. When humans routinely rewrite most outputs or miss the same failure, change the workflow rather than blaming the reviewer. Document overrides and their reasons so patterns can reveal unclear instructions, data gaps, model limitations, or inappropriate task selection.
Practice
Design review for a fictional AI-drafted supplier-risk summary and test two reviewers with seeded unsupported claims, omitted caveats, and a conflicting source.
Evidence to keep
Submit the review checklist, decision-rights matrix, seeded-defect results, override log, and escalation rule.
Ready to move on when
Module provenance
External authorities, common professional practice, and Academy-selected controls are identified separately. A source supports only the stated scope; it does not convert this lesson into legal, licensed, accredited, or regulatory advice.
Module 5
Objective: Build a representative evaluation set and use decision-linked metrics to determine whether a workflow is fit for its bounded purpose.
Open Lesson 5: examples, worksheet & feedback →An evaluation set should represent ordinary work, difficult cases, rare but harmful failures, missing information, conflicting sources, multilingual input where relevant, and attempts to bypass instructions. Preserve the inputs, authorized sources, expected qualities, critical failures, and scoring rubric under a version. Keep a holdout subset from routine prompt tuning so improvement is not measured only on familiar examples. Use synthetic data unless approved real records are necessary and governed. Evaluation should include groups or conditions that could experience different error rates, while avoiding unsupported demographic inference.
Choose metrics based on the decision. Useful measures may include source-supported claim rate, critical-failure rate, abstention quality, extraction accuracy, reviewer agreement, override rate, processing time, and cost per accepted output. An overall average can conceal unacceptable high-impact errors, so define non-compensable failures and release thresholds in advance. Compare against the current human or non-AI process when feasible; faster is not better if rework or harm increases. Record confidence intervals or sample limitations rather than presenting small tests as certainty. Re-evaluate after material changes and monitor real exceptions because a pre-release set cannot represent every future condition.
Practice
Create a 20-case evaluation set for a synthetic policy-answering workflow, including normal, conflicting, missing-source, injection, and high-impact cases, then score two instruction versions.
Evidence to keep
Submit the versioned set, rubric, critical-failure definitions, results, limitations, comparison, and release recommendation.
Ready to move on when
Module provenance
External authorities, common professional practice, and Academy-selected controls are identified separately. A source supports only the stated scope; it does not convert this lesson into legal, licensed, accredited, or regulatory advice.
Module 6
Objective: Operate an AI workflow with visible exceptions, monitoring, rollback, version control, and a manual path when approved conditions are not met.
Open Lesson 6: examples, worksheet & feedback →Define exceptions before launch: unavailable sources, unsupported formats, low-quality scans, conflicting instructions, sensitive data, policy gaps, high-impact requests, unusual volume, model outage, suspected prompt injection, or outputs that fail validation. The system should fail safely by abstaining, preserving the original input, and routing to an authorized person. Users need a clear way to report a wrong or harmful result and continue service without being forced through the same failure. Time-sensitive exceptions require ownership and service targets; a manual path that nobody staffs is not a control.
Every material change should have a request, reason, owner, affected components, risk review, test scope, approval, release record, and rollback plan. Changes include model versions, vendors, prompts, source sets, connectors, thresholds, user groups, and downstream actions. Monitor critical failures, overrides, abstentions, complaints, latency, cost, security events, and data-boundary breaches with thresholds that trigger investigation or pause. Preserve enough evidence to reconstruct an incident without retaining unnecessary sensitive content. Emergency changes still require after-action documentation. If the workflow drifts outside its intended purpose, stop or reclassify it rather than quietly expanding scope.
Practice
Run a tabletop in which an approved model changes, a source connector becomes stale, and a prompt-injection pattern appears during a deadline-sensitive process.
Evidence to keep
Submit the exception queue design, manual service path, monitoring thresholds, change record, regression scope, rollback decision, and after-action review.
Ready to move on when
Module provenance
External authorities, common professional practice, and Academy-selected controls are identified separately. A source supports only the stated scope; it does not convert this lesson into legal, licensed, accredited, or regulatory advice.
Free operational lesson
Rate the task by harm if wrong, sensitivity of the data, reversibility, need for expertise, and frequency. High-risk or sensitive work needs stronger controls—or should remain human-led.
Define the task, affected people, data, harm, reversibility, expertise, scale, and whether AI should assist at all.
Limit instructions, sources, data, tools, outputs, and decision rights to an approved purpose.
Test representative and failure-oriented cases against a versioned rubric and predetermined release thresholds.
Give capable people the context, time, authority, and evidence needed to catch material errors before action.
Track exceptions and changes, preserve a manual path, and pause or roll back when risk exceeds the approved boundary.
Worked example
An operations team wants AI to answer employee travel-policy questions using controlled policy documents, but the documents contain an expired regional exception.
Paste all company files into a chatbot and tell it to answer every travel question confidently.
Provide only approved travel-policy versions, require a source identifier for each material claim, instruct the system to abstain on conflicts or missing locations, prevent it from approving expenses, and route exceptions to the travel-policy owner. Test the expired exception, unsupported regions, prompt injection, and incomplete questions before release.
Why it matters: The improved workflow limits sources and authority, makes uncertainty visible, tests a known failure, and preserves human ownership of policy decisions.
03 Control the work
Common failure modes
Starting with a tool obscures the decision, owner, data, failure consequence, and non-AI alternatives that determine whether automation is appropriate.
Style and structure can create unwarranted confidence; material claims still require source and rule verification.
Oversight is ineffective when reviewers lack time, expertise, context, authority, or evidence of known failure modes.
Unrecorded model, prompt, source, or connector changes destroy reproducibility and can invalidate prior evaluation.
Operating measures
The percentage of material output claims that are correctly supported by an authorized source under the workflow's traceability rule.
Use it to decide: Investigate instruction, retrieval, source quality, or reviewer failures when support drops below the approved threshold.
The percentage of evaluated or monitored cases containing a predefined non-compensable failure such as harmful advice, protected-data exposure, or unauthorized action.
Use it to decide: Block release or pause the workflow according to the predetermined threshold, even if average quality is high.
The percentage of seeded material defects correctly identified by assigned human reviewers under realistic conditions.
Use it to decide: Improve review interface, evidence, staffing, training, or workflow scope when reviewers cannot reliably detect known defects.
The elapsed time from exception detection to safe routing, service continuation, and an owned resolution path.
Use it to decide: Increase manual-path capacity or simplify routing when exceptions leave users blocked beyond the approved service target.
04 · Applied case lab · 2026.08.31-practice-3
The packet contains contradiction and incomplete evidence on purpose. Each assignment now identifies the exact fictional inputs available to the learner. Complete a bounded starter, mark missing records instead of inventing them, and compare your reasoning with the debrief—not with a pretend single perfect answer.
This is a fictional, sanitized AI-governance training case. All records are fabricated, and the exercise provides no legal, privacy, employment, medical, financial, security, or model-performance guarantee.
CB01 · citable versioned case brief
Redwood Assist is a fictional services company considering an AI workflow to summarize new client intake and draft a routing recommendation. The current form receives about 420 submissions each month. It includes name, business email, phone, company, project description, budget range, requested timing, and an optional attachment. In a sample of 60 fabricated records, seven attachments contain government identifiers, four contain medical details unrelated to the service, nine include third-party personal information, and 16 lack enough context for reliable routing. The proposed model vendor offers a standard account with model-improvement use enabled by default and a 30-day content retention statement. No security, privacy, contract, data-location, deletion, or subprocessors review has been completed. A pilot prompt tells the model to identify serious founders and reject low-quality leads. The output labels applicants high, medium, or low potential, but neither potential nor serious is defined. In a 40-record test, the model routes 31 correctly according to one manager, while a second manager disagrees on 11 of those decisions. Two Spanish-language submissions are summarized with missing budget conditions. The team proposes automatically rejecting low-rated applicants and sending their attachment to a funding partner. There is no consent for partner disclosure, no human-review standard, no protected exception path, and no versioned evaluation set. Leadership wants the automation live next week to save eight hours of staff time. Learners must classify risk, constrain instructions and sources, establish data boundaries, design meaningful human review, build an evaluation set, and control exceptions and changes. The exercise cannot establish vendor suitability, lawful processing, fairness, or permission to automate decisions.
Judgment required: Establish an authorized reference standard and adjudication method before reporting accuracy.
Do not assume: Do not treat one manager's label or majority agreement as objective truth.
Judgment required: Decompose the workflow and decide which components, if any, are suitable for a bounded pilot.
Do not assume: Do not let an acceptable drafting use authorize a high-impact automated decision.
Bounded case starter
Define the task, affected people, harm paths, reversibility, prohibited uses, and approval boundary.
Deliverable: An AI use-case and risk card with an initial proceed, redesign, or stop recommendation.
Completion boundary: Complete a bounded starter and gap analysis using only CB01, F01, F02, F03, F04, F07, F10, F11, and the assignment-scope record below. Populate supported fields, label every unavailable field “not supplied,” and cite the input ID for each material statement. You may design a proposed template, control, question, or decision rule, but must label it as a learner proposal rather than observed case evidence. Do not contact people, access live systems, run tests, sign records, claim approval, or invent names, dates, quotations, transactions, results, or source documents.
Workbook plan: 2 authored artifacts with 3 stable practice rows. The download includes a prerequisite check, six-to-eight task-specific operating steps, field-by-field guidance, completed supported and known-gap examples, supporting-artifact examples where authored, stop/go/escalate rules, and an exact completion test.
Provided inputs
Evidence criteria
Debrief lens: Risk changes when a summary becomes a rejection or disclosure. The use-case card should analyze each action separately and refuse to hide high-impact decisions inside an efficiency project.
Bounded case starter
Replace subjective labels with an evidence-grounded extraction and routing specification.
Deliverable: A versioned instruction, source schema, output schema, and unsupported-output rule.
Completion boundary: Complete a bounded starter and gap analysis using only CB01, F04, F07, F08, F09, F10, and the assignment-scope record below. Populate supported fields, label every unavailable field “not supplied,” and cite the input ID for each material statement. You may design a proposed template, control, question, or decision rule, but must label it as a learner proposal rather than observed case evidence. Do not contact people, access live systems, run tests, sign records, claim approval, or invent names, dates, quotations, transactions, results, or source documents.
Workbook plan: 4 authored artifacts with 14 stable practice rows. The download includes a prerequisite check, six-to-eight task-specific operating steps, field-by-field guidance, completed supported and known-gap examples, supporting-artifact examples where authored, stop/go/escalate rules, and an exact completion test.
Provided inputs
Evidence criteria
Debrief lens: Observable extraction is more governable than undefined worthiness labels. Grounding improves traceability but does not remove the need for correction, uncertainty, and review.
Bounded case starter
Design minimization, redaction, attachment handling, vendor review, retention, access, and disclosure controls.
Deliverable: A data-flow map, prohibited-data table, and vendor-review question set.
Completion boundary: Complete a bounded starter and gap analysis using only CB01, F02, F03, F05, F06, F11, and the assignment-scope record below. Populate supported fields, label every unavailable field “not supplied,” and cite the input ID for each material statement. You may design a proposed template, control, question, or decision rule, but must label it as a learner proposal rather than observed case evidence. Do not contact people, access live systems, run tests, sign records, claim approval, or invent names, dates, quotations, transactions, results, or source documents.
Workbook plan: 3 authored artifacts with 14 stable practice rows. The download includes a prerequisite check, six-to-eight task-specific operating steps, field-by-field guidance, completed supported and known-gap examples, supporting-artifact examples where authored, stop/go/escalate rules, and an exact completion test.
Provided inputs
Evidence criteria
Debrief lens: Redaction alone may not make an unreviewed vendor appropriate. The strongest design reduces collection, blocks sensitive attachments, and obtains contractual and technical answers before use.
Bounded case starter
Define reviewer competence, evidence, authority, workload, and reject or escalate controls.
Deliverable: A human-review standard and two review-record starters: one for missing routing context and one for a Spanish-language condition omission, with unavailable source text and model output marked not supplied.
Completion boundary: Complete a bounded starter and gap analysis using only CB01, F02, F03, F04, F08, F09, F10, and the assignment-scope record below. Populate supported fields, label every unavailable field “not supplied,” and cite the input ID for each material statement. You may design a proposed template, control, question, or decision rule, but must label it as a learner proposal rather than observed case evidence. Do not contact people, access live systems, run tests, sign records, claim approval, or invent names, dates, quotations, transactions, results, or source documents.
Workbook plan: 3 authored artifacts with 7 stable practice rows. The download includes a prerequisite check, six-to-eight task-specific operating steps, field-by-field guidance, completed supported and known-gap examples, supporting-artifact examples where authored, stop/go/escalate rules, and an exact completion test.
Provided inputs
Evidence criteria
Debrief lens: A human click is not oversight. Reviewers need source visibility, competence, time, authority, and a protected way to reject the model recommendation.
Bounded case starter
Build a versioned test set with routine, difficult, multilingual, missing-data, and critical-failure cases.
Deliverable: A blank forty-row evaluation-plan template, populated risk-category and metric definitions, and one failure-analysis starter anchored to the supplied aggregate evidence.
Completion boundary: Complete a bounded starter and gap analysis using only CB01, F02, F03, F04, F08, F09, and the assignment-scope record below. Populate supported fields, label every unavailable field “not supplied,” and cite the input ID for each material statement. You may design a proposed template, control, question, or decision rule, but must label it as a learner proposal rather than observed case evidence. Do not contact people, access live systems, run tests, sign records, claim approval, or invent names, dates, quotations, transactions, results, or source documents.
Workbook plan: 3 authored artifacts with 43 stable practice rows. The download includes a prerequisite check, six-to-eight task-specific operating steps, field-by-field guidance, completed supported and known-gap examples, supporting-artifact examples where authored, stop/go/escalate rules, and an exact completion test.
Provided inputs
Evidence criteria
Debrief lens: A single accuracy percentage conceals disagreement and critical harm. Evaluation should separate routine errors from privacy leakage, unsupported rejection, and language omissions.
Bounded case starter
Draft monitoring, incident, override, change, rollback, and retirement controls without operating the workflow or executing rollback.
Deliverable: A workflow-runbook draft, exception-log template, change-record template, and rollback-test plan with all execution evidence pending.
Completion boundary: Complete a bounded starter and gap analysis using only CB01, F05, F06, F11, F12, and the assignment-scope record below. Populate supported fields, label every unavailable field “not supplied,” and cite the input ID for each material statement. You may design a proposed template, control, question, or decision rule, but must label it as a learner proposal rather than observed case evidence. Do not contact people, access live systems, run tests, sign records, claim approval, or invent names, dates, quotations, transactions, results, or source documents.
Workbook plan: 4 authored artifacts with 18 stable practice rows. The download includes a prerequisite check, six-to-eight task-specific operating steps, field-by-field guidance, completed supported and known-gap examples, supporting-artifact examples where authored, stop/go/escalate rules, and an exact completion test.
Provided inputs
Evidence criteria
Debrief lens: A governed pilot anticipates failure and change. Regression evidence and a tested manual fallback matter more than preserving automation uptime after the evidence boundary changes.
Self-review standard
There is no single universal model, prompt, or accuracy threshold. Strong governance decomposes the use, limits data and authority, and tests consequential failures explicitly. The downloadable rubric is formative guidance only—not a grade, verified completion, credential assessment, or professional approval.
Route to: Qualified privacy, legal, compliance, and responsible-AI owners
Learner boundary: The learner maps evidence and controls but cannot authorize processing or high-impact automation.
Route to: Authorized security, platform, procurement, and incident-response owners
Learner boundary: Training evaluation does not certify vendor or system security.
05 Editable working tool
Document one bounded workflow step at a time. Name the AI contribution precisely, list only approved data, define the human decision and check, and state what happens when evidence, system behavior, or risk falls outside the approved condition. Add as many rows as needed, export a CSV, or print only this tool. Entries are not transmitted. They last only in this tab unless you opt into device-only saving in the learning-work panel. No account or cross-device sync is provided.
| Workflow step | AI role | Approved data | Human check | Failure response |
|---|---|---|---|---|
Completed example
Data boundary: Work only in an approved system. Do not place passwords, payment-card data, government identifiers, health information, export-controlled material, client secrets, or unpublished invention details in this public worksheet.
Each two-question check works independently. Review any answered question immediately; you do not need to finish all twelve. Feedback is formative, not a verified grade or credential.
07 Sources and handoffs
Module provenance distinguishes external authorities, legal-status evidence, and Wealth Synergy internal operating standards. Requirements change; verify current source text and obtain qualified advice when a decision carries legal, financial, technical, employment, safety, or regulatory consequences.
nist-ai-rmf · Official guidance
Primary NIST resource for governing, mapping, measuring, and managing AI risk across the lifecycle.
Open reviewed external source ↗nist-ai-600-1 · Official guidance
NIST's cross-sector profile describing generative-AI risks and actions aligned with AI RMF 1.0.
Open reviewed external source ↗nist-privacy-framework · Official guidance
A voluntary primary framework for identifying and managing privacy risks created by products, services, and data processing.
Open reviewed external source ↗ws-ai-workflow-operating-standard · Academy internal operating standard
Academy-selected task classification, prompt, evidence, human-review, evaluation, exception, and change controls. This is an internal operating standard selected by Foundry Academy; it is not law, accreditation, licensure, or an external-standard requirement.
Version 1.0 · reviewed 2026-09-01 · owner: Foundry Academy curriculum ownerExplore software implementation only after the workflow purpose, data boundary, evaluation standard, human ownership, and exception path are documented.
Explore the handoff →Request a controlled team cohort to practice on synthetic cases and adapt governance to approved organizational policy and tools.
Explore the handoff →Future facilitated pathway · not open
The public case lab is available now. A future facilitated cohort may add assignments, feedback, and human capstone review only after a named qualified facilitator and reviewer, schedule, rubric, privacy controls, support, price, refund terms, and delivery terms are published in writing. No enrollment or payment is accepted here.
Course material supports professional development. It does not replace legal, tax, accounting, cybersecurity, engineering, regulatory, employment, translation, or other qualified professional advice where that expertise is required.
The public knowledge check and portal review markers are learning aids only. They do not award a credential. Pilot curriculum and credential assessment are in quality review; Foundry Academy is not currently representing these pages as accredited instruction or an automatic path to a certificate.
Participation does not guarantee employment, client work, funding, licensure, certification by a public authority, or any business outcome.
Use the public foundation
All public materials and case files are available without enrollment. A fit conversation is non-binding and does not reserve a place in a future pilot.
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