aequai ~ / departments

$ aequai page --departments --full-maps

Every department needs a different first gate.

Department maps help teams pick one workflow, define the owner, set the approval point, and decide what evidence must be logged before AI output affects real work.

start with a diagnostic industry handbooks

// boundary: Local review copy. AequAI provides readiness, diagnostic, workflow-design, governance, simulation, and decision-support work. No publication, regulated conclusion, partner claim, production authority, token/chain/network action, carbon-asset action, autonomous operation, or professional advice is implied.

$ aequai department-graph --shared-gates --service-routes

Department workflow graph.

A relationship view of departments, shared control layers, and service routes. Connected departments share source discipline, approval gates, evidence logs, customer-facing review, supplier flows, or implementation risk.

departments · gates hover a department to show related teams and controls · fixed view · click service nodes

Customer-facing departments

Sales Marketing and content Customer support Leadership

Operational departments

Operations Finance and accounting Procurement and vendor management Engineering and product

Control layers

Approval gates Evidence logs Data boundaries Simulation before expansion

Service routes

AI Adoption Diagnostic Department AI Pilot Design AI Governance Starter Kit AI Workflow Simulation Lab Agent Operations Safety Audit AI Literacy Training

Graph connections are workflow-design relationships only: shared review owners, common evidence logs, adjacent data boundaries, or service paths. They are not implementation claims.

$ aequai company-map --snapshot --roadmap

Company-wide adoption snapshot.

This source-backed snapshot helps a company choose where AI adoption should begin, what must be reviewed first, and which first workflow should become a controlled pilot.

[01]Page Purposesource-backed handbook section+ open- close

Help a company choose where AI adoption should begin, what must be reviewed first, and which first workflow should become a controlled pilot.

Training paths are personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[02]Company-Wide Adoption Snapshotsource-backed handbook section+ open- close

Question	What To Capture	Output
Where is AI already used?	Teams, tools, workflows, outputs	AI use inventory
What data enters tools?	Public, internal, restricted, private, excluded	Data boundary map
What outputs leave the team?	Drafts, customer messages, reports, records, system changes	Output risk map
Who reviews?	Owner, reviewer, fallback, stop condition	Approval gate model
What evidence exists?	Sources, logs, versions, reviewer notes	Evidence map
What should be trained?	Role gaps, tool gaps, review habits	Training roadmap

[03]Risk And Evidence Heatmapsource-backed handbook section+ open- close

Area	Typical Workflows	Early AI Level	Evidence Need	First Safe Pilot
Leadership	Briefs, risk registers, decisions	L1 Draft	Sources and reviewer	Morning brief draft
Sales	Proposals, CRM notes, follow-up	L1 Draft	Approved account facts	Proposal draft
Marketing	Copy, campaigns, claims	L1 Draft	Claim/source table	Claim-safety review
Support	Escalations, FAQs, replies	L1 Draft	Ticket refs	Escalation summary
Operations	SOPs, exceptions, handoffs	L1 Draft / reviewed L2	Process source	SOP update draft
Finance	Checklists, variance questions	L0 Assist / L1 Draft	Source sheets	Close checklist support
HR	Onboarding, policy summaries	L1 Draft	Approved policy source	Onboarding FAQ draft
Engineering/Product	Specs, tests, review gates	L1 Draft	Issue/PR refs	Review checklist
Legal/Compliance coordination	Advisor prep, policy inventory	L0 Assist / L1 Draft	Source index	Advisor-prep pack
Procurement	Supplier evidence, vendor notes	L1 Draft	Supplier source map	Evidence request pack

[04]Pilot Prioritization Criteriasource-backed handbook section+ open- close

Score each workflow from 1 to 5:

+pain and frequency;
+reversibility;
+source availability;
+review-owner clarity;
+data sensitivity;
+customer or public impact;
+system-action risk;
+training readiness.

Choose the workflow with high pain, clear evidence, a known reviewer, and low action risk.

[05]30/60/90-Day Roadmapsource-backed handbook section+ open- close

Period	Company Focus	Department Focus	Training Focus	Decision
30 days	Inventory AI use and boundaries	Pick 1-2 L1 pilot candidates	Role/tool baseline	Proceed/shrink/park/kill
60 days	Define approval gates and evidence logs	Simulate one workflow	Tool-specific practice	Proceed/shrink/park/kill
90 days	Decide whether to expand, govern, or pause	Pilot with measured review burden	Team playbook	Proceed/shrink/park/kill

[06]Recommended AequAI Pathsource-backed handbook section+ open- close

+AI Adoption Diagnostic.
+Department AI Pilot Design for the selected department.
+AI Literacy Training for role/tool practice.
+AI Governance Starter Kit if usage spreads across teams.
+AI Workflow Simulation Lab before any L2 workflow expands.

[07]What Not To Automate Or Claimsource-backed handbook section+ open- close

Do not promise autonomous operation, production action, public claims, customer outcomes, regulated conclusions, financial returns, partner endorsement, DPP operation, carbon-asset action, or chain-release support from a company-wide adoption map.

$ aequai department-maps --all --required-fields

All department maps.

Each department entry exposes the required fields: first safe pilot, 30/60/90 roadmap, simulation scenario, service path, training path, approval gate, evidence to log, and no-go line. Extra context covers workflows, opportunity level, training needs, data sensitivity, and KPIs.

[01]LeadershipStrategy, updates, risk, decisions, meetings.+ open- close

First safe pilot

Morning brief, risk summary, options memo.

Approval gate

Leader review before decisions or public use.

Evidence to log

Sources, assumptions, reviewer.

Simulation scenario

Synthetic board-update brief.

Service path

AI Adoption Diagnostic, AI Governance Starter Kit.

Training path

Executive AI Literacy Briefing.

No-go line

Forecast certainty, public statements, investor/partner commitments.

Data sensitivity

Strategy and financial context can be sensitive.

KPIs

Brief quality, review time, decision clarity.

30/60/90 roadmap

+30 days: inventory
+60 days: approval model
+90 days: adoption roadmap.

Opportunity and training needs

+L0-L4 opportunity: L0 summaries, L1 decision briefs, L2 reviewed task prep, L3 exception-only, L4 not promised.
+Training needs: Source discipline, decision review, tool choice.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[02]SalesAccount research, proposals, CRM notes, follow-up.+ open- close

First safe pilot

Account summary, proposal draft, objection library.

Approval gate

Sales owner before send or CRM update.

Evidence to log

CRM refs, source facts, reviewer.

Simulation scenario

Fictional proposal workflow.

Service path

Department AI Pilot Design.

Training path

Sales manager path.

No-go line

Automated outreach, pricing, unsupported customer facts.

Data sensitivity

Customer and pricing context restricted.

KPIs

Draft acceptance, CRM hygiene, error rate.

30/60/90 roadmap

+30 days: CRM facts
+60 days: proposal gate
+90 days: pilot.

Opportunity and training needs

+L0-L4 opportunity: L0 research support, L1 proposal drafts, L2 reviewed CRM prep, L3 exception-only, L4 not promised.
+Training needs: CRM evidence, safe follow-up, claim-safe offers.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[03]Marketing and ContentCampaigns, claims, pages, posts, briefs.+ open- close

First safe pilot

Claim-safety review, campaign brief, content repurpose.

Approval gate

Marketing owner before publication.

Evidence to log

Source/claim map, reviewer.

Simulation scenario

Fictional campaign review.

Service path

Department AI Pilot Design, AI Governance Starter Kit.

Training path

Marketer/content path.

No-go line

Unsupported public claims or auto-publishing.

Data sensitivity

Public-claim and brand risk.

KPIs

Claim issues found, review cycle time.

30/60/90 roadmap

+30 days: source map
+60 days: review gates
+90 days: content ops pilot.

Opportunity and training needs

+L0-L4 opportunity: L0 source review, L1 drafts, L2 reviewed scheduling prep, L3 exception-only, L4 not promised.
+Training needs: Source discipline, brand review, approval gates.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[04]Customer SupportTickets, escalations, FAQs, macros.+ open- close

First safe pilot

Escalation summary, FAQ draft, response-quality review.

Approval gate

Support lead before replies or policy notes.

Evidence to log

Ticket refs, source policy, reviewer.

Simulation scenario

Synthetic escalation thread.

Service path

Department AI Pilot Design.

Training path

Support lead path.

No-go line

Automated replies or policy decisions.

Data sensitivity

Customer private data may be sensitive.

KPIs

Resolution support time, escalation clarity.

30/60/90 roadmap

+30 days: summaries
+60 days: FAQ gate
+90 days: macro pilot.

Opportunity and training needs

+L0-L4 opportunity: L0 summaries, L1 reply drafts, L2 reviewed routing prep, L3 exception-only, L4 not promised.
+Training needs: Data boundaries, response review, escalation criteria.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[05]OperationsSOPs, exceptions, checklists, handoffs.+ open- close

First safe pilot

SOP draft, exception summary, shift briefing.

Approval gate

Process owner before changes.

Evidence to log

Version, source steps, reviewer.

Simulation scenario

Synthetic SOP update.

Service path

AI Workflow Simulation Lab.

Training path

Operations manager path.

No-go line

Unreviewed process changes or system actions.

Data sensitivity

Internal process and system details.

KPIs

SOP freshness, rework, exception handling.

30/60/90 roadmap

+30 days: process map
+60 days: simulation
+90 days: pilot.

Opportunity and training needs

+L0-L4 opportunity: L0 compare, L1 SOP drafts, L2 reviewed task prep, L3 exception-only, L4 not promised.
+Training needs: Workflow mapping, logs, handoff quality.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[06]Finance and AccountingClose checklists, variance questions, invoices, approvals.+ open- close

First safe pilot

Close checklist, variance question pack, invoice triage notes.

Approval gate

Finance owner before entries, payments, filings.

Evidence to log

Source sheets, assumptions, reviewer.

Simulation scenario

Fictional close checklist.

Service path

AI Adoption Diagnostic.

Training path

Finance/accounting path.

No-go line

Filings, payments, accounting entries, advisor conclusions.

Data sensitivity

Financial, payment, tax, and payroll data can be excluded.

KPIs

Checklist completeness, review burden.

30/60/90 roadmap

+30 days: boundary map
+60 days: checklist
+90 days: reviewed pilot.

Opportunity and training needs

+L0-L4 opportunity: L0 explanation, L1 checklist drafts, L2 reviewed prep only, L3 exception-only, L4 not promised.
+Training needs: Data boundaries, evidence, reviewer discipline.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[07]HR and People OpsOnboarding, policy, training, employee questions.+ open- close

First safe pilot

Onboarding FAQ, policy summary, training note.

Approval gate

HR owner before employee-facing use.

Evidence to log

Policy refs, reviewer.

Simulation scenario

Synthetic onboarding FAQ.

Service path

AI Governance Starter Kit.

Training path

HR/people ops path.

No-go line

Personnel decisions or private employee processing.

Data sensitivity

Employee records and personnel issues sensitive.

KPIs

FAQ clarity, review errors.

30/60/90 roadmap

+30 days: policy source map
+60 days: FAQ gate
+90 days: team playbook.

Opportunity and training needs

+L0-L4 opportunity: L0 summarize, L1 FAQ drafts, L2 reviewed workflow prep, L3 exception-only, L4 not promised.
+Training needs: Privacy hygiene, employee-data boundaries.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[08]Engineering and ProductSpecs, issues, tests, reviews, code-agent support.+ open- close

First safe pilot

Spec draft, test checklist, PR review gate.

Approval gate

Engineer owner before repo/system change.

Evidence to log

Issue/PR refs, diffs, reviewer.

Simulation scenario

Synthetic PR review workflow.

Service path

Agent Operations Safety Audit.

Training path

Engineer/product path.

No-go line

Deploys, repo mutation, production changes without approval.

Data sensitivity

Code, credentials, production systems sensitive.

KPIs

Review quality, defect escape, rollback clarity.

30/60/90 roadmap

+30 days: authority map
+60 days: review gates
+90 days: simulation.

Opportunity and training needs

+L0-L4 opportunity: L0 explanation, L1 specs/tests, L2 reviewed repo prep, L3 exception-only, L4 not promised.
+Training needs: Agent boundaries, repo review, source discipline.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[09]Legal and Compliance CoordinationPolicy inventory, advisor prep, review routing.+ open- close

First safe pilot

Document inventory, counsel question pack, policy comparison.

Approval gate

Counsel/owner before conclusions or external use.

Evidence to log

Source index, question list, reviewer.

Simulation scenario

Synthetic policy review prep.

Service path

AI Governance Starter Kit.

Training path

Legal/compliance coordinator path.

No-go line

Professional conclusions or regulator-facing statements.

Data sensitivity

Legal files and regulated topics sensitive.

KPIs

Prep completeness, review clarity.

30/60/90 roadmap

+30 days: inventory
+60 days: question pack
+90 days: gate model.

Opportunity and training needs

+L0-L4 opportunity: L0 organize, L1 question packs, L2 reviewed routing prep, L3 exception-only, L4 not promised.
+Training needs: Source indexing, boundaries, advisor-prep discipline.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

[10]Procurement and Vendor ManagementSupplier requests, vendor comparison, evidence gaps.+ open- close

First safe pilot

Supplier evidence request, vendor comparison, renewal checklist.

Approval gate

Procurement owner before supplier contact or decisions.

Evidence to log

Supplier refs, request version, reviewer.

Simulation scenario

Synthetic supplier request.

Service path

DPP Readiness Review.

Training path

Procurement/vendor path.

No-go line

Supplier acceptance, claims, contract changes.

Data sensitivity

Supplier private documents and contracts sensitive.

KPIs

Evidence completeness, request quality.

30/60/90 roadmap

+30 days: evidence fields
+60 days: supplier pack
+90 days: readiness pilot.

Opportunity and training needs

+L0-L4 opportunity: L0 summarize, L1 request drafts, L2 reviewed vendor prep, L3 exception-only, L4 not promised.
+Training needs: Evidence readiness, data boundaries, request wording.
+Training personalization: Personalized to the person, team, role, workflow, tool stack, risk profile, business context, workflow maturity, data boundaries, and approval responsibilities.

Autonomy language: L0 Assist is safe for low-risk internal help. L1 Draft is the default early promise. L2 Execute with approval is possible only with human review and gates. L3 Supervised operation is exception-only. L4 Autonomous operation must not be promised.

$ aequai related --next

Related routes.

↗

services

Service catalogue

Compare diagnostics, pilot design, governance, DPP readiness, tokenization readiness, simulation, and training.

↗

solutions

Solution routes

Find the route by department, industry, business type, or evidence need.

↗

training

Personalized training

Role, workflow, tool stack, risk profile, data boundary, and approval responsibility practice.

$ aequai contact --book diagnostic

Map one workflow first.

Bring one workflow, one team, or one evidence problem. The useful next step may be proceed, shrink, park, or stop.

open contact browse resources