// productivity assumptions, readiness evidence, risk-adjusted value

$ model --adoption_roi="evidence_based"

AIducation turns AI adoption ROI into an evidence dashboard. Leaders can see role-by-role productivity assumptions, risk adjustments, workflow proof, and executive actions before scaling AI workflows.

[SUPPORT_ROI_API][ROI_CALCULATOR]

Adoption ROI dashboard

15
Departments
9910
Hours protected
4
Evidence-ready
42
Risk controls
[!] ROI is modeled as planning evidence until readiness, verification, and manager-review artifacts prove lift.

// ROI_evidence_loop

The dashboard avoids vague productivity claims by connecting every estimate to readiness status, workflow artifacts, risk controls, and manager actions.

Assume

Start with transparent learner count, workflow count, minutes saved, monthly runs, and rework assumptions.

Prove

Require baseline, exit, workflow portfolio, tool-lab, and manager-review evidence before claiming lift.

Adjust

Discount value where policy safety, verification, approvals, or high-risk workflows are still weak.

Scale

Move only evidence-ready departments into expansion, templates, and approved workflow libraries.

// Department_value_map

Each role has transparent assumptions, monthly impact estimates, productivity levers, evidence links, and risk-adjusted executive actions.

View planning-stage API filter
AIducation for Support

Support

early signal
320h saved
38h rework
78 target
  • [+] Billing escalations and refunds
  • [+] Account verification and sensitive-data handling
  • [+] Tone, empathy, and next-step setting
AcademyAPI
AIducation for Sales

Sales

early signal
576h saved
69h rework
76 target
  • [+] Prospect research and account briefs
  • [+] Cold outreach and personalization
  • [+] Discovery call preparation
AcademyAPI
AIducation for Marketing

Marketing

evidence ready
1200h saved
180h rework
72 target
  • [+] Product launch copy
  • [+] Campaign brief generation
  • [+] SEO and content planning
AcademyAPI
AIducation for HR

HR

planning
224h saved
18h rework
82 target
  • [+] Policy drafting and explanation
  • [+] Recruiting and candidate screening support
  • [+] Performance review preparation
AcademyAPI
AIducation for Finance

Finance

planning
224h saved
18h rework
82 target
  • [+] Expense review and policy checks
  • [+] Variance analysis commentary
  • [+] Forecasting support
AcademyAPI
AIducation for Engineering

Engineering

early signal
576h saved
69h rework
76 target
  • [+] AI-assisted code review
  • [+] Debugging and root cause analysis
  • [+] Architecture tradeoff review
AcademyAPI
AIducation for Product Managers

Product

early signal
576h saved
69h rework
76 target
  • [+] PRD review and requirement tightening
  • [+] Research synthesis
  • [+] Roadmap and prioritization analysis
AcademyAPI
AIducation for Executives

Executives

planning
224h saved
18h rework
82 target
  • [+] AI strategy and governance
  • [+] Department rollout planning
  • [+] Risk and policy review
AcademyAPI
AIducation for Operations

Operations

early signal
576h saved
69h rework
76 target
  • [+] SOP generation and review
  • [+] Workflow automation planning
  • [+] Operational reporting
AcademyAPI

// Support_first_ROI

Support stays the first commercial wedge, but the ROI model is built for every department. The same evidence standard can expand from support to sales, marketing, HR, finance, product, engineering, and operations.

Connect ROI to org readiness
Planning assumptions
  • [+] 25 learner pilot
  • [+] 4 workflows per learner
  • [+] 12 minutes saved per workflow
  • [+] 12% rework reduction assumption
Value evidence
  • [+] Support AI Workflow Lab with 23 artifacts
  • [+] 5 reusable workflow template kits
  • [+] Support Assistant selection lab for approved tool selection
  • [+] 78+ readiness target with baseline and exit comparison
Risk adjustments
  • [+] Treat ROI as a planning estimate until baseline and exit evidence are complete.
  • [+] Discount value from workflows that lack manager review, verification notes, or approved-tool evidence.
Executive actions
  • [+] Coach Support learners below target before claiming productivity impact.
  • [+] Run another week of daily missions on the weakest verification or policy dimension.
  • [+] Review tool-comparison evidence before approving more automated workflows.