Skip to main content
COMPEL Body of Knowledge
Get Certified Platform

COMPEL Specialization Stream · AITE-VDT

COMPEL Academy — AI Value & Analytics Expert

Expert certification for practitioners who design and run AI value-realization programs: benefit hypotheses, evidence instrumentation, and data-driven transformation reporting.

Expert Technical Track 50 hours 50 CE credits

Profession title: AI Value & Analytics Expert

Audience: Transformation PMOs, finance partners, and value-realization leads accountable for AI outcomes.

AI Value & Analytics Expert badge

Enroll in the AITE-VDT track

Registration, enablement, and the proctored assessment are delivered through compel.one. Seats open continuously.

Register on compel.one →

Prerequisite chain

  1. AITF AI Transformation Foundations
  2. → AITE-VDT (this credential)

Learning outcomes

The learning journey is sequenced to cover each outcome below in order. Every article in the journey maps to at least one outcome.

  1. 1. Design benefit hypotheses and evidence instrumentation end-to-end.
  2. 2. Report portfolio-level value with defensible, auditable metrics.
  3. 3. Translate AI outcomes into executive-grade transformation narratives.

Body of Knowledge articles (48)

Module M1.3 (48 items)

  1. Article
    The AI Value Chain
    M1.3-Art01
  2. Article
    Shipped Value vs Realized Value
    M1.3-Art02
  3. Article
    Counterfactual Thinking for AI
    M1.3-Art03
  4. Article
    The Measurement Plan Artifact
    M1.3-Art04
  5. Article
    Leading and Lagging Indicators
    M1.3-Art05
  6. Article
    Structuring the AI Business Case
    M1.3-Art06
  7. Article
    Risk-Adjusted NPV for AI Features
    M1.3-Art07
  8. Article
    Total Cost of Ownership for AI
    M1.3-Art08
  9. Article
    Unit Economics of an AI Feature
    M1.3-Art09
  10. Article
    Token Economics of Generative Systems
    M1.3-Art10
  11. Article
    Sensitivity Analysis and Scenario Planning
    M1.3-Art11
  12. Article
    Building a KPI Tree for an AI Program
    M1.3-Art12
  13. Article
    Applying the Balanced Scorecard to AI
    M1.3-Art13
  14. Article
    OKRs and AI Delivery Cadence
    M1.3-Art14
  15. Article
    Control Performance Reports for AI Programs
    M1.3-Art15
  16. Article
    The Value Realization Report
    M1.3-Art16
  17. Article
    Dashboard Design for AI Value
    M1.3-Art17
  18. Article
    Choosing Between Experimental and Observational Designs
    M1.3-Art18
  19. Article
    A/B Testing for AI Features
    M1.3-Art19
  20. Article
    Difference-in-Differences in AI Rollouts
    M1.3-Art20
  21. Article
    Regression Discontinuity for Threshold-Based AI Decisions
    M1.3-Art21
  22. Article
    Synthetic Control for Unique Deployments
    M1.3-Art22
  23. Article
    Propensity-Score Matching for Observational AI Studies
    M1.3-Art23
  24. Article
    Designing an Evaluation Harness for Value
    M1.3-Art24
  25. Article
    Drift Detection and Value Erosion
    M1.3-Art25
  26. Article
    Attribution Modeling for AI Outcomes
    M1.3-Art26
  27. Article
    FinOps for AI
    M1.3-Art27
  28. Article
    Observability Platform Selection for AI Value
    M1.3-Art28
  29. Article
    Compute Budgets and Token-Aware Governance
    M1.3-Art29
  30. Article
    Building a Portfolio Scorecard
    M1.3-Art30
  31. Article
    Stage-Gate Value Reviews in COMPEL
    M1.3-Art31
  32. Article
    The Sunset and Decommission Case
    M1.3-Art32
  33. Article
    Externality Accounting: Carbon, Water, and Social
    M1.3-Art33
  34. Article
    Sustainability-Adjusted Value
    M1.3-Art34
  35. Article
    Board-Grade AI Value Reporting
    M1.3-Art35
  36. Lab
    Lab 1: Write a Measurement Plan for an AI Feature
    M1.3-Art51
  37. Lab
    Lab 2: Build an rNPV Model with Monte Carlo Sensitivity
    M1.3-Art52
  38. Lab
    Lab 3: Decompose Token Economics and Redesign for 40% Cost Reduction
    M1.3-Art53
  39. Lab
    Lab 4: Design a Difference-in-Differences Rollout for an Enterprise Copilot
    M1.3-Art54
  40. Lab
    Lab 5: Build a Portfolio Scorecard for a Ten-Feature Program
    M1.3-Art55
  41. Case Study
    Case Study 1: Zillow Offers — Shipped But Not Realized
    M1.3-Art61
  42. Case Study
    Case Study 2: Enterprise Copilot Rollout — A DiD Design in Practice
    M1.3-Art62
  43. Case Study
    Case Study 3: Dutch Toeslagenaffaire — Counterfactual Failure and Externality Accounting
    M1.3-Art63
  44. Template
    Template 1: Measurement Plan (11 Sections)
    M1.3-Art71
  45. Template
    Template 2: AI Business Case with Risk-Adjusted NPV
    M1.3-Art72
  46. Template
    Template 3: KPI Tree Builder
    M1.3-Art73
  47. Template
    Template 4: Value Realization Report (VRR)
    M1.3-Art74
  48. Template
    Template 5: Portfolio Scorecard
    M1.3-Art75

Competencies demonstrated

  • → Benefit hypothesis design and tracking
  • → Evidence instrumentation through the COMPEL lifecycle
  • → Portfolio-level value reporting and benchmarking
  • → Data-driven transformation narratives

Exam blueprint summary

Assessment
Proctored examination
Passing score
75% passing score
Portfolio
Required
Renewal
Every 24 months
Recommended hours
50
CE credits
50

Linked Core Mastery context

The Specialization Stream assumes AITF Foundations fluency. These Core Mastery resources are the recommended grounding before entering the AITE-VDT learning journey.

Formal credential definition

The machine-readable Open Badges 3.0 / W3C Verifiable Credential definition for AITE-VDT is published at /credential/aite-value-realization . HR platforms and AI citation engines can fetch the JSON-LD document at /credential/aite-value-realization.json .