Four-tab interactive workflow economics framework for assessing enterprise AI displacement, token efficiency scaling curves, signal thresholds, and capital allocation posture.
This artifact converts AI cost/performance improvements into a workflow-level decision instrument for enterprise AI portfolio reviews.
It is designed for CIO, CFO, and board-level discussions where the key question is not “Can AI do this?” but:
When does this workflow cross the economic displacement threshold, and what should we do before it does?
https://grikard.github.io/token-efficiency-framework/
The artifact includes four tabs:
-
Displacement Equation
Defines the economic threshold for workflow displacement. -
Scaling Curves
Compares token cost efficiency, GPU AI performance, and Moore’s Law. -
Decision Matrix
Maps representative workflows by signal threshold and position on the token efficiency curve. -
Workflow Assessor
Lets users score any workflow across four dimensions and receive a capital allocation posture.
Displacement Ratio =
(Intelligence Fidelity × Human Baseline Cost)
÷
(AI Execution Cost + Error Rate × Error Cost)
A workflow becomes economically displaceable when:
Displacement Ratio > 1.0
Compute efficiency alone does not determine AI displacement.
Displacement occurs when intelligence fidelity and human baseline cost exceed AI execution cost plus workflow-specific error penalty.
High-consequence workflows require:
- fidelity improvement
- governance
- proprietary training data
- error-cost controls
- human-in-the-loop design
not merely cheaper tokens.
| Posture | Typical Horizon | Meaning |
|---|---|---|
| Automate Now | 0–6 months | Economics already cross the displacement threshold |
| Sequence Next | 12–24 months | Prepare infrastructure before capability catches up |
| Build & Defend | 24–48 months | High-value workflows approaching threshold; build data moat |
| Protect & Invest | 36+ months | Human judgment remains primary; protect proprietary expertise and data |
The interactive assessor scores workflows on four dimensions:
| Input | Definition |
|---|---|
| Signal threshold | Cognitive fidelity required for acceptable output |
| Annual volume | Leverage from automation across repeated workflow instances |
| Error cost | Consequence of a wrong output |
| Training data coverage | Availability of proprietary signal and workflow examples |
The assessor includes presets for:
- Invoice processing
- Tier 1 support
- Compliance monitoring
- Wealth advisory preparation
- M&A judgment
Use this framework to support:
- enterprise AI portfolio reviews
- agentic AI investment sequencing
- CIO/CFO capital allocation discussions
- workflow economics analysis
- AI governance and risk triage
- ROIC-driven automation planning
Gary Rikard, MBA
LinkedIn: https://www.linkedin.com/in/garyrikard
Suggested GitHub topics:
enterprise-ai
agentic-ai
workflow-economics
token-efficiency
ai-displacement
ai-governance
capital-allocation
roic
automation-economics
The scaling curve panel uses Chart.js loaded from a public CDN. The chart requires internet access to render.
The model is an executive decision framework, not a deterministic forecast. Workflow-specific assumptions should be validated with operational telemetry, error-rate testing, and governance review before production deployment.