Interactive Companion -- EICT Diagnostic | iProDecisions Research Issue 05

Tool 01

Intelligence Capital Diagnostic Simulator

All five IROC drivers normalized to 0-100 index scores. IROC Composite is the average of the five indices. Comparable across firms, benchmarkable against percentile bands.

Positioning: This is a Diagnostic Simulator, not a certified benchmark. All outputs are directional estimates for strategic planning. Certified benchmarks require empirical calibration from the EICT validation roadmap (Phases 1-4).

IROC Drivers -- Raw Inputs

Note: This is a Diagnostic Simulator, not a certified benchmark. All outputs are directional estimates for strategic planning. Certified benchmarks require empirical calibration from the EICT validation roadmap (Phases 1-4).

ICR (Conversion Rate) 0.08 [Index: 16]

IT (Throughput / qtr) 120 [Index: 22]

IL (Latency, days) 14 [Index: 78]

Q (Decision Quality) 0.60 [Index: 56]

SL (Learning Velocity) 1.05 [Index: 30]

Driver Index Bars (0-100)

IROC Composite Score

Average of five normalized driver indices (0-100 scale)

Experimental (<50)

Level 1

Experimental

<50

Level 2

Emerging

50-70

Level 3

Scaling

70-85

Level 4

Leading

85-95

Level 5

Intelligence-Native

95+

Bottleneck Diagnostic

Tool 02

Financial IROC Calculator

Translate IROC into the language boards speak: revenue, cost savings, risk reduction, payback period. Financial IROC = Economic Value Realized / Intelligence Capital Invested.

Intelligence Capital Invested (Denominator)

AI/ML Infrastructure

$/yr

Data & Governance

$/yr

Talent & FDEs

$/yr

Change & Adoption

$/yr

Total Intelligence Capital Invested $7,000,000

Economic Value Realized (Numerator)

Revenue Gain

$/yr

Cost Reduction

$/yr

Risk Reduction

$/yr

Productivity Gains

$/yr

Total Economic Value Realized (EVR) $3,000,000

Financial IROC

0.43x

EVR / Capital Invested

Payback Period

2.3 yr

Capital / EVR

Verdict

Below Breakeven

<1.0 Destruction / 1.0-1.5 Realization / 1.5-3.0 Acceleration / 3.0+ Compounding

To Break Even

+$4.0M EVR

Additional EVR needed for IROC = 1.0

Financial Translation Summary

Tool 03

Intelligence Capital Compounding Model

Model: V(t) = V(0) × (1 + learning_rate)^t. Adjust annual learning rates to see how intelligence value diverges over 10 years.

Annual Learning Rates

Firm A -- rate 2%

Firm B -- rate 10%

Firm C -- rate 20%

Firm A Y10

122

Firm B Y10

259

Firm C Y10

619

C vs A Gap

5.1x

V(t) = 100 × (1 + r)^t -- 10-Year Trajectory

Tool 04

Testable Predictions

A theory that cannot be falsified is not a theory. EICT makes specific, testable predictions. If the following are systematically wrong, the theory is wrong.

Prediction 01 -- ICR and Enterprise Value

If a firm has an IROC Composite Score above 85 (all five drivers in the top quartile), then it should outperform industry peers on revenue growth and margin expansion over a 3-year period. If it does not, the Intelligence Capital thesis is wrong.

Prediction 02 -- Friction Reduction vs Model Improvement

If a firm reduces organizational friction by 30% without upgrading its AI models, then it should see greater improvement in Financial IROC than a comparable firm that upgrades its models by 30% without reducing friction. If model upgrades consistently outperform friction reduction, the GenAI Divide thesis is wrong.

Prediction 03 -- FDE Maturity and IROC

If a firm has FDE Maturity at Level 4+, then its Financial IROC should be at least 2x higher than comparable firms at Level 1-2. If FDE maturity shows no correlation with Financial IROC, the deployment mechanism thesis is wrong.

Prediction 04 -- Compounding Divergence

If two firms start with identical Intelligence Capital stocks but different learning rates (2% vs 20%), then after 10 years the high-learning firm should have 5x+ the intelligence value. If the divergence is linear rather than exponential, the compounding thesis is wrong.

Prediction 05 -- Financial IROC vs ROIC

If Financial IROC is measured across a panel of knowledge-intensive firms, then Financial IROC should explain more variance in total shareholder return than ROIC alone over a 5-year period. If ROIC remains the superior predictor, Intelligence Capital is not a distinct factor of production.

Why Falsifiability Matters

These predictions distinguish EICT from a consulting framework. A consulting framework prescribes. A theory predicts. If these predictions are systematically confirmed, EICT has explanatory power. If they are systematically falsified, the theory needs revision. Either outcome advances understanding.

Tool 05

Intellectual Lineage

EICT builds on, extends, and in some cases challenges established management and economic theory.

Peter Drucker

The Knowledge Worker (1959)

EICT extends: Drucker identified knowledge as the key resource. EICT formalizes what Drucker intuited: knowledge must be operationalized to create value. Intelligence Capital is the measurable successor. EICT adds the conversion pipeline and return metric Drucker's framework lacked.

Ronald Coase

The Nature of the Firm (1937)

EICT extends: Coase explained firm boundaries through transaction costs. EICT proposes that conversion costs (converting intelligence into action) are becoming more determinative than transaction costs. The GenAI Divide is a conversion cost theory of enterprise AI failure.

Michael Porter

Competitive Advantage (1985)

EICT challenges: Porter's value chain assumed physical activities. The Cognitive Supply Chain replaces it with an intelligence conversion pipeline. Competitive advantage comes from conversion efficiency (ICR) and compounding velocity (SL), not activity optimization.

Herbert Simon

Administrative Behavior (1947)

EICT extends: Simon's bounded rationality explains suboptimal decisions. EICT's Intelligence Latency and Cognitive Debt formalize the organizational mechanisms through which bounded rationality manifests at enterprise scale. Decision Quality (Q) is a direct descendant.

Barney / Wernerfelt

Resource-Based View (1984/1991)

EICT extends: RBV identifies VRIN resources as competitive advantage. EICT specifies Intelligence Capital as the defining resource and adds what RBV lacks: a conversion mechanism, a failure architecture, and a return metric.

Nonaka / Takeuchi

Knowledge-Based View (1995)

EICT extends: KBV describes knowledge creation and transfer. EICT adds a formalized conversion pipeline, a failure architecture, and a return metric. The Compound stage is the measurable equivalent of Nonaka's knowledge spiral.

Thomas Davenport

Competing on Analytics (2007)

EICT extends: Davenport showed analytics as competitive advantage. EICT generalizes to Intelligence Capital and adds the deployment layer (FDEs) and measurement framework (IROC) that Davenport's framework lacked.

MIT CISR

Digital Operating Models (2019)

EICT extends: MIT CISR describes how firms organize for digital. EICT identifies the Intelligence Economy Operating Model as the next research frontier (Issue 06). EICT provides the economic theory; the operating model provides the organizational blueprint.

EICT Interactive Companion