Workflow economics — revenue intelligence

Revenue Intelligence Amplification Framework

A board-ready framework for evaluating Revenue Intelligence across industries. The model treats AI as a revenue-amplification system, not a labor-replacement system, and tests whether each workflow clears a WACC-adjusted ROIC hurdle.

Panel 1 of 4 — the amplification equation

Revenue intelligence amplifies; it does not displace

Cost-side workflows cross when AI execution plus error penalty falls below human baseline cost. Revenue-side workflows cross when the lift from AI guidance, multiplied by opportunity volume and conversion probability, clears the WACC-adjusted hurdle rate net of misguidance cost. Variables in green move in favor of investment. Variables in amber are the risk side.

Revenue Intelligence amplification equation Revenue Intelligence crosses the hurdle when fidelity times revenue opportunity divided by program cost plus misguidance penalty exceeds the WACC-adjusted ROIC hurdle. ↑ RISING — algorithmic curve + integration Fidelity guidance relevance × accuracy × → STRUCTURAL — set by market and motion Revenue opportunity pipeline × deal value × conversion ↓ FALLING — token efficiency curve, ~10×/12–18mo Program cost AI + implementation + change mgmt + ↓ FALLING with semantic integration depth Misguidance penalty error rate × cost of wrong action > 2.0× WACC-adjusted ROIC hurdle Cost-side displacement crosses at 1.0. Revenue-side amplification must clear a hurdle — break-even is not enough; capital allocation requires risk-adjusted return. Revenue intelligence is an amplification equation, not a labor-replacement equation.

Panel 2 of 4 — three scaling regimes

Compute economics now run on three curves, not one

Token cost efficiency improves roughly 10× every 12–18 months — orders of magnitude faster than Moore's Law ever delivered. For revenue intelligence specifically, the implication is that the cost of generating high-fidelity guidance per opportunity is collapsing faster than the cost of generating the opportunity itself. The amplification ratio improves on the cost denominator before it improves on the revenue numerator.

Token cost efficiency GPU AI performance Moore's Law

Index: 2022 = 1.0 baseline · log scale · 2026 onward projected. Sources: synthesis of frontier model pricing trajectories, NVIDIA datasheets, and historical CMOS scaling.

Panel 3 of 4 — the decision matrix

Two axes — signal threshold and position on the token efficiency curve — yield four capital postures

Each dot is a representative revenue intelligence workflow placed by where its signal-to-noise threshold sits today and how soon AI economics cross the hurdle rate. The slope of the threshold curve is the analytical point: high-judgment workflows require disproportionately more progress before amplification clears the hurdle. Click any workflow to read its capital allocation logic. Switch industries to see how the same framework places vertical-specific workflows.

On mobile, swipe horizontally to view the full matrix.

PROTECT & INVEST 36+ months · deepen the data moat BUILD & DEFEND 24–48 months · proprietary signal SEQUENCE NEXT 12–24 months · prepare infrastructure AUTOMATE NOW 0–6 months · capture lift hurdle frontier far from hurdle above ROIC hurdle Position on token efficiency curve → ← Signal threshold of workflow high low

Click a workflow above

Each workflow is placed by its signal threshold (vertical axis) and how close current AI economics sit to its hurdle rate (horizontal axis). Switch industries above to see how the same framework places vertical-specific revenue intelligence workflows.

Panel 4 of 4 — workflow assessor

Score a workflow on four dimensions to place it on the matrix

Two dimensions on the left determine the signal threshold — how much cognitive fidelity the workflow requires. Two on the right determine token efficiency proximity — how close current AI economics sit to clearing the hurdle for this specific workflow. The four scores combine into a quadrant placement and a capital allocation decision.

Signal threshold dimensions

Judgment complexity

How nuanced is the reasoning the workflow requires?

Routine Expert only 3

Error consequence

Cost of acting on a wrong recommendation in this workflow.

Trivial Catastrophic 3

Token efficiency dimensions

AI capability today

How well current AI handles this specific task — given models, proprietary data, and integration depth.

Nascent Expert-level 3

Unit economics today

AI cost per decision versus the value created per decision.

Unfavorable Favorable 3

Automate now

Low signal · High proximity

Deploy agents immediately. Track adoption and conversion lift, not just throughput. Reinvest into governance.

Capital deployment window: 0–6 months

Build & defend

High signal · High proximity

AI is approaching your threshold. Invest now in proprietary data and semantic integration. Build governance ahead of capability.

Capital deployment window: 24–48 months

Sequence next

Low signal · Low proximity

Capability arrives in 12–24 months. Pre-position the substrate now — standardize the workflow, begin vendor evaluation, build integration scaffolding. Preparation work, not waiting.

Capital deployment window: 12–24 months

Protect & invest

High signal · Low proximity

Far from the hurdle. Deepen domain expertise and identify the proprietary data assets in this workflow.

Capital deployment window: 36+ months

Signal score

60/100

Proximity score

60/100

Capital allocation decision

Build & defend

Amplification capability is approaching this signal threshold. Capital priority is the proprietary substrate — deal data, customer context, semantic integration — that lifts fidelity and dampens misguidance. Build the seller adoption infrastructure now so amplification capture starts the day capability lands.

How to read the deployment window. The window is when capability and economics are favorable enough to deploy this workflow with confidence — it is not a payback period. For Build & Defend at 24–48 months, the capital you allocate now (proprietary data, semantic integration, seller adoption infrastructure) is what lets you capture amplification the day capability lands. The window is a readiness timeline, not a payback timeline.

Try a preset workflow