Human Advantage Framework

Your team is optimized for work
AI is already doing.

HAF gives leaders a precise view of where human effort is deployed — and where it needs to go. Not culture. Not capabilities. Contribution allocation.

For HR leaders and organizational teams. Typically 15–30 person minimum.

Human Leverage Ratio
44%
Moderate — target: 55%  ·  gap: 11pp
0355065100
WAI Distribution — Q2 2025
Ideator 24% Executor 57% T/S 19%
EOS
38/100
DRS
62%
IGS
18.4
The Challenge

AI doesn't eliminate work.
It eliminates a specific kind of work.

The work most organizations have built around — defined execution, structured troubleshooting — is precisely what AI compresses fastest. The rebalancing isn't optional.

McKinsey & Company · 2023
60–70%
of employee time is at meaningful AI automation risk across knowledge work functions
McKinsey Global Institute · 2025
+12%
projected demand for creativity and ideation by 2030. Routine cognitive demand: −19%
Goldman Sachs · 2023
300M
job equivalents exposed globally to AI substitution — concentrated in execution-heavy roles

Most organizations are structured around the work AI eliminates fastest. Execution-heavy teams — built for reliability, throughput, and defined-scope delivery — are precisely the configurations most compressed by AI acceleration.

The scarcest human contribution is now ideation: working upstream of defined problems, generating novel approaches, challenging framing before solutions are even attempted. AI cannot originate from a blank canvas.

High-complexity troubleshooting retains full premium. Low-complexity troubleshooting does not. Most organizations cannot tell the difference — and cannot see the imbalance in their own teams.

HAF makes the imbalance visible. It gives leaders the precise view they need to act — not a sentiment survey, not a capability inventory, but a measurement of where human effort actually goes.

"The fundamental question is not whether AI will change what people do. It is whether organizations can see clearly enough to change where people are deployed — before the market does it for them."

Doshi & Hauser (2024, Science Advances) demonstrate that AI systematically homogenizes collective creative output — further concentrating premium on the small proportion of human contributors who work at the edges of ideation rather than the center.

The Framework

Three levers.
Not personality types.

The Human Advantage Framework measures how effort is actually deployed across three levers — Ideator, Executor, and Troubleshooter. A person's profile is derived from how they allocate effort across these levers. The same person may lean Ideator in one team and Executor in another.

◈
Lever 01

Ideator

Proactive, open-ended, curiosity-driven. Works upstream — defining problems, generating approaches, challenging framing before solutions are attempted.

AI signal: Highest premium — AI cannot originate from a blank canvas
⬡
Lever 02

Executor

Converts defined solutions into delivered outcomes. High-throughput, scope-clear delivery. The role most accelerated — and most compressed — by AI tooling.

AI signal: Highest exposure — increasingly augmented and compressed
⬟
Lever 03

Troubleshooter

Reactive, scoped, pressure-tested. Resolves defined problems within constraints. High-complexity troubleshooting retains full premium. Low-complexity is AI-absorbable.

AI signal: Partial — complexity level determines premium retention
Work Allocation Index

Three inputs. Three signals.
One clear picture.

WAI asks each person to allocate 100 points across the three roles — not abstractly, but anchored to their current development plan. This solves the gameability problem.

Three Inputs
1

Self-Assessment

Individual allocates 100 points anchored to their current development plan — not what they believe they are, but what their plan says they're building toward.

2

Manager Current Reality

Manager allocates 100 points reflecting how the individual is actually deployed right now — not aspirationally, but in current operating reality.

3

Manager Strategic Vision

Manager allocates 100 points reflecting where this person needs to go — given the team's strategic needs over the next 12–18 months.

Three Signals Generated

Perception Gap

How accurately is this person seeing their own contribution? A large gap — in either direction — warrants a direct conversation before development planning begins.

Self-Assessment vs. Manager Current Reality

Development Gap

How far does this person need to travel? The distance between where they are and where the team needs them to go — across all three roles simultaneously.

Manager Current Reality vs. Manager Strategic Vision

Alignment Gap

Is this person developing in the right direction? Even active, motivated development can compound misalignment if it's pointed the wrong way.

Self-Assessment vs. Manager Strategic Vision
Executive Metrics

Precise measurement.
Board-ready language.

Four aggregate metrics that translate individual WAI profiles into team-level intelligence — each calibrated to surface a distinct class of organizational risk.

EOS

Execution Overload Score

Human effort over-concentrated in execution relative to the team's own stated baseline. Measures the degree to which the team is structured around work AI compresses fastest.

Higher score = greater overload risk
Low Risk
Moderate
High Risk
HLR

Human Leverage Ratio

The proportion of effort allocated to work AI cannot fully replicate. Combines ideation (full premium) and high-complexity troubleshooting (partial premium).

I_avg + 0.7 × T_avg
Low <35%
Mod 35–50%
Strong 50–65%
Elite 65%+
DRS

Dependency Risk Score

Critical thinking concentration — the proportion of high-value ideation held by a small number of individuals. High DRS indicates strategic fragility from key-person dependency.

Top 20% HVC ÷ Total HVC
Healthy <50%
Elevated 50–70%
Critical >70%
IGS

Ideal Gap Score

Distance between the team's current WAI distribution and its self-defined target allocation. There is no universal benchmark — teams define their own target; IGS measures the gap.

Euclidean distance from target allocation
Aligned <10
Moderate 10–20
Misaligned >20
Team Composition

The 3×3 matrix.
Current state vs. strategic vision.

Current state
Manager vision
Priority conversation

What the matrix surfaces

Each cell represents a primary→secondary role combination. Team members appear in the cell matching their profile. Three views reveal the gap between present reality and strategic intent.

⚠
Coverage gap: I→T cell has only one member (Jamie L.) — over-reliance risk on a single pioneer profile.
↗
Development moves: 3 members require significant role reallocation — Morgan R., Riley M., Jordan P. Manager vision shows intentional redistribution.
◎
Concentration risk: E→T cell holds 3 members in current state — 37.5% of team in the highest AI-exposure profile.
Research Foundation

Built on a decade of
organizational research.

HAF is grounded in peer-reviewed work on team effectiveness, human creativity, and the economics of AI displacement — not consulting intuition.

McKinsey & Company
2023

60–70% of employee time at meaningful AI automation risk across knowledge work functions.

McKinsey Global Institute
2025

By 2030: creativity demand +12%, routine cognitive demand −19%. The rebalancing from execution to ideation is structural, not cyclical.

Goldman Sachs Research
2023

300M full-time job equivalents exposed globally to AI automation, concentrated in execution-heavy knowledge work.

World Economic Forum
2025

97M new roles requiring judgment, origination, and novel problem-setting — the precise capabilities HAF measures as Ideation.

Doshi & Hauser · Science Advances
2024

AI systematically homogenizes collective creative output, further concentrating premium on human contributors at the edges of ideation.

Edmondson · Harvard Business School
1999 · 2018

Psychological safety is the #1 predictor of team effectiveness. HAF gap conversations are designed within this framework.

Dweck · Stanford
2006 · 2019

Human capacities are malleable — the core assumption underlying WAI's three-input design and development gap measurement.

Get Started

Ready to see your team's
Human Leverage Ratio?

Request an assessment. We'll scope the engagement, calibrate the instrument to your team structure, and deliver results with a facilitated debrief.

Team Overview

Analytics & Credit Management  ·  Q2 2025 Assessment  ·  8 members assessed
EOS · Execution Overload Score
38
out of 100
Moderate
↑ from 31
HLR · Human Leverage Ratio
44%
target: 55%
Moderate
↑ gap: 11pp
DRS · Dependency Risk Score
62%
critical threshold: 70%
High
↑ action needed
IGS · Ideal Gap Score
18.4
moderate misalignment
Moderate

WAI Distribution — Current State

8 members · Q2 2025
24%
57%
19%
Ideator 24%
Executor 57%
Troubleshooter 19%
◈
HLR target gap: 11 percentage points. Current Human Leverage Ratio of 44% sits below the team's stated target of 55%. Three development moves in the Manager Vision state are designed to close this gap — Morgan R. and Riley M. are priority cases.
⚠
EOS trending up — execution overload increasing. Score moved from 31 to 38 since the last assessment period. The team is absorbing more execution-heavy work without a compensating increase in ideation capacity. Monitor closely.

EOS Trend — Last 4 Quarters

Q3'24 Q4'24 Q1'25 Q2'25 28 31 33 38

Team Matrix

3×3 composition view — primary role × secondary role

Composition Matrix — Analytics & Credit Management

Current state
Manager vision
Priority conversation

Priority Conversations

Member
Current Profile
Vision Profile
Alignment Gap
Morgan R. Priority
E→T
I→E
High
Riley M. Priority
E→T
T→I
High
Jordan P.
T→E
T→I
Medium
Alex C.
I→E
I→T
Medium

Talent View

Full WAI profile and gap analysis — 8 team members
Name Self Profile Mgr Current Mgr Vision Perception Gap Dev Gap Alignment Gap
Alex C. I→E I→E I→T Low Medium Medium
Jamie L. I→T I→T I→T None None None
Morgan R. Priority E→T E→T I→E Low High High
Sam K. E→T E→T E→T None None None
Taylor B. E→I E→I E→I None None None
Jordan P. T→E T→E T→I Low Medium Medium
Casey W. T→E T→E T→E None None None
Riley M. Priority E→T E→T T→I Low High High

Insights

Auto-generated from Q2 2025 assessment data · 4 active signals

DRS approaching critical threshold — ideation concentration risk

Critical

62% of meaningful ideation capacity is concentrated in 2 members: Jamie L. and Alex C. The critical threshold is 70%. If either member exits or is redeployed, the team loses more than half its upstream problem-setting capability in a single move.

Recommended Actions
  • Prioritize Morgan R.'s development move toward I→E profile — this directly distributes ideation load
  • Create structured ideation pairing: assign Jamie L. as a deliberate upstream collaborator for Morgan R. on one active project
  • Review succession scenario: what happens if Alex C. is promoted or exits in the next 12 months?

EOS trending up — execution overload increasing across quarters

Warning

Execution Overload Score has risen from 28 (Q3'24) to 38 (Q2'25) — a 36% increase in four quarters. The team is absorbing more defined, scope-clear execution work without a compensating shift in ideation or complex troubleshooting capacity.

Recommended Actions
  • Audit whether Sam K. and Casey W.'s stable E→T and T→E profiles are absorbing scope creep from adjacent teams
  • Review workload distribution — are ideators being pulled into execution coverage?
  • Set a Q3 checkpoint: if EOS reaches 42, trigger a formal team design review

3 members require development direction conversations this quarter

Action Required

Morgan R., Riley M., and Jordan P. each show a meaningful gap between current profile and manager vision. These are not performance issues — they are direction issues. The conversations need to happen before development plans are written.

Recommended Actions
  • Morgan R.: highest priority — I→E vision requires significant ideation development. Schedule a structured WAI debrief this month
  • Riley M.: large profile shift (E→T to T→I) — assess whether this is realistic in current team context
  • Jordan P.: secondary role shift only (E to I) — moderate conversation, good candidate for early pilot

HLR 11pp below target — structural gap, not performance gap

Monitor

The 44% Human Leverage Ratio against a 55% target is a structural allocation gap — the team doesn't have enough effort in AI-durable work. This won't be solved by individual performance improvements; it requires deliberate team composition change.

Recommended Actions
  • Model the HLR impact of Morgan R. and Riley M. completing their development moves — this closes approximately 6–7pp of the gap
  • Consider whether next hire should be profiled as I→T or I→E to close remaining gap structurally
  • Revisit target: is 55% the right goal given team charter, or should it be recalibrated?