Sextant · Areté Intelligence

Methodology & Validation

Every diagnostic claim Sextant produces is anchored to public research and industry-standard frameworks. This page documents the methodology stack — the frameworks Sextant inherits, the validated psychometric instruments the Diagnostic administers, and the third-party research that grounds every figure surfaced to a CEO. For the deeper research writeup behind the instrument, see the Research Appendix.

Representative Sextant output — fictional company and modeled assumptions.

Section 1

Standards-stack foundation

Sextant is not a proprietary framework. It composes three industry-standard frameworks the buyer can independently verify and cross-reference.

NIST AI Risk Management Framework (AI RMF 1.0)
Released 2023, with the GenAI Profile (AI 600-1) added 2024. Four functions — Govern, Map, Measure, Manage — provide the structural backbone for Pillar 4 (Governance, Risk & Compliance) and the 0–4 maturity scale used across all pillars.
nist.gov/itl/ai-risk-management-framework · public, freely citable
ISO/IEC 42001 — AI Management Systems
Published December 2023 as the first certifiable AI management standard. Annex A controls map directly into Sextant's Pillar 4 sub-dimensions. Auditability of Sextant outputs against ISO 42001 is a documented design goal.
iso.org/standard/81230.html · public ISO standard
CMMI Maturity Model
Capability Maturity Model Integration — the 30-year-old standard for process maturity. Sextant's named anchors (absent · ad hoc · defined · managed · optimized) inherit CMMI vocabulary directly, ensuring buyers familiar with CMMI recognize the scoring instantly.
cmmiinstitute.com · public framework
Section 2

Seven-pillar framework · source frameworks per pillar

Each pillar synthesizes the strongest vendor and academic frameworks for its dimension. Vendor names appear here for internal traceability but are paraphrased in client-facing copy to avoid jargon and avoid promoting the originating firm.

PillarSource frameworks (internal traceability)
1 · Strategy, Value & Portfolio PostureBCG Build for the Future · McKinsey Rewired · HBR portfolio approach · PwC use-case-trap diagnosis · Andrew Ng AI Transformation Playbook
2 · Data FoundationSnowflake 6 Cs of AI-Ready Data (Clean / Contextual / Consumable / Current / Correlated / Compliant) · Rogati AI Hierarchy of Needs · IBM AI Ladder · Microsoft AI Landing Zone
3 · Technology & Tool AdoptionMicrosoft Cloud Adoption Framework for AI · Iansiti & Lakhani AI Factory · Microsoft MLOps Levels L0–L4 · Google ML Test Score · AWS Well-Architected ML Lens · Salesforce 5-step agentic readiness
4 · Governance, Risk & ComplianceNIST AI RMF + AI 600-1 GenAI Profile · ISO/IEC 42001 Annex A · KPMG Trusted AI · Gartner AI TRiSM · OWASP LLM Top 10 · Fed SR 11-7 · FDA PCCP · NAIC AIS Program
5 · Operating Model & TalentMicrosoft CoE 5-driver playbook · Andrew Ng AI Transformation Playbook · Mollick Centaur / Cyborg / Self-Automator typology · Accenture AI talent research
6 · Culture & Psychological Readiness9 validated psychometric instruments — see Section 3 below
7 · Value Capture & MeasurementBrynjolfsson J-curve · McKinsey transformative-change criteria · MIT NANDA GenAI Divide · BCG Deploy / Reshape / Invent · Bain leaders' 10–25% EBITDA benchmark

Pillars 6 and 7 are Sextant's differentiating pillars. Vendor frameworks underweight culture (Pillar 6) where the cohort actually fails; PE sponsors immediately recognize pilot purgatory (Pillar 7) as a board-legible metric.

Section 3

Pillar 6 · nine validated psychometric instruments

Culture & Psychological Readiness is the most heavily-instrumented pillar in Sextant. Nine peer-reviewed instruments triangulate attitude, anxiety, trust, literacy (self-report and objective), behavioral intent, and change readiness. Items are administered verbatim from published validation papers to preserve norm comparability.

ConstructInstrumentItemsScale
Psychological safetyEdmondson TPS-7 + 4 AI items117-pt Likert
AI attitudes (positive)GAAIS-10 positive subscale · Schepman & Rodway 2020/202555-pt Likert
AI attitudes (negative)GAAIS-10 negative subscale55-pt Likert
AI anxietyWang & Wang AIAS-21 (full or 12-item short)12-215-pt Likert
Trust calibrationTIAS-AI · Scharowski & Perrig 2024 adaptation127-pt Likert
AI literacy (self-report)Wang ALS-12 · Wang, Rau & Yuan 2023125-pt Likert
AI knowledge (objective)AICOS · Markus, Carolus & Wienrich 2025 (arXiv 2503.12921)per inst.multi-choice
Behavioral intentUTAUT2 · Venkatesh, Thong & Xu 2012147-pt Likert
Org readiness for changeShea ORIC 2014125-pt Likert

Plus three behavioral / typology layers — Microsoft 4-tier user typology (Skeptic / Novice / Explorer / Power User), resistance archetype (Champion / Early Adopter / Fence-sitter / Skeptic / Saboteur), shadow-AI disclosure — administered as multi-choice and scenario judgment rather than Likert.

Four analysis rules · pre-registered before fielding

  1. Measure dispersion, not just averages. Every Pillar 6 sub-score reported as mean + variance + by-layer slice (C-suite / VP / Director / Manager / IC). High mean + high variance = pockets of fear hidden behind a healthy average.
  2. Observe behaviors, not just attitudes. Self-report Likert pairs with multi-choice objective knowledge (AICOS), scenario judgment, and Sensor C telemetry when available.
  3. Measure attitudinal AND structural signal at the management layer. Sensor A captures attitudinal (TPS-7, AIAS); Sensor B/C captures structural (budget authority, change-management time, sanctioned-tool access). "Binding constraint sits in middle management" claim comes from triangulation, not attitudinal data alone.
  4. Cell-size collapse rule. N<5 layers collapsed with adjacent layer in fixed order (C-suite + VP → VP + Director → Director + Manager). Applied uniformly, never post-hoc.
Section 4

Score scale · 0–4 maturity

All seven pillars use the same uniform 0–4 scale. Anchors are named, not numeric — buyers understand "you're at ad hoc on Data" more clearly than "you're at 1.4 out of 5."

ScoreMaturity anchorWhat it means
0AbsentNo activity, no policy, no infrastructure for this pillar
1Ad hocActivity exists but is uncoordinated, individual-driven, undocumented
2DefinedCoordinated activity with documented processes; not yet measured systematically
3ManagedMeasured, governed, owned with named accountability and recurring review
4OptimizedContinuously improving, externally audit-ready, integrated with enterprise strategy

Continuous interpolation between integer anchors is allowed (e.g., 2.7 = "managed approaching optimized"). Scale consistent with NIST AI RMF maturity language and Phoenix backend evaluation primitives.

Section 5

Measurement methodology · how Sextant computes ROI

Every dollar value, percentage, and classification a CEO sees in the Sextant Command Center is produced by a documented formula — never editorial copy. This section is the audit trail. Five computations drive the entire surface: Drift Profile cost math, the Σ-Score composite, the Quantum opportunity envelope, the Reallocation Index (with its headcount + cultural risk classification rules), and the Agent Architecture savings model.

Defend any number — three levels deep
  1. Plain meaning — the one-line "say it" under each formula below. Lead here.
  2. The formula — it's documented on this page; offer to walk the math live.
  3. The source — every framing stat is public research (NIST, MIT, McKinsey, Bain), and every figure recalibrates to the client's own workflow telemetry during the Sextant Discover phase.
Climb only as far as the question demands — the depth is already here, so you never have to bluff.

5.1 · Drift Profile · loaded-labor + tooling cost math

Say it"We add up what a recurring job costs you today — people, hours, software — then what it costs once the right AI tier handles the routine parts. The gap is recoverable drift, in dollars you can take to a board."

Per-workflow cost decomposition. Drift = the gap between what a recurring SOP costs today (loaded labor + tooling + other) and what the same SOP costs after Sextant routes the right agent tier to each subtask. Computed independently for every workflow, then rolled up to the Annual rollup line in the Drift Profile table.

// Per-cycle cost — current state current = cycles × hrs_per_cycle × people_per_cycle × avg_loaded_rate + tool_cost + other_cost // Per-cycle cost — Sextant-projected state future = cycles × future_hrs × future_people × avg_loaded_rate + future_tool_cost // Drift recovered (annual) savings = current − future pct_reduction = savings ÷ current // Roll-up across all workflows annual_drift_recovered = Σ savings_per_workflow

Worked example — Helios "Monthly financial close": 12 × 80 × 3 × $325 + $90K tools = $1.025M baseline; future 12 × 18 × 2 × $325 + $30K = $270K; savings = $755K (74% lower).

5.2 · Composite readiness (Σ-Score) · pillar-weighted maturity

Say it"Think of it as a weighted GPA across seven readiness subjects — and we weight the subjects that matter most in your industry. One number, 0 to 4, that moves as you commit to tracks."

The single 0–4 score a CEO sees on the Quantum chart and Σ-Score panel. Weights vary by industry — manufacturing weights Data and Tech higher; SaaS weights Talent and Culture higher; finserv weights Risk higher. Weights sum to 1.0 and are published per-profile in the data layer.

// Composite at time t (current or projected) σ_score(t) = Σ weight[i] × pillar_score[i](t) for i in 1..7 // Projected pillar score = current + lift from selected tracks pillar_projected[i] = pillar_current[i] + Σ track.lift[i] for track in selected // Delta from no-action floor delta = σ_projected − σ_no_action_floor

Worked: Helios industry weights (mfg) → Strategy 0.15, Data 0.10, Tech 0.10, Risk 0.15, Talent 0.15, Culture 0.20, Value 0.15. Current composite = 1.44; with all 6 tracks selected → 3.15.

5.3 · Quantum opportunity envelope · ceiling, floor, selection cone

Say it"The band is your full range of outcomes — do nothing and you slide down the floor; commit fully and you reach the ceiling. The more you commit, the tighter and more certain the path becomes."

The band on the Quantum chart shows the full range of outcomes from no-action floor (declining) to all-tracks ceiling. The user's live selection line moves through that band; the selection cone tightens as commitment increases (more tracks selected = lower variance).

// Ceiling — composite if all applicable tracks adopted ceiling_composite = σ_score(t+18mo | all_tracks_selected) // No-action floor — composite decays at 5% per quarter (industry default) no_action(t) = σ_current × (1 − decay_rate)t/quarters // Selection uncertainty cone — ±σ shrinks with commitment selection_σ(t) = base_σ × (1 − commitment_factor) commitment_factor = (tracks_selected ÷ tracks_applicable) × 0.65

Worked: Helios ceiling = 3.15, no-action 18-month floor = 1.28, selection at 6/6 tracks → cone narrows 65% (uncertainty band ±0.35 → ±0.12 by +18mo).

5.4 · Reallocation Index · headcount + cultural-risk classification

Say it"This is the layoffs question, answered before they ask it. The index is the share of freed-up capacity we redeploy to higher-value work instead of cutting — higher means more growth and lower cultural risk."

The 0–100 score that headlines the Reallocation Map. Sextant treats AI as augmentation, not reduction: the Reallocation Index = percentage of reclaimed capacity that gets redeployed (to higher-value work, training, new initiative, or reserve) rather than eliminated. Pairs with two classification metrics that drive the Index narrative line.

// Reallocation Index — % NOT going to elimination reallocation_index = (1 − pct_eliminated) × 100 // Net headcount classification (thresholds calibrated to curated narratives) net_headcount = "growth" if pct_eliminated ≤ 5% AND (higher_value + new_initiative) ≥ 55% "stable" if pct_eliminated ≤ 15% "modest reduction" if pct_eliminated ≤ 25% "reduction" otherwise // Cultural risk classification cultural_risk = "minimal" if pct_eliminated ≤ 10% AND pct_training ≥ 15% "low" if pct_eliminated ≤ 18% OR pct_training ≥ 25% "moderate" if pct_eliminated ≤ 30% "elevated" otherwise

Worked: Helios reallocation = 8% eliminated, 23% training, 41% higher-value, 15% new-initiative, 13% reserve. Index = 92. Net headcount = "stable" (elim ≤ 15%). Cultural risk = "minimal" (elim ≤ 10% AND training ≥ 15%).

5.5 · Agent Architecture savings · tiered-mix vs. all-frontier

Say it"Same output, a fraction of the cost. We put the frontier model only where judgment matters, let mid-tier handle delegation, and run value-tier autonomously overnight. The tiering is where the savings live."

Per-workflow cost savings from routing the right model tier to each subtask: Frontier for judgment, Mid-tier for delegation, Value-tier for autonomous background. Baseline is "everything on the frontier tier" — the most expensive but simplest architecture. Sextant's recommendation is the mix that preserves output quality at a fraction of the all-frontier cost.

// Per-cycle cost of recommended architecture mix_cost_per_cycle = primary.cost + Σ delegation_agent.cost + Σ autonomous_agent.cost // All-frontier baseline (same task hours, frontier-tier pricing for everything) all_frontier_per_cycle = total_hours × frontier_rate_per_hour // Annual savings vs. all-frontier baseline annual_savings = (all_frontier_per_cycle − mix_cost_per_cycle) × cycles_per_year pct_lower = (all_frontier − mix) ÷ all_frontier

Worked: Helios "Monthly financial close" — mix = $558/cycle (frontier $320 + 2× mid-tier $96+$128 + value $14); all-frontier = $1,880/cycle; per-cycle savings = $1,322 (70% lower); annual = $1,322 × 12 = $15.9K.

5.6 · Projected Impact panel · headline aggregation

Say it"This rolls the five computations into the four numbers your CEO repeats in the boardroom — where you land, what it costs, your single biggest lever, and how fast it pays back."

The "Adopting N tracks" card on the Command Center aggregates the first five computations into the four KPIs a CEO will quote back: composite readiness, total investment, top-leverage pillar, and projected payback. Each is a pure roll-up — no new math, just composition of 5.1–5.5.

// Composite readiness — projected with selected tracks (from 5.2) composite_readiness = σ_score(t+18mo | tracks_selected) delta_from_today = composite_projected − composite_current // Investment total — sum of track costs investment_total = Σ track.cost for track in selected duration_weeks = max(track.duration_weeks) // tracks run in parallel // Top lift — the pillar with the largest projected gain pillar_gain[i] = pillar_projected[i] − pillar_current[i] top_lift = argmax(pillar_gain) across i in 1..7 // Annual hours reclaimed + FTE equivalent (from 5.1) annual_hours_reclaimed = Σ workflow.hours_saved × cycles_per_year fte_equivalents = annual_hours_reclaimed ÷ 2080 // Drift payback — days to recoup investment from annual savings annual_savings = Σ workflow.savings_per_cycle × cycles_per_year payback_days = investment_total ÷ (annual_savings ÷ 365)

Worked: Helios all 6 tracks → composite 3.15 (Δ +1.71), top lift = Value (1.3 → 3.6 = +2.3), 10,096 hrs reclaimed = 4.9 FTE-equivalents, $2.1M annual recurring savings, full-program payback ≈ 219 days (vs. the 49-day Drift-payback tile, which is the first track alone). Investment total is scoped per proposal and not shown here.

Limitations · when not to trust these numbers

Demo data is industry-typical, not company-specific. The Sextant Discover phase calibrates these formulas to a client's actual workflow telemetry (cycles, hours, rates, tool spend) within the 28-day engagement — at which point every value above reflects the client's reality, not a default. Until then: treat demo numbers as the shape of the answer, not the answer itself. Industry weights, decay rate (5%/quarter), commitment factor (0.65), and classification thresholds are documented in the methodology and reviewable on request.

Section 6

Agent-readiness ladder · cross-cutting

Three tiers; clients walk up one rung at a time. Roadmap rule: never recommend a tier the client is not adjacent to.

TierPostureIndicators (cross-pillar)
Chat-basedEmployees use ChatGPT / Claude / Copilot for individual-task assistance. AI as a tool the human drives.Pillar 6 frequency-of-use ≥ "sometimes" · Pillar 3 zero automated workflows · Pillar 5 reports no orchestration infrastructure
AutomationWorkflows redesigned around scheduled or triggered AI execution. Human decides, AI executes (Centaur mode per Mollick).At least one workflow has named owner + documented redesign · Pillar 7 workflow-redesign rate ≥ 20%
Autonomous agentsMulti-step agentic systems making decisions inside guardrails, with human-in-the-loop checkpoints.Pillar 4 AgentOps controls in place · Pillar 3 action logging + rollback paths · Pillar 5 explicit agent-owner accountability
Section 7

Discover → Empower → Build

Sextant is the entry point of the delivery model, not a standalone product. Discover determines where AI should create value; Empower and Build turn that agenda into realized capability.

Discover
Determines where AI should create value. This is the Sextant deliverable — a 28-day diagnostic (Day 0–28) that maps current state, decides which opportunities to fund, validate, defer, or decline, and mobilizes owners and workplans.
Empower
Builds the ownership, skills, strategic vision, risk model, and adoption capacity to act on the Discover findings — instructor-led, executive education, and custom curriculum among the channels.
Build
Creates the technical capability that makes it real: data strategy, engineering, warehouses, statistical / forecasting / ML models, AI agents, multi-agent systems. Build scopes follow Discover findings.
Realized impact is multiplicative: Build quality × Empower effectiveness. This is a conceptual model, not an ROI formula — modeled value ranges carry explicit assumptions and confidence, not guaranteed outcomes.

Discover, Empower, and Build are sequenced but not one-and-done — findings from a completed cycle routinely reopen the Discover agenda as priorities, capabilities, and constraints evolve.

Section 8

External validation · public research

Every diagnostic figure surfaced to a CEO is anchored to public research the buyer can independently verify.

95% pilot failure
Of enterprise AI pilots delivered zero measurable P&L impact through 2025. THE headline stat — opens every demo.
MIT NANDA GenAI Divide Report · 2025 · n=312 enterprises
61% no measurement
Of enterprise AI projects had no post-deployment measurement. Justifies Sextant's measurement-discipline scoring in Pillar 7.
MIT Sloan Management Review · 2025
74% struggle to scale
Of companies struggle to achieve and scale AI value beyond proofs of concept. Only 26% have built the capabilities to move past pilots into tangible value.
39% any EBIT impact
Of respondents report any enterprise-level EBIT impact from AI use. Most attribute less than 5% of EBIT to AI. Only 6% qualify as "AI high performers" capturing 5%+ EBIT.
10–25% EBITDA leaders
Top-quartile benchmark for AI-attributable EBITDA improvement. Leaders are compounding the gap as laggards fall further behind — the time-to-act window is narrowing.
67% vs 33% build success
Partner-led AI builds succeed at 67% rate; in-house at 33%. Confirms Areté's build/buy/partner discipline as the default recommendation.
McKinsey Global Survey
$665B global TAM
Global enterprise AI spend 2026. Frames the market opportunity.
Bloomberg Intelligence · 2026
FOMO > performance
"FOMO is stronger than poor performance" in driving AI spend. Tonal counter-anchor: Sextant = the antidote to FOMO-driven AI spend.
Goldman Sachs via Fortune · May 6 2026
Sextant · Areté Intelligence · methodology documentation ← Return to Command Center