Strategy & Vision
Executive sponsorship, AI–business alignment, investment thesis, competitive intelligence.
AI Assessment
ASCEND
Diagnose AI maturity. Quantify the path. Sequence the execution.
A score that can't be defended to a CFO isn't worth the slide it's printed on. ASCEND is the assessment that can.
ASCEND is a research-backed AI maturity and value system. It blends MIT CISR's stage logic, Harvard's operating-model lens, Deloitte's scaling mechanics and NIST/ISO assurance into an evidence-triangulated diagnostic — and a roadmap built to be executed, not filed.
Not a self-rating questionnaire.
Not five generic dimensions and a slide deck.
Not maturity theatre with no evidence behind the score.
Evidence. Math. Then action.
Every score is backed by at least two independent evidence sources. Every recommendation has a probability range. Every horizon hands off to the team that will build it.
The Framework
Eight pillars. Forty sub-dimensions. One honest score.
Most assessments stop at five high-level dimensions. ASCEND digs deeper because that's where the binding constraints actually live — and where remediation plans become operational instead of aspirational.
Data & Analytics
Data quality, accessibility, governance & lineage, analytical maturity, architecture.
Technology & Infrastructure
Cloud & compute readiness, MLOps tooling, integration, security, scalability.
People & Skills
AI/ML talent depth, enterprise AI literacy, cross-functional capability, talent strategy.
Governance & Ethics
AI policy framework, responsible AI practices, regulatory compliance, accountability.
Process & Operations
Process mapping, automation maturity, workflow integration, operational metrics discipline.
Culture & Change Readiness
Leadership alignment, organizational agility, change capacity, innovation mindset.
Innovation & Experimentation
Experimentation infrastructure, pilot-to-production pipeline, R&D allocation, iteration speed.
The Scoring Engine
AIMx — math that doesn't average away your weakest pillar.
AIMx is ASCEND's proprietary scoring engine. It triangulates evidence, applies a constraint-based harmonic model so a single weak pillar can't be hidden by strong ones, and runs 10,000 Monte Carlo simulations per use case to produce probability-backed ROI ranges instead of fake-precision point estimates.
Sub-dimension
Each of the 40 sub-dimensions scored 0.0–5.0 against three evidence indicators (120+ total). Every score carries an evidence-confidence rating.
Pillar
Each pillar score is the weighted average of its five sub-dimensions, with weights configurable by industry and strategic priority.
AIMx Composite
Harmonic weighted mean with floor drag from the lowest pillar. Critical weaknesses can't be averaged away — they pull the whole score down, as they should.
Maturity stages
< 1.0
Nascent
No formal AI strategy. Individual experiments without coordination.
1.0 – 2.0
Exploring
Initial projects. Awareness growing but capabilities siloed.
2.0 – 3.0
Building
Repeatable processes. Multiple teams engaged. Foundation forming.
3.0 – 4.0
Scaling
AI embedded in operations. Governance operational. Cross-functional delivery.
4.0 – 5.0
Leading
AI-first culture. Continuous innovation. Organization-wide democratization.
Triangulation rule
Every score is backed by at least two of four independent evidence sources: stakeholder interviews, structured questionnaires, document & system review, and hands-on technical observation. If interviews say the data is great but document review shows no dictionary and sampling finds 30% null rates — the score reflects reality, not perception.
The Engagement
Four to six weeks. Five phases. No ambiguity.
01 Week 0
Scoping & Intake
Executive alignment, scope and access provisioning, questionnaire distribution to 20–50 stakeholders, interview schedule locked.
02 Weeks 1–2
Discovery
12–20 stakeholder interviews, document and artifact review, technical environment walkthroughs, data-quality sampling, use-case discovery workshop.
03 Week 3
Analysis & Scoring
Evidence triangulation across all 40 sub-dimensions, AIMx scoring, constraint analysis, Monte Carlo simulation for the top 10–15 use cases.
04 Week 4
Synthesis & Roadmapping
Use cases prioritized into Quick Wins / Foundation / Scale horizons, reference architecture, governance framework, change playbook.
05 Weeks 4–5
Delivery & Activation
Preliminary findings review, executive presentation, Q&A and strategic discussion, transition proposal into execution.
What You Walk Away With
Two core deliverables. Three supporting artifacts. One executable path.
AI Readiness & Findings Report
The comprehensive diagnostic. 40–60 pages of evidence-backed analysis with the AIMx scorecard, gap analysis, constraint map, data landscape inventory and risk register. Designed to be read by the board and acted on by the executive team.
AI Journey Roadmap
The action plan. Monte Carlo–validated priority matrix, 18-month phased timeline (Quick Wins / Foundation / Scale), reference architecture, investment framework, governance blueprint and change playbook.
Data Readiness Assessment
Standalone data maturity report: source inventory, quality scores, governance gaps, AI-ready infrastructure recommendations.
Architecture Blueprint
Target-state technical architecture: integration patterns, tool selections, MLOps pipeline design, migration strategy.
Executive Presentation
Board-ready slide deck (20–25 slides): distilled findings, AIMx scorecard, top recommendations, investment case, next steps.
Ready for a score you can defend?
Tell us about your organization and we'll send back a scoped ASCEND proposal — tier, timeline, evidence sources, deliverables.
Start an Assessment