How We Actually Evaluate Platforms
12 dimensions. 7 evidence tiers. Every score backed by documented evidence, not marketing claims.
Dimensions
Scale
Evidence Tiers
What is ASCS?
The AI Sales Capability Score (ASCS) is a standardized, evidence-based framework for enterprise AI sales platforms. Every platform is scored across 12 weighted dimensions on a 0–10 scale, backed by classified evidence sources.
Every score cites its evidence class — from Observed (lab-tested) to Not Tested.
The 12 ASCS Dimensions
Weighted by importance — cross-system reasoning and AI quality carry the highest weights (12% each).
ERP Connectivity
Depth of native ERP integration (SAP, Oracle, NetSuite, etc.)
Why it matters
Enterprise sales AI must reason over operational data, not just CRM records
CRM Data Grounding
Ability to ingest, normalize, and reason over CRM data (Salesforce, Dynamics 365, etc.)
Why it matters
Foundation of all sales AI use cases
Cross-System Reasoning
Ability to combine data from CRM + ERP + external sources in a single reasoning chain
Why it matters
A defining capability gap in 2026 — most systems face challenges here
AI Reasoning Quality
Accuracy, faithfulness, hallucination rate, response quality and task reliability
Why it matters
Core intelligence metric
Workflow Automation
End-to-end task completion (not just recommendations, but actions taken)
Why it matters
Separates copilots from autonomous agents
Agentic Capability
Multi-step planning, tool selection, sub-agent coordination, trajectory accuracy
Why it matters
Reflects the shift to agentic AI in 2026
Data Accessibility & Openness
API-first architecture, data export, programmatic access, open standards
Why it matters
Enterprise buyers need data portability
Security & Governance
Encryption, SSO/SAML, SCIM, SOC 2, ISO, data isolation, audit trails
Why it matters
Non-negotiable for enterprise adoption
Explainability & Transparency
Can the system explain why it made a recommendation? Source attribution?
Why it matters
Critical for CXO trust and regulatory compliance
Deployment Flexibility
Cloud, on-prem, hybrid, VPC, edge deployment options
Why it matters
Many enterprises cannot go fully cloud
Integrations Ecosystem
Breadth and depth of third-party integrations, marketplace, connectors
Why it matters
Ecosystem maturity indicator
Market Adoption & Ecosystem
Customer base, community, third-party analyst coverage, hiring signals
Why it matters
Proxy for long-term viability
Scoring Rubric
Standardized 0–10 scale applied consistently across all vendors and dimensions.
Not available / no evidence found
Minimal / experimental / pre-release
Basic capability / limited documentation
Functional / documented / meets baseline enterprise requirements
Strong / well-documented / differentiated capability
Recognized / extensive evidence / highly-rated
Evidence Classification
Every score cites its evidence class — full transparency on confidence levels.
Weight Distribution
Benchmark Protocol
7-step protocol ensuring reproducibility, transparency, and scientific rigor.
Research Question
Clear buyer-oriented hypothesis.
Dataset Design
Documented ground truth labels.
Environments
Representative lab configurations.
Metrics
Pre-defined evaluation criteria.
Execution
Multiple runs with significance testing.
Analysis
Confidence intervals and limitations.
Publication
Open datasets and code.
Evaluation Process
Reviews CXO considers documented capabilities, architecture, enterprise fit, customer-market evidence, cost considerations, strengths, limitations, and uncertainty when forming its assessment.
Open & Auditable
All datasets are versioned and checksummed. Raw data is preserved and available for audit. Methodology changes are documented in published changelogs.
External Advisory Review
Reviewed by 2–3 domain experts before publication. Audits for bias, ensures methodological rigor, and signs off before release.