Maturity Maps Benchmark AI Gaps Beyond Use Cases
AI Maturity Maps score enterprise readiness across 6 dimensions using 480+ studies (150k+ respondents); reveal 'adoption mirage'—high claimed use but lags in data (8/10 functions score 1), people (7/10 score 1), governance, turning capability overhang into applied gaps.
Six Dimensions to Measure True AI Readiness
Assess AI maturity beyond raw use cases with Deployment Depth (assistants to autonomous agents), Systems Integration (AI embedded in CRM/workflows vs. standalone ChatGPT), Data (proprietary access like codebases/customer history vs. PDF drops), Outcomes (measured ROI vs. pilots), People (upskilling + attitudes), and Governance (clear rules/permissions). Plot on a 5-point scale: 3=on-track (where orgs should be), 4=ahead, 5=leader; 2=behind, 1=significant lag. 'On-track' derives from AIDB/Super Intelligent data (thousands of agent interviews), aggregated 480+ Q2 studies (150k+ pros, 50+ countries) from Big Four, Gartner, Forrester, Stack Overflow, Jellyfish (20M PRs from 200k engineers), etc.—most orgs trail on-track, visualizing capability overhang.
Dominant Patterns: Adoption Mirage and Human Bottlenecks
High adoption claims mask shallow depth: e.g., marketing/sales report 30% content growth but peers hit 50%; sales 88% 'use AI' but only 24% in revenue workflows (browser-tab drafting, not autonomous SDRs). Universal gaps: Data caps everything (8/10 functions score 1-1.5, no pipelines for context); People neglected (7/10 score 1, 93% AI spend on infra vs. 7% people—leaders overreport training, e.g., CS 72% leaders say adequate vs. 55% workers disagree); Outcomes thin (rushed adoption skips ROI metrics); Governance weak (IT: 54% centralized frameworks, 50% agents unmonitored, 88% security incidents). Worker-leader disconnects amplify: HR leaders prioritize AI but 2/3 staff say no upskilling.
Function Benchmarks and Harbingers
Customer Service on-track in deployment/systems but stressed (87% workers high stress, 75% leaders see AI worsening; absorbs routines, humans get emotional cases sans training). Engineering/IT on-track in depth/systems/people (technical edge, measurable workflows). Operations: 90% 'investing' but thin GenAI layer on legacy automation (23% formal strategy). Finance leads governance (69% CFOs advanced frameworks from SOX/compliance) but lags deployment. Sales/others show 'embedding gap'—adoption without integration. CS as canary: AI + underinvestment = burnout; finance may tortoise-ahead with safe deployment.
Apply Maps to Close Gaps
Use radars for use cases (Prime/Emerging/Frontier by function/readiness). Benchmark vs. peers/on-track at bsup.ai (quiz plots your org). Predict ROI measurement glow-up soon; prioritize data/people/governance as floors—without them, adoption stays assistive, not transformative.