Assessment Methodology
Detailed explanation of how we calculate scores, interpret results, and provide actionable recommendations across all assessment tools.
Scoring Scales
Organization Maturity Level (1-5)
Grades the organization as a whole. The AI Execution Maturity Assessment averages its eight domain scores into a 0–100 score and maps it to a level in 20-point bands.
Experimental (score 0–19)
Ad hoc AI usage with no governance or measurement
Fragmented (score 20–39)
Scattered policies with inconsistent implementation
Structured (score 40–59)
Defined controls for key use cases with baseline measurement
Governed (score 60–79)
ISO 42001-style discipline across AI lifecycle
Compounding (score 80–100)
Continuous improvement with measurable competitive advantage
Used in: AI Execution Maturity Assessment
Practice Maturity Rating (0-4)
Rates one practice, control or capability at a time. Tools convert ratings to 0–100 before combining them, so a 4 counts as full marks for that item.
None
Not in place, or in place in name only
Initial
Informal, inconsistent or reactive
Developing
Some formal process, not yet comprehensive
Defined
Documented, standardized and consistently applied
Optimized
Measured, continuously improved and proactive
Used in: AI Governance Gap Analyzer, Prompt Lifecycle Governance, AI Telemetry Readiness Audit, Execution Intelligence Opportunity Model
Percentage Scale (0-100%)
Used for measuring coverage, adoption rates, and utilization metrics
Direct percentage representation of coverage or completion
Used in: License Utilization Optimizer, Shadow AI Risk Model
Friction Scores (1-5)
Used for measuring user experience and adoption barriers. Ratings are converted to 0–100, where a 1 scores 0 and a 5 scores 100 (higher means less friction).
Very High Friction
Major barriers preventing adoption
High Friction
Significant obstacles hindering usage
Moderate Friction
Some challenges but manageable
Low Friction
Minor issues, smooth experience
Very Low Friction
Seamless, intuitive, easy to use
Used in: Employee AI Friction Assessment
Calculation Methods
Dimension Scoring
Each dimension is scored on 0–100, then combined into a single overall score.
The AI Execution Maturity Assessment takes a simple average of its eight domains. The Telemetry Readiness Audit weights its six dimensions (data capture counts most). The Risk Exposure Index uses domain weights only to rank risks, not to inflate their scores. The weights are PromptFluent scoring choices, not an external standard.
Used in: AI Maturity Assessment, Telemetry Readiness Audit, AI Risk Exposure Index
Coverage Percentage
Measures the proportion of items assessed or implemented relative to the total.
Completed or implemented items are expressed as a share of the total applicable items, giving a clear read on how much ground has been covered versus how much remains.
Used in: Shadow AI Risk Model, License Utilization Optimizer, AI Governance Gap Analyzer
Cost Estimation
Turns time and spend you enter into a monthly cost.
Time cost = people × occurrences per week × minutes ÷ 60 × loaded hourly cost × 4.33 weeks per month. Seat cost = unused seats × price per seat. The tools do not estimate savings, ROI or payback, because those depend on how a rollout goes; the CFO example dashboard shows a worked ROI calculation on fictional figures.
Used in: Prompt Debt Calculator, License Utilization Optimizer, Waste & Redundancy Detector
Risk Score Aggregation
Combines multiple risk factors — likelihood, impact, and severity — into a prioritized view.
Individual risks are evaluated on how likely they are, how much damage they could cause, and how severe the consequences would be. More severe exposures carry more weight, so the most material risks rise to the top of the priority list.
Used in: AI Risk Exposure Index, Shadow AI Risk Model
Compounding Index
An internal index of how much AI work is turning into reusable organizational assets.
The index starts at 1.0 (no uplift) and rises with your base score, time in operation (capped at two years) and the share of employees using AI. It is a relative indicator for tracking your own progress, not a financial projection.
Used in: Institutional Intelligence Compounding Score
Maturity Progression Guide
Where you are and what to focus on next, by organization maturity level (1–5) from the AI Execution Maturity Assessment.
Level 1: Experimental
Characteristics
- ✓Ad-hoc AI usage
- ✓No formal policies
- ✓Individual experimentation
- ✓Untracked spending
Common Risks
- Shadow AI proliferation
- Security vulnerabilities
- Compliance violations
- Wasted resources
Next Priority
Establish basic governance foundation
Level 2: Fragmented
Characteristics
- ✓Some policies documented
- ✓Central AI team forming
- ✓Basic tracking
- ✓Vendor management starting
Common Risks
- Inconsistent application
- Coverage gaps
- Limited visibility
- Scaling challenges
Next Priority
Standardize processes and expand coverage
Level 3: Structured
Characteristics
- ✓Comprehensive policies
- ✓Cross-functional governance
- ✓Systematic monitoring
- ✓Clear accountability
Common Risks
- Process rigidity
- Innovation bottlenecks
- Maintenance burden
- Change resistance
Next Priority
Optimize efficiency and enable innovation
Levels 4-5: Governed and Compounding
Characteristics
- ✓Continuous improvement
- ✓Data-driven decisions
- ✓Proactive risk management
- ✓Innovation enablement
Common Risks
- Complacency
- Over-optimization
- Adaptation lag
- Complexity creep
Next Priority
Maintain agility and strategic alignment
Score Interpretation Guide
Scores are Context-Dependent
A score of 60% for a company just starting AI adoption may be excellent, while the same score for a mature AI-first company may indicate gaps. Compare against your own earlier results first; any peer comparison is only as good as its documented sample.
Prioritize High-Impact Areas
Not all dimensions are equally important. Focus on areas with high business impact and high risk first. A 70% score in security is more concerning than 70% in documentation.
Trends Matter More Than Absolute Scores
Regular assessments showing upward trends (even if scores are moderate) indicate healthy progress. Stagnant or declining scores warrant investigation even if absolute values seem acceptable.
Perfect Scores May Not Be Optimal
Achieving 100% in all areas may indicate over-investment in governance at the expense of innovation. Balance is key—aim for “good enough” governance that enables, not hinders, AI adoption.