Assessment Methodology
Detailed explanation of how we calculate scores, interpret results, and provide actionable recommendations across all assessment tools.
Scoring Scales
Maturity Levels (0-4)
Used for assessing the maturity of practices, processes, and capabilities
Not Implemented
No action taken or capability does not exist
Initial/Ad-hoc
Informal, inconsistent, or reactive approach
Developing
Some formal processes, but not comprehensive
Defined
Documented, standardized, and consistently applied
Optimized
Continuously improved, measured, and proactive
Used in: AI Maturity Assessment, Telemetry Readiness Audit, Prompt Governance
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 Discovery, Training completion rates
Friction Scores (1-5)
Used for measuring user experience and adoption barriers
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 Friction Assessment
Calculation Methods
Weighted Dimension Scoring
Different dimensions are assigned weights based on their relative importance, then combined into a single overall score.
Each dimension is scored independently and then combined, with higher-priority dimensions contributing proportionally more to the overall result. Weightings reflect business impact and risk, so a strong score in a critical area counts for more than the same score in a lower-stakes one.
Used in: AI Maturity Assessment, Risk Assessment, Telemetry Audit
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 Discovery, License Optimizer, Compliance assessments
ROI Estimation
Estimates return on investment from AI initiatives or optimizations.
Projected net benefit is weighed against the cost to achieve it, producing a return estimate and payback view that leaders can use to prioritize investments.
Used in: License Optimizer, Waste Detector, Value realization tools
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 Assessment, Security assessments, Compliance gap analysis
Compounding Value
Models how value grows over time as AI capabilities mature and adoption increases.
Value is projected to build on itself as maturity, time in operation, and adoption rise together — reflecting how governed, reusable AI work compounds into organizational assets rather than one-off outputs.
Used in: Intelligence Compounding, Execution Intelligence, Long-term value projection
Maturity Progression Guide
Understanding where you are and what to focus on next based on your maturity level.
Level 0-1: Initial/Reactive
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: Developing
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: Defined
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
Level 4: Optimized
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. Always compare against your industry peers and your own historical performance.
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.