Standard : AI systems deliver measurable improvement over non-AI alternatives
Purpose and Strategic Importance
This standard requires that every AI system in production must demonstrate a measurable improvement over the best available non-AI alternative — whether that is a rules-based system, a manual process, or a statistical heuristic. It supports the policy of measuring what AI delivers, not just what it predicts, by anchoring AI value in a concrete comparative context. The question is never whether the AI works in isolation; it is whether the AI is the best use of investment for the problem at hand.
Strategic Impact
- Ensures that AI investment is directed only at problems where it genuinely outperforms alternatives, maximising portfolio ROI
- Creates a clear and defensible value narrative for business stakeholders who fund AI initiatives
- Prevents the displacement of effective non-AI solutions with AI systems that are more complex but no more effective
- Provides the evidence base for scaling successful AI systems and retiring those that do not justify their operational cost
- Encourages a problem-first mindset that considers the full solution landscape rather than defaulting to AI
Risks of Not Having This Standard
- AI systems replace simpler, cheaper, more explainable alternatives without delivering superior outcomes
- Business stakeholders fund increasingly complex AI portfolios that generate activity rather than value
- Non-AI solutions with high reliability are retired in favour of AI systems that require more oversight and maintenance
- The organisation develops a blind spot for the opportunity cost of AI complexity relative to simpler alternatives
- Trust in AI deteriorates when users recognise that existing methods were actually better for their needs
CMMI Maturity Model
Level 1 – Initial
| Category |
Description |
| People & Culture |
- Improvement over alternatives is assumed rather than measured; AI is pursued because it is perceived as modern |
| Process & Governance |
- No requirement to compare AI to non-AI alternatives before deployment; the comparison is not part of the project lifecycle |
| Technology & Tools |
- No tooling enables controlled comparison between AI and non-AI approaches in a live environment |
| Measurement & Metrics |
- No comparative performance data exists; the AI system's relative value over alternatives is unquantified |
Level 2 – Managed
| Category |
Description |
| People & Culture |
- Teams informally describe the non-AI alternative in project documentation; the case for AI is articulated qualitatively |
| Process & Governance |
- Business cases include a description of the non-AI baseline; comparative rationale is reviewed at project approval |
| Technology & Tools |
- Teams run the non-AI baseline in parallel with the AI system during pilot to generate comparison data |
| Measurement & Metrics |
- A summary comparison of AI versus non-AI performance on key metrics is included in pilot review reports |
Level 3 – Defined
| Category |
Description |
| People & Culture |
- Comparative evaluation against non-AI alternatives is a required phase of the AI project lifecycle |
| Process & Governance |
- A defined evaluation methodology specifies how AI and non-AI alternatives will be compared on performance, cost, and user experience |
| Technology & Tools |
- A/B testing or shadow mode deployment infrastructure enables live comparison between AI and non-AI approaches |
| Measurement & Metrics |
- Improvement over non-AI baseline is calculated on a defined set of metrics; a minimum improvement threshold gates production deployment |
Level 4 – Quantitatively Managed
| Category |
Description |
| People & Culture |
- Teams are held accountable for sustaining the demonstrated improvement over the non-AI baseline throughout the production lifecycle |
| Process & Governance |
- Ongoing monitoring tracks whether the AI system continues to outperform the non-AI alternative as the operating environment changes |
| Technology & Tools |
- Shadow comparison systems maintain a live non-AI baseline in production for continuous relative performance tracking |
| Measurement & Metrics |
- Comparative advantage metrics are reviewed quarterly; systems that lose their advantage trigger a review of continued investment |
Level 5 – Optimising
| Category |
Description |
| People & Culture |
- Teams regularly revisit whether AI remains the best solution as non-AI approaches improve and problem contexts evolve |
| Process & Governance |
- Comparative evaluation standards are updated as the landscape of non-AI alternatives changes, including improvements in automation and process redesign |
| Technology & Tools |
- Automated comparative evaluation continuously tests AI against evolving rule-based and statistical alternatives |
| Measurement & Metrics |
- Organisation-wide data on AI versus non-AI performance feeds strategic decisions about where to invest in AI versus process improvement |
Key Measures
- Percentage of deployed AI systems with a documented comparative evaluation against a non-AI baseline
- Average measured improvement of AI systems over their non-AI alternatives across the production portfolio
- Number of AI systems retired or replaced with simpler alternatives following comparative evaluation
- Rate of production AI systems that have maintained their performance advantage over the non-AI baseline for more than 12 months
- Cost-adjusted comparative performance ratio (value delivered per pound of operating cost, AI versus non-AI)