Practical implications
| IIL depth/DC activation | Sensing | Seizing | Reconfiguring |
|---|---|---|---|
| Deep | Continuously validate and refine GenAI outputs with providers to maintain contextual accuracy and proactively identify emerging risks | Integrate trusted GenAI recommendations into planning while retaining human oversight for context-specific decisions | Formalize iterative feedback and embed GenAI into planning, reporting and sustainability management to sustain long-term social impact |
| Moderate | Jointly interpret outputs, document inaccuracies and seek provider recalibration before relying on recommendations in higher-risk decisions | Embed recurring AI-supported monitoring into daily workflows and train employees to critically interpret recommendations | Increase provider feedback and evaluate whether AI-supported changes consistently improve operational and social outcomes |
| Shallow | Avoid relying on generic or poorly contextualized outputs; provide detailed feedback to improve relevance | Seek provider support when AI recommendations are repeatedly ignored, overridden or inconsistently applied | Reassess data quality, provider fit and implementation readiness before scaling GenAI if iterative improvement remains limited |
| Sensing | Seizing | Reconfiguring | |
|---|---|---|---|
| Deep | Continuously validate and refine GenAI outputs with providers to maintain contextual accuracy and proactively identify emerging risks | Integrate trusted GenAI recommendations into planning while retaining human oversight for context-specific decisions | Formalize iterative feedback and embed GenAI into planning, reporting and sustainability management to sustain long-term social impact |
| Moderate | Jointly interpret outputs, document inaccuracies and seek provider recalibration before relying on recommendations in higher-risk decisions | Embed recurring AI-supported monitoring into daily workflows and train employees to critically interpret recommendations | Increase provider feedback and evaluate whether AI-supported changes consistently improve operational and social outcomes |
| Shallow | Avoid relying on generic or poorly contextualized outputs; provide detailed feedback to improve relevance | Seek provider support when | Reassess data quality, provider fit and implementation readiness before scaling GenAI if iterative improvement remains limited |
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