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Context & Scope
Impact assessment is a critical business function that involves measuring and evaluating the consequences of specific actions, policies, or changes within an organisation. Traditionally, human analysts perform this role by collecting data, conducting surveys, analysing metrics, and producing reports to gauge the effectiveness of interventions.
AI Solution Overview
- Organisation defines assessment parameters and goals
- AI accesses relevant data sources (e.g. financial records, employee surveys, productivity metrics)
- AI collects and preprocesses data, ensuring quality and consistency
- AI applies advanced analytics to identify patterns and correlations
- AI generates baseline metrics for comparison
- Organisation implements change or intervention
- AI continuously monitors relevant metrics in real-time
- AI compares post-intervention data to baseline
- AI generates comprehensive impact reports with visualisations
- AI provides actionable insights and recommendations
- Organisation reviews findings and makes informed decisions
If needed at any point:
- AI flags anomalies or unexpected results for human review
- Human analysts can input additional context or qualitative data
- AI can adjust analysis based on new information or changing priorities
Human vs AI
| Human Intelligence (HI) | Artificial Intelligence (AI) |
|---|---|
| HI can analyse limited data sets due to time and cognitive constraints | AI can process vast amounts of data from multiple sources simultaneously |
| HI may introduce unconscious biases into analysis | AI can maintain objectivity and consistency across assessments |
| HI requires significant time to collect and analyse data | AI can provide near real-time analysis and continuous monitoring |
| HI may miss subtle patterns or correlations in complex data | AI can identify intricate relationships and non-obvious impacts |
| HI can struggle to maintain consistency across multiple assessments | AI ensures standardised methodology and comparable results over time |
| HI has limited capacity to simulate multiple scenarios | AI can rapidly model and compare numerous potential outcomes |
| HI may have difficulty quantifying qualitative impacts | AI can apply sentiment analysis and natural language processing to assess qualitative data |
| HI can become fatigued, affecting the quality of long-term studies | AI can maintain consistent performance for extended periods |
| HI may face challenges in integrating diverse data types | AI can seamlessly combine and analyse structured and unstructured data |
| HI can overlook long-term or indirect effects | AI can track and predict long-term impacts and secondary effects |
Addressing Common Concerns
Data privacy and security AI systems are designed with robust security measures and can be configured to comply with data protection regulations. Sensitive information can be anonymised or aggregated to protect individual privacy while still providing valuable insights.
Complexity of organisational systems While organisations are indeed complex, AI excels at handling multifaceted systems with numerous variables. The AI can be trained on the specific context of the organisation, ensuring its analysis accounts for unique structural and cultural factors.
Overlooking qualitative impacts Modern AI is capable of processing qualitative data through natural language processing and sentiment analysis. It can integrate survey responses, employee feedback, and even social media sentiment into its assessments, providing a holistic view of impact.
Resistance to AI-driven decisions The AI system is designed to augment human decision-making, not replace it. It provides data-driven insights and recommendations, but final decisions remain in the hands of human leaders who can consider additional context and nuances.
Adaptability to changing conditions AI systems can be designed to continuously learn and adapt to changing organisational conditions. Regular updates and retraining ensure the AI remains relevant and accurate in its assessments over time.
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