Stakeholder Intelligence: Bridging the 81% Communication Efficiency Gap through AI-Driven Governance
Supervised by Dr. Ana Cocho Bermejo, this dissertation identifies how NLP bridges the “data fog” in multi-stakeholder project environments.
From Raw Data to Strategic Intelligence
Human-AI Collaboration Synergy
Project failure is rarely a technical issue; it is a communication breakdown. This framework establishes AI as a force-multiplier that automates synthesis, allowing the AI-Driven Project Professional to lead with high-value strategic mediation.
| Governance Domain | AI-Automated Efficiency | Human-in-the-Loop Strategy |
|---|---|---|
| Reporting | Real-time status & synthesis | Stakeholder contextualization |
| Risk Management | Linguistic sentiment flagging | Strategic Mediation & Mitigation |
| Resource Planning | Predictive bottleneck forecasting | Strategic talent & skill allocation |
Quantitative Evidence: Industry Professional Survey
Data derived from a primary research survey of 21 project and IT management professionals (Construction, IT, and Manufacturing) as part of the MSc Engineering Management dissertation (2024).
Communication Process Efficiency: Verified percentage of survey respondents who acknowledged AI’s ability to significantly improve communication productivity.
Stakeholder Demand Synthesis: Validated consensus that NLP advances will drastically improve the depth of knowledge regarding stakeholder demands.
Governance Transparency: Percentage of respondents who believe AI enhances stakeholder transparency and decision-making data quality.
Managed Scalability: Preference for phased integration strategy, ensuring cultural acceptance through small-scale AI pilots.
Strategic Implementation Roadmap
Automating low-value tasks: meeting transcriptions, schedule syncs, and status report generation.
Implementing sentiment analysis and ML to forecast resource strain and communication lag.
AI suggesting specific engagement strategies for high-priority stakeholders based on historical trend data.