Stakeholder Intelligence
Using AI and natural language processing to close the communication gap in multi-stakeholder project governance.
MSc Engineering Management dissertation, Anglia Ruskin University. Supervised by Dr. Ana Cocho Bermejo.
From raw data to strategic intelligence
The framework moves project communication through four stages, turning unstructured discussion into governance action.
Human and AI collaboration
Project failure is rarely a technical issue; it is a communication breakdown. The framework positions AI as a force multiplier that automates synthesis, freeing the 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 contextualisation |
| Risk management | Linguistic sentiment flagging | Strategic mediation & mitigation |
| Resource planning | Predictive bottleneck forecasting | Strategic talent & skill allocation |
What the survey found
Primary research surveying 21 project and IT management professionals across construction, IT and manufacturing, as part of the MSc Engineering Management dissertation (2024). Each figure below is the share of respondents who agreed with the statement.
Communication efficiency: agreed AI can significantly improve communication productivity in projects.
Stakeholder understanding: agreed NLP advances will improve the depth of insight into stakeholder demands.
Governance transparency: believe AI enhances stakeholder transparency and the quality of decision data.
Phased adoption: preferred a staged rollout, piloting AI at small scale first to build cultural acceptance.
Strategic implementation
Automating low-value tasks: meeting transcriptions, schedule syncs and status report generation.
Sentiment analysis and machine learning to forecast resource strain and communication lag before they escalate.
AI suggesting specific engagement strategies for high-priority stakeholders, based on historical trend data.