AI-Driven Organizational Network Analysis (ONA)

Choose: Microsoft Viva Insights, TrustSphere, Confirm, or academic tools (Gephi + custom data). Critical: anonymize personal data, aggregate at team level, comply with privacy laws (GDPR). Communicate transparently: how data is used, who has access, opt-out options. In ASEAN jurisdictions, pay particular attention to Malaysia's PDPA and Singapore's PDPA which have specific provisions around employee monitoring. Use metadata only, never content, and present results at the team level, not individual level, in leadership reports. Run the privacy framework past your local legal counsel before collecting any data.

AI analyzes: email metadata (who emails whom, not content), Slack/Teams messages (frequency, not content), meeting attendance, code collaboration (GitHub PRs), document sharing (Google Drive). Builds network graph: nodes = people, edges = collaboration strength. Collect at least 90 days of data to account for project cycles and seasonal patterns. Weight recent data more heavily than older data to reflect the current organisational reality. Exclude automated messages like bot notifications and system alerts from the analysis to avoid inflating connection strength.

AI detects: central influencers (high betweenness centrality), collaboration bottlenecks (single points of failure), siloed teams (low cross-team connections), over-connected individuals (burnout risk), peripheral employees (may need support). Generates visualizations and reports. Focus the initial analysis on three actionable metrics: betweenness centrality to find bottleneck individuals, cluster coefficient to identify siloed teams, and degree centrality to spot overloaded collaborators at burnout risk. Present findings as network visualisations because executives understand visual patterns faster than tables of numbers.

Use insights for: restructuring (move teams closer who collaborate frequently), succession planning (identify backup for key people), onboarding (connect new hires to central people), cross-functional project staffing (leverage existing relationships). Use ONA insights to validate or challenge proposed restructuring plans before announcing them. If ONA shows two teams collaborate heavily despite being in different departments, a reorganisation that separates them further will fail. For ASEAN conglomerates with multiple subsidiaries, ONA reveals cross-entity collaboration gaps that org charts cannot show.

Track trends: is collaboration improving? Are silos breaking down? Are key person dependencies reducing? Use as leading indicator of organizational health. Quarterly ONA reports for leadership. Combine with engagement surveys for holistic view. Run ONA quarterly and track trends in cross-team collaboration index and key-person dependency count. Set a target of reducing single-point-of-failure dependencies by 20 percent year-over-year. Combine ONA data with engagement survey results to identify teams that are both isolated and disengaged, which is the highest-risk combination.