Den Bosch included all stakeholder dialogues recorded over a month in their final report
“ECHO let us see the fault-lines and the common ground in minutes, not months. It’s the first time our whole team felt ahead of the debate.” - Marloes Engelhardt; Project manager
Date
1 apr 2024
Context
Government, Mobility, Interviews, Surveys, Focus Groups, Breakout groups
Participants
200+ City Stakeholders
Benefits
70× faster | 25x coverage | 40% cheaper
The Municipality of 's-Hertogenbosch (Den Bosch) partnered with Dembrane to implement AI-assisted public participation processes across three pilot programs in 2024. Using Dembrane's ECHO platform, the municipality was able to process months of stakeholder dialogues, street interviews, and public consultations in minutes rather than months. The result was a clear mandate for an car-free city center (autoluwe stad), significant ROI for the municipality, and a replicable methodology now being applied to healthcare and other public participation programs.
Background
The Challenge of Modern Public Participation
The Municipality of 's-Hertogenbosch faced a common challenge in modern governance: how to meaningfully process and synthesize large volumes of citizen input across multiple participation channels. Traditional methods of analyzing public consultation data were time-intensive, often taking months to process and synthesize, by which time the political momentum for decision-making had often dissipated.
The municipality was particularly focused on mobility and urban planning decisions that required broad citizen buy-in. Previous consultation processes had generated extensive data but lacked the analytical capacity to quickly identify patterns, consensus points, and areas of disagreement that could inform policy decisions.
The 's-Hertogenbosch Context
's-Hertogenbosch, a historic city in North Brabant with approximately 155,000 residents, was grappling with typical urban challenges: balancing accessibility, sustainability, and livability in its city center. The municipality recognised that effective policy-making required not just citizen input, but the ability to rapidly process and understand that input to maintain democratic legitimacy and political momentum.
Using ECHO
Dembrane's ECHO platform was deployed to handle the complete workflow of public participation data processing:
Transcription: Automatic conversion of recorded dialogues and consultations
Anonymization: Privacy-/GDPR-compliant processing of citizen input
Topic Modeling: AI-powered identification of key themes and discussion points
Synthesis: Generation of actionable insights and consensus identification
Dembrane’s implementation supported many different participation channels such as traditional public consultation meetings (inspraakavonden), street interviews with citizens, stakeholder dialogues with organised groups and digital participation platforms.
Implementation: Three Pilot Programs
The municipality chose to test Dembrane's solution across three distinct pilot programs, each designed to explore different aspects of public participation. The first pilot focused on citizen assemblies, building on previous deliberative sessions with selected citizen representatives. These deep deliberative sessions allowed for comprehensive policy exploration with diverse stakeholder input, testing the system's ability to handle complex, nuanced discussions.
The second pilot addressed the broad mobility transition facing the city center. This city-wide consultation involved the general public, local businesses, and advocacy groups in developing sustainable mobility policies. The scope and diversity of participants made this pilot particularly challenging, but ultimately most successful. The AI-assisted processing enabled the municipality to identify clear patterns in citizen preferences, ultimately generating a strong mandate for car-free city center implementation.
The third pilot was planned around neighborhood engagement, specifically with the Zandbewoners residential community, focusing on hyperlocal consultation processes. While this pilot was planned during the initial phase, it wasn't fully implemented, providing useful insights about the importance of sequencing and resource allocation in rolling out new consultation technologies.
Stakeholder Experience Analysis
The implementation revealed five distinct stakeholder categories within the municipal structure, each with different needs and experiences of the AI-assisted consultation process. Understanding these different perspectives proved crucial to the project's success and offers valuable insights for other municipalities considering similar implementations.
At the executive level, wethouders and aldermen gained confidence in evidence-based policy decisions through access to clear, synthesized citizen input. Political leaders often struggle with the challenge of balancing democratic consultation with the need for decisive action. The AI-assisted process provided them with defensible evidence for controversial decisions while reducing political risk through demonstrated citizen engagement.
Senior policy advisors like Marloes Engelhart served as crucial influencers, bridging political leadership and operational teams. Marloes's experience captured the transformative potential of the approach: "ECHO let us see the fault-lines and the common ground in minutes, not months. It's the first time our whole team felt ahead of the debate." This stakeholder group particularly valued the enhanced ability to provide strategic advice based on comprehensive citizen input, allowing them to anticipate areas of controversy and consensus before they became political challenges.
Project managers and facilitators found themselves freed from the administrative burden of data processing to focus on what they did best: meaningful citizen engagement and process facilitation. Rather than spending weeks manually coding consultation transcripts and trying to identify patterns, they could focus on designing better participation processes and providing thoughtful interpretation of results. This shift from data wrangling to strategic thinking proved energizing for municipal staff.
Technical evaluators responsible for compliance and IT integration were initially concerned about privacy and data protection requirements, but found that Dembrane's robust anonymization and data protection measures actually exceeded municipal standards. The successful integration with existing municipal systems demonstrated that innovative technology could work within established governance frameworks.
Procurement specialists, often skeptical of new technology implementations, found the clear ROI demonstration and measurable outcomes made the case for continued investment straightforward. The ability to point to specific policy decisions enabled by the technology, combined with documented time savings, provided the kind of concrete evidence that procurement processes require.
Results and Impact
The most visible immediate outcome was a clear mandate for implementing a car-free city center. This controversial urban planning decision had been under discussion for years, but previous consultation processes had failed to generate the kind of clear citizen guidance that political leaders needed to move forward. The AI-assisted processing revealed both areas of strong consensus and specific concerns that needed to be addressed, providing a roadmap for implementation rather than just general support.
The efficiency gains were dramatic. Analysis that had previously taken months was completed in minutes, allowing the municipal communications team to focus on interpretation and strategy rather than data processing. This speed meant that consultation results could be integrated into decision-making processes while the issues were still politically current, maintaining the connection between citizen input and policy outcomes that is essential for democratic legitimacy.
Perhaps most importantly, the approach generated buy-in across the municipal organization. Cross-departmental alignment emerged because different stakeholder groups could see how the process served their specific needs while contributing to overall municipal effectiveness. Political leadership gained confidence in evidence-based decisions, operational staff could focus on their core competencies, and technical teams saw successful innovation within established governance frameworks.
The success of the initial implementation led to methodology replication across other municipal functions. The approach developed for mobility consultation has been adapted for healthcare policy consultations and other public participation programs. Rather than treating AI-assisted consultation as a special case, the municipality has integrated it into their standard operating procedures for major policy decisions.
The Municipality of 's-Hertogenbosch (Den Bosch) partnered with Dembrane to implement AI-assisted public participation processes across three pilot programs in 2024. Using Dembrane's ECHO platform, the municipality was able to process months of stakeholder dialogues, street interviews, and public consultations in minutes rather than months. The result was a clear mandate for an car-free city center (autoluwe stad), significant ROI for the municipality, and a replicable methodology now being applied to healthcare and other public participation programs.
Background
The Challenge of Modern Public Participation
The Municipality of 's-Hertogenbosch faced a common challenge in modern governance: how to meaningfully process and synthesize large volumes of citizen input across multiple participation channels. Traditional methods of analyzing public consultation data were time-intensive, often taking months to process and synthesize, by which time the political momentum for decision-making had often dissipated.
The municipality was particularly focused on mobility and urban planning decisions that required broad citizen buy-in. Previous consultation processes had generated extensive data but lacked the analytical capacity to quickly identify patterns, consensus points, and areas of disagreement that could inform policy decisions.
The 's-Hertogenbosch Context
's-Hertogenbosch, a historic city in North Brabant with approximately 155,000 residents, was grappling with typical urban challenges: balancing accessibility, sustainability, and livability in its city center. The municipality recognised that effective policy-making required not just citizen input, but the ability to rapidly process and understand that input to maintain democratic legitimacy and political momentum.
Using ECHO
Dembrane's ECHO platform was deployed to handle the complete workflow of public participation data processing:
Transcription: Automatic conversion of recorded dialogues and consultations
Anonymization: Privacy-/GDPR-compliant processing of citizen input
Topic Modeling: AI-powered identification of key themes and discussion points
Synthesis: Generation of actionable insights and consensus identification
Dembrane’s implementation supported many different participation channels such as traditional public consultation meetings (inspraakavonden), street interviews with citizens, stakeholder dialogues with organised groups and digital participation platforms.
Implementation: Three Pilot Programs
The municipality chose to test Dembrane's solution across three distinct pilot programs, each designed to explore different aspects of public participation. The first pilot focused on citizen assemblies, building on previous deliberative sessions with selected citizen representatives. These deep deliberative sessions allowed for comprehensive policy exploration with diverse stakeholder input, testing the system's ability to handle complex, nuanced discussions.
The second pilot addressed the broad mobility transition facing the city center. This city-wide consultation involved the general public, local businesses, and advocacy groups in developing sustainable mobility policies. The scope and diversity of participants made this pilot particularly challenging, but ultimately most successful. The AI-assisted processing enabled the municipality to identify clear patterns in citizen preferences, ultimately generating a strong mandate for car-free city center implementation.
The third pilot was planned around neighborhood engagement, specifically with the Zandbewoners residential community, focusing on hyperlocal consultation processes. While this pilot was planned during the initial phase, it wasn't fully implemented, providing useful insights about the importance of sequencing and resource allocation in rolling out new consultation technologies.
Stakeholder Experience Analysis
The implementation revealed five distinct stakeholder categories within the municipal structure, each with different needs and experiences of the AI-assisted consultation process. Understanding these different perspectives proved crucial to the project's success and offers valuable insights for other municipalities considering similar implementations.
At the executive level, wethouders and aldermen gained confidence in evidence-based policy decisions through access to clear, synthesized citizen input. Political leaders often struggle with the challenge of balancing democratic consultation with the need for decisive action. The AI-assisted process provided them with defensible evidence for controversial decisions while reducing political risk through demonstrated citizen engagement.
Senior policy advisors like Marloes Engelhart served as crucial influencers, bridging political leadership and operational teams. Marloes's experience captured the transformative potential of the approach: "ECHO let us see the fault-lines and the common ground in minutes, not months. It's the first time our whole team felt ahead of the debate." This stakeholder group particularly valued the enhanced ability to provide strategic advice based on comprehensive citizen input, allowing them to anticipate areas of controversy and consensus before they became political challenges.
Project managers and facilitators found themselves freed from the administrative burden of data processing to focus on what they did best: meaningful citizen engagement and process facilitation. Rather than spending weeks manually coding consultation transcripts and trying to identify patterns, they could focus on designing better participation processes and providing thoughtful interpretation of results. This shift from data wrangling to strategic thinking proved energizing for municipal staff.
Technical evaluators responsible for compliance and IT integration were initially concerned about privacy and data protection requirements, but found that Dembrane's robust anonymization and data protection measures actually exceeded municipal standards. The successful integration with existing municipal systems demonstrated that innovative technology could work within established governance frameworks.
Procurement specialists, often skeptical of new technology implementations, found the clear ROI demonstration and measurable outcomes made the case for continued investment straightforward. The ability to point to specific policy decisions enabled by the technology, combined with documented time savings, provided the kind of concrete evidence that procurement processes require.
Results and Impact
The most visible immediate outcome was a clear mandate for implementing a car-free city center. This controversial urban planning decision had been under discussion for years, but previous consultation processes had failed to generate the kind of clear citizen guidance that political leaders needed to move forward. The AI-assisted processing revealed both areas of strong consensus and specific concerns that needed to be addressed, providing a roadmap for implementation rather than just general support.
The efficiency gains were dramatic. Analysis that had previously taken months was completed in minutes, allowing the municipal communications team to focus on interpretation and strategy rather than data processing. This speed meant that consultation results could be integrated into decision-making processes while the issues were still politically current, maintaining the connection between citizen input and policy outcomes that is essential for democratic legitimacy.
Perhaps most importantly, the approach generated buy-in across the municipal organization. Cross-departmental alignment emerged because different stakeholder groups could see how the process served their specific needs while contributing to overall municipal effectiveness. Political leadership gained confidence in evidence-based decisions, operational staff could focus on their core competencies, and technical teams saw successful innovation within established governance frameworks.
The success of the initial implementation led to methodology replication across other municipal functions. The approach developed for mobility consultation has been adapted for healthcare policy consultations and other public participation programs. Rather than treating AI-assisted consultation as a special case, the municipality has integrated it into their standard operating procedures for major policy decisions.