---
title: Making Digital Government More Human
description: Granicus Chief Product Officer Bob Ainsbury explains how AI, relevant outreach and resident insights can make digital government more human.
---

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# Making Digital Government More Human

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<iframe class="hs-responsive-embed-iframe" style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; border: none;" title="The Future Public Servant: Bob Ainsbury, Granicus" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" xml="lang" src="https://www.youtube.com/embed/7hQQfAQpjts?si=83i3r8tJ2l4EkH9n" width="960" height="540" frameborder="0" allowfullscreen data-service="youtube"></iframe>

*Presented by Granicus in partnership with Carahsoft*

Government has made substantial progress moving services online. But a digital service’s availability tells only part of the story. The next challenge is understanding who still isn’t using it, why they aren’t connecting, and how agencies can reach them more effectively.

![Bob Ainsbury, Chief Product Officer at Granicus, on The Future Public Servant](https://www.stategovtoday.com/hs-fs/hubfs/Screenshot%202026-11-02%20at%201.52.29%20PM.png?width=550&name=Screenshot%202026-11-02%20at%201.52.29%20PM.png)In this episode of **The Future Public Servant**, **Bob Ainsbury, Chief Product Officer at Granicus**, examines how a deeper understanding of residents can improve digital services—and how artificial intelligence can give public servants more time to exercise the judgment those services require.

Ainsbury describes today’s digital government landscape as uneven. Agencies have expanded online access, while residents’ expectations continue to rise through their experiences with banking, shopping, entertainment, and other services. People increasingly expect organizations to understand their circumstances and deliver information that is relevant to them. Those expectations extend to government.

For Ainsbury, that makes participation a critical measure of progress. Satisfaction among existing users matters, but agencies also need to understand the people who never connect. A missed notification can mean a resident fails to renew a benefit or remains unaware of a service for which they may qualify. Identifying those gaps requires looking beyond the audiences an agency already reaches.

“Instead of looking at what you have, look at what you need,” Ainsbury says.

That shift changes the starting point for outreach. Rather than choosing the largest available email list, an agency should first identify the people affected by a program, decision, or service. It can then determine whether its existing communications actually reach them.

Making those messages more human also means making them more specific. Ainsbury discusses earthquake-warning outreach as an example: a general invitation to download an app can become more useful when it explains why that warning system matters to a particular resident and their community. Relevance gives people a clearer reason to act.

The same principle applies to gathering feedback. When government considers changes to a road, understanding which respondents live nearby or use it can help officials interpret the impact of their decisions. Ainsbury emphasizes that collecting this context must serve a clear purpose, with protections that prevent individual information from being exposed.

He describes Granicus’ work on Audiences as an effort to connect a broader understanding of residents with communication and engagement tools. The goal, he explains, is to help agencies reach people who may benefit from a service even when they have not already subscribed to that agency’s updates.

AI and machine learning can also change the employee side of that relationship. Ainsbury acknowledges public servants’ concerns about how emerging technology could affect their roles. He sees an opportunity to reduce the repetitive work of drafting, distributing, and scaling communications so employees can focus more attention on judgment: recognizing underserved communities, interpreting residents’ needs, and deciding how best to respond.

Measuring that impact requires more than counting hours saved. Ainsbury advocates combining feedback from individual service interactions with broader measures of satisfaction and trust. Machine learning can help agencies identify patterns across those signals, giving leaders a fuller understanding of whether digital investments are improving the experiences of the people they serve.

### Three Key Takeaways

1. **Measure who isn’t being reached.** Digital government progress depends on identifying residents who are missing services, benefits, or important communications—not just evaluating existing users.
2. **Make relevance and privacy work together.** Understanding residents can improve outreach and feedback, provided personal information is protected and collected for a clear service purpose.
3. **Use AI to strengthen human judgment.** Reducing repetitive execution can give public servants more time to address community needs, while success should be measured through service outcomes, satisfaction, and trust.

*The Future Public Servant is presented by Granicus and Carahsoft.*

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