The Future Public Servant: 7 of the 20 FGX capabilities that make Gov 3.0 real (North America)

What does the next generation of public servant actually need to be good at? And as AI reshapes everyday government work, where does it help and where do we need to be careful?

In this webinar from Public Sector Network and the Future Government Institute (FGI), a panel of senior government practitioners unpacks seven of the 20 capabilities from the Future Government Transformation (FGX) framework, the practical skills and mindsets that move organizations from Gov 2.0 to Gov 3.0 and beyond.

The panel covers service design and orchestration, analytics-driven decision making, experimentation and learning, agile governance and funding, trust by design, transparency and accountability, and AI and automation assurance. Rather than theory, the conversation focuses on real delivery experience, including how to cut through bureaucratic friction, build multidisciplinary innovation pods, bring citizens in as co-designers, and prepare government data for responsible AI adoption.

Key learning moments:

• The risk of the status quo is the risk nobody names. Government decision-making is wired to measure the risk of doing something new, but rarely asks what it costs to keep doing what isn't working. Reframing that conversation is one of the simplest and most powerful shifts a public servant can make.
• Not all friction is created equal. Some processes exist to protect citizens and should stay. Others are legacy checkboxes that no one has questioned in years. Learning to tell the difference, and having the confidence to challenge bad friction, is a core capability for Gov 3.0.
• Cross-functional innovation pods break the governance bottleneck. When proofs of concept get routed through the same oversight as multi-year enterprise projects, nothing moves. Small, mixed teams with executive sponsorship can deliver results faster while still meeting accountability requirements.
• Citizens belong in the design process, not just at the receiving end. Programs like IRS DirectFile showed that bringing real users in as co-designers through tight feedback loops produces better outcomes than launching a finished product and hoping it works.
• AI should accelerate the human, not replace the human. Government data is too inconsistent and the stakes are too high to remove people from the process. The safe path is keeping humans in the loop while using AI to speed up their work, and investing in data readiness before scaling.