AI Forward Series: From Pilot Programs to Real Government Operations

July 8, Aug. 12, Sept. 9, Oct. 14, 2026 | 1.5 Hours Each | 2:00PM EDT | 6 CPE

Government agencies are under increasing pressure to move artificial intelligence beyond pilots and into real-world operations – while navigating evolving policy requirements, data challenges, and growing security risks.

The AI Forward Roundtable Series – July 8, Aug 12, Sept 9, and October 14 – is designed to help public sector leaders work through what it actually takes to expand AI utilization throughout their agencies. Across four focused sessions, this training will explore how agencies are moving AI into production, preparing and governing their data, managing new risks, and scaling successful efforts across the enterprise.

These are not theoretical discussions. Each session is grounded in the real decisions, tradeoffs, and constraints agencies are dealing with right now — offering practical insight into what’s working, what’s not, and what needs to happen next. For industry partners, this series creates a unique opportunity to engage alongside government leaders in conversations that reflect current priorities — where solutions are aligned to real challenges, not hypothetical use cases.

July 8: Moving AI from Pilot to Production

Agencies have launched dozens of AI pilots – but many never make it into production. They stall for many reasons, including integration challenges, policy friction, unclear ROI, and the difficulty of embedding AI into real mission workflows.

It is relatively easy to set up a pilot program – target a particular process, make sure the datasets are clean and accessible to your AI tools, upgrade key hardware and software, and test AI-driven outcomes for quality results. But the same steps are arduous and time-consuming when scaling up; untrustworthy data, out-of-date or obsolete infrastructure, and poor identity control systems, to name a few factors, all must be addressed before an AI pilot can be scaled across the enterprise.

Join us as thought leaders from government and industry discuss their own experiences moving from AI pilot programs to agencywide implementations, the challenges they had to overcome, what they would do differently, and the conditions that enabled their successes.

Learning Objectives:

• Outline the elements that were improved to start the pilot and assess how much upgrading and improvement is needed to expand the pilot across the agency
• Assess the talent available within the agency to implement an enterprise-wide AI program and the hiring needed to execute
• Delineate budget requirements and whether performance improvements can generate savings that can be applied to expand the program

Distinguished Speakers:

August 12: Data Readiness, Governance & Trusted AI

As agencies move to implement AI throughout their organizations, most are finding their AI efforts disrupted by fragmented, low-quality, or inaccessible data. This is a problem government shares with the private sector – a recent survey found that 79% of respondents said their AI initiatives are being hindered by limited access to data across environments.

These are not new problems for the government. There are still issues with data fragmentation and silos, poor data quality, complexities in applying governance and security requirements, and technical debt – legacy systems aren’t designed for modern analytics needs. And. of course, the pace of data generation continues to accelerate, adding to the pressure to clean and restructure massive numbers of datasets. That recent survey found that 60% of AI projects may be abandoned due to poor data readiness.

Join us as thought leaders in government and industry discuss their experiences addressing data readiness, the tradeoffs between speed and control, their data ownership and governance challenges, and their first-hand experience balancing policy expectations with the reality of “trusted AI.”

Learning Objectives:

• Outline the particular challenges in data readiness faced by your agency
• Delineate the steps to address those challenges, including prioritization and resource allocation
• Establish metrics to measure improvements in data quality so your agency datasets can be used by AI tools to produce trusted solutions

Distinguished Speakers:

September 9: Managing Risk & Securing AI Systems

AI introduces new vulnerabilities – such as data leakage, model manipulation, and uncontrolled access – even as agencies are still figuring out how existing risk and security frameworks apply. Recent news articles have reported that Anthropic’s newest AI model, Mythos, found 2,000 vulnerabilities in just seven weeks of testing commercially available software; Mozilla, for instance, reported Mythos identified 271 security vulnerabilities in Firefox 150. There have been instances where security teams are pulled in late and asked to “make it safe” after deployment decisions are already underway.

There are cybersecurity constructs in place that can help control access to AI tools and data. For example, the Zero Trust mandate already in place – “never trust, always verify” – strengthens requirements for access. Having an “identity-first” security structure can minimize the risks associated with AI adoption.

Join us as thought leaders from government and industry discuss their experiences adapting risk management and cybersecurity measures at their agencies to enable the use of AI while protecting against the new vulnerabilities it opens.

Learning Objectives:

• Identify existing cybersecurity weaknesses in existing processes, such as where security is being bypassed or bolted on too late
• Understand how to apply Zero Trust concepts to AI workflows
• Confirm cybersecurity alignment with guidance provided by the National Institute of Science and Technology (NIST) and the Cybersecurity and Infrastructure Security Agency (CISA)

Distinguished Speakers:

October 14: Scaling AI Across the Enterprise

AI success is often isolated, limited to pilot efforts that tackle one specific challenge or workflow. AI pilots are most focused on getting the technology right; implementing an enterprisewide AI strategy requires alignment across leadership, workforce, procurement, and mission teams, though definitions of success, ownership, and ROI remain unclear.

How agencies can move beyond the isolated success in a pilot and achieve similar results across their organizations is more a strategic challenge than a technical one. It requires shifting from a use case to capability, from technology experiment to an operational effort, by prioritizing scalable infrastructure, robust data governance, and change management.

Join us as thought leaders from government and industry share their insights on automating deployment, integrating with existing workflows rather than creating standalone tools, and ensuring clear executive sponsorship and workforce buy-in to manage organizational change.

Learning Objectives:

• Identify organizational and work culture barriers that need to be addressed
• Review your agency’s budget and procurement activities to ensure funding is available as scaling up takes place
• Delineate your agency’s automation and AI priorities to maximize both effectiveness and impact

Distinguished Speakers: