Las Vegas

11,000+ people. Many conversations. Lots of movement between sessions, meetings, the massive Ai4 exhibition hall, and back to the hotel.

Last week at the Venetian Las Vegas, Ai4 brought together leaders, builders, investors, policymakers and companies shaping what comes next in AI. I arrived particularly interested in a few questions:

• How are organizations actually deploying AI?

• Where is governance showing up?

• What are leaders prioritizing?

• How are personal data and data retention being handled?

• And where are the risks beginning to surface?

Between sessions, I spent time speaking directly with companies about their AI tools, asking how governance is being operationalized, and comparing those conversations with what I was hearing from leaders on stage.

Three themes stood out for me.

01 — AI Use in the Public Sector

Government agencies are already using AI to reduce administrative work, improve citizen services and process information more efficiently.

One discussion explored how processes that historically required documents to be mailed, scanned and manually reviewed can increasingly be supported by AI.

Contract review was another example. One session described work involving agentic AI to support that process, with analysts reportedly saving approximately 75% of their time.

As much as AI increases efficiency in government, the question about data sharing and guardrails is also raised. Is service delivery improved while maintaining digital trust through systems?

That is a conversation I believe deserves deeper examination.

02 — AI Leadership

Another theme was the gap between using AI and scaling it.

Many organizations have AI initiatives, but few have moved from experimentation to sustainable enterprise adoption.

Using AI is not the same as transforming an organization with AI.

One speaker emphasized the importance of focusing on impact.

Some initiatives should create quick wins by helping an organization operate faster or removing repetitive work. Others will require a longer horizon as companies rethink processes, data, technology and workforce capabilities.

There was also an interesting discussion about the emotions surrounding technological change, including fear, anxiety, nostalgia and trust.

The ATM was used as an example. When ATMs first appeared, many people feared that bank tellers would disappear. The work changed. Roles evolved.

AI will likely produce similar shifts across many professions.

The leadership challenge is helping people understand what is changing, what remains human-led and how work may evolve.

Reliable data also remains fundamental.

Organizations looking to fine-tune models or use proprietary information with powerful LLMs will need strong data foundations if they want AI initiatives to move beyond experimentation.

03 — AI in the Ballot Box

One of the more unexpected discussions at Ai4 focused on data centers and their growing political significance.

Data centers are becoming part of the public conversation around AI.

Nevada has become an important location for data center infrastructure, and communities are increasingly paying attention to the physical footprint of the facilities supporting AI.

People are talking about electricity demand, tax incentives, jobs, infrastructure, and the economic benefits.

As demand from hyperscalers continues to grow, states and counties may also find themselves in stronger negotiating positions when determining what communities receive in return.

The workforce discussion was equally interesting.

AI may not simply eliminate jobs. It may change them. An electrician, for example, may increasingly develop specialized skills to work within sophisticated data-center environments.

That is another reminder that the impact of AI extends far beyond software.

AI has a physical footprint, an energy footprint, a workforce footprint, and increasingly, it has a political footprint.

One comment from Dr. Kevin Frazier, University of Texas School of Law, stayed with me:

“Folks aren’t opposed to technology. They’re opposed to uncertainty.”

That may have been one of the most important observations of the week. This is because uncertainty sits underneath many of the AI conversations happening right now.

The Conversation Has Changed

People want to understand what happens to their personal data. Employees want to understand what happens to their work. Communities want to understand what AI infrastructure means for them, and executives want to understand how quickly they should move.

The AI conversation has moved well beyond what the technology can do. It is increasingly about leadership, trust, people, infrastructure and public confidence.

One issue in particular deserves a deeper examination: What happens to digital trust when government begins using agentic AI with sensitive personal data?

I’ll explore that next in THEEAIR Executive Insights.

Ai4 was a fantastic event. I made some great connections, had thoughtful conversations across the AI ecosystem, and I’m looking forward to returning for Ai4 2027.