The public sector stands on the edge of a seismic technology shift. As AI accelerates across industries, government agencies are under increasing pressure to modernize infrastructure, ensure security, and unlock efficiencies—without losing focus on mission delivery. But while interest in AI is soaring, readiness is another story.
In a recent episode of Tech in Translation, we sat down with Dan Morehead, AI Account Strategist at Cisco, and Nick Serapiglia, Managing Director of Digital Transformation at Iron Bow Technologies, to talk about what it takes to move from AI curiosity to execution. Their message to public sector leaders was clear: the future belongs to those who prepare.
AI Is Here—But Most Networks Aren’t Ready
AI is no longer hypothetical. In fact, 78% of organizations report using AI in at least one business function, and 92% expect to increase AI investment over the next three years. Demand for AI-ready data centers is projected to grow by 33% annually through 2030. The public sector is feeling that pressure, and recent federal directives have made it clear: agencies must build secure, scalable infrastructure and designate leadership roles to oversee AI strategy.
And yet, true readiness remains rare.
“There are promising pockets of momentum,” said Dan. “But on the whole, most agencies aren’t ready for enterprise-wide AI adoption.”
From Pilot to Production: What’s Getting in the Way?
Both Dan and Nick agreed—many agencies are stuck in pilot mode. Projects often show strong initial promise but stall before scaling. Why?
“IT and cybersecurity are often left out of early planning,” Dan explained. “Without early involvement, you miss foundational requirements like network latency, computational power, or model security. That kills momentum.”
Nick echoed that sentiment: “AI isn’t just about the model anymore. It’s about the entire foundation—data, infrastructure, and security. If those aren’t aligned, you’re not going to scale.”
Where AI Is Already Creating Value
Despite the challenges, AI is already delivering tangible results across public sector use cases:
-
Help Desk Automation: Gen AI is being used as a Tier 1 support agent, streamlining tickets and freeing human technicians for more complex work.
-
Physical Security: Computer vision enables faster, smarter surveillance and incident detection.
-
Predictive Maintenance: Especially on military installations, machine learning is helping reduce downtime and operational costs.
-
Intelligent Document Processing: Agencies are using AI to digitize and analyze paper-based processes, speeding up workflows and improving fraud detection.
One standout example? The Department of Defense used AI to forecast unspent budget line items—freeing up hundreds of millions of dollars for repurposing. “That use case started with warfighter support but ended up in financial analytics,” said Dan. “The return on investment was undeniable.”
What’s Slowing Progress? Fear, Fragmentation, and Foundation Gaps
So what’s holding agencies back?
-
Fear of rapid change: “Things are evolving so fast, people worry they’ll invest today and be outdated tomorrow,” Nick said.
-
Fragmented leadership: Security, data, and infrastructure teams often operate in silos.
-
Lack of foundational readiness: According to Nick, 80% of AI project time is spent preparing data. Without clean, accessible data, AI can’t deliver.
Both experts stressed the need to bring technical and business teams into the same room—and soon.
Where to Start: A Framework for AI Readiness
Whether you're launching your first AI initiative or looking to expand, Dan and Nick recommend focusing on these pillars:
1. AI-Ready Data
Clean, curated, and accessible data is essential. Agencies should invest in data governance and make data part of everyday operations.
2. AI-Ready Infrastructure
Agencies need scalable, power-efficient infrastructure that won’t overload budgets or data centers. Predictable compute and network performance are non-negotiable.
3. AI-Ready Security
AI creates new threat surfaces. Agencies need to evaluate risks across the AI lifecycle and ensure both the architecture and models are secure.
“AI isn’t magic,” Nick emphasized. “You can’t bolt it onto a broken foundation.”
The Power of Workshops and Cross-Functional Planning
Both experts agree—AI success starts with conversation. They recommend hosting structured workshops to identify high-impact use cases, assess infrastructure gaps, and align key stakeholders.
“You’ll be surprised what happens when you get IT, data teams, and leadership in one room,” Dan said. “That’s where we’ve seen real breakthroughs.”
The Role of Partnerships
With 96% of CEOs relying on trusted partners to future-proof their networks, agencies don’t have to go it alone.
“There’s no single provider that can do everything,” Dan acknowledged. “But Cisco brings secure infrastructure, AI-driven security, and deep trust. Partnering with Iron Bow adds data expertise and public sector know-how. That’s how we help agencies move from interest to action.”
Final Advice for Public Sector Leaders
If you remember one thing from this conversation, let it be this:
“There’s no AI outcome without AI readiness,” said Nick. “And readiness starts with infrastructure, security, and data.”
Dan added, “IT needs to demand a seat at the table. Because if you’re not part of the AI conversation now, you’ll be scrambling to catch up later.”
What’s Next for AI in Government?
Both experts see a future where AI becomes an everyday assistant—not a threat.
“We’re heading toward a world where every employee has an AI assistant,” said Dan. “It’s not about replacing people—it’s about supercharging them.”
On the infrastructure front, Nick predicts a shift away from massive GPU farms toward smarter, edge-based deployments: “We’re going from ‘AI first’ to ‘AI smart.’ It’s going to make AI more affordable, more flexible, and more impactful across government.”
Key Takeaways:
-
Start with the foundation—data, infrastructure, and security must be aligned.
-
Bring IT to the table—don’t let AI pilots bypass the teams who will scale them.
-
Lean on trusted partners—no one can tackle AI alone.
-
Think big, but start small—target high-impact use cases to build momentum.
-
Prepare for a future of AI assistants and edge-based intelligence.
Liked these takeaways? Listen to the full episode now.
COMMENTS