Federal AI and Innovation: 5 Questions with Jesús Jackson

Innovation has been a defining theme throughout Jesús Jackson’s career. Over the past two decades, he has helped federal agencies modernize technology, build secure cloud platforms, develop AI-powered solutions, and transform ideas into mission-ready capabilities. Along the way, he has led engineering organizations, established innovation centers, and guided the technical strategy behind hundreds of millions of dollars in successful federal pursuits. 

Jesús recently joined Evolver as Vice President of Growth Solutions and works across the company’s business units to shape technical strategy, develop differentiated solutions, and strengthen capture and proposal efforts. Drawing on deep experience in artificial intelligence, cloud engineering, DevSecOps, and software development, he helps customers navigate complex modernization challenges while identifying practical ways to improve mission outcomes. 

We sat down with Jesús to discuss where agencies are seeing the greatest value from AI, how innovation programs succeed, and what it takes to introduce emerging technologies without disrupting mission-critical operations. 

I think it’s a challenge a lot of agencies are still struggling with today. 

The first thing you need is a champion within the agency who’s already hit the “I believe” button. They’re willing to dedicate some political capital to sponsor the technology, and they understand the mission impact and benefits. 

But getting one person on board isn’t enough. You can’t force people to adopt new technology. It’s better to build consensus. Let people touch and feel it. Let them see how it helps them do their jobs and advances the mission. 

I think proofs of concept are one of the best ways to do that. Instead of a big-bang deployment, start with focused use cases, especially painful use cases that people deal with every day. If users can quickly see value, you’re going to get traction. If the use case doesn’t resonate, they won’t care about it. 

The other piece is demonstrating that security is baked in from the beginning. If you’re thinking about mission impact, I can’t have AI making mission-critical decisions on our behalf without a human evaluating the data first. Having a human in the loop and establishing guardrails early are really important. 

One of the biggest game changers right now is cybersecurity. The tools available today compared to eight years ago are like night and day. Before AI, SOC analysts spent a huge amount of time dealing with alert fatigue, sorting through thousands of alerts and trying to determine what was a false positive and what needed immediate attention. 

AI helps reduce that fatigue and allows analysts to focus on the data points most likely to lead to meaningful findings. That improves investigations and strengthens overall security posture. 

I’m also excited about what AI is doing for software engineering. These tools have matured significantly over the last couple of years. People without traditional software engineering backgrounds can now move from an idea to a proof of concept, and sometimes even a production-level system, through prompting. 

The impact on agencies is significant, especially when it comes to legacy modernization. Many organizations are still running COBOL mainframes or other aging infrastructure because those systems are deeply embedded in the mission. AI is helping transform those environments into modern technology stacks and changing what’s possible from a modernization standpoint. . 
 

Where I’ve seen agencies struggle is when they take too long to adopt AI capabilities, rely on outdated AI models, or make approved tools cumbersome to use. Their workforce still wants to use AI, so employees start turning to public, cloud-based tools where those protections don’t exist. This can lead to shadow IT, creating unnecessary security risks for the organization. 

I always tell customers that if you don’t provide AI tools your workforce can use, they’re going to use AI anyway. The genie is out of the bottle. The productivity gains are too significant. That’s why trying to stop AI use altogether isn’t a realistic strategy. 

The challenge is that those public-facing cloud services aren’t private. If employees upload PII, health information, or other sensitive data, it’s now living on someone else’s servers. Every agency needs to decide what data should remain within its own infrastructure. 

The better approach is to provide secure alternatives, establish guardrails as early as possible, and give employees clear guidance around acceptable use. That allows organizations to capture the benefits of AI without pushing users toward unapproved solutions. 

Sometimes that means starting with mock or dummy data. The structure mirrors mission data, but there’s no risk if it’s exposed. That’s a responsible way to test, learn, and build confidence before moving forward. 
 

The first step is making sure you’re solving actual client problems.

A common trap innovation groups fall into is getting hung up on the latest and greatest technology. They build what I call science projects. They’re interesting and may solve a niche problem, but when you ask who’s going to buy it, there’s no clear answer. 

Everything should tie back to a real client pain point. Even better if it’s a challenge shared by multiple agencies or organizations. 

I also think innovation doesn’t always have to mean cutting-edge technology. There’s a phrase I heard a long time ago: the money is in the boring stuff. Sometimes the biggest opportunities come from automating manual processes or improving workflows. Whether it’s AI, robotic process automation, or another capability, what matters is solving the problem. 

And if you’re building capabilities yourself, you need top talent and a lean team. Run it like you would any strong agile organization. Have a methodology, maintain accountability, and create transparent communication. Those fundamentals are critical to building and sustaining innovation. 
 

What stands out to me most is the need to be an effective communicator and storyteller. 

As a teacher, I taught AP Computer Science and introduced students to concepts that were completely new and often very challenging. The goal was to take complex information, break it down, and present it in a way that created that lightbulb moment where students could say, “I get it. Now I can go build something.” 

The technology world isn’t that different. 

A lot of leadership is serving as a bridge between customers and development teams. You have to understand requirements, communicate them clearly, and make sure the right solution gets built. 

Then you have to explain why that solution matters. Sometimes that’s easy because it’s a visible application that people can immediately interact with. Other times it’s a backend system doing something incredibly complex behind the scenes. 

Either way, you have to connect the technology to the outcome. You have to explain what problems it’s solving, why it’s valuable, and why people should care. I think that ability to communicate clearly and tell a compelling story is a strong parallel between both worlds. 

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Conclusion

Throughout the conversation, we surfaced a consistent throughline: successful innovation is built on solving real mission problems and giving people the confidence to embrace new ways of working. AI may be accelerating what’s possible, but lasting transformation still depends on thoughtful leadership, clear communication, and keeping people at the center of every decision.

For Jesús and the growth team at Evolver, the future of federal technology is about helping agencies adopt innovation with a clear focus on mission outcomes. When technology is grounded in purpose and people understand its value, innovation becomes a true catalyst for lasting impact. As Evolver pursues our innovation strategy, we’ll continue leveraging storytelling to bridge the gap between emerging technologies and our customers’ mission, helping them turn modern tools into measurable impact.