Modernizing Federal Technology: From Cloud Strategy to AI Reality

Government agencies face growing pressure to modernize technology while protecting mission-critical systems and national security operations. How can leaders balance innovation, cybersecurity, and operational reliability at the same time? 

In this episode of FORUM Power Podcast, we explore how cloud computing, artificial intelligence, data analytics, DevSecOps, and automation are reshaping federal technology strategy. 

Our guest, Siva Sivaraj, Vice President of Technology at ITC Federal, shares insights from more than 20 years leading technology innovation and solution architecture for complex federal missions. 

We discuss how agencies supporting organizations like the U.S. Department of Homeland Security are approaching cloud migration, AI adoption, data-driven decision-making, and supply-chain resilience. 

You’ll hear practical insights on separating AI hype from real automation value, modernizing legacy government systems, and designing secure, mission-focused technology solutions that actually deliver results. 

If you work in federal IT, government technology, cybersecurity, cloud infrastructure, AI, or digital transformation, this episode offers valuable perspective on what’s changing—and what challenges remain the same. 

Key topics in this episode:

• Federal technology modernization and cloud strategy
 • Artificial intelligence vs automation in government operations
 • DevSecOps, cybersecurity, and mission reliability
 • Data analytics and data-driven government leadership
 • Supply-chain resilience and operational risk management
 • Modernizing legacy federal systems safely
 • Designing solution architectures that deliver real outcomes 

#FORUMPowerPodcast #FederalIT #GovTech #CloudComputing #ArtificialIntelligence #DevSecOps #Cybersecurity #DataAnalytics #Leadership #Innovation #ITCFederal

FORUM POWER Podcast delivers insider conversations on GovCon and federal IT leadership, hosted by Mary Ann Brown, President of FORUM.
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Transcript

SPEAKER_00 0:02

The choices leaders make today shape how government works tomorrow. Featuring candid conversations with leaders at the forefront of federal innovation at GovCon, Forum Power examines modernization, procurement, cybersecurity, AI, and more. This is Forum Power. And now, here's your host, Mary Ann Brown.

SPEAKER_01 0:24

I'm being joined today by Siva Savaraj, the VP of Technology at ITC Federal. Siva, thank you for hopping out with me today.

SPEAKER_02 0:32

Thanks, Mary. Thanks for having me here.

SPEAKER_01 0:34

So tell me a little bit about what it is you guys do there at ITC Federal.

SPEAKER_02 0:37

ITC Federal is um is the mid-market uh company, and uh we support uh customers predominantly in the national security and law enforcement space. And our support is around uh IT modernization, IT operations, like end-user services and federal financial services. So we do the whole realm of overall IT management for our customers.

SPEAKER_01 1:00

With over 20 years delivering mission solutions to the federal government, what has changed most in how agencies think about technology and what challenges remain stubbornly the same?

SPEAKER_02 1:11

So, what's changed most is the speed of expectation. Agencies are no longer just consumers of technology. They're expected to operate like digital enterprises. Uh, the biggest shift is that federal leaders don't see technology as a back office support anymore. They see it as mission capability. Also, what has not changed is the mission can't pass and the stakes are still incredibly high, especially with the law enforcement and the national security domain. Over the last two decades supporting federal missions, the most noticeable change is how proactive agencies have become about technology. There's a growing recognition, especially across DHS and TV, that they simply can't execute the most challenging missions on outdated legacy technology. The mindset has moved from keep the lights on to how do we build the foundation for innovation so the mission can adapt to tomorrow's threats and demands. Another major change is the way agencies talk about partnership. It's not just about buying a tool or awarding a contract. Agencies want a partner that understands large enterprise systems in the unique context of government missions and can modernize while sustaining critical operations. That's why we emphasize on a force multiplier helping agencies anticipate how modern tech will interact with what's already in place and orchestrating solutions that drive meaningful outcomes without destabilizing the environment. Also, what has not changed is equally important, right? The constraints are real, risk tolerance is low, missions are complex, and continuity matters. The pressure to deliver is constant, often with surges driven by policy, operations, and deadlines. There is also a persistent challenge of operating across large interconnected ecosystems where many stakeholders need to align in the environment, even small inefficiencies are scale quickly because if you're inefficient, it shows up. That has not changed at all.

SPEAKER_01 3:19

You work at the intersection of advanced analytics, AI, and RPA. How do you decide which problems truly need AI versus those better solved with simpler automation or process improvement?

SPEAKER_02 3:32

The question is no longer if agencies should use AI. It's how to integrate it, right? So it actually impacts the mission. And the first step is being harnessed about whether AI is needed at all. My decision framework is to start simple, prove value, and scale responsibly. In practice, we look at three layers. One is process improvement. If the issue is unclear, ownership, inconsistent steps are avoidable handoffs. The fastest improvement is often workflow redesign and not AI. AI won't fix it, it'll just automate inconsistency. In those cases, we start with standard operating procedures, decision trees, and streamlined workforce. Next, the automation slash RPA. If the process is repetitive, rule-based work moving data, validating fields, routing tasks, automation is usually the highest ROI and lowest risk. AI and advanced analytics will come in when problem involves ambiguity, language, pattern detection, or skill, especially when humans are spending time interpreting unstructured information or making repeated judgment calls. If you can write stable rules, don't use AI. If you can't, because the inputs are messy and the decisions require context, AI can be the right tool. Even when AI fits, it must meet operational standards, clear accuracy targets, and human oversight. Also, it provides governance on what data it can see and how answers are controlled. The ability for mission owners to update content and keep it current. I default to process fixes first and automation second, and AI only when ambiguity or unstructured information makes rules impractical. And only if we can govern it, measure it, and sustain it, I recommend using AI. But we cannot spell IT without AI today.

SPEAKER_01 5:29

Supply chain bottlenecks are a growing concern across government. How are data-driven, mission-based solutions helping agencies move from reactive fixes to proactive resilience?

SPEAKER_02 5:40

Historically, agencies responded after disruptions occurred. Now they're moving towards predictive mission assurance. Proactive resilience starts when you turn scattered information into timely decision advantage. So leaders in the federal market space see issues before they become mission-impacting events. What we see across our customers and the government agencies is that supply chain problems often look like logistics, but they are really data problems. Incomplete visibility, delayed information, inconsistent interpretation, and difficulty coordinating decisions across many stakeholders. So those causes these bottleneck issues in supply chain. A data-driven mission-based approach starts with building the technological foundation for innovation in the environment where information can be trusted, accessed quickly, and updated. When agencies modernize their IT infrastructure and workflows, they can shift from after-the-fact reporting to continuous awareness, which is what proactive resilience requires. From there, advanced analytics and AI become meaningful because they are built on a stable, modernized foundation, AI can improve operations by enabling better situational awareness and helping teams sort and analyze large volumes of information. Apply to supply chain, that becomes earlier detection of anomalies, faster prioritization of constraints, and more informed decisions about mitigation actions. But out of all that I talked about AI, the most important leadership lesson is don't treat supply chain as a single system problem. Treat it as a mission outcome problem. The goals are not dashboards. The goal is continuity, ensuring critical services stay reliable under stress. I think that is the need of the work.

SPEAKER_01 7:34

You've supported agencies like DHS, where risk tolerance is low but stakes are really high. How do you design innovative solutions while ensuring security, reliability, and mission continuity?

SPEAKER_02 7:46

In high-stakes environments like the DHS, innovation has to be operationally safe, meaning it strengthens continuity rather than introducing fragility. In high-risk environments, innovation means reducing operational risk, not introducing novelty. Our goal is mission assurance. Systems must perform reliably even during disruption. So the first principle is simple. Mission continuity is non-negotiable in DHS. That shapes how we innovate. We focus on solutions that can modernize capability while still supporting critical existing systems because national security and federal law enforcement missions can't afford downtime or destabilization. That's why partnership matters. Agencies need a technology partner that can navigate large enterprise systems and implement innovation in a way that drives outcomes while maximizing efficiency. We emphasize this balance, anticipate the challenges ahead, understand how modern tech interacts with existing infrastructure and orchestrate solutions that improve capability without breaking what already works. So a practical way to talk about this is to describe the safe innovation pattern that IDC follows. So we start with clearly scoped, controlled solutions aligned to real user demand. Next one is we use platforms the customer already operates. In that case, SaaS solutions available in Azure environment. So you reduce integration and sustainment risk, control in the hands of program owners. So you keep governance and trust close to the mission. So these are the ways like we innovate in a high-stake environment like DHS and D1G.

SPEAKER_01 9:36

As vice presidents of solutions architecture, you see both strategy and execution. What separates solution architectures that deliver real outcomes from those that look good on paper but fail in practice?

SPEAKER_02 9:48

Architecture is not a design artifact, right? Because a lot of people they think, okay, these are like design patterns, that is architecture, that is the output of architecture. It's a living operational capability. Real architecture is designed for operations, adoption, and sustainment, not just pretty looking diagrams, right? So what I've learned as a solution leader is that architecture fails when they're optimized for approval instead of outcomes. The architectures that work do a few things consistently. They start from the mission context, especially in government where you're dealing with large enterprise systems and high-stakes continuity requirements. They assume the system must evolve so they create room for the agency to anticipate tomorrow's challenges and incorporate innovation without rewriting everything, because the architecture has to be evolving as the need evolves. The architecture should build in governance and ownership so the customer can operate the solution, not just receive it. So, for instance, we use the grants chatbot. It shows architecture that is built for reality. Because the critical design element was not just the agent that we used in that chatbot scenario, but it was the operational control plane. Program managers can view and update their questions and answers, as I was mentioning earlier, right? So we give the control to the program owners. That way they control any of the innovations. So what we propose is always a sustainment first architecture. That's why it delivered maturable outcomes, reduced call volumes, and yielded cost savings in wherever we used it.

SPEAKER_01 11:27

Many agencies struggle to turn data into action. What's the biggest mistake organizations make when trying to become data-driven, and how can leaders avoid that?

SPEAKER_02 11:36

Becoming data-driven starts with what decisions must we make better, faster, or safer. Then you align the architecture, analytics, and governance to support those decisions. The biggest mistake is treating data as the goal instead of treating decisions as the goal. Many organizations equate being data driven with building dashboards, doing like cool analytics, collecting more data, or adopting new tools. But being data-driven is actually about actionable outcomes. Better decisions, faster responses, improved service delivery, and reduced risk, especially in national security and federal law enforcement missions. Leaders avoid this mistake by focusing on the mission journey and the voice of the customer. How users actually need to operate and decide. We emphasize connecting modern technology to meaningful mission outcomes and enabling greater insights to support decision making. That is the standard. If the data initiative doesn't change decisions or reduce friction for users, it's not delivering what is supposed to deliver. I think the good way to explain this is to start with the decisions, then work backward, identifying the highest impact decisions, what needs to be faster, what needs to be safer, what needs to be more reliable, and build a data foundation and automation around these decisions.

SPEAKER_01 13:03

Your experience spans cloud, devsecops, AI, and emerging technologies. How do you help agencies modernize legacy systems without disrupting critical mission operations?

SPEAKER_02 13:14

I always feel like modernization must be evolutionary, not disruptive. We always, based on our company principle, we avoid like big bank transformations. Instead, we deliver incremental modernization that improves reliability while reducing risk. So, based on our experience, modernization always worked in government when it respects continuity because the mission can't stop while technology catches up. Legacy modernization in federal environment is rarely a clean slate rebuild. Agencies must look ahead to solutions that anticipate tomorrow's challenge, as I mentioned earlier, all the questions, while also providing support for critical existing systems to maximize efficiency when national security is at stake. So the approach is to modernize in a way that strengthens the underlying technology foundation, that enables innovation, which improves operational efficiency and security, and also allows the agency to adapt quickly to mission needs. This also ties back to our broader solution strengths like cloud native environments, automation, IT operations, data engineering, and AI. Because modernization is not one motion. It's a coordinated change across delivery and operations.

SPEAKER_01 14:28

Okay, so the Form 100 recognizes leaders making measurable impact. Do you have a time when your approach reduces risk or improves service delivery for a federal mission?

SPEAKER_02 14:38

Yes. Measurable impact comes from practical solutions that improve service without increasing operational burden. So I'd like to bring out one example that we did for a public safety grants program use case. This program serves a critical public safety mission, managing and administering grants used by well over 13,000 organizations, state, local, territorial, and tribal law enforcements, and others. So always users needed timely, accurate guidance on timing missions and application procedures. The existing response center helped this process, but it becomes so expensive and the demand spikes increased wait times, especially near deadlines, right? So this actually impacted the user experience. We looked at the scenario and then we implemented a chatbot agent using existing SaaS capabilities available in Azure. The key insight was that a small curator library could handle most user questions. So program managers can control the responses by updating questions and answer pairs, which also made it easier to keep the information current. But the measured outcome was we had a 20% drop in manual call volume and reduction of one response center position, yielding a saving of 120k annual savings, and faster user experience with immediate answers, supporting faster application completion time, and quarterly updates timed to program changes. This improved the reliability of information. That the overall thing improved the service delivery and reduced the operational risk in a way that program can sustain.

SPEAKER_01 16:21

Looking ahead, what capabilities should federal leaders be investing in now to prepare for the next decade of mission challenges and what technologies are they really overhyping?

SPEAKER_02 16:31

So the federal leaders should invest in the foundations, right? That make innovation safe and repeatable. Modernized infrastructure, automation, and the ability to integrate AI into operations with mission impact. From what we are seeing and what we are experiencing, agencies know they must modernize because they can't take on future challenges with outdated technology. But it is also clear they can't do it alone. Success requires partners that can navigate large enterprise systems and implement innovation while sustaining critical operations. I see the capabilities worth investing in now are IT modernization foundations that connect workforce, customers, and programs for operational efficiency and security, hyperautomation and AI-driven solutions intentionally applied to improve operations and citizen services. AI-enabled mission support that improves situational awareness and helps team manage massive volumes of information, especially when manual analysis becomes a bottleneck. On overhype, it's not that AI is overhyped because AI is proven to make operations more effective when applied intentionally. What's overhyped is the idea that AI is a shortcut. If leaders don't invest in integration, governance, and operational fit, AI becomes a pilot that never scales. The real shift is moving from try AI to integrate AI so it changes outcomes. If I could say one last thing, the mission outcomes improve fastest when modernization, automation, and AI are applied intentionally. When the Technology Foundation is built to keep the mission running while it evolves. So the next decade of federal technology is not about adopting newest tools. It's about building resilient, intelligent mission systems that can adopt to uncertainty.

SPEAKER_01 18:25

Well, Siva, thank you for hopping on and speaking with us today. This has been great.

SPEAKER_00 18:29

Thanks, Mary, for the opportunity.io. Thank you for listening. Be sure to join us again for a new episode and a deeper look at leadership and action.