For warfighters, the defense industrial base’s (DIB) strength comes down to speed: whether industry can anticipate demand, solve production problems, and deliver critical systems on time. Breaking Defense discussed the challenges facing the DIB with Ana Garcia Olson, Managing Director, Navy & Marine Corps Business Lead, Accenture Federal Services, and Amy Bahrani, Managing Director, AI and Data, Defense Industrial Base Lead, Accenture Federal Services.
Breaking Defense: In today’s security landscape, technological dominance isn’t just about who has the best asset on the frontline; it’s about who can design, build, and field capabilities the fastest. How do we move the Joint Force and the defense industrial base away from legacy, multi-year development timelines and into a true pace of rapid innovation?
Garcia Olson: The reality we’re facing is that the traditional way we build military platforms—where it takes years, sometimes a decade, to move a system from a whiteboard to operational deployment—is a strategic liability. The biggest move underway impacting the DIB is the evolution of the way the Department of War is aligning the acquisition process to prioritize commercial innovation, speed, and outcomes to ensure greater impact for the mission and the warfighter. I see both small and large companies seeking more information on how to move quickly to make that happen. We know the services want to move faster and having those avenues to meet that demand is a bit of a challenge. The ability to out-engineer and out-field the adversary is a baseline necessity. To do that, the DIB has to evolve into a highly agile, software-defined enterprise.
The actual engines driving this acceleration are digital engineering and modern manufacturing. We are talking about putting high-fidelity digital twins, Internet of Things (IoT) sensors, and robotics directly onto the factory floors and into the shipyards. By creating a seamless digital thread that links initial mission requirements straight through to the assembly line, you get total lifecycle visibility. It allows distributed teams to collaborate on sensitive design tasks in real-time, compressing engineering cycles and automating the dense documentation that typically stalls programs.
Bahrani: When you look at the immediate constraints, it almost always comes down to capacity. It’s the material constraints, space constraints, and workforce shortages putting pressure on the system from every angle.
To break through that pressure, we must stop treating the DIB as a reactive or even passive supply chain function. For example, we recently looked at the military’s global supply chain and brought in the same world-class industrial expertise that moves inventory for the world’s largest commercial retailers. By building a predictive global system where AI, robotics, and digital twins work together, we can ensure the right asset gets to the warfighter when every single second counts.
We know this works because we are delivering these secure, cloud-based digital engineering environments right now. We are supporting hundreds of concurrent workloads across military organizations and complex weapon systems at the highest classification levels, all built on a zero-trust architecture. When you can securely share cross-domain data and run high-performance simulations, you unlock the ability to field advanced military technology at commercial speed and industrial scale. That is how you build a resilient ecosystem where industry partners can see their innovations reaching the frontline faster.
Breaking Defense: The DIB is massive – how does AI shift that paradigm from a reactive planning to a predictive enterprise that can move as fast as the mission?
Bahrani: We need both government and industry to accelerate their own internal operations into an Intelligent Enterprise so they can collectively support a true National Enterprise. That means driving end-to-end data integration with agentic AI across the entire product lifecycle—from initial ideation and design through engineering, manufacturing, and field service.
Our approach is straightforward: we should simulate before we fabricate. AI allows us to build highly complex system models and expand the simulation space. We can learn hard lessons faster (and with far less expense) in a lab or on a shop floor, catching quality assurance issues at the design stage rather than correcting costly failures out in the field.
Garcia Olson: But to make that work, you must look at how data is used. It’s not enough to drop information into data lakes or insert a human into a legacy business process at discrete points. The most effective AI-powered operations are redesigned from the ground up with humans in the lead. You need real-time data to give leaders decision advantage, but humans must be positioned from the start to steer the system, not just validate a machine’s work.
Skeptics in the defense ecosystem often look at AI as a collection of interesting pilots that struggle to cross the “valley of death” into actual production. Where are you seeing success in deploying AI at scale?
Garcia Olson: We already see it playing out in the resurgence of advanced domestic manufacturing. When you use industrial AI to pipe real-time factory-floor data into a secure digital core, you create a shared intelligence layer. An aerospace facility, a clean-energy plant, or a commercial shipyard can suddenly adapt and surge production based on real-time national demand.
Bahrani: A concrete example puts it in real perspective on where AI is moving the needle. For one defense client, we completely redesigned their end-to-end ordering function. After building a proof of concept, we delivered an operational transformation. The result was a 76% cut in front-end cycle time, a 51% productivity gain, and $53.5 million in ROI in the first year. That is how you scale innovation to support the warfighter.
You mentioned workforce constraints earlier. If the physical infrastructure is modernizing, how do you upskill the workforce on the shop floor or in the maintenance depots to keep pace?
Bahrani: Historically, training meant taking people offline for months of reading technical manuals because we assumed they needed to understand the entire lifecycle before touching a machine. We are flipping that model to focus instead on the tasks a person is responsible for today, showing them the immediate impact, and widening the scope of their training over time.
If a technician encounters an error code they’ve never seen on an assembly line, they shouldn’t have to halt production or track down a supervisor. We are deploying AI-powered systems that ingest standard operating procedures, documentation, and blueprints in real time. The technician can ask a question, get the exact fix surfaced instantly, and keep the line moving.
Garcia Olson: To upskill the workforce, blend hands-on peer mentorship with digital training such as bite-sized microlearning and virtual simulations. This allows employees to learn at their own pace on the shop floor without severely disrupting daily operations. It also changes the recruitment equation where there is high demand for critical digital roles The younger generation entering the workforce grew up in a real-time data environment. They are incredibly sophisticated with all things digital. If they step onto a submarine or a shipyard floor and find themselves cut off from basic data access, they are underutilized. We must give them the intuitive, modern tools that match their aptitudes, so they are empowered to solve problems on the fly.
While the major defense primes have the capital to invest in these capabilities, the DIB relies on a tiered network of over 60,000 companies, many of which are small and mid-sized. How do these smaller suppliers adopt advanced AI without jeopardizing cybersecurity or breaking their margins?
Bahrani: That is a critical vulnerability. For a Tier 3 or Tier 4 supplier, the cost of bringing on board an experienced Chief Information Security Officer (CISO) could completely erase their margins, making it incredibly difficult to meet strict Cybersecurity Maturity Model Certification (CMMC) requirements.
If the question is how should we manage sensitive data across a fractured supply chain, the answer has to be automated governance. Security controls and guardrails must be baked directly into the digital infrastructure itself, creating systemic protections that don’t rely solely on human compliance or a single point of failure.
Garcia Olson: We also have amazing dual-use commercial tech companies that want to bring their capabilities into the national security space, but they are locked out by the sheer complexity of facility clearances and timelines that can take up to a year just to get a person cleared.
We routinely hear the frustration from emerging tech leaders who say, “I have the technology to solve your manufacturing bottleneck right now, but I don’t know how to navigate the security barriers to get it to you.”
Breaking down those informational and security silos isn’t a regulatory preference anymore. It’s a requirement for modern national security and homeland defense. We have to build a foundation that allows startup innovators and early tech companies to securely plug into the national enterprise, because operational readiness begins long before a platform ever reaches the frontline.
For more from Accenture on the DIB and closing the delivery gap, click here.
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