I spoke in a media interview about the challenges facing the global defense industry and how AI is starting to change the way organizations respond to these. I want to share some of the main thoughts from that conversation, because those challenges, as well as the opportunities, are becoming more urgent by the day.
As defense forces around the world scale up amid rising geopolitical tensions, suppliers are under increasing strain. Requirements are getting more complex, cybersecurity is a must and AI is unavoidable. All of this can slow down design cycles, jam up manufacturing and delay delivery.
Our defense clients often sit on significant backlogs, creating operational bottlenecks at a time when they’re being pushed to increase their speed to market, be more resilient and maintain control, all while operating in highly secure, regulated environments.
Many of these bottlenecks are rooted in fragmented digital backbones, where enterprise resource planning, product lifecycle management, application lifecycle management and manufacturing execution system environments don’t provide a shared view of requirements, configurations, production status and lifecycle impact.
An industry stretched at every level
What’s unique about the defense industry is the prevalence of consortia, which is how many manufacturers go to market. You already have one company dealing with speed-to-market issues, operational complexity and supply chain logistics. Now multiply that by 10 when you bring in partners across the wider ecosystem.
On top of that, most of our clients are trying to double or even triple their output over the next few years, with fewer people and in a far more complex world.
The broader context is also shifting. Governments and national militaries are increasing spending dramatically, which means their suppliers must respond faster to deliver far more material but still meet strict national and regional security, sovereignty and compliance requirements.
Defense technology is also evolving rapidly. New products need to be engineered, time to market has to shrink and production has to scale — all while raw materials remain in short supply.
Even with funding available, many suppliers struggle to keep up with demand. This is where data, AI and related technologies are becoming central, both to improve efficiency and to build more resilient, secure and interoperable industrial capabilities.
Where AI starts to make a real difference
The next step is not simply adding AI to existing workflows. Defense organizations need software-defined operating models that connect requirements, engineering, production, logistics and lifecycle feedback across a secure digital backbone. This is where agentic AI becomes valuable: it can interpret requirements, coordinate tasks, trigger compliance checks, recommend next-best actions and escalate exceptions to human experts under defined governance policies.
It can also sort through large volumes of data to pinpoint requirements, run simulations through digital twins and identify more efficient logistics paths. In the defense context, this makes AI a capability multiplier — but only when it’s embedded in secure, sovereign digital architectures that support faster and more informed decision-making without compromising control.
One of the most immediate impacts we’re seeing is in how organizations handle increasingly complex military requirements and specifications. In the past, every national military defined its own specifications, which quickly became highly complex. Now those requirements are often consolidated into massive documents — not 20 pages, but 2,000 or more.
You can imagine how long it would take an engineer to sift through all of that to determine what’s relevant and whether anything is redundant. GenAI can cut that effort down significantly. Instead of reading everything, an engineer can go straight to the relevant information — for example, isolating all requirements related to a specific technology — and focus on what needs to be done.
AI also enables trusted, federated decision-making in complex ecosystems. Data still needs to stay where it belongs — within different parts of an organization or partner network, inside a zero trust environment — but AI can surface the most relevant insights under the right policies without breaking those boundaries.
This approach supports interoperability between North Atlantic Treaty Organization (NATO) and partner networks while maintaining national control, governance and data sovereignty.
The reality check: Data, risk and human readiness
Of course, none of this comes without challenges. The first and most obvious is data quality. If your data is bad, the outcome will be bad, no matter how good the AI is. The risk becomes even greater as systems become more autonomous. If something goes wrong, it needs to be detected quickly. Otherwise, you risk triggering processes unintentionally.
In the past, there were more points of human oversight, so issues could be caught early. As automation increases, problems may only become visible much later. By then, fixing them can be very expensive.
Some constraints are specific to defense. Operating across multiple organizations in highly secure environments means you can’t experiment as freely as in other industries. Solutions must meet defense-grade security requirements, prioritize auditability and comply with national and EU regulatory frameworks.
In defense, AI governance must be designed into the operating model: data lineage, model validation, audit trails, human approval points, red team testing and secure deployment pipelines are not optional add-ons.
Then there’s the human factor. There’s a persistent myth that AI is here to replace people. What we actually see is greater value in supporting people and removing time-consuming work. That said, some repetitive tasks will shrink, which means employees need to be upskilled to take on more analytical, decision-focused roles.
This makes change management critical, as human readiness for AI is a significant hurdle. Do people trust it? Do they accept it? Whether it’s leadership, operations or frontline teams, the human side of AI is often overlooked. Without it, however, the technology won’t deliver on its promise.
No more “starting small”
Right now, there’s still mixed messaging around AI in many organizations. What can AI really do, and how do we prove its value? That uncertainty often leads to a “start small” mindset — test a few use cases and look for quick wins.
The more effective approach is to start focused but to design for scale from day one. Select one or two high-impact value streams, such as requirements engineering, supplier risk, production planning or predictive maintenance, and implement AI end to end with governance, security, controls and measurable outcomes from the start.
Building momentum, governance and trust is what ultimately allows AI to scale in a secure, compliant and operationally effective way within an organization. And it also helps you overcome resistance.
Strike the right balance with AI
What’s changed most in defense technology over the past year is the mindset. Organizations have moved from observing AI to asking how to execute it, seeing it as necessary rather than optional.
AI can be incredibly powerful, but it’s also disruptive. Getting it right in execution means balancing speed with control, innovation with security, and technology with people.
For the defense industry, that balance is becoming critical because the goal is not just to move faster but to do so in a way that strengthens resilience, preserves privacy and sovereignty, and maintains trust across allied ecosystems.
WHAT TO DO NEXT
Learn more about NTT DATA’s Defense and Space solutions to see how we support defense organizations and manufacturers with secure, sovereign and business‑critical IT, infrastructure and data capabilities.