Accelerate outcomes with mission-ready AI

No IT project fails because developers didn’t write code fast enough. Projects fail when teams build the wrong solution, misread their users, work or deploy in inappropriate environments, or create systems so complex they collapse under their own weight. Generative AI should take aim at these problems. After all, smarter coding agents without smarter processes only help build the wrong things—faster.

In the race to embed artificial intelligence into every system, speed is winning—but trust is becoming more critical than ever. Each week brings headlines about AI breakthroughs, from generative models capable of drafting reports to predictive tools that promise to transform logistics and battlefield decision-making. Yet behind the buzz lies an uncomfortable reality: too many federal solutions treat AI as an afterthought—something bolted on rather than built in. That approach may accelerate deployment, but it leaves mission-critical systems fragile, exposed, and untrusted in the moments that matter most.

The successful modern technology enterprise depends on the frequent, low-risk flow of value to end users. Flow, in turn, depends directly on a deliberate approach to how that value traverses from developer to user, including the infrastructure, security, networks, data, application architecture, user experience, and team-to-team interactions that comprise delivery. “Continuous delivery” isn’t the sum of all these parts; instead, it describes the state of these elements seamlessly working together in concert to enable value. A network of interconnected value that links the necessary handoffs between teams and systems is, ironically, a tight analogy for modern logistics and supply chains.