McKinsey’s article “Seizing the Agentic Advantage[1]”puts its finger on a problem many organizations already recognize: generative AI is everywhere, but measurable impact is still hard to find. Nearly eight in ten companies now report using gen AI in at least one business function, yet more than 80 percent still say it has not materially contributed to earnings. McKinsey calls this the “gen AI paradox,” and it is the right phrase for a market flooded with excitement, experimentation, and very little transformation. That is even more true for federal civilian and defense applications.

The majority of the AI market has stopped at the chatbot layer. Horizontal copilots and generic assistants are easy to deploy, but they often yield diffuse, difficult-to-measure benefits and are disconnected from the workflows where real value is created. Many higher-value, function-specific use cases remain stuck in pilot mode. McKinsey notes that about 90 percent of these vertical use cases never make it out of pilot, which means many organizations are still experimenting around the edges rather than redesigning how work actually gets done.

That is where agentic AI changes the conversation. McKinsey argues that agents can move AI from a reactive tool to a proactive, goal-driven collaborator because they combine autonomy, planning, memory, and integration to automate more complex business processes. Just as important, the firm warns that organizations cannot simply plug agents into legacy workflows and expect transformational outcomes. To unlock real advantage, they have to rethink those workflows with agents at the core, supported by governance, observability, and controlled autonomy.

That message lands even harder in defense and federal environments, where the cost of slow decisions, fragmented data, and manual reporting is not just inefficiency. It is mission drag. In these settings, AI has to do more than summarize text or answer prompts. It has to operate inside the mission, understand constraints, surface risks, preserve traceability, and keep humans firmly in control. The future is not a prettier chatbot. It is a secure digital workforce that helps teams execute, decide, andadapt faster. This is exactly the lane CORAS.ai has been building toward.

At its core, CORAS is an agentic decision intelligence platform for government and defense, and GARY is the LLM agent (with many sub-agents with specific roles and responsibilities) that turns that concept into action. GARY analyzes data, runs models, and delivers transparent, defensible recommendations, and because he is embedded within the CORAS engine, he too has the security of FedRAMP High and IL4/5-ready deployments across NIPR, SIPR, and other secure environments.

Just as important, CORAS' agentic AI is operational infrastructure. Customers can launch with pre-built, mission-ready agents for POM planning, portfolio optimization, readiness analysis, workforce forecasting, governance, and contracting, without requiring custom development before value starts to appear. That is especially relevant to one of the biggest barriers McKinsey identifies – a lack of mature packaged solutions for vertical, high-impact use cases. With CORAS, you have a Commercial Off the Shelf (COTS) agentic AI that is ready to drive real workflows.

GARY offers a key differentiator as an agent versus chatbot AI. GARY works in various roles or tasks as a decision agent that orchestrates data, models, workflows, and scenarios in real time; runs what-if analyses; surfaces blind spots; suggests next actions; and documents the logic behind each move. Just as significant, GARY does not hallucinate, so if information is needed to complete an action, he requests what is needed to finish the prompt given. He is a digital ally and analyst, connecting information across silos, proactively identifying issues, and delivering recommendations with logict races, source citations, and caveats to preserve human oversight. That is a much stronger answer to enterprise frustration than “AI that chats.” It is AI that works with 50x productivity.

The strongest case for agentic AI, though, is operational. An interesting use case with a CORAS customer in a U.S. Navy program office used GARY to reduce Contract Data Requirements List Setup (CDRLS) from an estimated 120 to 160 staff hours and multiple coordination meetings to roughly 30 minutes, with approximately $45,000 in avoided labor and a reported 240x to 320x productivity gain. That is a single instance for a process that is repeated thousands of times across departments.

The implications are endless for GARY to be tasked with repetitive processes like CDRLS for starters. Beyond the headline number, the significance is what GARY actually produced: requirements identification, a data model, workflow configuration, notifications, dashboards, and a rollout plan. In other words, it did not just summarize the work but helped structure and accelerate the work itself. That is the difference between AI as assistance and AI as advantage.

McKinsey is right that the next chapter of AI will be won by organizations that move from scattered experimentation to process redesign, from passive tools to governed agents, and from novelty to measurable value. The companies that break the AI paradox will be the ones that stop asking how to add AI to yesterday’s workflow and start asking how work should be redesigned now that agents can reason, act, and collaborate. CORAS.ai and GARY offer a concrete answer to that shift.

We are advancing a model in which agentic AI becomes part of the operating environment itself: secure, explainable, mission-aware, and built to help humans move faster on the decisions that matter most. In a market still crowded with copilots and pilots, that is the real advantage.

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[1]Seizing the Agentic Advantage, June 13, 2025, mckinsey.com,  https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage?stcr=EF8615FF03D548EE88F9764DFBBAB3FB&cid=mgp_opr-eml-alt-dna_mt-mgp-glb--&hlkid=4a0149a9d55641cab4abfda433c9cb5c&hdpid=05eaa0e3-51f6-454e-95e7-b2492dd3d3f4

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