During the Global War on Terror (GWOT), U.S. defense and intelligence operations achieved something unprecedented: near-total visibility.
Drones, persistent ISR, satellite coverage, wide-area sensors, and more meant we could see almost everything, almost everywhere, almost all the time.
By any reasonable assumption, that should have made us smarter.
Instead, it made us dependent.
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Data silos and information overload remain persistent problems, but there’s a deeper issue underneath them — one that rarely gets discussed.
When you can see everything, you stop asking what you should be looking for. And when you stop asking, the skill of figuring it out atrophies.
In permissive environments where U.S. forces had air superiority and satellite coverage, there was a tendency for the command structure to increasingly lean on persistent collection. Analysts shifted from hypothesis-driven work to "watch and report."
Budgets, similarly, followed sensors, not sense-making.
Federal intelligence funding grew 125% from 1980-1989, but that investment flowed to collection platforms. The money went toward getting more data, not toward reasoning about what that data meant.
As a result, foundational analytic processes, once the backbone of intelligence work during the Cold War era, fell out of practice.
Tradecraft, or the skills and tools behind gathering information, looks nothing like it did 50 years ago. More vitally, the way we think critically about the information gathered has waned.
Structured techniques like Analysis of Competing Hypotheses and Critical Factors Analysis have become optional rather than standard. When you have all the data, the time-intensive work of reasoning through ambiguity feels unnecessary.
They were subsequently abandoned because of their labor-intensive nature, which began to feel like mere overhead.
Leading to situations like these, which happen on a regular basis:
All of this stands in the way of gathering the right data that guides you to the correct course of action.
We talk a lot about "the right data" at Certus Core, but that phrase gets misunderstood, only perpetuating these problems.
The "right data" is a matter of reasoning. If you've never defined what you're looking for, you won't recognize it when you see it.
The same dynamic plays out in organizations of any size or sector. Strategic questions get answered with whatever data is easiest to access, not whatever data is most relevant.
Without a framework for what matters, every data point competes equally for attention. And when everything competes equally, nothing gets prioritized.
In military operations specifically, the process of defining what matters is supposed to happen through a Commander's PIRs (Priority Intelligence Requirements).
Done well, PIRs distill what actually matters for a mission. This means having a clearly stated intelligence question, the indicators that would answer it, and the time frame by which the answer is needed.
In practice, the process became ceremonial. It took too much labor, so it ranked low on the priority list. Once complete, it was often ignored by most of the staff and recommunicated in different, more distilled ways.
The U.S. built exquisite collection architectures for domains where forces operated with relative impunity. That worked in Iraq and Afghanistan.
But in great-power competition, where adversaries contest the air, degrade or deny space assets, jam sensors, and exploit cyber and electronic attack vectors, the assumption that "we can see it, therefore we understand it" isn’t an option. Assumptions that things will just “work” are not only a threat to the mission but potentially national security.
In these contested environments, sensor coverage becomes intermittent or degraded. Adversaries employ concealment, deception, and adaptive tactics rather than symmetric mass exposure. The intelligence advantage shifts to whoever can infer most accurately from incomplete information.
Organizations that never built that reasoning muscle leave agencies and teams flat-footed when navigating murky situations.
This is where software enters the picture.
Platforms like IBIS™ (Information Bridging and Integration System) change the equation. Natural language queries force outcome thinking. Instead of, "Give me all the drone data collected on Tuesday," you ask, "Show material movement patterns in NAI Seven over the past 96 hours."™
Knowledge graphs can pull together data from these autonomous platforms together with human intelligence, creating mission context instead of a slew of disconnected feeds.
Our AI governance ensures collection activities serve a strategy that’s more than “fill up all the storage possible.”
AI governance creates repeatability and re-usability between deployments of IBIS™ or within the same software being used for a different mission.
Think of governance as the abstract stitching that makes multiple uses and use cases for AI possible as you switch what you're doing and the data you're doing it with.
In a recent airborne autonomous systems project, what once required days of data wrangling became queries answered in seconds. Operators could ask mission-critical questions in plain English and receive verified, visualized answers drawn from multiple intelligence sources.
Collection shifted from generating data to answering questions.
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Deception detection, denial-resilient collection, or human-source networks all fall under tradecraft. These capabilities weren’t built by watching drone feeds. Yet all of them become critical the moment the feeds go dark.
As with many other disciplines, AI can change the equation.
The processes that were too time-consuming to run consistently can now be automated. These include::
The goal is systems that reason with you, not just retrieve for you.
IBIS™ is built around this principle. Knowledge graphs create a continuously updated network of meaning.
Agentic AI orchestrates queries across disparate sources. Natural language access removes the bottleneck of technical translation. And governance ensures every output is verified against your organization's rulebook.
This way, your organization gets structured reasoning at speed, accessible to everyone who needs it.
In a recent Vendor Threat Mitigation project with the U.S. Air Force, analysts faced a familiar challenge: manually searching multiple disconnected databases while racing against procurement deadlines. Complex vendor assessments took several hours – sometimes days..
Instead of analysts thinking through what they might be looking for, they were stuck with a tedious amount of work.
IBIS™ integrated 4TB of vendor intelligence from standard VTM data sources: SAM.gov, USA Spending databases, the Consolidated Sanctions List, and finished intelligence documents.
Rather than maintaining expertise in multiple query languages, analysts could query in plain English:
"Show me all telecommunications vendors with recent contracts over $1M and trace their beneficial ownership to countries of concern."
The system orchestrates queries across every database, correlates findings, and delivers comprehensive results in under a minute. The platform surfaces data and helps analysts think through its implications with traceable logic.
Like any muscle, rebuilding tradecraft skills takes time under tension and reps. With IBIS™ automating the processes in between, these analysts can be thinking deeply about the questions they’re looking to answer rather than figuring out how to work with the data itself.
The GWOT era taught us that dependency on uncontested collection creates vulnerability. Everybody has data now. What will separate victory from defeat in any given engagement won’t be more data.
The ability to reason fastest under constraints will be.
The tools to rebuild reasoning-layer intelligence exist today. The question is whether organizations will invest in them before constraints force the issue.
Our 8-week Pilot Partnership Program lets you validate what reasoning-layer AI can do for your operations.
Up to 2TB across three data sources, full platform access for three users, and a complete refund if you decide not to continue.
See how chat-based queries + mission-derived context + AI governance eliminates the tradeoff between speed and accuracy with IBIS™.
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