“Simply put, we must continue to leverage AI – and every emerging technology that we can – to maintain the U.S. space-based intelligence advantage as we protect our satellites; enhance our ability to monitor adversary activity; and provide timely, accurate intelligence to warfighters, analysts, first responders, and decision makers,” said National Reconnaissance Office Director Chris Scolese during the 2026 USGIF GEOINT Symposium in Aurora, Colorado.
Chris Scolese frames AI as an operational imperative
Scolese’s remark at the 2026 GEOINT symposium framed the problem plainly: collection capability has outpaced the workflows that produce useful intelligence. Satellites now deliver imagery and geospatial data at a scale and cadence that strain traditional analyst workstreams. The director tied AI adoption directly to a set of operational priorities — protecting satellites, monitoring adversary activity, and delivering timely, accurate intelligence to a range of users from warfighters to first responders — making the technology more than a technical convenience and instead a mission necessity.
Robert Conway: a metadata problem, not a sensors problem
“The government gets petabytes of images on a daily basis, and this is thrown into a library somewhere with probably not a very good indexing capability,” explained Robert Conway, Senior Client Partner at Verizon. Conway described how many imagery stores remain cataloged by narrow fields such as geographic coordinates and timestamps, leaving analysts to perform time-consuming searches that often miss the contextual cues needed for operational decisions.
He listed the sorts of contextual markers analysts want but do not always find in catalogs: “Weather patterns, terrain elevation, infrastructure proximity, seasonal activity, transportation routes, atmospheric conditions, and even religious criteria can all influence the intelligence value of an image.” Conway added a blunt assessment of the consequence: “If you’re not leveraging AI or a large language model, you’re just missing the whole picture.”
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End the scrambleHow AI is changing GEOINT processing and delivery
The source describes AI not as a single tool but as a set of capabilities that change how imagery is processed. Machine learning models can automatically classify imagery, detect objects, recognize patterns, and enrich images by correlating them with weather feeds, topographical data, infrastructure maps, maritime traffic information, and historical activity patterns. That technical shift moves agencies away from static imagery archives and toward what the source calls more dynamic intelligence environments, where analysts can query mission-specific criteria rather than comb through repositories.
That approach is reflected in public comments from senior NGA leadership. NGA Director Army Lt. Gen. Michelle Bredenkamp said the agency is finalizing a “blueprint for becoming an AI first organization,” while stressing that AI is meant to augment human expertise rather than replace it: “AI handles the volume, the speed, the pattern recognition across massive data sets, but critical thinking, contextual understanding, the ability to ask, ‘What does this actually mean?’ that is irreplaceable human expertise,” she said.
Deputy Director Brett Markham underscored the operational endgame: the agency is looking to apply AI to workflows that can reduce timelines “from hours down to minutes” for analysts and decision makers.
NGA’s Rapid Capabilities Office and the turn to commercial partners
One concrete institutional step in this direction is NGA’s October 2025 establishment of a Rapid Capabilities Office (RCO). The RCO, the agency says, is part of its effort to accelerate adoption of commercially developed geospatial intelligence and AI technologies. Markham said, “We are constantly surveying the marketplace for innovative capabilities that meet our needs.”
The source paints procurement speed as a core driver of commercial engagement: traditional acquisition cycles often move too slowly for fast-evolving AI and geospatial tools, so agencies are actively seeking industry-developed solutions that can be piloted quickly and scaled if successful. Conway summarized that dynamic: “They want industry to be able to develop this for them,” and agencies are “trying to remove the red tape.”
What this means for the NGA, the NRO, and commercial partners
- NGA: The agency is institutionalizing AI with a blueprint to become “AI first” and an RCO to rapidly ingest commercial capabilities, while explicitly preserving human analytic roles even as it seeks to shorten processing timelines.
- NRO: Leadership has cast AI as essential to maintaining space-based intelligence advantage and to the protection and operational use of satellites, linking technical adoption directly to national-security mission outcomes.
- Commercial partners (including firms represented by Robert Conway): Expect accelerated demand for deployable, mission-ready AI that can be piloted and scaled, and continued pressure to deliver rapid, integrated solutions that enrich imagery with the contextual layers analysts need.
The picture the symposium painted is simple and consequential: sensors deliver more than they once did, but raw imagery is not the same as usable intelligence. Agencies are betting that the combination of AI, commercial innovation, and new acquisition pathways will convert volume into velocity — turning petabytes on a shelf into minutes of actionable insight for people at the tactical edge. Whether those systems will meet operational expectations, preserve human judgment, and do so at the speed leaders promise remains the immediate test.
