When I began writing to this title, I thought it would be straightforward to discuss educating and training the geospatial intelligence workforce for sovereign and coalition operations. I expected only to apply a few well-researched recommendations for intelligence education and training. But, to borrow the metaphor of falling down a rabbit hole, the path forward was anything but straight. However, I emerged with a more fundamental understanding and suggestion.

The changing character of war is exposing shortcomings in Western military preparedness. Major General Mick Ryan (2026) describes a 2026 US Army exercise in Germany in which a smaller Ukrainian unmanned systems contingent detected American armor by following dust trails and attacked the exposed vehicles with drones. Losses accumulated so rapidly that vehicles had to be repeatedly returned to the exercise. His point is that the outcome exposed an institutional failure to learn and adapt quickly enough.

My concern is that without immediate changes in GEOINT education and training, we will fail to provide today's and tomorrow's commanders with the battlefield knowledge they need. To use an analogy, years ago in the Pacific region, I encountered the mudskipper, a fish with independently moving eyes that watch in different directions at the same time. GEOINT education and training need to do something similar. One of our eyes must focus on rapidly changing operational needs while the other focuses on anticipating future requirements and helping develop the GEOINT doctrine and concepts that will guide professional development.

The Changing Character of Warfare

The war in Ukraine reveals a battlefield shaped by AI-enabled analysis and targeting, persistent sensing, inexpensive precision systems, autonomous platforms, and decision cycles approaching machine speed. In 2023, Vice Admiral Frank Whitworth called this a "reluctant revolution in military affairs" and argued that technology was advancing faster than Western institutions could adapt (Air & Space Forces Association, 2023). Robert A. Pape (2026), writing about Iran, describes the spread of commercially available precision capabilities as a "second precision revolution." The implication is significant. Access to relatively inexpensive precision technology allows states such as Iran to accumulate large inventories of missiles and drones that can challenge Western forces dependent on smaller numbers of costly and exquisite defensive systems. Western militaries are therefore in a race to learn and adapt faster than their competitors. Ryan (2025) calls this the "adaptation war." The imperative is to adjust doctrine, weapons systems, organizations, and concepts rapidly enough to retain a decision advantage.

General David Petraeus argues that warfare is moving toward autonomous operations (Stretch, 2026). Petraeus points to Ukraine's use of 10,000 small drones per day as evidence that remotely operated systems are approaching a manpower threshold. Counter-drone measures also expose their dependence on communications. These pressures are pushing autonomy onto the platform and accelerating swarm development. Future engagements may increasingly pit distributed human-machine systems against one another.

The human role will change as well. General Dagvin R. M. Anderson distinguishes between a human in the loop who decides before action and a human on the loop who supervises automated processes (VICE News, 2026). GEOINT professionals may similarly shift from individual analytic tasks to directing and validating machine processes. General Sir Nick Carter cautions that drones and AI create a new way of war only when integrated with doctrine, force structure, training, experimentation, and human leadership (Center for Strategic and International Studies [CSIS], 2026).

GEOINT Education and Training for Human-Machine Warfare

Ryan (2026) argues that Western military institutions suffer from a systemic learning deficit that favors existing competencies and slows adaptation. GEOINT education and training cannot wait for warfare to be fully defined. A professional trained around today's tradecraft risks arriving at tomorrow's battlefield already obsolete. Educators should help the profession recognize change, challenge assumptions, test alternatives, and rapidly translate lessons into training.

I argue that GEOINT will increasingly provide trusted geographic context directly to autonomous systems. Those systems will require machine-usable terrain, imagery, location, provenance, confidence, and geographic constraints at operational speed. GEOINT professionals must understand what machines know, how they know it, where uncertainty enters, and when conclusions should be questioned. Education must prepare them to direct and validate machine processes while retaining human responsibility for intent, deception, geographic context, and judgment.

Human-machine warfare also makes the distinction between sovereign and coalition GEOINT more consequential. A nation's GEOINT reflects its institutions, authorities, and security requirements. Coalition GEOINT is shaped by multinational cooperation, releasability, and shared intelligence infrastructure (Bacastow et al., 2020). Technical interoperability does not produce common authorities, identical information access, shared risk tolerances, or identical judgments. GEOINT professionals must learn to connect sovereign capabilities without erasing the distinctions that make them sovereign.

My research and experience suggest seven priorities for GEOINT education and training.

1. Differentiate AI competence across the workforce. Most professionals need enough literacy to use AI-enabled systems and recognize their limits. Smaller numbers need deeper skills to maintain, modify, or develop them (Cruickshank, 2023).

2. Recognize when geographic models stop representing reality. Seasonal change, terrain modification, new sensors, patterns of life, camouflage, deception, and adversary adaptation can make reliable models wrong (Cruickshank, 2023).

3. Develop educators as deliberately as students. Faculty must understand AI capabilities, limitations, governance, and operational boundaries. An education system cannot adapt faster than its educators (Joshi, 2025; Smith, 2025).

4. Practice calibrated trust. Learners should confront biased outputs, plausible errors, and misleading confidence so they learn when to rely on, challenge, or reject a system (Smith, 2025).

5. Train in degraded and deceptive conditions. Exercises should include failed communications, incomplete data, spoofed positioning, conflicting sources, and uncertain geographic knowledge (Cruickshank, 2023; Hamilton et al., 2023).

6. Train across sovereign and coalition constraints. Professionals should practice with different national data, models, releasability rules, risk tolerances, and authorities for autonomous action (Smith, 2025).

7. Build a continuous curriculum adaptation cycle. Operational feedback should move rapidly into skills assessment, experimentation, curriculum design, evaluation, and revision (Hamilton et al., 2023; Joshi, 2025).

The objective is not simply to produce GEOINT professionals who know how to use GIS, remote sensing software, and AI or how to support drone operations. It is to develop professionals who understand geography deeply enough to direct, question, constrain, and correct machines that increasingly depend on geographic knowledge. They must judge when machine representations of geography are trustworthy, when they are wrong, and what machines should be permitted to do with them.

Conclusion

Preparing GEOINT professionals is now an adaptation problem as much as an education and training problem. GEOINT education and training must keep one eye on today's battlefield and the other on the battlefield that is emerging. We must meet immediate needs while developing the doctrine and competencies required for future human-machine warfare. The purpose of GEOINT education should not be to substitute machine capability for geographic reasoning and human judgment, but to use machine capability to extend the speed and reach of both. To paraphrase an idea often associated with futurist Alvin Toffler, the successful GEOINT professionals of the future will not necessarily be those most skilled with technology, but those who can learn, unlearn, and relearn as rapidly as warfare changes.