The U.S. Army is at a crossroads. It’s not just swapping horses for tanks. It’s wrestling with a deeper shift: moving from analog habits to truly data-driven decision-making. A century ago, cavalry faithful resisted mechanization even as tanks proved their worth. Today’s Army faces parallel resistance, not from lack of tech, but from mindset. Some leaders still lean on map boards and acetate overlays, wary of relying on computers and networks. That hesitation slows the transformation the service desperately needs.
The Mojave Falcon Experiment
In June 2025, the Army Reserve sent its first operational data team (ODT) to Exercise Mojave Falcon at Fort Hunter Liggett. Their mission? Track and optimize food, fuel, and ammunition distribution across a sprawling, simulated operations zone. Over 10 days, the handpicked team used platforms like Palantir, Tableau, and PowerBI to build data pipelines and craft visualizations to support commanders in real time.
Breakthroughs and Roadblocks
The exercise produced clear wins in logistics efficiency. When data was clean and accessible, commanders could make faster, smarter decisions. But the pilot also revealed serious friction:
Cultural barriers
Staff conditioned by outdated reporting habits missed chances to ask deeper questions. As one intel officer put it, “we are just calling all routes red because we don’t really see a pattern.” Unless commanders demand insight, data becomes theater, not enabler.
Poor data fidelity
Simulated data lagged behind the richness of real-world sources like the Army’s Total Ammunition Management Information System. Analysts spent hours massaging oversimplified data, like drilling with muskets in a tank war.
Tool unfamiliarity
The exercise revealed limited training on digital platforms. One officer admitted he didn’t have time to learn new tools mid-exercise. That shows where the burden falls: leaders must invest in skill development before deploying data teams.
Policy confusion
Data governance and security rules were murky. The team struggled to export data from classified networks without lengthy approvals. That highlights the need to treat data requests like ammunition and train the force accordingly.
What Worked
Despite resistance, some units fully embraced the ODT. These early adopters—likely future commanders and senior leaders—understood the potential of predictive analytics. That open mindset sets the stage for deeper integration in next year’s exercises.
Key Takeaways
Match teams to readiness: Deploy ODTs where baseline digital literacy exists. Otherwise, talent goes underutilized.
Optimize deployment: Start co-locating teams during kickoffs, then move most analysts to stable, connected environments for iterative follow-through.
Clarify policy: Make data governance and export processes part of training. The Army’s data strategy needs to be practiced, not just published.
From Experimentation to Doctrine
The ODT pilot at Mojave Falcon shows potential, not permanence. Real progress depends on transforming doctrine, training, and leadership culture. Just as the 20th-century Army had to embed the tank into its doctrine and mindset, today’s force must internalize data. Not just as tools, but as part of the observe-orient-decide-act cycle. Digital systems aren’t a sideshow. They’re the engine.
Final Word: Will You Drive the Tank?
The analog versus digital debate isn’t theoretical. It shapes the battlefield. Units that cling to analog risk being outmaneuvered by those that embrace data. The Army is investing in platforms, policy, and people. What remains to be seen is whether institutional will matches that investment. If not, some will keep riding horses while others drive tanks.
Jim Perkins is an Army Reserve officer assigned to the Digital and AI Office at the Office of the Chief of Army Reserve. His views reflect his own perspectives, not the U.S. Army’s position.
