AI-Enabled Rocket Artillery: Greece’s PULS Signal

Greece’s expected purchase of 36 PULS rocket artillery systems—reported at $757 million—isn’t just another European defense procurement story. It’s a clear marker of where land warfare is headed: faster targeting cycles, tighter sensor-to-shooter links, and decision support that increasingly depends on AI-enabled software.

Elbit Systems has said it anticipates receiving the contract, contingent on final negotiations with Greece’s Ministry of National Defense. The timing matters. Greek lawmakers have approved the 2026 budget, and Athens is simultaneously discussing a wider modernization effort that includes an integrated air defense concept often described as an “Achilles’ Shield.” If you follow the “AI in Defense & National Security” space, you’ve seen this pattern across Europe: modernization is less about buying a platform and more about buying an ecosystem—munitions, data flows, integration, training, sustainment, and the software to connect it all.

Here’s the stance I’ll take: rocket artillery becomes strategically decisive when it’s digitally integrated. Range matters, payload matters, but the real advantage is time—time to detect, decide, deconflict, and strike.

What Greece is really buying with PULS (beyond rockets)

At the surface level, PULS is marketed as a flexible launcher that can fire unguided rockets, precision-guided munitions, and missiles across multiple ranges, and that can be adapted to existing wheeled or tracked platforms. That’s attractive for any military that wants more capability without rebuilding its logistics pipeline from scratch.

But the deeper value proposition is this: modern rocket artillery is increasingly a software-defined fires node. The launcher is the visible part. The critical part is how well the system plugs into:

• ISR feeds (drones, counter-battery radars, tactical recon)
• C2 and fires networks (fire mission planning, approvals, deconfliction)
• Targeting workflows (coordinate quality, collateral estimation, restricted target lists)
• Electromagnetic and cyber resilience (operating when networks degrade)

When Elbit describes PULS as “comprehensive” and “cost-effective,” the subtext is interoperability and sustainment. Greece is already juggling multiple suppliers and legacy systems. A modular launcher reduces training burden—but integration reduces operational friction.

Why the “sensor-to-shooter” chain is the real weapon

The most relevant metric for AI-enabled fires isn’t just maximum range. It’s the kill chain speed: how quickly a unit can go from detection to effects while maintaining control and reducing mistakes.

AI doesn’t replace commanders here. It compresses the steps that waste time:

• prioritizing targets from competing inputs
• checking target confidence and coordinate validity
• suggesting munition/trajectory options
• flagging conflicts (friendly forces, no-strike areas)
• generating a recommended fire plan that humans approve

If you’re leading modernization, you should treat rocket artillery as part of a mission planning and battlefield awareness problem, not as a standalone “fires” purchase.

Why Europe is adopting PULS—and what Greece adds to the trend

PULS has already landed with several European buyers:

• Germany selected it in February 2025 through the Euro PULS collaboration (involving KNDS Deutschland)
• The Netherlands signed in 2023
• Denmark acquired eight systems in 2023

That matters because European procurement is rarely a one-off. Once a capability becomes a de facto standard among neighbors and allies, it changes:

• training interoperability
• munition stockpiling decisions
• maintenance and spares ecosystems
• how quickly coalition forces can coordinate fires

Greece’s expected deal is especially interesting because it’s happening alongside talks about integrated air defense modernization. Taken together, Athens appears to be positioning for a layered defense and deterrence posture: protect key assets (air defense), then hold adversary forces and infrastructure at risk (long-range fires).

A practical read on “Achilles’ Shield” + long-range fires

Air defense and rocket artillery are often discussed separately. Operationally, they’re linked.

• Air defense tries to preserve freedom of action by reducing the threat from aircraft, missiles, and drones.
• Rocket artillery provides rapid, scalable strike options that can shape battles without immediate airpower.

In modern conflicts, drones and loitering munitions stress air defense while simultaneously feeding targeting. That’s where AI becomes central: air defense and fires both generate and consume massive volumes of data. Nations that manage that data flow well act faster.

Where AI actually shows up in modern artillery operations

People hear “AI-enabled artillery” and imagine autonomous launch decisions. That’s not the direction serious militaries are taking. The valuable AI is quieter: decision support, prediction, and automation of the boring-but-critical steps.

1) Target triage and confidence scoring

When multiple sensors report activity—drones, SIGINT, radar, human observers—operators need help sorting signal from noise.

AI-enabled tools can:

• correlate tracks across sensors
• detect patterns consistent with artillery prep or logistics buildup
• flag likely decoys or repeat false positives
• assign confidence levels that guide human review

This is battlefield awareness under time pressure. The goal isn’t “perfect truth.” It’s actionable clarity.

2) Fire mission planning under constraints

Every fire mission has constraints: munition availability, firing unit location, terrain, airspace coordination, collateral limits, and rules of engagement.

AI-assisted planning can generate candidate solutions quickly:

• suggested munition types for a target
• recommended aim points for effects desired
• trajectory options to reduce risk to friendly air or neighboring units
• timing suggestions synchronized with maneuver

It’s the same idea as route optimization in logistics—except the cost of a bad recommendation is far higher, so human approval stays central.

3) Counter-battery and survivability logic

Rocket artillery lives under constant threat from counter-battery radars, drones, and loitering munitions. Survivability depends on shoot-and-scoot discipline and minimizing signature.

AI tools can support:

• predicting enemy counter-battery response windows
• recommending displacement routes and alternate firing points
• monitoring EMCON adherence and communication patterns

A launcher that fires quickly but relocates slowly becomes an expensive target. AI-enabled recommendations help keep crews alive.

4) Training and sustainment analytics

This part doesn’t get headlines, but it’s often where readiness is won.

Software can identify:

• which crews are slow in digital fire mission workflows
• recurring maintenance faults by subsystem
• munition handling errors in training repetitions
• simulator scenarios that correlate to field performance

If Greece wants PULS to be credible deterrence, it needs more than delivery. It needs high-availability units and high-confidence crews.

What procurement teams should ask before signing an AI-enabled fires deal

If you’re a defense leader, integrator, or program office stakeholder, the smart questions aren’t “does it shoot far?” They’re “does it connect, endure, and scale?” Here are the questions I’d put on the table.

Integration questions (the ones that hurt later if ignored)

1. What data standards and interfaces are supported for integrating national C2 and ISR feeds?
2. What’s the degradation plan when GPS is jammed, comms are intermittent, or the cloud is unreachable?
3. How are targets validated and audited—can you replay a mission for after-action review and accountability?
4. Who owns the integration layer—the military, a prime integrator, or the OEM?

AI governance questions (the ones that keep you out of trouble)

1. Where is AI used in the workflow—and where is it explicitly prohibited?
2. What training data assumptions exist (terrain, weather, decoy prevalence, sensor types)?
3. How is model behavior monitored over time to prevent drift?
4. What’s the human override and approval design for every high-consequence step?

A simple rule works well: AI can recommend. Humans decide. Systems record. That triad is how you build speed without losing control.

Why this deal matters for national security leaders watching the region

Greece’s defense buildup is happening in a Europe that has re-learned an old lesson: massed fires and air defense aren’t legacy concepts—they’re daily realities. What’s new is the pace and the data density.

A country that modernizes artillery without modernizing its data and decision workflows ends up with impressive hardware and mediocre outcomes. The countries getting the most from new fires systems are those treating them as part of a broader AI-enabled national security architecture:

• persistent surveillance feeds
• fused intelligence and target development
• resilient C2 networks
• disciplined governance for automation

This is also why Israeli-Greek defense ties matter beyond the transaction. Greece has already worked with Israeli industry on other defense programs, and the pattern suggests a longer-term shift toward capability bundles rather than standalone buys.

The lead-generation reality: AI-enabled fires is an integration problem first

If you’re building or buying in this space—whether you’re a defense ministry, a systems integrator, or a technology vendor—the opportunity is clear: the next competitive edge is in the connective tissue.

The most valuable contributions tend to be:

• secure data pipelines from sensors to fires cells
• AI-assisted decision tools that reduce cognitive load
• model governance and auditability that commanders trust
• training systems that translate digital workflows into battlefield speed

If you’re evaluating an AI-enabled artillery modernization path, map your kill chain end-to-end before you compare brochures. The launcher is only one node.

Greece’s expected PULS deal is a useful signal for the rest of the market: European buyers are paying for speed, integration, and resilience—not just range.

What would change in your organization if you treated every new “platform buy” as a data and decision program from day one?