AI Endpoint Security ROI: What 273% Really Means

Security leaders don’t usually struggle to explain risk. They struggle to explain value.

When a commissioned Forrester Total Economic Impact (TEI) study reports a 273% ROI over three years for AI-native endpoint security (modeled on a 15,000-employee, 12,000-endpoint enterprise), it gives CISOs and IT leaders something they can actually use in budget season: a business case with numbers, not fear.

This post is part of our AI in Cybersecurity series, and I want to focus on the practical question behind that headline: what drives endpoint security ROI in 2025—and how do you validate it in your environment without getting dazzled by a percentage?

Why endpoint security ROI is suddenly a board-level topic

Endpoint security ROI matters now because endpoint scope has exploded while staffing hasn’t. Laptops, VDI, contractor devices, servers, cloud workloads with “endpoint-like” characteristics, and OT-adjacent systems keep piling up. Meanwhile, adversaries move laterally across identity, endpoint, and cloud faster than most teams can coordinate across tools.

The uncomfortable truth: most endpoint programs aren’t “bad,” they’re just operationally expensive. The cost hides in:

• Tool sprawl (multiple agents, consoles, contracts)
• Alert fatigue and repetitive triage
• Slow investigation due to incomplete telemetry
• Time lost onboarding new sites, acquisitions, and remote teams

The TEI study’s headline ROI is really a proxy for something more specific: AI-powered endpoint security can reduce both breach probability and operational drag at the same time—if it’s deployed as a platform foundation rather than another point tool.

What the 273% ROI is actually made of (and what to challenge)

A credible ROI claim should decompose into three buckets: risk reduction, labor efficiency, and time-to-value. The Forrester TEI findings (commissioned by CrowdStrike) highlight all three.

Here are the specific outcomes cited for organizations using CrowdStrike Endpoint Security:

• 273% ROI over three years, with payback in under six months
• 80% lower risk of endpoint-related breaches (stronger protection + faster investigation/response)
• 95% reduction in technology management labor (tool consolidation + SaaS console with no maintenance)
• 30,500+ hours saved across security and technical teams
• 66% faster time-to-value for new sites and acquisitions
• Net present value (NPV) reported as $3.7M for the modeled organization

The stance I take on ROI studies

ROI studies are useful, but only if you treat them like a checklist for your own assumptions. TEI models are built from interviews and a composite organization. That’s not a flaw—it’s the point. But your job is to interrogate the inputs.

When you see “273% ROI,” challenge these four items immediately:

1. Baseline tool stack: Are you replacing multiple endpoint tools (AV, EDR, device control), or adding one more agent?
2. Maturity level: Do you already have strong processes and low noise, or are analysts drowning?
3. Incident profile: Are endpoint-related incidents a major driver of downtime and response cost for you?
4. Rollout speed: Can you deploy quickly (single sensor, cloud managed), or will internal friction slow adoption?

If your current environment looks like “lots of tools + lots of noise + slow investigations,” ROI is usually real—and it’s usually bigger than you expect.

The operational mechanics: how AI endpoint security creates measurable value

The biggest ROI driver isn’t “AI” in the abstract. It’s what AI changes about the daily work. In endpoint security and XDR, AI matters when it compresses the time between signal and action.

1) Less alert noise, more analyst throughput

AI-native endpoint security aims to reduce false positives and cluster related activity into higher-confidence detections. That’s not a vanity metric. It determines whether your SOC is doing:

• Triage work (acknowledge, close, repeat)
• or threat work (contain, hunt, fix root cause)

The TEI study includes a customer quote describing investigations shrinking from “hours” to “minutes.” That’s exactly where ROI appears first—because every minute saved compounds across shifts, incidents, and escalations.

A practical way to quantify this in your own SOC is to measure:

• Average time spent per endpoint alert (before and after)
• Percent of alerts escalated to incident
• Investigation time to reach a containment decision

If you only track MTTR at the incident level, you miss the real drain: the 200 small investigations that never become “incidents,” but still consume the week.

2) Tool consolidation reduces “security tax” on IT

The TEI results cite a 95% reduction in technology management labor, attributed to consolidating legacy tools and simplifying operations with a SaaS console.

Here’s what that means in real organizations:

• Fewer agents fighting for CPU and breaking endpoints
• Fewer exceptions, GPO conflicts, and version mismatches
• Less time patching on-prem infrastructure (management servers, databases)
• Less “ticket ping-pong” between desktop engineering and security

I’ve found that consolidation is also where you win political capital: end-user performance improves, IT ticket volume drops, and security stops being the team that “adds another agent.”

3) Faster protection during change events (acquisitions, new sites)

The TEI study reports 66% faster time-to-value for new sites and acquisitions. That matters because late-year M&A and restructuring is common, and security teams often get pulled into January integration chaos.

AI endpoint security contributes when you can:

• Deploy a lightweight sensor quickly
• Standardize policies across diverse endpoint fleets
• Get immediate visibility into what was inherited
• Contain threats without rebuilding the whole stack

This is one of the most under-modeled benefits in security ROI discussions. Boards care about integration speed because it affects revenue capture and operational stability.

How to evaluate AI endpoint security ROI in your own environment

The cleanest way to build your business case is to run a 30–60 day proof-of-value focused on economics, not features. Most companies get this wrong by measuring “detections” instead of “hours saved” and “tools removed.”

A practical ROI scorecard (steal this)

Track these metrics weekly during your evaluation:

1. Analyst time

   • Hours spent on triage
   • Hours spent on investigations
   • After-hours pages (count and duration)

2. Coverage and visibility

   • Percent of endpoints fully onboarded
   • Time from install to first usable telemetry
   • Gaps: unmanaged devices, stale agents, offline endpoints

3. Response performance

   • Time to contain (isolation, kill process, quarantine)
   • Percent of incidents contained without escalation

4. Platform consolidation potential

   • Tools you can retire (and contract dates)
   • On-prem servers you can shut down
   • Agents you can remove

5. Risk posture proxy

   • Dwell time on confirmed activity
   • Repeat infections on same endpoint/user
   • Endpoint-to-identity correlation success rate

Then translate into dollars:

• Hours saved × loaded labor rate
• Tools removed × annual license + infra + admin time
• Reduced breach likelihood × expected loss (use your own risk model)

This is how you make ROI defensible without relying on anyone else’s composite organization.

Where AI fits in the bigger “AI in Cybersecurity” story

AI endpoint security is the foundation layer that makes the rest of AI-driven defense work. If your endpoint telemetry is weak, every downstream AI system—SIEM analytics, agentic SOC workflows, exposure prioritization—runs on bad inputs.

Endpoint security also sits at the intersection of the three forces shaping cybersecurity right now:

• Speed: adversaries chain tactics quickly across endpoint and identity
• Scale: distributed work and cloud workloads expand the attack surface
• Automation: defenders need automated response to keep up with machine-paced attacks

That’s why ROI shows up so clearly in endpoint modernization. You’re not just buying “better detection.” You’re buying a lower cost per protected endpoint and a lower cost per investigated alert.

People also ask: common ROI questions (answered plainly)

Is 273% ROI realistic for mid-market organizations?

It can be, but only if you’re replacing multiple tools and reducing manual work. If you keep the old stack and add an extra EDR, ROI shrinks fast.

What’s the fastest way to see value from AI endpoint security?

Start with alert reduction and response speed. Those improvements show up in week-one operations and create momentum for broader platform adoption.

What should I watch out for when adopting AI-native endpoint security?

Three traps:

• Not retiring legacy tools (you pay twice and keep complexity)
• Not integrating with identity and cloud signals (you miss cross-domain attacks)
• Treating automation as “later” (you keep the same labor model)

Next steps if you’re building an endpoint security business case

If you’re heading into 2026 planning, here’s the move I’d recommend: stop pitching endpoint security as “protection,” and start pitching it as “operational efficiency + reduced breach exposure.” The TEI study numbers resonate because they combine both.

Build your case around three outcomes your CFO will recognize:

• Lower operating cost (hours saved, tools removed)
• Lower loss expectancy (reduced breach likelihood and impact)
• Faster integration and growth (onboarding endpoints during change)

If you want, map your current endpoint stack, contracts, and SOC workflows, then run a proof-of-value using the scorecard above. You’ll either validate the ROI story—or learn exactly what needs to change to make AI-powered endpoint security pay off.

Where do you think your biggest endpoint ROI is hiding right now: tool consolidation, analyst time, or faster response when things break?