Automox just hit a major milestone with support from its customers – earning seven G2 Summer 2025 badges across three continents and multiple market segments. This significant recognition demonstrates that IT teams worldwide are finding substantial value in Automox’s approach to endpoint management.
G2, the world’s largest software marketplace, is where millions of users share verified reviews about business software. Unlike paid placements or vendor-submitted content, G2 badges are earned through authentic customer feedback and satisfaction scores, making this recognition particularly meaningful as it reflects genuine user experiences.
The recognition demonstrates Automox’s strength across multiple dimensions. Grid Leader badges in mid-market, enterprise, and overall categories indicate both high customer satisfaction and substantial market presence — the gold standard of G2 recognition.
The Momentum Leader badge places Automox in the top 25% of category products by user ratings, while the High Performer Mid-Market badge for Asia reflects strong customer satisfaction scores. Regional Leader and Leader Mid-Market badges for Asia and Asia Pacific, respectively, round out the global recognition.
“These badges reflect what our customers tell us every day: they trust Automox to make their jobs easier and their organizations more secure,” said Justin Talerico, CEO of Automox. “When IT teams can patch thousands of endpoints with a single click, troubleshoot issues 49% faster, and sleep better at night knowing their systems are protected, that’s the kind of value that shows up in customer reviews.”
Automox’s cloud-native strategy eliminates the need for VPNs, on-premises infrastructure, and associated complications. IT teams can manage endpoints anywhere in the world through a single console, transforming what used to be a complex and time-consuming process into a streamlined, easy operation.
The Latest
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...
40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...
Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...