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LiveAction LiveWire Now Allows Users to Export Network Packet Data into AI

LiveAction announced that users can now leverage LiveWire in concert with Artificial Intelligence (AI) to better refine network operations.

LiveWire will now allow users to easily export their network packet data for use in AIs to find patterns that human operators might otherwise miss.

LiveWire now allows users to easily export their data and use it with the AIs – such as ChatGPT. From there, they can more easily address a wide range of network issues by combining these network data sets with AI capabilities.

This can make addressing previously complex network issues into natural language questions. For example, when voice jitter becomes a problem, administrators can use their network monitoring data in an AI to merely ask questions like “do we have any voice problems?”, “where?”, “for how long?” and “how do I address it?”

These capabilities can also be used to unearth previously unnoticed patterns in text-heavy data, uncover contextual relationships in network events that would be difficult for a human administrator and simplify previously complex workflows.

John Smith at LiveAction notes “AIs and Large Language Models (LLM) are having rapid and transformative effects on the world around us. However, many still aren’t aware of how they can leverage them with the data they already own. LiveWire gives users an easy way to supercharge their network monitoring data with these emerging technologies.”

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LiveAction LiveWire Now Allows Users to Export Network Packet Data into AI

LiveAction announced that users can now leverage LiveWire in concert with Artificial Intelligence (AI) to better refine network operations.

LiveWire will now allow users to easily export their network packet data for use in AIs to find patterns that human operators might otherwise miss.

LiveWire now allows users to easily export their data and use it with the AIs – such as ChatGPT. From there, they can more easily address a wide range of network issues by combining these network data sets with AI capabilities.

This can make addressing previously complex network issues into natural language questions. For example, when voice jitter becomes a problem, administrators can use their network monitoring data in an AI to merely ask questions like “do we have any voice problems?”, “where?”, “for how long?” and “how do I address it?”

These capabilities can also be used to unearth previously unnoticed patterns in text-heavy data, uncover contextual relationships in network events that would be difficult for a human administrator and simplify previously complex workflows.

John Smith at LiveAction notes “AIs and Large Language Models (LLM) are having rapid and transformative effects on the world around us. However, many still aren’t aware of how they can leverage them with the data they already own. LiveWire gives users an easy way to supercharge their network monitoring data with these emerging technologies.”

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AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...