Skip to main content

Numerify Releases Change Risk Prediction Solution

Numerify announced the release of its Change Risk Prediction solution, aimed at helping organizations reduce their IT Change Management costs and risks while increasing change velocity, and therefore agility.

The new solution is part of Numerify's broader IT Business Analytics portfolio that spans the Infrastructure and Operations (I&O), Application Development / DevOps, and IT leadership and strategy functions.

"As IT landscapes become more complex, large organizations are challenged to balance more frequent changes in production with the risk of service disruptions. Our new solution helps them manage change risk by blending data across their entire landscape and applying Artificial Intelligence (AI) to deliver actionable insights," said Srikant Gokulnatha, Co-Founder and Chief Product Officer at Numerify. "Based on our work through scores of deployments across Fortune 500 leaders, we have now packaged our best practices to deliver these insights even faster," said Gokulnatha.

The solution integrates data from a range of IT sources including development, build, test, deployment, IT Service Management, and Application Performance Management systems to create a unified view of all change-related problems and incidents. Numerify's Machine Learning (ML) models then process this data to predict both failure rates for specific changes and uncover systemic causes of change failure, including signals from both upstream code development and downstream performance monitoring applications. The Numerify Change Risk Prediction solution also provides a complementary descriptive analytics view, enabling IT executives to use an analytical lens to rapidly come up with a strategy to eliminate these systemic causes of change failure.

Organizations that have adopted Numerify's Change Risk Prediction solution, experience the following benefits:

- Increased overall efficiency: By reducing Mean-Time-to-Resolution (MTTR) of change-related incidents, and reducing rework and recovery costs, IT organizations can decrease the total costs of their Change Management function.

- Accelerated agility: By reducing change lead times and increasing change frequency, IT organizations can not only deliver a better customer experience but also expedite their journey to Agile and DevOps adoption.

- Reduced risk: By accurately predicting the causes of change risk as well as systemic causes of change failure, IT organizations can mitigate risk while focusing their attention on the riskiest changes.

The Latest

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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 ...

Numerify Releases Change Risk Prediction Solution

Numerify announced the release of its Change Risk Prediction solution, aimed at helping organizations reduce their IT Change Management costs and risks while increasing change velocity, and therefore agility.

The new solution is part of Numerify's broader IT Business Analytics portfolio that spans the Infrastructure and Operations (I&O), Application Development / DevOps, and IT leadership and strategy functions.

"As IT landscapes become more complex, large organizations are challenged to balance more frequent changes in production with the risk of service disruptions. Our new solution helps them manage change risk by blending data across their entire landscape and applying Artificial Intelligence (AI) to deliver actionable insights," said Srikant Gokulnatha, Co-Founder and Chief Product Officer at Numerify. "Based on our work through scores of deployments across Fortune 500 leaders, we have now packaged our best practices to deliver these insights even faster," said Gokulnatha.

The solution integrates data from a range of IT sources including development, build, test, deployment, IT Service Management, and Application Performance Management systems to create a unified view of all change-related problems and incidents. Numerify's Machine Learning (ML) models then process this data to predict both failure rates for specific changes and uncover systemic causes of change failure, including signals from both upstream code development and downstream performance monitoring applications. The Numerify Change Risk Prediction solution also provides a complementary descriptive analytics view, enabling IT executives to use an analytical lens to rapidly come up with a strategy to eliminate these systemic causes of change failure.

Organizations that have adopted Numerify's Change Risk Prediction solution, experience the following benefits:

- Increased overall efficiency: By reducing Mean-Time-to-Resolution (MTTR) of change-related incidents, and reducing rework and recovery costs, IT organizations can decrease the total costs of their Change Management function.

- Accelerated agility: By reducing change lead times and increasing change frequency, IT organizations can not only deliver a better customer experience but also expedite their journey to Agile and DevOps adoption.

- Reduced risk: By accurately predicting the causes of change risk as well as systemic causes of change failure, IT organizations can mitigate risk while focusing their attention on the riskiest changes.

The Latest

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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 ...