
IBM unveiled IBM Watson AIOps, a new offering that uses AI to automate how enterprises self-detect, diagnose and respond to IT anomalies in real time.
Watson AIOps enables organizations to introduce automation at the infrastructure level and is designed to help CIOs better predict and shape future outcomes, focus resources on higher-value work and build more responsive and intelligent networks that can stay up and running longer.
The new solution is built on the latest release of Red Hat OpenShift to run across hybrid cloud environments and works in concert with technologies at the center of today's distributed work environment, such as Slack and Box. It also works with providers of traditional IT monitoring solutions, such as Mattermost and ServiceNow.
As part of the rollout, IBM is also announcing the Accelerator for Application Modernization with AI, within the IBM's Cloud Modernization service. This new capability is designed to help clients reduce the overall effort and costs associated with application modernization. It provides a series of tools designed to optimize the end to end modernization journey, accelerating the analysis and recommendations for various architectural and microservices options. The accelerator leverages continuous learning and interpretable AI models to adapt to the client's preferred software engineering practices and stays up-to-date with the evolution of technology and platforms.
Many of the technologies underlying Watson AIOps and the Accelerator for Application Modernization were developed in IBM Research.
"What we've learned from companies all over the world is that there are three major factors that will determine the success of AI in business – language, automation and trust," said Rob Thomas, SVP, Cloud and Data Platform, IBM. "The COVID-19 crisis and increased demand for remote work capabilities are driving the need for AI automation at an unprecedented rate and pace. With automation, we are empowering next generation CIOs and their teams to prioritize the crucial work of today's digital enterprises—managing and mining data to apply predictive insights that help lead to more impactful business results and lower cost."
The Latest
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 ...
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 ...