The new integration of AT&T and IBM technology enables enterprises to conduct comprehensive testing and analysis of their apps' network and battery usage on mobile devices, and quickly make necessary changes; creating higher-quality, better performing mobile apps.
For the first time, IBM clients will be able to determine how their enterprise mobile app is performing on any wireless network, and then rapidly improve performance through development tools.
The integration of AT&T’s Application Resource Optimizer (ARO) with IBM’s software development solution for Collaborative Lifecycle Management (CLM) expands the development capabilities of the IBM MobileFirst strategy.
“ARO tackles a fundamental coding challenge developers face today – finding and fixing performance and power bottlenecks that detract from a great user experience,” said Carlton Hill, VP Developer Services, AT&T. “ARO can help developers create apps that conserve battery life, load pages faster and consume network resources in a smarter way, all of which improve the customer experience.”
AT&T’s ARO is a free, carrier-agnostic, open source diagnostic tool that enables developers to analyze the performance of their apps, whether they be business or consumer apps. Launched in January 2012, ARO is now used by more than 1,500 developers, and users are seeing better results across the board, from a 35 percent reduction in excess data usage to apps that run up to 60 percent faster. Improving app performance and creating better customer experiences, ARO saved more than 500 terabytes of data in its first year.
ARO enables developers to diagnose previously undetectable inefficiencies in app-to-network interaction. ARO can identify the events happening at multiple layers within an app and pinpoint inefficient resource usage. ARO can then make specific recommendations on how developers can optimize their apps to improve performance, speed and battery utilization while also minimizing the network impact.
Rapid growth in mobile computing is driving demand for faster and more frequent software delivery with rapid response to customer feedback, increasingly achieved with a continuous delivery, or DevOps, approach. User choice and ease of movement means that performance speed and quality are of the essence for businesses. A sluggish, battery-draining and high data-consuming app can cripple customer relationships and an enterprise’s ability to conduct business. By testing for these pitfalls early, businesses will now be able to develop apps that are battery life and data network friendly, increasing customer use and satisfaction.
“Businesses are challenged with the need to provide rich mobile applications, while avoiding device power and network usage pitfalls that frustrate and turn away customers,” said Kristof Kloeckner, GM IBM Rational Software. “With today’s news, we are advancing our DevOps strategy in support of better mobile application delivery.”
Built on an open source platform and independent of any specific wireless carrier, ARO provides direct feedback to developers about how their app is behaving on any network, allowing them to test and deliver apps with improved battery life, faster response times, and more efficient network handling. In addition to graphically presenting network, device, application and user behaviors and interactions, ARO also applies radio and power models to provide feedback about exactly where power and data drains are happening.
By linking ARO analysis with mobile development capabilities from IBM, development and testing teams will be able to run battery and data analysis, instantly creating a set of defects in the Rational CLM solution with one click. With this comprehensive view, directly from customer usage models, business analysts will be able to plan for new requirements, developers will efficiently understand which areas need improvement around network and battery usage, and testers will be better enabled to communicate about defects with the operations team. This streamlined, click-through process has the option to be partially automated through Rational Test Workbench, and even further optimized by virtualizing services not available or ready for testing, increasing overall productivity.
The Latest
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...