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Cyara Releases Customer Experience Test Automation Maturity Model

Cyara announced the release of its CX Test Automation Maturity Model — an easy-to-navigate self-assessment that enables organizations to determine how advanced their approach to automated CX testing is and see how their current level of test automation measures up to industry best practices.

The model provides organizations with actionable steps to deliver improved customer journeys across digital and voice channels and reduce the risk of customer-facing defects.

“Automated CX assurance is an increasingly critical practice that highly impacts and benefits modern contact centers. Cyara’s Test Automation Maturity Model empowers organizations that want to modernize and adapt their testing process, regardless of where they are in their digital transformation journeys,” said Alok Kulkarni, CEO of Cyara. “Since so many organizations are looking for guidance on how to successfully implement CX testing and assurance, we want to deliver a simple, cost-free solution that will assist businesses in discovering and deploying the best test automation practices for their CX.”

The Test Automation Maturity Model provides a full report on a company’s automated CX testing , providing a maturity score and comparing the organization’s test automation practices to the industry standards. Furthermore, the model uses self-reported data to export a CX testing recommendation that is specifically tailored to the organization, providing an actionable path forward to improve CX testing based on personalized survey results. With these features, enterprises can make smarter decisions to improve their CX testing technology and processes.

Cyara’s CX Test Automation Maturity Model is available now on the Cyara website and is free to use.

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Cyara Releases Customer Experience Test Automation Maturity Model

Cyara announced the release of its CX Test Automation Maturity Model — an easy-to-navigate self-assessment that enables organizations to determine how advanced their approach to automated CX testing is and see how their current level of test automation measures up to industry best practices.

The model provides organizations with actionable steps to deliver improved customer journeys across digital and voice channels and reduce the risk of customer-facing defects.

“Automated CX assurance is an increasingly critical practice that highly impacts and benefits modern contact centers. Cyara’s Test Automation Maturity Model empowers organizations that want to modernize and adapt their testing process, regardless of where they are in their digital transformation journeys,” said Alok Kulkarni, CEO of Cyara. “Since so many organizations are looking for guidance on how to successfully implement CX testing and assurance, we want to deliver a simple, cost-free solution that will assist businesses in discovering and deploying the best test automation practices for their CX.”

The Test Automation Maturity Model provides a full report on a company’s automated CX testing , providing a maturity score and comparing the organization’s test automation practices to the industry standards. Furthermore, the model uses self-reported data to export a CX testing recommendation that is specifically tailored to the organization, providing an actionable path forward to improve CX testing based on personalized survey results. With these features, enterprises can make smarter decisions to improve their CX testing technology and processes.

Cyara’s CX Test Automation Maturity Model is available now on the Cyara website and is free to use.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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 ...

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