SAN MATEO, Calif., September 20, 2021 -- Applitools (https://applitools.com/) announced its inclusion in new research published by Enterprise Management Associates (EMA) entitled, "Disrupting the Economics of Software Testing Through AI." According to the report, Visual AI has the highest impact on software testing as compared to other available applications of AI technology in the market today. As the first ever in-depth research report on the impact of AI on automated testing, the report found organizations reliant upon traditional testing tools and techniques fail to scale to the needs of today's digital demands and are quickly falling behind their competitors. The report identifies critical factors that hinder software engineering and DevOps teams including the escalating costs of quality control, and growing complexity associated with the increasing release velocity, number of smart devices, operating systems, and programming languages. As such, EMA Research examined the impact of six real world scenarios of traditional test automation practices.

Paxata, the pioneer in self-service data preparation, today announced that it was named an innovator in Enterprise Management Associates (EMA) "Innovation in the Use of Artificial Intelligence (AI) and Machine Learning (ML) for Data Integration and Preparation" Top 3 report. According to the findings, more than half of all participants (52 percent) said that the use of AI or ML to automate the data preparation or integration process is important to their organization. Because of the prominent role of data integration and preparation in any analytics project, the report stated that AI-enablement should be a priority for analytics leaders at all levels as it provides organizations with the ability to overcome the constraints of legacy or less-automated data processing. The complimentary report can be downloaded here. "The next major shift in the analytics, business intelligence, and data management markets is coming from the use of AI and ML across the entire information supply chain. Along with using machine learning to find the next-best offer, companies can now point algorithms at modern data platforms to find links between data sets, automate data preparation, or breaches in data governance," said John Santaferraro, Research Director at EMA and lead author of the report.

It is hard to believe it was less than half a year ago we were still putting the final touches on SL1 – the engineering teams hard at work making sure the platform was ready, the marketing team getting logos, branding and messaging polished. Our goal when we launched SL1 was to refashion what AI and ML meant to the IT operations, DevOps and IT management community. We'd been watching for years as ephemeral, complex IT infrastructure became the enterprise IT norm and consequently threaten enterprises by that complexity. We realized the market had an emerging need for a solution that could manage that complexity and help IT organizations build and maintain the resilient, robust digital experiences that were growing in demand. By now you are all familiar with the story – we started with the basic premise that enterprises needed greater visibility into their IT ecosystems, the context to understand that visibility, and then the tools to act on what they found.