"More than two-thirds of respondents (68%) report their organization has adopted an internal developer platform (IDP). The primary goal is to improve operational efficiency (57%), provide better user experiences (48%), and improve observability and security (47%), the survey finds."
"However, a significant percentage of respondents also noted that their development teams are still encountering friction, with systems integration (25%) and security and compliance restrictions (23%) identified as the two primary sources. Additionally, the survey identifies testing and quality assurance (22%) and integration, deployment and implementation (18%) as the two biggest bottlenecks in their organization's software engineering workflows."
"IT leaders, as a result, are investing more in automation in areas such as security (47%), testing (45%) and monitoring (43%), the survey finds. More than two-thirds of survey respondents (67%) said their organization is spending significantly on generative AI. The issue is only 31% said they have achieved moderate success, which suggests many organizations are still struggling with how to operationalize it."
"While many individuals within organizations have been able to automate a range of tasks, organizations are now appreciating the amount of effort and expertise that is needed to automate actual workflows, he added. Additionally, as more AI agents are deployed, organizations are not only contending with token costs but also AI agent sprawl, he added. At its core, AI is not necessarily so much a tool problem so much as it is a people and process issue."
A survey of 954 IT decision-makers reports increased investment in reducing friction across the software development lifecycle. More than two-thirds of organizations have adopted an internal developer platform, aiming to improve operational efficiency, user experiences, and observability and security. Despite these efforts, teams still encounter friction, especially from systems integration and security and compliance restrictions. Testing and quality assurance and integration, deployment, and implementation are identified as the biggest workflow bottlenecks. IT leaders are investing in automation for security, testing, and monitoring. Many organizations are spending significantly on generative AI, but only a minority report moderate success, indicating challenges in operationalizing it. Automation requires substantial effort and expertise, and AI agent deployment introduces token costs and agent sprawl, making it a people and process issue.
#internal-developer-platforms #sdlc-automation #security-and-compliance #software-testing-and-qa #generative-ai-operations
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