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Survey Surfaces Significant Levels of IDP Investment to Reduce SDLC Friction

A survey of 954 IT decision-makers suggests more resources are now being allocated to reducing friction across the software development lifecycle (SDLC).

Conducted by CDW, the survey finds 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.

Marc Litten, senior manager for AI factory and data center strategy at CDW, said artificial intelligence (AI) is clearly a significant element of any approach to automation. 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, noted Litten.

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, said Litten. The simple truth is rather than falling behind most organizations are encountering the same challenges at relatively the same pace, he noted.

DevOps teams are, of course, generally at the forefront of agentic AI adoption. Therefore, many of the governance and security issues that organizations encounter are first experienced by their software engineers. The one thing that is certain, however, is that software engineering workflows are now evolving more rapidly than ever.

Hopefully, the end result of that change will be higher quality software. In the short term at least, there appears to have been more emphasis placed on generating code faster. Many DevOps teams, as result, have been overwhelmed by the volume of code generated by AI coding tools, with much of that code for one reason or another not actually making it into a production environment.

Eventually, however, the quality of code being generated by AI coding tools will continue to improve. In the meantime, DevOps teams would be well-advised to review existing code review and testing capabilities with an eye toward relying more on AI agents to validate the massive volumes of code being generated by AI agents that depend on large language models (LLMs) trained on flawed code initially created by human developers that might not always reflect best practices.



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