A report published by the Futurum Group finds organizations are exploring multiple paths to employing artificial intelligence (AI) capabilities across the software development lifecycle (SDLC).

Over the next 12 to 18 months organizations plan to increase spending on not only on AI code generation (83%) and agentic AI technologies (76%), but also existing familiar tools that have been augmented with AI, according to a survey of 855 IT leaders conducted by the Futurum Group.

While many DevOps teams will undoubtedly employ a mix of these approaches, it’s clear there is a battle underway for the hearts and minds of software engineers, said Mitch Ashley, vice president and practice lead for DevOps and application development at the Futurum Group.

Providers of application development tools and platforms have reached a pivotal AI moment as DevOps teams weigh the merits of switching platforms versus relying on incumbent vendors to bolt on additional AI capabilities, he added. Ultimately, success will hinge on lowering barriers to entry, go-to-market models and demonstrating tangible value at a time when the way software is being created is fundamentally changing, noted Ashley.

It’s still early days so far as adoption of AI is concerned but application development teams are clearly at the forefront. Previously, research from the Futurum Group revealed that 41% of the respondents it surveyed expect generative AI tools and platforms will be used to generate, review and test code.

Less clear is how much faith application development teams will have in those tools and platforms. It’s one thing to rely on a generative AI tool to explain how an existing code base is structured, but the quality of the code that might be generated can vary substantially. In some instances, such as generating a script for invoking an application programming interface (API), the quality of the code might not matter. However, the quality of the code being used to create business logic is critical, especially if that code might contain known vulnerabilities that were never properly scanned.

Of course, depending on the experience of the application developer, there are also plenty of instances where the code being generated might be better than the code that a human developer might have created on their own. As a result, the return on investment in AI tools and platforms will tend to vary widely from one DevOps team to the next.

Ultimately, advances in AI should bring about more positive than negative changes, especially as more AI agents that have been narrowly trained to automate a specific set of tasks become more widely employed. The simple fact is that there are still far too many manual tasks that make building and deploying software more tedious than anyone really enjoys, which only leads to higher turnover rates as software engineers inevitably become burnt out.

Conversely, however, DevOps teams should also be wary of becoming overly dependent on AI. After all, the one golden rule of application development that still applies in the age of AI is that no code should be deployed in a production environment that one or more humans don’t thoroughly understand.