Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Matt Voget, Director of Technology at Ambassador
There’s no doubt that AI’s growing role in the tech sector has consumed the majority of the thought space for most tech leaders as they look toward their 2025 budget. In fact, the focus for many has shifted from “How do we save money?” to “How do we invest more in the right tools (i.e., AI) to meet business goals?”
Gartner’s 2024 tech roadmap study for software engineers backs this claim up, stating that the number one concern for tech executives was that “rapidly evolving business and customer needs are driving software engineering leaders to place significant emphasis on the speed and agility with which they’re able to deliver new solutions,” and “cost considerations are becoming less of a factor in software engineering leaders’ technology deployment decisions.”
To validate that further-my team at Ambassador ran a survey at the recent API World 2024 conference, and after security, manual work came in (23%) as the next greatest pain point for development teams. The most obvious (and frankly popular) solution to that pain point lies in automating tasks, presumably with AI. For developers, the next frontier isn’t just adopting AI-it’s mastering how to leverage AI to eliminate repetitive tasks while retaining control over the creative and problem-solving aspects of coding.
AI: An Assist, Not a Replacement
The promise of AI isn’t to replace developers but to augment their capabilities, allowing them to focus on solving business problems rather than wrestling with boilerplate code. Tools like GitHub Copilot have already demonstrated the power of AI in assisting with coding tasks, but the future holds even greater potential. By 2025, developers may find themselves working with a variety of large language models (LLMs) tailored to specific tasks, such as structuring a project, generating unit tests, or debugging.
The key challenge will be selecting the right tool for the job. With the proliferation of LLMs-each offering unique strengths like handling edge cases, larger context windows, or multiple input types-developers will need to navigate a complex ecosystem. This could mean chaining together multiple AI tools in cohesive workflows to address diverse requirements.
The Evolution of Developer Workflows
One area where AI is set to have a profound impact is in solving niche pain points within developer workflows (such as with a tool like Blackbird). Today, developers use a variety of tooling to build, test, and release code – configuring and using this tooling may be tedious or place high demands on multiple skill sets.
Using development tools that correctly infuse AI can speed up and amplify the tasks that developers are already doing as part of their workflows, things like writing, debugging, reviewing, and testing code. These tools not only reduce the tedious manual work but can also fill in the expertise gaps that developers or teams may be missing. For example, an AI tool that reviews code may find crucial security vulnerabilities that teams without that expertise would miss.
This shift doesn’t necessarily discount the need for developers. Rather, it allows developers to make higher-impact contributions by allowing them to focus on ensuring positive business outcomes versus spending that time on the repetitive, tedious, or arcane problems. AI tooling in development workflows should be sought after by developers and teams as an enhancement, but not as a replacement for them.
This evolution also underscores the importance of flexibility and interoperability with the tools that tech teams are choosing to invest in. We can’t just build AI on top of everything- our developers will need tools that integrate seamlessly into their existing workflows while also understanding how cost considerations will play a role. And by this, I mean, all of those fancy advanced AI capabilities may come at a premium, and organizations will need to weigh the value of these tools against their budgets. Although cost is no longer the greatest concern for tech teams anymore, it will play a role.
As AI continues to evolve, the developers who succeed will be those who can navigate this new landscape with agility, selecting the right tools and workflows to maximize their impact for the right price. For organizations, investing in AI tools that align with their teams’ needs will be critical to staying ahead in this rapidly changing environment. The future is clear: AI isn’t just a trend-it’s the key to unlocking the next level of developer productivity and eliminating manual work where necessary.
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