Today, it is nearly impossible for anyone in a software development role to avoid AI technology. That’s not a bad thing; individuals and teams can realize numerous potential benefits from AI-driven development.
One thing is certain: Software developers who haven’t already done so must adapt their skills to meet the demands of this new era. And they should do it quickly.
The Stack Overflow 2025 Developer Survey, based on responses from more than 49,000 professional developers worldwide, showed that 84 percent of respondents were using or planning to use AI tools in their development workflow.
That’s an increase from the previous year, when 76 percent of respondents used or planned to use AI. By late 2025, about half of the survey respondents said they were already using AI tools daily.
“Developers everywhere are being pushed to adjust their skills for a world where AI is now part of the daily workflow,” says Chris Camacho, COO and co-founder of security-platform provider Abstract Security.
“In our industry, the shift is obvious,” Camacho says. “Internal workforce studies across several large enterprises show that a strong majority of developers are already using some form of AI assistance, and more than half of those companies now list AI and data-related skills as top hiring priorities. It feels similar to the early cloud wave, but the adoption curve is moving much faster.”
Developers are already feeling the shift from a development workflow based on writing code to one where they supervise, constrain, and reason about code generated by agents, says Sameer Agarwal, CTO and co-founder of Deductive AI, a provider of development tools. “The skill set that matters is changing accordingly,” he says.
Many developers are already adapting their skills to better fit in the world of AI-driven development. We asked developers and tech leaders how they are making the shift.
Structured training
Formal and ongoing training, whether it’s offered within your organization or externally, is one of the best ways to keep up with the latest trends in using AI coding agents.
“Inside engineering teams, the best learning will come from structured internal training,” Agarwal says. “We’re already seeing that many enterprises are beginning to run sessions on prompt design, agent behavior, reliability risks, and the failure modes of AI-generated code.” The most valuable courses are no longer about how to use AI coding agents, he says. Instead, they now cover debugging AI agents and evaluating the quality and relevance of their actions.
Every developer can benefit from an organized approach to learning, says Brady Lewis, senior director of AI innovation at fractional marketing firm Marketri. “A structured path for acquiring knowledge, by way of traditional coursework and/or specific certification programs in the areas of machine learning, data engineering, and/or statistics, provides developers with a basis upon which they can build applications that act as interfaces to AI models,” he says.
Structured learning does not require developers to become data scientists, Lewis says. Instead, it will educate developers on the limitations of AI systems in order to make application design more predictable and resilient.
Also see: AI developer certifications tech companies want.
Employer support for early adopters
Much of the AI training a developer receives might come from employers, as they look to broaden the use of AI in development.
“As the need for internal AI training programs continues to grow, an increasing number of employers are differentiating themselves by offering additional support to employees who are early adopters of their internal training programs,” Lewis says.
In addition to the learning, some of this training could open the door to other opportunities.
Many organizations are still trying to understand their standards regarding AI, Lewis says, “and developers who are engaged early in developing these standards have a greater impact than those who are not.”
Mentorship programs
Team-based mentorship programs can also help spread knowledge about AI tools and processes.
“A quiet trend happening inside many teams is that junior developers are asking fewer questions because AI tools can answer quickly,” Camacho says. “This may speed up a task, but it slows down long-term growth. The strongest teams I know are pairing juniors and seniors more often, encouraging code reviews that focus on how AI suggestions were validated.”
As the number of companies implementing AI-driven processes continues to grow, “there are also increasing opportunities for mentorship,” Lewis says. “Developers who partner with those leading the charge in developing AI-based processes can often receive important hands-on experience faster than those who are learning in isolation.”
Continuous learning is becoming a baseline expectation, Camacho says. “Developers need stronger literacy around data, safety, and security because AI features depend on them,” he says. “Teams that keep mentorship at the center will grow faster than those that rely entirely on tools.”
Also see: What you need to know about AI governance.
Support from AI providers
To help adapt to AI-driven development, why not go directly to the AI providers?
“My approach to adapting my skills and resume to AI was to go directly to the sources of the latest AI technologies and read their documentation and training materials,” says Chris Minnick, a software developer and CEO of WatzThis, a provider of books about software development and related topics.
“For example, OpenAI has OpenAI Academy, and most companies that do work in AI have similar resources,” Minnick says. “I didn’t consider going back to college because college courses can’t possibly keep up with the pace of change. I did study for and earn the Amazon AWS AI Practitioner certification, which is their foundational certification for showing that you’re familiar with how to use the AWS AI-related tools and with how generative AI works.”
Adapting to the AI-first mindset
For many developers, the first move in the transition to AI-driven development may be the most challenging, because it requires a shift in mindset.
Software developers first need to accept that their work will fundamentally change, says Ray Kok, CEO of Mendix, a provider of development platforms. The AI-first mindset is a muscle that must be exercised daily, he says.
Developers also need to train themselves on the higher levels of abstraction for software development, Kok says.”Get out of your programming mindset and start adopting model-based software development as a complementary tool for software composition and application development,” he says.
“I’m seeing developers adapt fastest when they shift from learning individual AI tools to understanding the underlying behaviors of AI systems,” Lewis says. “The developers who do well focus less on memorizing syntax and more on learning how orchestration, data quality, and workflow structure affect the reliability of AI-assisted development.”
Learning by trial and error
One way to become more familiar with AI-driven development is by taking on projects, on a trial-and-error basis, to see what works and what doesn’t work.
“I think where developers can adapt and improve their skills is the same as any great developer has done in the past: by dipping their toes in the water, reading the documentation/user stories, then jumping into building projects,” says Jackson White, founder and chief developer at Launch Turtle, a provider of website and application development services.
“The first AI-driven website I built with Launch Turtle was terrible, and lots had to be mended with traditional coding practices,” White says. “However, as both models and my own prompting became more sophisticated through the process of trial and error, AI has been able to take over substantially heavier loads. I think other developers would find similar results.”
Developers would be wise to focus on self-guided, experiential learning, says Joshua McKenty, CEO and co-founder of Polyguard, a company providing defenses against deepfakes and AI-powered fraud. “Get your hands dirty!” he says. “Try a new AI tool every few weeks. Ask one AI chatbot for help using another. The true sign of mastery is when you know how to use a tool—and when not to. So push these tools to their limits, and learn what happens when you hit them.”
Don’t forget to update your resume
Employers want to know about your experience with AI-driven development, so including this on your resume is important.
“As more companies build internal AI capabilities and as the demand for AI capabilities continues to grow, resumes are changing too,” Lewis says. “Developers that highlight real-world experience in these areas—agentic patterns, workflow design, prompt evaluation, quality control—are putting themselves far ahead of developers who simply list AI tools.”
Hiring managers are increasingly looking for developers who can articulate how, where, and why AI adds value; where it introduces risk; and how to make it reliable in the real world, Lewis says.
“It’s essential to update your resume as you gain new AI skills, especially if you’re looking for a job or if you might soon be looking for a job,” Minnick says. Even for job listings that don’t specifically mention AI skills, understanding how to use and integrate generative AI into software is rapidly becoming a standard requirement for software development jobs, he says.
