To remain competitive in today’s software development landscape, adopting AI technologies is crucial. Discover how various developers are maintaining their edge.
In the contemporary software development sphere, encountering AI technology is practically unavoidable. This isn’t a negative development; in fact, AI-powered development offers a wealth of potential advantages for both individuals and teams.
One undeniable truth is that software developers who haven’t yet done so need to swiftly update their skill sets to align with the requirements of this evolving era.
The 2025 Stack Overflow Developer Survey, compiling insights from over 49,000 professional developers globally, indicated that 84% of participants were either already utilizing or intended to integrate AI tools into their development processes.
This marked a rise from the prior year, where 76% of respondents either used or intended to use AI. By the end of 2025, approximately half of those surveyed reported daily use of AI tools.
Chris Camacho, COO and co-founder of Abstract Security, a security-platform provider, observes, “Software developers globally are compelled to refine their capabilities for an environment where AI is now a regular component of daily operations.”
Camacho adds, “Within our sector, this transformation is evident. Workforce analyses within numerous major companies reveal that a significant majority of developers are already leveraging some form of AI assistance, and over half of these organizations now prioritize AI and data-related proficiencies in their hiring strategies. It mirrors the early stages of cloud adoption, but the pace of integration is considerably quicker.”
Sameer Agarwal, CTO and co-founder of Deductive AI, a development tools provider, notes that developers are experiencing a transition from a coding-centric workflow to one focused on overseeing, limiting, and understanding agent-generated code. He states, “The critical skill set is evolving in response.”
Numerous developers are already adjusting their capabilities to thrive in the realm of AI-powered development. We sought insights from developers and technology leaders on their adaptation strategies.
Formalized Learning Programs
Consistent and formal training, whether provided internally or by external entities, stands as a premier method for staying current with emerging practices in AI coding agents.
Agarwal explains, “Within engineering teams, the most effective learning will stem from structured internal programs. We’re observing a trend where numerous enterprises are initiating workshops on prompt engineering, understanding agent conduct, identifying reliability hazards, and recognizing potential failures in AI-produced code.” He adds that the most impactful training now focuses less on basic AI coding agent usage and more on debugging these agents and assessing the quality and pertinence of their outputs.
According to Brady Lewis, senior director of AI innovation at fractional marketing firm Marketri, every developer can gain from a systematic learning strategy. He asserts, “A defined route for knowledge acquisition, through conventional academic courses and/or specialized certification programs in machine learning, data engineering, and/or statistics, equips developers with a foundational understanding to construct applications that interface with AI models.”
Lewis clarifies that structured education doesn’t necessitate developers becoming data scientists; rather, it aims to inform them about AI system limitations, fostering more predictable and robust application design.
Related: Essential AI Developer Certifications Sought by Tech Firms.
Organizational backing for pioneers
A significant portion of AI education for developers will likely originate from their employers, who aim to expand AI integration into development workflows.
Lewis notes, “With the escalating demand for internal AI training initiatives, a growing number of employers are distinguishing themselves by providing enhanced assistance to staff members who are among the first to engage with these programs.”
Beyond mere skill acquisition, such training could unveil new career avenues.
Lewis points out that numerous organizations are still formulating their AI guidelines, “and developers who actively participate early in shaping these standards exert a more substantial influence compared to those who remain disengaged.”
Guidance Initiatives
Collaborative mentorship initiatives can also facilitate the dissemination of expertise regarding AI tools and methodologies.
Camacho observes, “A subtle shift within many teams is that junior developers are posing fewer questions due to the rapid answers provided by AI tools. While this might accelerate individual tasks, it can hinder long-term professional development. The most effective teams I’ve encountered are increasingly pairing junior and senior members, promoting code reviews that emphasize the validation of AI-generated suggestions.”
As more businesses adopt AI-powered procedures, Lewis states, “the prospects for mentorship are also expanding. Developers who collaborate with those pioneering AI-driven processes frequently gain valuable practical experience more quickly than individuals learning in isolation.”
Camacho highlights that ongoing education is becoming a fundamental requirement. He elaborates, “Developers require enhanced proficiency in data, safety, and security, as AI functionalities are contingent upon these aspects. Teams that prioritize mentorship will achieve growth more rapidly than those solely dependent on technological aids.”
Related Reading: Key Aspects of AI Governance.
Assistance from AI Vendors
For assistance in transitioning to AI-powered development, why not engage directly with the AI solution providers themselves?
Chris Minnick, a software developer and CEO of WatzThis, a company specializing in books on software development and related subjects, shares, “My strategy for evolving my skills and professional profile for AI involved consulting the primary sources of cutting-edge AI technologies and delving into their documentation and educational content.”
Minnick points out, “OpenAI, for instance, offers OpenAI Academy, and most organizations active in AI maintain comparable resources. I chose not to return to university, as academic curricula struggle to keep pace with rapid technological advancements. Instead, I pursued and obtained the Amazon AWS AI Practitioner certification, which serves as a fundamental credential demonstrating proficiency in utilizing AWS’s AI-specific tools and understanding the principles of generative AI.”
Cultivating an AI-Centric Perspective
For numerous developers, the initial step in moving towards AI-powered development might be the toughest, as it demands a fundamental change in perspective.
Ray Kok, CEO of Mendix, a development platform provider, states that software developers must first acknowledge that their roles will undergo significant transformation. He emphasizes that an AI-first mentality is like a muscle requiring daily practice.
Kok further advises developers to educate themselves on higher levels of abstraction within software development. He recommends, “Move beyond a purely programming-focused mindset and begin to embrace model-based software development as a supplementary approach for software assembly and application creation.”
Lewis observes, “Developers demonstrate the quickest adaptation when they transition from mastering individual AI tools to grasping the foundational dynamics of AI systems. Successful developers prioritize comprehending how orchestration, data integrity, and workflow design influence the dependability of AI-supported development, rather than merely memorizing syntax.”
Experiential Learning
A practical method for gaining proficiency in AI-powered development involves undertaking projects through an iterative, experimental approach to discern effective strategies.
Jackson White, founder and chief developer at Launch Turtle, a company offering website and application development, remarks, “I believe developers can refine and enhance their skills in the same manner as accomplished developers historically have: by tentatively exploring, reviewing documentation and user narratives, and then diving into project creation.”
White recounts, “The initial AI-driven website I developed with Launch Turtle was quite flawed, necessitating extensive corrections using conventional coding methods. Nevertheless, as both the models and my prompting techniques matured through persistent experimentation, AI began to manage significantly greater workloads. I anticipate other developers would observe comparable outcomes.”
Joshua McKenty, CEO and co-founder of Polyguard, a firm dedicated to protecting against deepfakes and AI-enabled fraud, suggests that developers would benefit from emphasizing self-directed, hands-on learning. He encourages, “Engage practically! Experiment with a different AI tool every few weeks. Seek assistance from one AI chatbot on how to operate another. The genuine mark of expertise lies in knowing when and when not to deploy a tool. Therefore, test these tools to their boundaries and learn from the consequences.”
Remember to refresh your professional profile
Employers are eager to learn about your expertise in AI-powered development, making its inclusion on your resume crucial.
Lewis notes, “With more organizations developing internal AI capacities and the increasing need for AI proficiencies, professional profiles are also evolving. Developers who emphasize practical experience in domains like agentic design, workflow structuring, prompt assessment, and quality assurance position themselves significantly ahead of those who merely enumerate AI tools.”
Lewis states that recruitment managers are increasingly seeking developers capable of explaining the value AI brings, its potential risks, and strategies for ensuring its real-world dependability.
Minnick advises, “It’s vital to keep your resume current as you acquire new AI competencies, particularly if you are actively job searching or anticipate doing so soon.” He adds that even for roles not explicitly listing AI skills, a grasp of how to utilize and incorporate generative AI into software is swiftly becoming a fundamental expectation for software development positions.
