Engineering skills are being rewritten by AI

Estimated reading time: 4 minutes

The evolution of AI is forcing leaders to rethink upskilling – can organizations do more to help their engineers keep pace?

The rise of AI usage in software development has transformed not only how engineers work, but also the skills they need to succeed.

With tools like Cursor and GitHub Copilot automating repetitive coding tasks – from fixing bugs to updating minor features – leaders face a new challenge: ensuring their teams are leveraging these tools to their maximum potential, and safely managing any emerging risks they pose. 

LeadDev’s Engineering Team Performance Report found that 83% of engineering leaders see upskilling as critical, up from 70% last year. As AI becomes embedded in everyday engineering workflows, fluency with these technologies is no longer optional – it is a core business priority. 

When asked which specific skills they want to upskill around, AI skills like prompt engineering and managing agents jumped to the top of the list, with 47% looking to upskill in these areas.

Dennis Nerush, AI director at Elementor, explained that companies are now formalizing AI coding standards, integrating AI expectations into engineering career ladders, and requiring every team to define how AI fits into their workflows. 

Shopify, for instance, now treats “reflexive AI usage” as a baseline expectation. Teams must justify why AI can’t handle a task before requesting headcount, and AI is being integrated into prototyping, performance reviews, and peer feedback.

When AI becomes integrated into everyday engineering work, teams deliver faster and more efficiently. Deployed effectively, AI tools can reduce boilerplate code production, speed up prototyping, and improve testing and documentation.

“That’s why upskilling is no longer positioned as “professional development” but as a lever for productivity and competitiveness,” Nerush added.

Alexia Pedersen, SVP International at O’Reilly, echoed this sentiment, noting organizations are moving from “here’s how you use this tool” to “here’s how you understand and leverage AI in your role and across the business.”

Upskilling for retention 

Leaders see upskilling not just as a way to keep pace with technology change, but also as a means to retaining engineers at a time of AI-driven job insecurity. As a result, 61% of organizations are investing in learning and development primarily to improve talent retention and developer satisfaction.

“When companies provide access to high-quality learning pathways, particularly around emerging areas like AI, they’re signalling trust, value, and long-term commitment. This reduces anxiety about job displacement and fosters a sense of security,” Pedersen said. 

Nerush said Elementor launched an “AI foundations” program covering core topics like prompt engineering, differences between AI models, and hands-on practice with various tools. They also developed an engineering-specific track focusing on AI-enhanced development, testing, planning, design, documentation, and deep MCP integrations. In addition, Elementor hosts collaborative AI workshops to tackle real challenges using tools like Cursor and agent-based approaches. 

According to Nerush, this structured training, shared language, and repeated practice show employees that the company is committed to their long-term relevance.

“That increases psychological safety and reduces the fear of falling behind. This alone has a strong impact on retention,” he added. 

Engagement equals better performance 

Boosting team performance was the second leading motivation for learning initiatives, at 48%. “When learning is embedded into daily workflows, the impact becomes visible in performance,” Pederson noted. 

She explained that teams trained in AI tools often experience measurable improvements in speed, quality, and innovation – whether through automated testing, smarter analytics, or faster problem-solving.

Making embedded learning work

Even when the “why” is clear for AI upskilling, the “how” remains a challenge, with 64% of respondents saying that finding time for learning is difficult.

“Barriers to learning and development are often cultural because they hinge on mindset, priorities, and leadership behaviour, rather than technical limitations,” Pederson noted. 

She explained that most organizations have the tools for upskilling; what they lack is giving people time and permission to use them. 

“Some leaders inadvertently block learning by focusing purely on short-term delivery goals, leaving teams with little bandwidth to experiment, explore new tools, or develop new skills. Others may see learning as a “nice-to-have” rather than a strategic lever, creating a perception that investing time in skill development isn’t a priority,” Pederson explained. 

The most effective organizations break these barriers by having leaders model learning – sharing their own growth and failures – and creating structured opportunities for teams to experiment and collaborate.

Nerush explained that successful teams have leaders who openly use AI and normalize experimentation by sharing both wins and failures. They encourage curiosity by treating small failures as valuable learning experiences.

“Cultures that really thrive in this new era treat learning as collective progress, particularly with AI evolving so quickly; leaders must normalize experimentation and learning from mistakes,” Pendersen noted. 

They also make continuous learning visible through routines: discussing AI use in 1:1s, recognizing AI-driven improvements in performance reviews, and allocating roadmap time for platform and skills development – not just features, according to Nerush. 

“When people feel safe to explore and apply new skills, learning can become ingrained in the company’s rhythm, driving both innovation and long-term talent retention,” Pedersen said.

Deadline: January 4, 2026

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