Gartner Says Generative AI will Require 80% of Engineering Workforce to Upskill Through 2027
Gartner analysts expect AI will impact the software engineering role in three ways:
In the short term, AI will operate within boundaries
- AI tools will generate modest productivity increases by augmenting existing developer work patterns and tasks. The productivity benefits of AI will be most significant for senior developers in organizations with mature engineering practices.
In the medium term, the emergence of AI agents will push boundaries
- AI agents will transform developer work patterns by enabling developers to fully automate and offload more tasks. This will mark the emergence of AI-native software engineering when most code will be AI-generated rather than human-authored.
“In the AI-native era, software engineers will adopt an ‘AI-first’ mindset, where they primarily focus on steering AI agents toward the most relevant context and constraints for a given task,” said Walsh. This will make natural-language prompt engineering and retrieval-augmented generation (RAG) skills essential for software engineers.
In the long term, advances in AI will break boundaries and will mark the rise of AI engineering
- While AI will make engineering more efficient, organizations will need even more skilled software engineers to meet the rapidly increasing demand for AI-empowered software.
“Building AI-empowered software will demand a new breed of software professional, the AI engineer,” said Walsh. “The AI engineer possesses a unique combination of skills in software engineering, data science and AI/machine learning (ML), skills that are sought after.”
According to a Gartner survey conducted in the fourth quarter of 2023 among 300 U.S. and U.K. organizations, 56% of software engineering leaders rated AI/machine learning (ML) engineer as the most in-demand role for 2024, and they rated applying AI/ML to applications as the biggest skills gap.
To support AI engineers, organizations will need to invest in AI developer platforms. AI developer platforms will help organizations build AI capabilities more efficiently and integrate AI into enterprise solutions at scale. “This investment will require organizations to upskill data engineering and platform engineering teams to adopt tools and processes that drive continuous integration and development for AI artifacts,” said Walsh.
