Longer Term, Organizations Will Require AI Engineers to Build AI-Empowered Applications
Through 2027, generative AI (GenAI) will spawn new roles in software engineering and operations, requiring 80% of the engineering workforce to upskill, according to Gartner, Inc.
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.
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.
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.
Gartner clients can read more in “AI Will Not Replace Software Engineers (and May, in Fact, Require More).”
Learn more in the complimentary Gartner webinar “What Mature Organizations Do Differently for AI Success.”
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