Over 80 per cent of software engineers need to pick up new skills such as natural-language prompt engineering and retrieval-augmented generation (RAG) in order to secure their jobs amid the generative AI boom, according to analysts at Gartner Inc.
In a recently published note, the global research firm said that AI was not going to replace software engineers but spawn new roles for them. “Bold claims on the ability of AI have led to speculation that AI could reduce demand for human engineers or even supplant them entirely […] While AI will transform the future role of software engineers, human expertise and creativity will always be essential to delivering complex, innovative software,” Philip Walsh, a senior principal analyst at Gartner, said.
Underlining the importance of investing in AI developer platforms, Walsh further said, “This investment will require organisations to upskill data engineering and platform engineering teams to adopt tools and processes that drive continuous integration and development for AI artifacts.”
The US-based IT research and consulting company laid out the following three stages of AI’s impact on software development:
– AI tools will operate within boundaries in the short term, which means that it will enhance productivity by improving the workflows of existing software engineers.
– Later, AI agents will push the boundaries by fully automating tasks carried out by software engineers. At this point, most code will be AI-generated rather than human-authored.
– In the long term, AI engineering will become more efficient and organisations will look to hire skilled software engineers to “meet the rapidly increasing demand for AI-empowered software.”
On recent trends, Gartner said its survey of 300 organisations in the US and UK found that around 56 per cent of software engineers believed that the role of an AI/ML engineer was most in-demand. Most of them also reportedly acknowledged that they lacked the skills to integrate apps with AI/ML.
Using AI to generate code has emerged as a potentially strong use case of the generative technology. This has led to a surge in AI coding tools, with the roll out of GitHub Copilot and Anthropic’s Claude. AI coding startup Supermaven created a lot of buzz last month after it managed to secure $12 million in its first outside funding round led by Bessemer Venture Partners.
However, the usefulness of AI in coding is still up for debate. Studies have found that there has been a decline in the quality of code generated using AI models. Additionally, AI coding tools may not be guaranteed to improve productivity. A survey of 800 software developers using GitHub Copilot found that none of them saw any productivity gains. To make matters worse, they reported experiencing a 41 per cent increase in bugs within pull requests while using the AI coding assistant to write code.
