Technological research and consulting firm Gartner says 75% of enterprise software engineers will be using an artificial intelligence (AI) code assistant by 2028.
That’s despite fewer than 10% of programmers using them in 2023.
Much as been made about Generative AI or GenAI assistance in recent months, with ChatGPT showing a startling ability to automate, streamline or reduce the time a basic task takes.
A study by MIT revealed AI used within the bounds of its ability by highly skilled workers can improve performance by as much as 40% compared to those who don’t use it at all.
For coding in particular, Westpac reports a 46% productivity gain with no reduction in code quality from software engineers that were aided by generative AI compared to a control group that performed the same tasks exclusively by hand, in a recent in-house experiment.
“When we looked at the time that it took the hand-coding team to actually complete their tasks on average it was three-and-a-half times longer than it took the other teams to do their tasks,” Westpac chief technology officer David Walker said.
The potential productivity gains led 63% of organisations to pilot, test or deploy AI code assistants, according to a survey of 598 global respondents.
How useful is GenAI, really?
Programmers generally cite GenAI’s ability to produce workable template code for a specific purpose that can then be reworked to suit more tailored purposes as one of the core time-savers of GenAI models, but Garner believes there’s much more to be gained than first meets the eye.
“Software engineering leaders must determine return on investment (ROI) and build a business case as they scale their rollouts of AI code assistants,” Gartner senior principal analyst Philip Walsh said.
“However, traditional ROI frameworks steer engineering leaders toward metrics centred on cost reduction.
“This narrow perspective fails to capture the full value of AI code assistants.”
During Westpac’s experiment, more junior software engineers expressed an appreciation for the AI tools, which 83% were “blown away” by.
For senior programmers, the tools reduced the need to complete “laborious tasks” and “allowed them to focus on more complicated aspects of the software”.
An Iceberg depiction of the hidden benefits of GenAI in programming:
Source: Gartner (April 2024).
“Calculating time savings on code generation is a good place to begin building a more robust value story,” said Walsh.
“To convey the full enterprise value story for AI code assistants, software engineering leaders should connect value enablers to impacts, and then analyse the overall return to the organisation.”
What about for the rest of us?
Looking at the broader economy, research from McKinsey estimates that GenAI “could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analysed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion”.
“This would increase the impact of all artificial intelligence by 15 to 40 percent,” the report read.
“This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.”
The report asserts that four areas in particular are likely to receive about 75% of the total value of GenAI – customer operations, marketing and sales, software engineering, and research and development.
“Excitement over this technology is palpable, and early pilots are compelling,” the report concludes, “But a full realisation of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address.
“These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills.”