Software engineers are becoming exasperated at having to embed AI capabilities in applications, according to research from Gartner – a problem the firm says isn’t likely to go away.
More than three-quarters (77%) of senior software engineers said demands to integrate AI within existing applications are a “significant or moderate pain point”.
Key factors behind this sharpened focus on AI integration lie in the need to improve features and functionality, the consultancy said, but the process appears to be creating more problems than it solves.
“With CEOs identifying AI as the technology that will most impact their industry, interest in offerings like AI agents is driving the most momentum,” said Gartner VP analyst Jim Scheibmeir.
Notably, Scheibmeir said that “execution is not easy” for engineering teams faced with the prospect of sprucing up existing applications with a dash of AI.
The push to modernize applications comes amidst surging investment in AI and C-suite demands to meet current industry trends, particularly agentic AI, which has emerged as the latest industry craze.
App modernization is a tricky task for any enterprise. A recent study from Pegasystems, for example, found that businesses face a delicate balancing act when approaching modernization efforts.
Many enterprises, Pegasystems noted, are caught in a “legacy dependency” which means they can ill afford to botch upgrades to existing legacy applications, many of which are business critical.
These challenges are not only hampering transformation efforts, the company warned, but are also impeding broader AI strategies, thereby leaving enterprises at a disadvantage compared to competitors in their respective industries.
Software engineers have a friend in AI
The plus side for engineering teams is that the use of AI to upgrade applications is becoming increasingly common. In fact, it has the potential to become a booming industry moving forward.
According to figures from Gartner, the AI application development platforms market is worth around $5.2 billion, and this is expected to rise significantly in the coming years.
“Both emerging vendors and established hyperscalers have developed and continue to enhance their platforms to alleviate the pain points experienced by enterprises,” the consultancy said. “Many dozens of emerging and established vendors are operating and innovating in this market.”
With a booming market comes a plethora of options, however, and Gartner warned engineering leaders should carefully consider which solutions or tools are best suited to their individual needs.
“Engineering leaders should opt for AI application development platforms or those with the best ecosystem, rather than a combination of disparate vendors, large language models (LLMs) and AI services,” Scheibmeir said.
Research from IBM earlier this year specifically highlighted tool sprawl as a key challenge in generative AI development, with nearly three-quarters (72%) of devs claiming to use between five and 15 AI tools in application development processes.
By opting for a more consolidated approach, Scheibmeir said teams can unlock marked benefits.
“This approach enables scaling, reuse and consistency in an area of technology and software engineering that is still very novel,” he said.
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