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Engineers are struggling to develop generative AI tools and applications, according to IBM research published Wednesday. Morning Consult conducted the survey of more than 1,000 U.S.-based enterprise AI developers.
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While AI developers say high performance, flexibility and ease of use are critical for enterprise tooling, those traits are also the rarest among available solutions, the report found. Engineers often point to the lack of standardization in the development process, difficulty developing ethical lifecycles and the need for customization as the top inhibitors of progress.
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Enterprise developers typically use between five and 15 tools to create AI applications. More than 1 in 10 developers leverage 15 or more in the process, and nearly all use coding assistants in some capacity to save time.
Developing enterprise-grade generative AI applications is critical for businesses across industries, but tool and tech stack complexities can derail the efforts.
AI developers have only grown in importance since the race to adopt generative AI kicked off more than two years ago. Last year, the role entered the top 10 highest-paid positions for the first time, according to a Stack Overflow survey published in September 2024. Last year, AI developers earned around $160,000 in annual compensation.
While AI-specific roles are still relatively new, enterprise interest is palpable. Machine learning and AI engineering positions have grown 27x since 2014, according to a SignalFire report. Comparatively, roles for cloud engineering and DevOps only jumped about three-fold in the same period.
Enterprises have worked to enhance the developer experience and keep employees engaged as their job market prospects proliferate. JPMorgan Chase, for example, focused on simplifying workflows even as the environment became more complex.
The potential benefits of AI skills have also pushed technology workers to beef up their own AI skills. Demand for prompt engineering courses increased by 456% year over year, according to O’Reilly data.
As generative AI reshapes existing workflows and job functions, Gartner predicts around 4 in 5 engineers will need to upskill by 2027.
It’s up to CIOs, who are most often tasked with guiding AI initiatives, to communicate what teams will need to be successful to the C-suite and board of directors as adoption moves forward.
“We need to be quite realistic about our enterprise’s ability to hire, train or source AI skills,” Tina Nunno, distinguished VP analyst at Gartner, said during a conference in October 2024. “This has become a particularly sticky area.”
