Navigating the Challenges of GenAI Implementation

Generative AI (GenAI)-enabled software development will improve productivity and work efficiency – the question is, how much? Most market research on this topic shows considerable gains in productivity. Research from Harvard found that specialists, depending on the task and seniority, saw a 43% increase in productivity. Likewise, a report from Goldman Sachs suggests that productivity could rise by 1.5 percentage points with GenAI after ten years of broad adoption, equating to almost double the pace of US productivity growth. While insightful, most of these findings come from controlled settings that don’t necessarily reflect the nuances of real-life use cases.

To better answer how much GenAI can enhance productivity in software development, a leading digital transformation services and product engineering company decided to record its practical findings and insights from a recent large-scale GenAI implementation project with one of its clients. This client wanted to adopt GenAI into the work processes of 10 development teams across three workstreams, entailing over 100 specialists. These real-life findings reveal the various challenges businesses will encounter along the journey; moreover, they underscore the necessity of a company-wide roadmap for scaling GenAI adoption.

Addressing Specialists’ Negative Attitudes and Expectations 

Many challenges can delay the success of a GenAI project, such as legal and regulatory concerns, a lack of processing capacity, security and privacy, etc. However, the most significant roadblock encountered during this large-scale implementation was the specialists’ attitudes and expectations around the technologies. During the implementation, the engineering company observed that the client’s specialists had certain expectations about GenAI and how it would augment their work. When these initial expectations didn’t align with the outcomes regarding quality or execution time, they would develop negative attitudes toward the technologies. In particular, when the GenAI didn’t, in their words, “Do the work for me,” they would respond with comments like: “I expected better and don’t want to waste my time anymore.”

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