How to Handle the Challenges of Implementing Generative AI in Your Business

Generative AI-enabled software development promises to boost productivity significantly. In fact, research by Harvard reveals a 43 percent increase in productivity, depending on the task and seniority of the specialist. Nevertheless, most market research on generative AI-attributed productivity improvement comes from controlled settings that don’t necessarily reflect real-world nuance.  

A leading digital transformation services and product engineering company sought to capture the components of a real-world integration by helping one of its clients integrate generative AI into the work processes of 10 development teams across three workstreams, including more than 100 specialists. The practical findings from this large-scale implementation can assist organizations as they overcome adoption challenges and craft a company-wide roadmap that scales AI tools, culture and practices. 

Addressing Generative AI Adoption Challenges  

Accounting for these variables when objectively measuring how new generative AI tools impact productivity can be nigh impossible on an individual level. As such, businesses should measure the change in productivity by examining the change in output for an entire team.

Several challenges impede adoption, such as compatibility with AI tools and integration issues. Likewise, data privacy and security concerns with tool usage can cause problems. Even when companies successfully resolve those challenges, the two main roadblocks encountered during this large-scale generative AI implementation were specialists’ misaligned attitudes and expectations regarding AI as well as the complexities of real-world project conditions.  

Before implementing generative AI, companies must navigate the attitudes and expectations of their workforce. Specialists’ negative attitudes around generative AI typically emerge when their initial expectations do not align with the outcomes concerning quality or execution time. Often, these attitudes amount to feeling that the tools should “Do the work for me.” When they don’t, specialists will say, “This won’t help me,” or “I don’t have time for this.”  

One approach organizations can take to encourage adoption is to analyze all of their specialists through surveys and assessments. These will allow companies to track attitudes and perceived personnel engagement, helping establish a baseline. Business leaders can then identify subgroups with similar attitudes and approach their coaching techniques differently.  

For example, two subgroups could include people with a high vs. a low self-perceived Gen AI proficiency score. Companies can create individual change management strategies for these groups, providing more coaching, training and resources for those who admitted to not being highly proficient with Gen AI. 

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Break through the hype and learn about Generative AI’s real value for your enterprise: https://www.epam.com/services/artificial-intelligence/generative-ai