Guest Post by Melissa Incera, S&P Global Market Intelligence
This is the second installment in our blog series on emerging Gen AI use cases (read the first blog here) and a companion to the analyst report, “Generative AI: Balancing Innovation and Privacy.”
In this report, we highlight two of the most prevalent applications of generative AI: code generation and contact center. While neither of these are new target areas for AI, generative AI unlocks significant value by shifting from rules-based systems with limited utility to models that are far more capable and context-aware, thus opening up net new ways of working.
Code generation
One particularly fast-growing application of generative AI is code generation. A recent 451 Research survey shows that 45% of organizations have already implemented some form of code generation technology, and an additional 39% intend to invest in it within the next 12 months. This surging interest suggests that code generation will soon become the second most popular generative AI modality in use, following text-based solutions (see our blog post on secure content generation), as organizations seek to accelerate their software development processes.
With code generation, automating rote tasks — such as writing boilerplate code — helps teams to focus on more strategic aspects of software development, leading to faster development cycles for new features and applications. Additionally, manual coding can be prone to mistakes, leading to bugs and software vulnerabilities. Automated code generation tools, meanwhile, are designed to follow best practices and coding standards, and many have built-in testing capabilities, resulting in more consistent and high-quality code. All of this has the effect of reducing development costs and improving code reliability.
Contact center
Customer experience has long been a leading application area for AI in the enterprise — second only to IT operations, according to 451 Research survey data — due to the wealth of data generated by customer interactions and the fact that many customer service tasks are easily automated due to their repetitive, rules-based nature. With generative AI, the value proposition has become even more compelling. In a 451 Research Customer Experience & Commerce survey, 37% of respondents said the integration of generative and predictive AI was “game-changing” in customer experience due to its potential to enhance service quality and boost staff productivity and satisfaction.
Top GenAI use cases in customer experience
Q. With the latest advancements in generative AI (GenAI), which of the following use cases are important for your organization to consider using? Please select all that apply. Base: All respondents (n=519). Source: 451 Research’s Voice of the Enterprise: Customer Experience & Commerce, Vendor Selection 2024.
In the contact center specifically, benefits extend to all parties. For customers, generative AI offers a more natural on-demand experience characterized by human-like conversations with AI interfaces that can understand complex requests and provide nuanced and accurate responses. As generative capabilities are better integrated with customer data, these applications also become more proactive, personalized, and contextualized compared to standard bots or service lines. For contact center staff, GenAI can be used to quickly find information within lengthy or complex documents, develop tailored scripts for interactions, and even facilitate real-time translation during multilingual conversations.
Looking ahead
In both cases, the integration of generative AI represents a fundamental evolution in how businesses work. In the contact center, the enhancement in speed and depth of service is critical as customer expectations are ever-increasing. For developers, AI-assisted coding can reshape the entire software development life cycle, from initial design to deployment and maintenance. In our next post, we will look at two evolving applications of generative AI: retrieval-augmented generation and agentic AI.
About the Author
Melissa Incera is a research analyst on the 451 Research Data, AI & Analytics team within S&P Global Market Intelligence. Her focus lies in generative AI, its emergent use cases in enterprise applications and the foundation models that underpin it. Specifically, she tracks the text-generation landscape and its offshoots in enterprise search, AI assistants, marketing and writing tools. Prior to joining the AI team, Melissa spent a few years covering technology M&A and financial markets looking at high-level trends, noteworthy deals and investments, and forecasting activity across technology subsectors. She also did some work with the Workforce Productivity & Collaboration team, where she researched the future of work and emerging applications to enhance the employee experience. Melissa holds a bachelor’s degree in English Literature from Boston College and an MBA from ESADE, Ramon Llull University in Barcelona.
