Optimising AI Performance with Generative Models

Introduction: In an era where Artificial Intelligence (AI) steers the competitive edge, this case study stands as a testament to innovative problem-solving in the customer service industry. This case study elucidates how research transcended the barriers of conventional AI models by employing Generative AI, marking a significant stride toward operational excellence and robust data-driven decision-making.

Challenge: The crux of the challenge was the recurring imprecision of AI outputs which necessitated exhaustive model refinements. This not only strained resources but also stymied the pace of innovation, a crucial deterrent in a highly competitive market.

Solution: The incorporation of Generative AI in the operational framework emerged as a cornerstone solution. Unlike their previous models, Generative AI’s capability to generate new data instances and discern complex patterns propelled a substantial improvement in output accuracy.


Enhanced Precision:

Real-World Evidence: The deployment of Generative AI slashed the Average Handle Time (AHT) from a cumbersome 45 minutes to an efficient 30 minutes, a metric indicative of the enhanced precision in resolving technical queries.

Operational Efficiency:

Real-World Evidence: Post the integration of Generative AI, Case Study One witnessed a significant uptick in resolution rates, from an initial 75% to an impressive 90%. This manifested in reduced operational bottlenecks and faster throughput.

Competitive Edge:

Real-World Evidence: The superior precision and efficiency fostered by Generative AI-enabled Case Study One to outperform competitors, as reflected in the Net Promoter Score (NPS) soaring from 70 to 85 post-integration.

Conclusion: This case study encapsulates the paradigm shift that Generative AI can instigate in optimizing AI-driven operations. The tangible improvements in key performance metrics like Average Handle Time, Resolution Rate, and Net Promoter Score underscore the pivotal role of Generative AI in not only addressing the inherent challenges but also in fostering a conducive environment for continuous innovation and competitive superiority in the customer service industry.


Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond (2023): Generative AI at Work. National Bureau of Economic Research working paper 31161. https://www.nber.org/papers/w31161