WorkDifferentWithAI.com Academic Paper Alert!
Written by Erik Brynjolfsson, Danielle Li, Lindsey R. Raymond
Category: “AI for IT”
Article Section: Practical AI Applications; Enterprise AI for Sales Forecasting
Publication Date: 2023
SEO Description: “Generative AI boosts worker productivity, aids in knowledge transfer, enhances customer sentiment, and encourages employee retention.”
AI-Generated Paper Summary
GPT-4 API
The research paper investigates the impact of a generative AI-based conversational assistant on the productivity and learning curve of customer support agents. Data from 5,179 agents reveal that using the AI tool increased productivity by 14% on average, with a significant 34% improvement observed for novice and low-skilled workers. The study suggests that the AI tool aids in disseminating best practices among less experienced workers and also affects customer sentiment positively, increases employee retention, and potentially facilitates worker learning. The report concludes that generative AI significantly enhances productivity, although the effects vary across different worker groups.
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Here are a few key thoughts on the novelty and commercial applications of this paper on generative AI in the workplace:
Novelty
- This is one of the first studies looking at the impact of a generative AI tool (chat assistant built on GPT) when deployed at scale in a real-world workplace setting.
- It provides new evidence that generative AI can increase worker productivity, especially for less experienced/skilled workers. This contrasts with prior automation technologies that tended to complement high-skill workers more.
- The paper offers new insights into how generative AI might improve outcomes – by helping disseminate best practices and accelerating learning. This is supported by evidence on worker adherence, learning during outages, and textual analysis.
Commercial Applications
- The results suggest generative AI could be valuable for improving onboarding/training for new hires in customer service roles. The AI helped inexperienced agents improve rapidly.
- More broadly, it points to opportunities for using generative AI to capture and spread expertise within organizations. The fact that low performers improved while high performers saw little change is consistent with their expertise being disseminated.
- For the AI vendor, it provides validation of their product’s capabilities and impact in a commercial deployment. This could support marketing and sales of their platform.
- For firms buying AI systems, the paper helps make the business case for adoption and provides guidance on where impact may be highest (less experienced workers).
- The results on lower attrition also suggest AI adoption could improve retention, especially of newer workers. This has direct cost benefits.
In summary, this rigorous study provides novel evidence that generative AI can create value in commercial settings by improving productivity and accelerating learning. The results open interesting avenues for using AI to capture and disseminate organizational knowledge.
Keywords
Generative AI, Work, Productivity, Customer Support Agents, AI-Assisted Learning
Author’s Abstract
New AI tools have the potential to change the way workers perform and learn, but little is known about their impacts on the job. In this paper, we study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents. Access to the tool increases productivity, as measured by issues resolved per hour, by 14% on average, including a 34% improvement for novice and low-skilled workers but with minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve. In addition, we find that AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning. Our results suggest that access to generative AI can increase productivity, with large heterogeneity in effects across workers.