How Generative AI Adoption Impacts Productivity

Recent advances in generative artificial intelligence (AI) have gleaned the interest of Teradata users across the globe, with specific focus on how this emerging “general purpose technology” will radically transform the way business is conducted.  A critical component of this includes the extent to which AI can reduce unit labor costs. A look at recent economic research attempts to answer this question and offers us a window into the future of how our workforce can be enhanced with AI.

During the first wave of the widespread adoption of information technology in business, MIT economist Robert Solow quipped that “you can see the computer age everywhere but in the productivity statistics.” The economic impact of the early adoption of a disruptive general-purpose technology (whether electric motors or AI) is difficult to identify and measure. As described by Eric Brynjolfsson, “the successful application of a general-purpose technology requires the development of complementary technologies, including co-invention of new processes, business models and human capital.”

A recent comprehensive analysis of the adoption and usage of AI by firms in the US (1) found evidence that AI adoption is coincident with the adoption of “high-potential technologies such as cloud computing, as well as building on the presence of digitally stored information within firms.” Another important factor influencing AI adoption within an organization is the nature of its workforce and the extent of the human capital available. Economists analyzed detailed employee resumes from social network sites that were subsequently matched to employers and found that “firms with higher initial shares of highly educated workers invest more in AI and, as firms invest in AI, they tend to transition to more educated workforces, with higher shares of workers with undergraduate and graduate degrees, and more specialization in engineering and information technology skills.” The study also found that “investments in AI were associated with a flattening of the firms’ hierarchical structure” noting that “as a predictive technology, AI improves individual employees’ ability to make predictions and decisions, which increases the autonomy of workers and reduces the demand for managerial positions.” 

A Harvard Business School study (2) revealed that a sample of Boston Consulting Group employees with access to the GPT-4 Large Language Model (LLM) increased their productivity on a selection of consulting tasks by 12.2%, while producing significantly higher quality results, as compared to a control group. Consultants across the skills distribution benefitted significantly from “AI augmentation”, with those below the average performance threshold increasing by 43% and those above the threshold increasing by 17%. The authors found evidence of a “jagged technology frontier where some tasks are easily done by AI, while others, though seemingly similar in difficulty level, are outside the current capability of AI”.

Another recent study (3) found that the “staggered introduction of generative AI conversational assistant to 5,179 customer support agents led to a 14% increase in productivity, as measured by issues resolved per hour with the greatest impact on novice and low-skilled workers; the AI assistance improves customer sentiment, reduces requests for managerial intervention and improves employee retention”. The authors identified “suggestive evidence that the AI model disseminates the best practices of more able workers and helps newer workers move down the experience curve.”

An organization’s return on investment in generative AI model development and deployment is driven by demonstrated increases in worker productivity. Holding wages constant and assuming a similar quality of output, an increase in labor productivity should lead to a proportionate decrease in unit labor costs. Teradata’s ClearScape Analytics™ provides the data and analytics infrastructure to support the deployment of AI models, both predictive and generative varieties. Additionally, Teradata’s ability to deliver customer journey analytics provides the necessary infrastructure to measure the benefits of generative AI deployment in customer service workflow applications. 

These recent studies of the adoption of AI by organizations as a task augmentation tool identified a material improvement in employee productivity, as both output and quality increased simultaneously after adoption, which leads directly to a decrease in unit labor costs. The productivity improvement was the greatest for employees with the least skills and work experience, which demonstrates the importance of investing in the comprehensive data management and advanced analytics solutions that only Teradata can provide. 

1. T. Babina, A. Fedyk, A. He, J. Hodson, “Firm Investments in Artificial Intelligence Technologies and Changes in Workforce Composition”, September 2022.
2. F. Dell’Acqua, E. McFowland, E. Mollick, H. Lifshitz-Assaf, K. Kellogg, S. Rajendran, L. Krayer, F. Candelon, K. Lakhani, “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality”, Harvard Business School, September 2023
3. E. Brynjolfsson, D. Li, L.R. Raymond, “Generative AI at Work”, NBER, April 2023.

While generative AI offers potential productivity benefits, there are other paths that financial institutions and banks are going down to stay one step ahead. On such focus is navigating the complex relationship banks have with their customers.  Find out how these institutions are continuously anticipating their customers’ needs to drive loyalty and retention, and how you can apply this to your business. Download the white paper, “Customer Banking Relationship in 5 Dimensions.”