Why Firm Productivity Might Drop in the Near Term as AI Gets Deployed

Among other issues, such as potential payback from deploying generative artificial intelligence, is the timing of the payback, and history suggests payback will take far longer than many expect. If AI does develop as a general-purpose technology, as were earlier GPTs including steam power and electricity, and even granting that many technological innovations--which are largely virtual--can propagate much faster than did earlier innovations.  

The initial impact of steam power and electricity on productivity was not as immediate or dramatic as expected. 

Consider steam power. Early adoption was slow. The first practical steam engine was invented by Thomas Newcomen in 1712. James Watt significantly improved the steam engine in 1765 and kicked off the process of commercialization. Still, by 1830, only 165,000 horsepower of steam was in use in Britain, for example.

Even in 1870, about two-thirds of steam power was concentrated in just three industries: coal mining, cotton textiles, and metal manufactures. So, while invented in the early 18th century, it took about 50 to 75 years for steam power to begin having truly widespread and transformative effects on industry and the economy.

The major productivity gains from electricity in the United States came in the 1920s, about 40 years after Thomas Edison first distributed electrical power in New York in 1882.

And there is ample prior evidence of actual productivity dips in  the early days of new technology diffusion. The J Curve, for example, illustrates the pattern that there is an early period of disruption and actual productivity decline when a major new technology is introduced. Only later are the tangible benefits seen. 

source: Flexible Production 

The J-curve effect in GPT adoption typically follows a few stages, from initial investment to realized productivity. AI clearly is in the early investment phase, which ought to imply significant costs without immediate financial returns.

Which ought to clue us in to the fact that investors are likely to be quite disappointed when most entities cannot show significant financial returns. 

And though the J curve might not apply when innovations do not require value chain disruption and displacement, Verizon’s experience with fiber-to-home upgrades still show that even innovations that do not require business model change can take a while to reach maturity. 

As significant as fiber-to-the-home was deemed to be by Verizon, one would be very hard pressed to show significant financial returns to Verizon for five years from mass deployment.

FTTH was not a GPT that required changes in consumer behavior or disruptions of Verizon’s supply and value chains. 

The thing about GPTs (and if AI is a GPT the J curve should apply) is that disruption is required. Still, Verizon arguably reached scale in about four to five years of construction, with very-significant revenue contributions for new video entertainment services enabled by the FiOS network. In the second quarter of 2011, for example, Verizon had about 4.5 million broadband accounts, as well as3.8 million video accounts. 

In the second quarter of  2011, FiOS generated 57 percent of consumer wireline revenues, up from 48 percent a year earlier, Verizon said that year. 

 

By the third quarter of 2011, FiOS accounted for nearly 60 percent of consumer wireline revenues. In the last quarter of 2014, FiOS contributed 75 percent of consumer wireline revenues. Keep in mind that statistic also includes the diminution of Verizon’s landline voice business, plus the maturation and decline of its linear video entertainment business as well. 

In other words, FiOS revenue became the driver of Verizon consumer fixed network revenue in part because the voice and video entertainment businesses declined. 

Year	Cumulative Capital Investment ($B)	Annual FiOS Revenue ($B)	FiOS Subscribers (Millions)
2006	3.6	0.5	0.7
2010	23.0	7.5	4.1
2014	30.0	12.7	6.6
2018	34.0	11.9	6.1
2022	36.5	12.8	6.3

The main take away is that productivity might actually dip in the near term as firms deploy AI technologies.

General-Purpose Technology	Initial Productivity Dip	Adaptation Period	Productivity Surge
Steam Engine	Slow adoption in early 19th century	1820s-1840s	1850s-1890s
Electricity	Limited productivity gains in 1890s-1910s	1920s-1930s	1940s-1950s
Computers	Productivity paradox in 1970s-1980s	1980s-1990s	Late 1990s-2000s
Internet	Initial investment costs in 1990s	Late 1990s-early 2000s	Mid 2000s-present

Generative AI Productivity is an Issue, But So is Most IT

Quantifying or documenting generative artificial intelligence value is a top issue, respondents said in a Gartner survey of information technology executives. That really should not come as a surprise, as documenting the value of most technologies in knowledge or office work is challenging. 

And since generative AI is used for customer service interactions, producing summaries, developing code, drafting documents or messages, the issue is how well we can document the productivity lift from virtually any IT tool, in those instances. 

source: Gartner 

Quantifying the productivity gains from new IT solutions in customer service can be surprisingly challenging, experts often say. As applied to customer service agent operations, IT tools are said to improve customer satisfaction, handle volume fluctuations, and reduce training times. But isolating the impact on individual agent output can be difficult.

Generative AI and other IT might increase the number of customer contacts per hour, for example. Chatbots are a substitute for human agents as well, so might contain customer service costs. But that all hinges on the quality of the chatbot to answer the questions customers actually have.  

In addition, customer service involves interactions with various channels (phone, email, chat), making it hard to isolate the impact of IT on a single metric. Improved customer satisfaction might not directly translate to a quantifiable reduction in call times.

But that might not always correlate with improved ability to actually solve a customer problem. In other words, quantity is not the same as quality. 

Also, changes in productivity may not be immediate.  Learning curves, process adjustments, and cultural shifts within the team can take time to settle before the full impact is realized.

Accurately measuring before-and-after states requires clean data and proper attribution. Factors like seasonal variations, changes in customer behavior, or external promotions can skew the results. 

Demonstrating a clear return on investment (ROI) for new IT implemented in customer service can be challenging. Here's a breakdown of the difficulties:

Generative AI also might not eliminate tasks, but rather shift them. Increased efficiency in handling routine inquiries might free up agents for more complex issues, making it difficult to show a direct reduction in overall workload or quality of outcomes. 

Improved agent morale, reduced stress, and better customer experiences are all positive outcomes, but they're not easily captured in traditional productivity metrics like call resolution times.

Source	Author(s)	Publication	Key Findings
The Impact of Information Technology on Customer Service Productivity	Brynjolfsson, Erik, et al.	Management Science (1993)	Found that the impact of IT on productivity depends on the specific technology and how it's implemented. Identifying productivity gains requires careful analysis.
Does IT Really Pay Off? Measuring the Effects of Information Technology Investment on Customer Service	Lee, Sang-Pil, and Byung-Il Park	Journal of Service Research (2001)	Highlights the difficulty of isolating the impact of IT on productivity due to the presence of confounding variables. Emphasizes the need for a multi-faceted approach to evaluation.
The Challenges of Measuring the Business Value of Customer Relationship Management (CRM) Initiatives	Rust, Roland T., et al.	Journal of Marketing (2004)	Argues that traditional ROI metrics might not capture the full value of CRM systems, which often include customer satisfaction and loyalty benefits alongside productivity improvements.

We might observe similar issues with other tasks GenAI might help with, such as creating documents and text. Past applications of word processing arguably provide speed and quality advantages that are hard to quantify. 

Study Name	Venue	Date	Key Conclusions
"The Effects of Word Processing Software on Writing Performance" by Keith S. McNeil	Educational Technology Research and Development	1988	Found that word processing had a minimal impact on writing speed but improved editing efficiency.
"The Impact of Technology on Writing: A Review of the Literature" by Charles A. MacArthur, et al.	Review of Educational Research	2001	Concluded that the impact of technology on writing quality is mixed and depends on factors like task and user skill level.
"The Myth of Increased Productivity: How New Technologies Slow Us Down" by Sherry Turkle	Basic Books	2015	Argues that constant connectivity and information overload can hinder focused work and deep thinking, potentially impacting writing productivity.
"Beyond the Efficiency Paradigm: Rethinking the Role of IT in Knowledge Work" by Wanda J. Orlikowski	Organization Science	2007	Shifts the focus from measuring just efficiency gains to considering how IT can enhance creativity, innovation, and collaboration in knowledge work like writing.
"The Myth of Increased Productivity: How New Technologies Can Slow Us Down" by Daniel H. Pink	Harvard Business Review	2016	Argues that constant connectivity and information overload can actually decrease focus and productivity, even with advanced tools.
"The Paradox of E-Mail: A Sociotechnical Perspective on Communication Overload" by Stefan Klein, Christian Bartsch, and Jan Marco Leimeister	Journal of Communication	2004	Highlights how email, a seemingly efficient communication tool, can lead to information overload and ultimately hinder productivity.
"The Impact of New Information Technologies on Task Performance: A Meta-Analysis" by Steven E. Fiore, James R. Salas, Michelle H. Cuevas, and Cheryl A. Bowers	Human Factors	2003	Reviews multiple studies on the impact of technology on task performance and concludes that the effect can be positive, negative, or neutral depending on the specific task and user characteristics.

Users Overestimate Value of IT

User perceptions often do not match underlying realities, whether that is the relative strengths of a sports team or the value of applying generative artificial intelligence. Overconfidence bias is one explanation. 

When surveyed or asked, people tend to overestimate their own abilities and knowledge, which can lead to an inflated perception of the impact of IT on their work. 

Confirmation bias also is at work. Confirmation bias essentially leads us to favor information that confirms our existing beliefs and downplay or ignore information that contradicts them. That obviously always is at work when decisions are made that have financial or strategic implications. Those who made the decisions have a vested interest in positive outcomes. 

The adage “You don’t get fired for recommending IBM” might be extended to any number of other buying decisions made related to IT. Risk aversion makes sense if your job might be imperiled by a bad decision. 

Going with firms with proven track records--rather than new upstarts--often makes perfect sense. The established “name” providers might cost more, or might not provide as much innovation, but the risk of unexpected failures is reduced. Backwards compatibility, domain knowledge, staff familiarity and training costs, conversion costs or ecosystem richness are other possible advantages of sticking with the known. 

The point is that users often overestimate the quantifiable advantages from new IT deployments and approaches, even as independent studies find mixed evidence of such clear improvements, at least within five to 10 years of a major technology deployment. 

Study Title	Authors	Year	Findings
"The Productivity Paradox in Information Technology"	Brynjolfsson, Erik and Lorin M.	1997	Found no clear evidence that IT adoption led to significant increases in overall worker productivity in the US economy.
"The Impact of Enterprise Resource Planning Systems on Firm Performance"	Brynjolfsson, Erik, Lorin M.	2002	Examined the impact of ERP systems on firms and found that while they can improve efficiency in some areas, the overall impact on profitability was mixed.
"From Hype to Reality: Exploring the Real Business Value of CRM Technology"	Reinartz, Werner and	2002	Studied the impact of Customer Relationship Management (CRM) systems and found that successful implementation required significant organizational changes beyond the technology itself.
"Does Investment in IT Really Pay Off?"	Brynjolfsson, Erik, Lorin M.	2016	Argued that the benefits of IT are often overstated, and that successful implementation requires a focus on complementary organizational changes and investments in worker skills.
"User Beliefs About Technology Usage: The Case of Enterprise Systems"	Dennis, Alan R. and Barbara	2001	Investigated user perceptions of enterprise systems and found a disconnect between user beliefs about the systems' capabilities and their actual effectiveness.

Also, most new IT implementations, especially those with important strategic impact, take time to bear fruit, as often, tangible outcomes require reshaping of whole business processes. So there often is a lag between the time new technologies are implemented and the time when people can cite clear positive outcomes. 

So watch for an avalanche of end user studies claiming benefits from deploying generative AI, for example, that cannot be independently verified, in terms of magnitude of gain. Vendors will want such attitudes and those making the spending decisions will want to keep their jobs.