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Generative AI at Work

Erik Brynjolfsson Danielle Li Lindsey Raymond

Stanford & NBER MIT & NBER MIT

April 25, 2023

Please see here for latest version

Abstract

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 percent on average, with the greatest impact on novice and

low-skilled workers, and minimal impact on experienced and highly skilled workers. We provide

suggestive evidence that the AI model disseminates the potentially tacit knowledge of more able

workers and helps newer workers move down the experience curve. In addition, we show that

AI assistance improves customer sentiment, reduces requests for managerial intervention, and

improves employee retention.

JEL Classiﬁcations: D80, J24, M15, M51, O33

Keywords: Generative AI, Large Language Models, Technology Adoption, Worker Productiv-

ity, Organizational Design.

Correspondence to erikb@stanford.edu, d_li@mit.edu, and lraymond@mit.edu. This paper is part of the National Bureau

of Economic Research (NBER)’s working paper series, #WP31161. We are grateful to Daron Acemoglu, David Autor, Amittai

Axelrod, Eleanor Dillon, Zayd Enam, Luis Garicano, Alex Frankel, Sam Manning, Sendhil Mullainathan, Emma Pierson, Scott

Stern, Ashesh Rambachan, John Van Reenen, Raﬀaella Sadun, Kathryn Shaw, Christopher Stanton, Sebastian Thrun, and

various seminar participants for helpful comments and suggestions and to the Stanford Digital Economy Lab for funding. The

content is solely the responsibility of the authors and does not necessarily represent the oﬃcial views of Stanford University,

MIT, or the NBER.

arXiv:2304.11771v1 [econ.GN] 23 Apr 2023

The emergence of generative artiﬁcial intelligence (AI) has attracted signiﬁcant attention, but

there have been few studies of its economic impact. While various generative AI tools have per-

formed well in laboratory settings, excitement about their potential has been tempered by concerns

that these tools are prone to “hallucination,” and may produce coherent sounding but inaccurate

information (Peng et al., 2023a; Roose, 2023).

In this paper, we study the adoption of a generative AI tool that provides conversational guidance

for customer support agents.

This is, to our knowledge, the ﬁrst study of the impact of generative

AI when deployed at scale in the workplace. We ﬁnd that access to AI assistance increases the

productivity of agents by 14 percent, as measured by the number of customer issues they are able to

resolve per hour. In contrast to studies of prior waves of computerization, we ﬁnd that these gains

accrue disproportionately to less-experienced and lower-skill workers.

We argue that this occurs

because ML systems work by capturing and disseminating the patterns of behavior that characterize

the most productive agents.

Computers and software have transformed the economy with their ability to perform certain

tasks with far more precision, speed, and consistency than humans. To be eﬀective, these systems

typically require explicit and detailed instructions for how to transform inputs into outputs: when

engineers write code to perform a task, they are codifying that task. Yet because many production

processes rely on tacit knowledge, these processes have so far deﬁed automation (Polanyi, 1966;

Autor, 2014).

Machine learning algorithms work diﬀerently. In contrast to traditional programming, these

systems implicitly infer instructions from examples. Given a training set of images, for instance,

ML systems can learn to recognize faces. This highlights a key, distinguishing aspect of ML systems:

they can learn to perform tasks even when no instructions exist–including tasks requiring tacit

knowledge that could previously only be gained through lived experience (Polanyi, 1966; Autor,

2014; Brynjolfsson and Mitchell, 2017).

We study the impact of generative AI on productivity in the customer service sector, an indus-

try with one of the highest rates of AI adoption (Chui et al., 2021). We examine the staggered

A note on terminology. There are many deﬁnitions of artiﬁcial intelligence and of intelligence itself— Legg et

al. (2007) list over 70 of them. In this paper, we deﬁne “artiﬁcial intelligence” (AI) as an umbrella term that refers

to a computer system that is able to sense, reason, or act like a human. “Machine learning” (ML) is a branch of AI

that uses algorithms to learn from data, identify patterns and make predictions or decisions without being explicitly

programmed (Google, n.d.). Large language models (LLMs) and tools built around LLMs such as ChatGPT are

an increasingly important application of machine learning. LLMs generate new content, making them a form of

“generative AI.”

We provide a discussion of this literature at the end of this section.

As Meijer (2018) puts it “where the Software 1.0 Engineer formally speciﬁes their problem, carefully designs algo-

rithms, composes systems out of subsystems or decomposes complex systems into smaller components, the Software

2.0 Engineer amasses training data and simply feeds it into an ML algorithm...”

deployment of a chat assistant using data from 5,000 agents working for a Fortune 500 software

ﬁrm that provides business process software. The tool we study is built on a recent version of the

Generative Pre-trained Transformer (GPT) family of large language models developed by OpenAI

(OpenAI, 2023). It monitors customer chats and provides agents with real-time suggestions for how

to respond. It is designed to augment agents, who remain responsible for the conversation and are

free to ignore its suggestions.

We have four sets of ﬁndings.

First, AI assistance increases worker productivity, resulting in a 13.8 percent increase in the

number of chats that an agent is able to successfully resolve per hour. This increase reﬂects shifts in

three components of productivity: a decline in the time it takes to an agent to handle an individual

chat, an increase in the number of chats that an agent is able to handle per hour (agents may handle

multiple calls at once), and a small increase in the share of chats that are successfully resolved.

Second, AI assistance disproportionately increases the performance less skilled and less experi-

enced workers across all productivity measures we consider. In addition, we ﬁnd that the AI tool

helps newer agents move more quickly down the experience curve: treated agents with two months

of tenure perform just as well as untreated agents with over six months of tenure. These results

contrast, in spirit, with studies that ﬁnd evidence of skill-biased technical change for earlier waves

of computer technology (Autor et al., 2003; Acemoglu and Restrepo, 2018; Bresnahan et al., 2002;

Bartel et al., 2007).

Our third set of results investigates the mechanism underlying our ﬁndings so far. We posit that

high-skill workers may have less to gain from AI assistance precisely because AI recommendations

capture the potentially tacit knowledge embodied in their own behaviors. Rather, low-skill workers

are more likely to improve by incorporating these behaviors by adhering to AI suggestions. Consis-

tent with this, we ﬁnd few positive eﬀects of AI access for the highest-skilled or most-experienced

workers. Instead, using textual analysis, we ﬁnd suggestive evidence that AI assistance leads lower-

skill agents to communicate more like high-skill agents.

Finally, we show that the introduction of AI systems can impact the experience and organization

of work. We show that AI assistance markedly improves how customers treat agents, as measured

by the sentiments of their chat messages. This change may be associated with other organizational

changes: turnover decreases, particularly for newer workers, and customers are less likely to escalate

a call by asking to speak to an agent’s supervisor.

Our overall ﬁndings demonstrate that generative AI working alongside humans can have a sig-

niﬁcant positive impact on the productivity and retention of individual workers. We emphasize,

however, that our paper is not designed to shed light on the aggregate employment or wage eﬀects

of generative AI tools.

Our paper is related to a large literature on the impact of various forms of technological adoption

on worker productivity and the organization of work (e.g. Rosen, 1981; Autor et al., 1998; Athey

and Stern, 2002; Bresnahan et al., 2002; Bartel et al., 2007; Acemoglu et al., 2007; Hoﬀman et al.,

2017; Bloom et al., 2014; Michaels et al., 2014; Garicano and Rossi-Hansberg, 2015; Acemoglu and

Restrepo, 2020). Many of these studies, particularly those focused on information technologies, ﬁnd

evidence that IT complements higher-skill workers (Akerman et al., 2015; Taniguchi and Yamada,

2022). Bartel et al. (2007) shows that ﬁrms that adopt IT tend to use more skilled labor and

increase skill requirements for their workers. Acemoglu and Restrepo (2020) study the diﬀusion of

robots and ﬁnd that the negative employment eﬀects of robots are most pronounced for workers

in blue-collar occupations and those with less than a college education. In contrast, we study a

diﬀerent type of technology—generative AI—and ﬁnd evidence that it most eﬀectively augments

lower-skill workers.

For example, Peng et al. (2023b) recruit software engineers for a speciﬁc coding task (writing an

HTTP server in JavaScript) and show that those given access to GitHub Copilot complete this task

twice as quickly. Similarly, Noy and Zhang (2023) run an online experiment showing that subjects

given access to ChatGPT complete professional writing tasks more quickly. In addition, they show

that ChatGPT compresses the productivity distribution, with lower-skill workers beneﬁting the

most.

To the best of our knowledge, however, there have been no studies of the impact of access to gen-

erative AI tools on productivity in real-world workplaces, nor over longer periods. Such studies are

important because the impact of AI on productivity may vary over time and interact with workers’

baseline level of experience or expertise. Technologies that look promising in laboratory settings

may have more limited eﬀects in practice because of the need for complementary organizational

investments, skill development and business process redesign. In addition, the introduction of AI

systems may have further impacts on worker and customer satisfaction, attrition, and patterns of

organizational behavior.

1 Generative AI and Large Language Models

In recent years, the rapid pace of AI development and public release tools such as ChatGPT,

GitHub Copilot, and DALL-E have attracted widespread attention, optimism, and alarm (The

White House, 2022). These technologies are all examples of “generative AI,” a class of machine

learning technologies that can generate new content—such as text, images, music, or video—by

analyzing patterns in existing data. In this section, we provide background on generative AI as a

technology and discuss its potential economic implications.

1.1 Technical Primer

In this paper, we focus on an important class of generative AI, large language models (LLMs). At

a high level, LLMs are neural network models designed to process sequential data (Bubeck et al.,

2023). For instance, an LLM can be trained by giving it access to a large corpus of text (such

as Wikipedia, digitized books, or portions of the Internet) and using that input text to learn to

predict the next word in a sequence, given what has come before. This knowledge of the statistical

co-occurrence of words allows it to generate new text that is grammatically correct and semantically

meaningful.

Though the name implies human language, the same techniques can be used to produce LLMs

that generate other forms of sequential data such as protein sequences, audio, computer code, or

chess moves (Eloundou et al., 2023).

Recent progress in generative AI has been driven by four factors: computing power, earlier

innovations in model architecture, the ability to “pre-train” using large amounts of unlabeled data,

and reﬁnements in training techniques.

Model performance depends strongly on scale, which

includes the amount of computing power used for training, the number of model parameters, and

dataset size (Kaplan et al., 2020). Pre-training an LLM requires thousands of GPUs and weeks to

months of dedicated training time. For example, estimates indicate that a single training run for a

GPT-3 model with 175 billion parameters, trained on 300 billion tokens, may cost $5 million dollars

in just computing costs (Li, 2020; Brown et al., 2020).

In terms of model architecture, modern LLMs make use of two earlier key innovations: positional

encoding and self-attention. Positional encodings keep track of the order in which a word occurs

in a given input.

This allows large bodies of input text to be broken into smaller segments that

can be processed simultaneously without “forgetting” earlier parts of the input (Vaswani et al.,

2017; Bahdanau et al., 2015). Meanwhile, self-attention assigns importance weights to each word

in the context of the entire input text. Older approaches that assign importance based on word

frequencies that may misrepresent a word’s true semantic importance. These older methods may

also base on semantic content within a small window. In contrast, self-attention enables models to

For a more detailed technical review of progress, see Radford and Narasimhan (2018); Radford et al. (2019); Liu

et al. (2023); Ouyang et al. (2022).

For instance, a model would keep track of “the, 1” instead of only “the” (if “the” was the ﬁrst word in the sentence).

capture long-range semantic relationships within an input text, even when that text is broken up

and processed in parallel (Vaswani et al., 2017).

Second, LLMs can be pre-trained on large amounts of unlabeled data. For instance, GPT is

trained on unlabeled text data, allowing it to learn patterns in human language without explicit

guidance (Radford and Narasimhan, 2018). Because unlabeled data is far more prevalent than

labeled data, this allows for LLMs to learn about natural language on a much larger training corpus

(Brown et al., 2020). The resulting model can be used in multiple applications because its training

is not speciﬁc to a particular set of tasks.

Finally, general-purpose LLMs can be further “ﬁne-tuned” to generate output that matches the

priorities of any speciﬁc setting (Ouyang et al., 2022; Liu et al., 2023). For example, an LLM may

generate several potential responses to a given query, but some of them may be factually incorrect

or biased. To discipline this model, human evaluators can rank these outputs to train a reward

function prioritizes some responses over others. Such reﬁnements can signiﬁcantly improve model

quality but making a general-purpose model better suited to its speciﬁc application (Ouyang et al.,

2022).

Together, these innovations have generated meaningful improvements in model performance.

The Generative Pre-trained Transformer (GPT) family of models, in particular, has attracted con-

siderable media attention for their rapidly expanding capabilities.

1.2 The Economic Impacts of Generative AI

Computers have historically excelled at executing pre-programmed instructions, making them par-

ticularly eﬀective at tasks that can be standardized and reduced to explicit rules (Autor, 2014).

Consequently, computerization has had a disproportionate impact on jobs that involve routine

tasks, such as data entry, bookkeeping, and assembly line work, and reducing demand for workers

performing “routine” tasks (Acemoglu and Autor, 2011). At the same time, computerization has

also increased the productivity of workers who possess complementary skills, such as programming,

data analysis, and research. Together, these changes have contributed to increasing wage inequality

in the United States and have been linked to a variety of organizational changes (Katz and Murphy,

1992; Autor et al., 2003; Michaels et al., 2014; Bresnahan et al., 2002; Baker and Hubbard, 2003).

In contrast, recent advances in AI, particularly those driven by generative AI, suggest that it

is possible for LLMs to perform a variety of non-routine tasks such as software coding, persuasive

For example, a model trained to generate tweets based on the history of Twitter will diﬀer depending on whether

its inputs are labeled with each tweet’s number of retweets or an assessment of its truthfulness.

For instance, GPT-4 has recently been shown to outperform humans in taking the US legal bar exam (Liu et al.,

2023; Bubeck et al., 2023; OpenAI, 2023).

writing, and graphic design (Bommasani et al., 2021; Eloundou et al., 2023). For example, Copilot,

an AI pair programmer that generates code suggestions for programmers, has achieved impressive

performance on technical coding questions and generates an average of 46 percent of code among

users (Nguyen and Nadi, 2022; Zhao, 2023). Similarly, services like Elicit and Casetext use LLMs to

ﬁnd and summarize key information in legal documents or research, tasks that were previously con-

sidered non-routine (Elicit, 2023; Casetext, 2023). Since many of these tasks are currently performed

by workers who have either been insulated or beneﬁted from prior waves of technology adoption,

the expansion of generative AI has the potential to shift the relationship between technology, labor

productivity, and inequality (The White House, 2022).

Unlike traditional programming, generative AI does not require explicit instructions as inputs.

Instead, it uses ML to mine vast amounts of human-generated data to recognize patterns, allowing

it to generate, summarize, and make inferences based on those patterns. For instance, if prompted

to provide a cover for a gothic novel, generative AI models will respond with an illustration that is

moody; if asked to write an email denying an employee a raise, generative AI will respond with a

note that is professional; this occurs even though no programmer has instructed the AI model as to

what tone would be appropriate in a given context. The ability to behave “appropriately” cannot

be reduced to a set of rules; instead people learn to do so from experience and apply unconscious

rules in the process. The fact that generative AI models display such skills suggests that they have

the potential to move past what has been termed “Polanyi’s Paradox,” the idea that knowledge is

diﬃcult to codify because individuals perform many tasks they cannot articulate (Polanyi, 1966;

Autor, 2014).

At the same time, LLMs have signiﬁcant limitations. Popular LLM-based tools such as ChatGPT

have been shown to produce false or misleading information in unpredictable ways. While these

models often perform well on speciﬁc tasks in the lab, these concerns have raised questions about

their ability to perform well in more complex real-world settings (Peng et al., 2023a).

2 Our Setting: LLMs for Customer Support

2.1 Customer Support and Generative AI

We study the impact of generative AI in the customer service industry, an area with one the highest

surveyed rates of AI adoption.

Customer support interactions are important for maintaining a

For instance, of the businesses that report using AI, 22 percent use AI in their customer service centers (Chui et

al., 2021).

company’s reputation and building strong customer relationships, yet as in many industries, there

is substantial variation in worker productivity (Berg et al., 2018; Syverson, 2011).

Newer workers are also often less productive and require signiﬁcant training. At the same time,

turnover is high: industry estimates suggest that 60 percent of agents in contact centers

leave each

year, costing ﬁrms $10,000 to $20,000 dollars per agent (Buesing et al., 2020; Gretz and Jacobson,

2018). To address these workforce challenges, the average supervisor spends at least 20 hours per

week coaching lower-performing agents (Berg et al., 2018). Faced with variable productivity, high

turnover, and high training costs, ﬁrms are increasingly turning toward AI tools (Chui et al., 2021).

At a technical level, customer support is well-suited for current generative AI tools. From an

AI’s perspective, customer-agent conversations can be thought of as a series of pattern-matching

problems in which one is looking for an optimal sequence of actions. When confronted with an issue

such as “I can’t login,” an AI/agent must identify which types of underlying problems are most likely

to lead a customer to be unable to log in and think about which solutions typically resolve these

problems (“Can you check that caps lock is not on?”). At the same time, they must be attuned to

a customer’s emotional response, making sure to use language that increases the likelihood that a

customer will respond positively (“that wasn’t stupid of you at all! I always forget to check that

too!”). Because customer service conversations are widely recorded and digitized, pre-trained LLMs

can be ﬁne-tuned for customer service using many examples of both successfully and unsuccessfully

resolved conversations.

In the remainder of this section, we provide details about the ﬁrm we study and the AI tool

they adopt.

2.2 Data Firm Background

We work with a company that provides AI-based customer service support software (hereafter, the

“AI ﬁrm”) to study the deployment of their tool at one of their client ﬁrms, (hereafter, the “data

ﬁrm”).

Our data ﬁrm is a Fortune 500 enterprise software company that specializes in business process

software for small and medium-sized businesses in the United States. It employs a variety of

chat-based technical support agents, both directly and through third-party ﬁrms. The majority of

agents in our sample work from oﬃces located in the Philippines, with a smaller group working in

the United States and in other countries. Across locations, agents are engaged in a fairly uniform

job: answering technical support questions from US-based small business owners.

The term “contact center” updates the term “call center,” to reﬂect the fact that a growing proportion of customer

service contacts no longer involve phone calls.

Chats are randomly assigned and support sessions are relatively lengthy, averaging 40 minutes

with much of the conversation spent trying to diagnose the underlying technical problem. The job

requires a combination of detailed product knowledge, problem-solving skills, and the ability to deal

with frustrated customers.

Our ﬁrm measures productivity using three metrics that are standard in the customer service

industry: “average handle time,” the average length of time an agent takes to ﬁnish a chat; “resolution

rate,” the share of conversations that the agent can successfully resolve; and “net promoter score,”

(customer satisfaction), which are calculated by randomly surveying customers after a chat and

calculating the percentage of customers who would recommend an agent minus the percentage who

would not. A productive agent is one who is able to ﬁeld customer chats quickly while maintaining

a high-resolution rate and net promoter score.

Across locations, agents are organized into teams with a manager that provides feedback and

training to agents. Once per week, managers hold one-on-one feedback sessions with each agent.

For example, a manager might share the solution to a new software bug, explain the implication of

a tax change, or suggest how to better manage customer frustration with technical issues. Agents

work individually and the quality of their output does not directly aﬀect others. Agents are paid

an hourly wage and bonuses based on their performance relative to other agents.

2.3 AI System Design

The AI system we study uses a generative model system that combines a recent version of GPT

with additional ML algorithms speciﬁcally ﬁne-tuned to focus on customer service interactions. The

system is further trained on a large set of customer-agent conversations that have been labeled with

a variety of outcomes and characteristics: whether the call was successfully resolved, how long it

took to handle the call, and whether the agent in charge of the call is considered a “top” performer

by the data ﬁrm. The AI ﬁrm then uses these data to look for conversational patterns that are

most predictive of call resolution and handle time.

The AI ﬁrm further trains its model using a process similar in spirit to Ouyang et al. (2022) to

prioritize agent responses that express empathy, surface appropriate technical documentation, and

limit unprofessional language. This additional training mitigates some of concerns associated with

relying on LLMs to generate text.

Once deployed, the AI system generates two main types of outputs: 1) real-time suggestions for

how agents should respond to customers and 2) links to the data ﬁrm’s internal documentation for

relevant technical issues. In both cases, recommendations are based on a history of the conversation.

For example, the correct response when a customer says “I can’t track my employee’s hours during

business trips” depends on what version of the data ﬁrm’s software the customer uses. Suppose

the customer has previously mentioned that they are using the premium version. In that case,

they should have access to remote mobile device timekeeping, meaning that the support agents

need to diagnose and resolve a technical issue preventing the software from working. If, however,

the customer stated that they are using the standard version, then the correct solution is for the

customer to upgrade to the premium version in order to access this feature.

Figure 1 illustrates sample output. In the chat window (Panel A), Alex, the customer, describes

their problem to the agent. Here, the AI assistant generates two suggested responses (Panel B). In

this example, it has learned that phrases like “I can deﬁnitely assist you with this!” and “Happy to

help you get this ﬁxed asap” are associated with positive outcomes. Panel A of Appendix Figure

A.1 shows an example of a technical recommendation from the AI system, which occurs when it

recommends a link to the data ﬁrm’s internal technical documentation.

Importantly, the AI system we study is designed to augment, rather than replace, human agents.

The output is shown only to the agent, who has full discretion over whether to incorporate (fully or

partially) the AI suggestions. This reduces the potential for output that is oﬀ-topic or incorrect to

make its way into customer conversations. Furthermore, the system does not provide suggestions

when it has insuﬃcient training data for that situation (this occurs in a large minority of cases). In

these situations, the agent must respond on their own.

Finally, we observe that the AI model is trained on human-generated data in a setting where

there is high variability in the abilities of individual agents. As a result, when the model identiﬁes

patterns that distinguish successful from unsuccessful calls, it is implicitly learning the diﬀerences

that characterize high- versus low-skill workers. For example, top-performing agents are often more

eﬀective at diagnosing the underlying technical issue given a customer’s problem description: in an

internal study, our AI ﬁrm found that top performers start researching a solution twice as quickly

as the average workers. An AI system, with access to many examples of diagnostic questions and

eventual solutions, may be able to encode some of the “best practices” top-performing agents use.

This suggests that an AI system may be able to more eﬀectively share knowledge across workers

both because it may capture tacit knowledge that was previously diﬃcult for managers to articulate

and because it can provide more real-time recommendations than a busy manager.

Indeed, AI

recommendations can be thought of as expanding the marginal productivity of high-skill workers

For more on context tracking, see, for instance, Dunn et al. (2021).

By necessity, managers can only base their feedback on a small subset of the hundreds of conversations an agent

conducts. And because managers are often pressed for time and may lack training, they may focus on a single metric

(“you need to solve problems faster”) rather than identifying strategies for how an agent could better approach a

problem (“you need to ask more questions at the beginning to diagnose the issue better.”) This type of coaching is

ineﬀective and often counterproductive for employee engagement (Berg et al., 2018).

by encoding their conversational patterns and disseminating them to other workers. In our setting,

high-skill workers are not compensated for these contributions.

3 Deployment, Data, and Empirical Strategy

3.1 AI Model Deployment

The AI assistant we study was gradually rolled out at the agent level after an initial seven-week

randomized pilot featuring 50 agents.

The deployment was largely uniform across both the data

ﬁrm’s own customer service agents, as well as its outsourced agents. Figure 2 documents the

progression of deployment among agents who are eventually treated. The bulk of the adoption

occurs between November 2020 and February 2021.

3.2 Summary Statistics

Table 1 provides details on sample characteristics, divided into three groups: agents who are never

given access to the AI tool during our sample period (“never treated”), pre-AI observations for

those who are eventually given access (“treated, pre”), and post-AI observations (“treated, post”).

In total, we observe the conversation text and outcomes associated with 3 million chats by 5,179

agents. Within this, we observe 1.2 million chats by 1,636 agents in the post-AI period. Most agents

in our sample, 83 percent, are located outside the United States, primarily in the Philippines. For

each agent, we observe their assigned manager, tenure, geographic location and ﬁrm information.

To examine the impacts of this deployment, we construct several key variables, all aggregated

to the agent-month level, which is our primary level of analysis.

Our primary measure of productivity is resolutions per hour (RPH), the number of chats that

a worker is able to successfully resolve per hour. We consider this measure to be the most eﬀective

summary of a worker’s productivity at the ﬁrm. An agent’s RPH is determined by several factors:

the average time it takes an agent to complete a conversation, the number of conversations they

are able to handle per hour (accounting for multiple simultaneous conversations), and the share of

conversations that are successfully resolved. We measure these individually as, respectively, average

handle time (AHT), chats per hour (CPH), and resolution rate (RR). In addition, we also observe

a measure of customer satisfaction through an agent’s net promoter score (NPS), which is collected

by the ﬁrm from post-call customer surveys.

Data from the RCT is included as part of our primary analysis but is not analyzed separately because of its small

sample size.

We observe these measures for diﬀerent numbers of agents. In particular, we are able to re-

construct measures of average handle time and chats per hour from our chat level data. Therefore

we observe AHT and CPH measures for all agents in our sample. Measures that involve an un-

derstanding of call quality—resolution rates, and customer satisfaction—are provided at the agent

month level by our data ﬁrm. Because our data outsources most of its customer service functions,

it does not have direct control over this information, which is kept by subcontracted ﬁrms. As a

result, we observe resolution rates and net promoter scores for a subset of agents in our data. This,

in turn, means that we only observe our omnibus productivity measure—resolutions per hour—for

this smaller subset.

Figure 3 plots the raw distributions of our outcomes for each of the never, pre-, and post-

treatment subgroups. Several of our main results are readily visible in these raw data. In Panels

A through D, we see that post-treatment agents have higher outcomes than both never-treated

agents and pre-treatment agents. In Panel E, we see no discernible diﬀerences in surveyed customer

satisfaction among pre- and post-AI groups.

Focusing on our main productivity measure, Panel A of Figure 3 and Table 1 show that never-

treated agents resolve an average of 1.7 chats per hour whereas post-treatment agents resolve 2.5

chats per hour. Some of this diﬀerence may be due to diﬀerences in the initial section: treated

agents have higher resolutions per hour prior to AI model deployment (2.0 chats) relative to never

treated agents (1.7). This same pattern appears for other outcomes: chats per hour (Panel C) and

resolution rates (Panel D). Panel B illustrates the clearest pattern with average handle times: both

pre-treatment and never-treated agents had a similar distribution of average handle times, centered

at 40 minutes but post-treatment agents have a lower average handle time of 35 minutes.

3.3 Empirical Strategy

We isolate the causal impact of access to AI recommendations using a standard diﬀerence-in-

diﬀerences regression model:

“ δ

` α

` β

` γX

` 

(1)

Our outcome variables y

capture average handle times, resolution rates, resolutions per hour, and

customer satisfaction scores for agent i in year-month t. Because workers often work only for a

portion of the year, we include only year-month observations for an agent who is actively employed

(e.g. assigned to chats). Our main variable of interest is AI

, an indicator equal to one if agent

i has access to AI recommendations at time t. All regressions include year-month ﬁxed eﬀects δ

to control for common, time-varying factors such as tax season or business quarter end. In our

preferred speciﬁcation, we also include controls for time-invariant agent-level ﬁxed eﬀects α

and

time-varying agent tenure. Standard errors are clustered at the agent or agent-location level.

A rapidly growing literature has shown that two-way ﬁxed eﬀects regressions deliver consistent

estimates only with strong assumptions about the homogeneity of treatment eﬀects, and may be

biased when treatment eﬀects vary over time or by adoption cohort (Cengiz et al., 2019; de Chaise-

martin and D’Haultfœuille, 2020; Sun and Abraham, 2021; Goodman-Bacon, 2021; Callaway and

Sant’Anna, 2021; Borusyak et al., 2022). For instance, workers may take time to adjust to using the

AI system, in which case its impact in the ﬁrst month may be smaller. Alternatively, the onboarding

of later cohorts of agents may be smoother, so that their treatment eﬀects may be larger.

We study the dynamics of treatment eﬀects using the interaction weighted (IW) estimator pro-

posed in Sun and Abraham (2021). Sun and Abraham (2021) show that this estimator is consistent

assuming parallel trends, no anticipatory behavior, and cohort-speciﬁc treatment eﬀects that follow

the same dynamic proﬁle.

In the Appendix, we show that both our main diﬀerences-in-diﬀerences

and event study estimates are similar using robust estimators introduced in de Chaisemartin and

D’Haultfœuille (2020), Borusyak et al. (2022), Callaway and Sant’Anna (2021), and Sun and Abra-

ham (2021), as well as using traditional two-way ﬁxed eﬀects OLS.

4 Results

4.1 Productivity

Table 2 examines the impact of AI model deployment on our primary measure of productivity,

resolutions per hour, using a standard two-way ﬁxed eﬀects model. In Column 1, we show that,

controlling for time and location ﬁxed eﬀects, access to AI recommendations increases the number

of resolutions per hour by 0.47 chats, up 22.2 percent from an average of 2.12. In Column 2,

we include individual agent ﬁxed eﬀects to account for potential diﬀerences between treated and

untreated agents. In Column 3, we include further controls for time-varying agent tenure. As we

add controls, our eﬀects fall slightly so that, with agent and tenure ﬁxed eﬀects, we ﬁnd that the

deployment of AI increases RPH by 0.30 calls or 13.8 percent. Columns 4 though 6 produce these

same patterns and magnitudes for the log of RPH.

Appendix Table A.1 ﬁnds similar results using alternative diﬀerence-in-diﬀerence estimators in-

troduced in Callaway and Sant’Anna (2021), Borusyak et al. (2022), de Chaisemartin and D’Haultfœuille

This last assumption means that treatment eﬀects are allowed to vary over event-time and that average treatment

eﬀects can vary across adoption-cohorts (because even if they follow the same event-time proﬁle, we observe diﬀerent

cohorts for diﬀerent periods of event-time).

(2020), and Sun and Abraham (2021). Unlike traditional OLS, these estimators avoid comparing

between newly treated and already treated units. In most cases, we ﬁnd slightly larger eﬀects of AI

assistance using these alternatives.

Figure 4 shows the accompanying IW event study estimates of Sun and Abraham (2021) for the

impact of AI assistance on resolutions per hour, in levels and logs. For both outcomes, we ﬁnd a

substantial and immediate increase in productivity in the ﬁrst month of deployment. This eﬀect

grows slightly in the second month and remains stable and persistent up to the end of our sample.

Appendix Figure A.2 shows that this pattern can be seen using alternative event study estimators as

well: Callaway and Sant’Anna (2021), Borusyak et al. (2022), de Chaisemartin and D’Haultfœuille

(2020), and traditional two-way ﬁxed eﬀects.

In Table 3, we report additional results using our preferred speciﬁcation with year-month, agent,

and agent tenure ﬁxed eﬀects. Column 1 documents a 3.8 minute decrease in the average duration

of customer chats, a 9 percent decline from the baseline mean (shorter handle times are generally

considered better). Next, Column 2 indicates a 0.37 unit increase in the number of chats that an

agent can handle per hour. Relative to a baseline mean of 2.6, this represents a roughly 14 percent

increase. Unlike average handle time, chats per hour accounts for the possibility that agents may

handle multiple chats simultaneously. The fact that we ﬁnd a stronger eﬀect on this outcome

suggests that AI enables agents to both speed up chats and to multitask more eﬀectively.

Column 3 of Table 3 indicates a small 1.3 percentage point increase in chat resolution rates,

signiﬁcant at the 10 percent level. This eﬀect is economically modest, given a high baseline res-

olution rate of 82 percent; we interpret this as evidence that improvements in chat handling do

not come at the expense of problem solving on average. Finally, Column 4 ﬁnds no economically

signiﬁcant change in customer satisfaction, as measured by net promoter scores: the coeﬃcient is

-0.13 percentage points and the mean is 79.6 percent. Columns 5 through 8 report these results for

logged outcomes. Going forward we will report our estimates in logs, for ease of interpretation.

Figure 5 presents the accompanying event studies for additional outcomes. We see immediate

impacts on average handle time (Panel A) and chats per hour (Panel B), and relatively ﬂat patterns

for resolution rate (Panel C) and customer satisfaction (Panel D). We therefore interpret these ﬁnd-

ings as saying that, on average, AI assistance increases productivity without negatively impacting

resolution rates and surveyed customer satisfaction.

4.2 Impacts by Agent Skill and Tenure

As discussed earlier, generative AI tools may have a diﬀerent pattern of productivity consequences

relative to earlier waves of technology adoption.

In Panel A of Figure 6, we consider how our estimated productivity eﬀects diﬀer by an agent’s

pre-AI productivity. We divide agents into quintiles using a skill index based on their average call

eﬃciency, resolution rate, and surveyed customer satisfaction in the quarter prior to the adoption

of the AI system. These skill quintiles are deﬁned within a ﬁrm-month. In Panel A, we show that

the productivity impact of AI assistance is most pronounced for workers in the lowest skill quintile

(leftmost side), who see a 35 percent increase in resolutions per hour. In contrast, AI assistance

does not lead to any productivity increase for the most skilled workers (rightmost side).

In Figure 7 we show that less-skilled agents consistently see the largest gains across our other

outcomes. For the highest-skilled workers, we ﬁnd mixed results: a zero eﬀect on average handle time

(Panel A), a positive eﬀect for calls per hour (Panel B), and, interestingly, small but statistically

signiﬁcant decreases in resolution rates and customer satisfaction (Panels C and D). These ﬁndings

suggest that while lower-skill workers improve from having access to AI recommendations, they may

distract the highest-skilled workers, who are already doing their jobs eﬀectively.

Next, in Panel B of Figure 6, repeat this analysis for tenure by dividing agents into ﬁve groups

based on their tenure at the time that the AI model is introduced. Some agents have less than one

month of tenure when they receive AI access, while others have over a year of experience. We see a

clear, monotonic pattern in which the least experienced agents see the greatest gains in resolutions

per hour.

In Figure 8, we show that these patterns persist for other outcomes: AI assistance generates

larger gains in call handling and quality for the newest workers. For more experienced workers,

we ﬁnd positive eﬀects for average handle time and calls per hour (Panels A and B), zero eﬀects

on resolution rate (Panel C), and a small but statistically signiﬁcant negative eﬀect for customer

satisfaction (Panel D).

In Appendix Figures A.3 and A.4, we show that our skill-heterogeneity results are robust to

controlling for agent tenure, and vice versa. This suggests the AI system has distinct impacts both

by worker experience and ability.

4.3 Moving Down the Experience Curve

To further explore how AI assistance impacts newer workers, we examine how worker productivity

evolves on the job.

In Figure 9, we plot productivity variables by agent tenure for three distinct

groups: agents who never receive access to the AI model (“never treated”), those who have access

We avoid the term “learning curve” because we cannot distinguish if workers are learning or merely following

recommendations.

from the time they join the ﬁrm (“always treated”), and those who receive access in their ﬁfth month

with the ﬁrm (“treated 5 mo.”).

We see that all agents begin with around 2.0 resolutions per hour. Workers who are never

treated slowly improve their productivity with experience, reaching approximately 2.5 resolutions

8 to 10 months later. In contrast, workers who begin with access to AI assistance rapidly increase

their productivity to 2.5 resolutions only two months in. Furthermore, they continue to improve

at a rapid rate until they are resolving more than 3 calls an hour after ﬁve months of tenure.

Comparing just these two groups suggests that access to AI recommendations helps workers move

more quickly down the experience curve.

The ﬁnal line in Panel A tracks workers who begin their tenure with the ﬁrm without access to

AI assistance, but who receive access after ﬁve months on the job. These workers improve slowly in

the same way as never-treated workers for the ﬁrst ﬁve months of their tenure. Starting in month

ﬁve, however, these workers gain access and we see their productivity rapidly increase following

the same trajectory as the always-treated agents. In Appendix Figure A.5, we plot these curves

for other outcomes. We see clear evidence that the experience curve for always-treated agents is

steeper for handle time, chats per hour, and resolution rates (Panels A through C). Panel D follows

a similar but noisier pattern for customer satisfaction.

Taken together, these results indicate that access to AI helps new agents move more quickly

down the experience curve. Across many of the outcomes in Figure 9, agents with two months of

tenure and access to AI assistance perform as well as or better than agents with more than six

months of tenure who do not have access.

4.4 Adherence to AI recommendations

We emphasize that the AI tool we study is meant to augment— rather than replace—human agents.

The system makes suggestions, but agents may elect to ignore these suggestions entirely. In our

results above, we estimate intent-to-treat eﬀects, that is how access to the AI tool impacts outcomes

regardless of how frequently agents follow its recommendations. In this section, we examine how

closely agents adhere to AI recommendations, and document the association between adherence and

returns to adoption.

We measure “adherence” starting at the chat level, by calculating the share of AI recommen-

dations that each agent follows. Agents are coded as having adhered to a recommendation if they

either click to copy the suggested AI text or if they self-input something very similar. We then

aggregate this to the agent-month level.

Our sample ends here because we have very few observations more than ﬁve months after treatment.

Panel A of Figure 10 plots the distribution of average agent-month-level adherence for our

post-AI sample, weighted by the log of the number of AI recommendations given to that agent in

that month. The average adherence rate is 38 percent with an interquartile range of 23 percent

to 50 percent: agents frequently ignore recommendations. In fact, the share of recommendations

followed is similar to the share of other publicly reported numbers for generative AI tools; a study

of GitHub Copilot reports that individual developers use 27 to 46 percent of code recommendations

(Zhao, 2023). Such behavior could be optimal: the AI model may make incorrect or non-sensical

suggestions.

Panel B of Figure 10 shows that returns to AI model deployment tend to be higher among

agents who follow a greater share of recommendations. We divide agents into quintiles based on the

percent of AI recommendations they follow in the ﬁrst month of deployment and separately estimate

the impact of access to the AI model for each group. These estimates control for year-month and

agent ﬁxed eﬀects as in Column 5 of Table 2.

For agents in the lowest quintile of adherence, we still see a 10 percent gain in productivity, but

for agents in the highest quintile, the estimated impact is much higher, close to 25 percent. Appendix

Figure A.6 shows the results for our other four outcome measures. The positive correlation between

adherence and returns holds most strongly for average handle time (Panel A) and calls per hour

(Panel B), and more noisily for resolution rate (Panel C) and customer satisfaction (Panel D).

We note that this relationship could be driven by a variety of factors: the treatment eﬀect

of adherence (agents have greater productivity because they listen to recommendations); selection

(agents who choose to adhere are more productive for other reasons); or selection on gains (agents

who follow recommendations are those with the greatest returns).

Finally, Figure 11 plots how adherence to AI recommendations evolves for workers of diﬀerent

experience or skill. In Panel A, we see that more senior workers are initially less likely to follow AI

recommendations: 30 percent for those with over a year of tenure compared to 37 percent for those

with under three months of tenure.

Over time, however, all workers increase their adherence, with

more senior workers doing so faster so that the groups converge ﬁve months after tenure. In Panel

B, we see a similar but more muted pattern for worker skill: lower-skill workers are initially more

likely to comply, but the highest-skilled workers converge over time.

These ﬁndings are consistent with both the possibility that workers who are initially more

skeptical may come to see the value of AI recommendations over time or that workers who strongly

dislike working with the AI system may exit at higher rates. Other studies on the use of AI tools

have found diﬀerences in the desire to follow AI recommendations; for instance, in a study of a

Agents below three months of experience are in their “onboarding” phase.

writing suggestion tool, Singh et al. (2022) ﬁnds that four of the 23 study participants refused to

engage with AI suggestions.

4.5 Textual Evidence

Our evidence so far suggests that access to AI suggestions improves productivity and, for the lower-

skilled and less-experienced agents, increases conversation quality as measured by resolution rates

and surveyed customer satisfaction.

Next, we consider why AI may have these impacts. This section provides evidence from prelim-

inary analysis of the textual content of chat conversations. Our goal is to understand whether and

how AI recommendations change the way agents communicate.

In particular, we are interested in AI model’s ability to encode the potentially tacit knowledge of

high performers: does AI assistance lead lower-skill workers to communicate more like higher-skill

workers? Because tacit knowledge is, by deﬁnition, not something that can be codiﬁed as a set of

rules, we examine the overall textual similarity of conversations rather than looking for the presence

of speciﬁc formulaic phrases.

We begin by constructing textual embeddings of agent-customer conversations. These embed-

dings allow us to represent their semantic and stylistic content as a vector so that we can compare

one conversation to another using cosine similarity. Cosine similarity is a widely-used metric for

measuring the similarity of two embeddings. It calculates the cosine of the angle between two n-

dimensional vectors, where values close to 1 indicate similarity (Koroteev, 2021). We form our text

embeddings using all-MiniLM-L6-v2, an LLM that is speciﬁcally intended to capture and cluster

semantic information in order to assess similarity (Hugging Face, 2023). We then compare how an

agent’s conversations change over time (e.g. within-person similarity over time), as well as how high-

and low-skill agents’ conversations compare with each other over time (between-person similarity

at a given point in time).

4.5.1 Within-worker changes in communication

To examine how AI changes agent conversations with customers, we begin by comparing an individ-

ual agent’s text pre- and post-AI model deployment. We take a given agent’s corpus of text from

an eight-week window before deployment and compare it with text from the same-sized window af-

terward. We exclude the three weeks around deployment to account for disruptions due to training

and individual adjustment to the AI model. We exclude messages from the customer, and focus

only on agent-generated language. Then, for each individual agent, we plot textual dissimilarity be-

tween pre- and post-AI conversations as one minus the cosine similarity. The within-person textual

diﬀerence in our data is roughly 0.3, indicating that, on average, agents continue to communicate

similarly after the deployment of the AI model. For context, the sentences “Can you help me with

this logging in?” and “Why is my login not working?” have a cosine similarity of 0.68.

We examine how language changes pre- and post-AI for workers who are initially high- and low-

skill, where high-skill workers are deﬁned as those in the top quintile of the skill index distribution,

while low-skill workers are those in the bottom quintile.

If the AI is able to embody and disseminate some of the tacit “best practices” of high-skill

workers, then we would expect low-skill workers to experience a greater shift in communication

patterns following access to AI assistance (high-skill workers would change less since the AI model is

suggesting practices they have already adopted). In Panel A of Figure 12, we ﬁnd evidence consistent

with this hypothesis: initially lower-skill agents shift their language more after AI model deployment,

relative to initially higher-skill workers. The scores we plot include controls for conversation timing

to account for seasonal changes in topics such as tax season or payroll cycles. These results also

control for agent tenure to account for the possibility that younger workers’ language may evolve

more quickly independent of access to the AI model.

If AI assistance merely leads workers to type the same things but faster, then we would not

expect this diﬀerential change. And because chats are randomly allocated to agents, we would not

also expect the pattern we document to be driven by diﬀerential changes in conversation topics

between high and low-skill workers.

4.5.2 Across worker comparisons

We next explore how lower-skill agents’ language choices change with AI assistance. In Panel B of

Figure 12, we provide suggestive evidence that AI assistance leads lower-skill agents to communicate

more like high-skill agents. In particular, we plot the cosine similarity between high- and low-skill

agents at speciﬁc moments in calendar time, separately for workers with (blue dots) and without

(red diamonds) access to AI assistance. Among agents without access, we deﬁne high- and low-skill

agents as those who are in the top or bottom quintile of our skill index for that month. Among

agents with access, we deﬁne high- and low-skill agents based on whether they are in the top or

bottom quintile of skill at the time of deployment.

Focusing on the blue dots, we see that the average textual similarity between high- and low-

productivity workers is 0.55. This ﬁgure is lower than our average within-person text similarity

(0.73), which makes sense given that these are across-person comparisons. Textual similarity is

stable over time, suggesting that high- and low-skill workers are not trending diﬀerently in the

absence of the AI assistant. Turning to the red diamonds, post-AI, the textual similarity between

high- and low-skill workers increases.

Combined with our results from Panel A, this suggests that low-skill workers are converging

toward high-skill workers, rather than the opposite. The magnitude of this change—moving from

0.55 similarity to 0.61 similarity—may appear small, but given that the average within-person

similarity for high-skill workers is 0.73, this result suggests that AI assistance is associated with a

substantial narrowing of language gaps.

4.6 Eﬀects on the Experience and Organization of Work

Access to AI assistance may impact workers and the ﬁrm organization as a whole through changes in

work experience, turnover, and task allocation. In this section, we examine some of these outcomes.

4.6.1 Sentiment

Customers often vent their frustrations on anonymous service agents and, in our data, we see regular

instances of swearing, verbal abuse, and “yelling” (typing in all caps). A key part of agents’ jobs is

to absorb customer frustrations while restraining one’s own emotional reaction (Hochschild, 2019).

The stress associated with this type of emotional labor is often cited as a key cause of burnout and

attrition among customer service workers (Lee, 2015). It is unclear what impact AI assistants may

have on the tenor of conversations: AI recommendations may help the agent more eﬀectively set the

customer’s expectations or resolve their problem, but customers may react poorly if AI-suggested

language feels “corporate” or insincere.

To assess this, we attempt to capture the aﬀective nature of both agent and customer text, using

sentiment analysis (Mejova, 2009). For this analysis, we use SiEBERT, an LLM that is ﬁne-tuned

for sentiment analysis using a variety of datasets, including product reviews and tweets (Hartmann

et al., 2023). Sentiment is measured on a scale from ´1 to 1. We compute sentiment scores for

the agent and customer text of each chat separately, and then aggregate across all chats for each

agent-year-month.

Panels A and B of Figure 13 describe the distributions of average customer and visitor sentiment

scores. On average, customer sentiments in our data are mildly positive and normally distributed

around a mean of 0.14 except for a mass of very positive and very negative scores. As one might

expect, customer service agents are trained to be extremely positive and professional so agent

sentiment scores are almost always highly positive, with a mean of 0.89.

Panels C and D consider how sentiment scores respond to the introduction of the AI assistant.

In Panel C, we see an immediate and persistent improvement in customer sentiment. This eﬀect is

economically large: according to Column 1 of Table 4, access to AI improves the mean customer

sentiments (averaged over an agent-month) by 0.18 points, equivalent to half of a standard deviation.

We see a much smaller impact on agent scores. In Panel D, we see no detectable eﬀect for agent

sentiment, which unsurprising because agent’s expressed sentiment is already very high. Column 2

of Table 4 indicates that agent sentiments increase by only 0.02 points or about one percent of a

standard deviation.

Appendix Figure A.7 examines heterogeneity sentiment impacts, by agent tenure and skill.

Consistent with our main results, we ﬁnd that most sentiment improvements occur for workers with

lower tenure and skills at the time of deployment. In contrast with our productivity results, the

eﬀects on customer sentiments are broader and no longer necessarily monotonic in tenure or skill.

We ﬁnd the largest eﬀects for workers with 3-6 months of tenure at AI model deployment, and we

ﬁnd a smaller impact of AI on sentiments only for agents in the highest quintile skill. These results

suggest that AI recommendations, which were explicitly designed to prioritize more empathetic

responses, may improve agents’ social skills and have a positive emotional impact on customers.

4.6.2 Attrition

Increases in worker-level productivity do not always lead workers to be happier with their jobs. If

workers become more productive but dislike being managed by an AI assistant, this may lead to

greater turnover. If, on the other hand, AI assistance reduces stress, then workers may be more

likely to stay.

Here, we examine how the deployment of the AI tool impacts worker attrition. For this analysis,

we compare agents who are never treated with treated agents after treatment. In particular, we drop

observations for treated agents prior to treatment because this group experiences no attrition in

this period by construction (they must survive to be treated in the future). Our analysis compares

the trajectories of agents with the same tenure but diﬀerent access to AI assistance, controlling for

location and time ﬁxed eﬀects.

Column 1 of Table 5 reports the main eﬀect of the AI assistant on attrition. We ﬁnd that,

on average, the likelihood that a worker leaves in the current month goes down by 8.6 percentage

points. Figure 14 considers how these patterns depend on an agent’s tenure or skill at the time the AI

system is introduced. We ﬁnd the strongest reductions in attrition among newer agents, those with

less than 6 months of experience. The magnitude of this coeﬃcient, around 10 percentage points,

is large given baseline attrition rates for newer workers of about 25 percent. We caution that this

eﬀect may be overstated if access to the AI tool is more likely to be given to agents whom the ﬁrm

deems more likely to stay. Without agent-ﬁxed eﬀects in our speciﬁcation, we cannot account for

baseline diﬀerences been treated and untreated agents. In Panel B, we examine attrition by worker

skill. Here, we ﬁnd a signiﬁcant decrease in attrition for all skill groups, but no systematic gradient.

4.6.3 Vertical and Horizontal Workﬂow

Changes in individual worker-level productivity may have broader implications for organizational

workﬂows (Garicano, 2000; Athey et al., 1994; Athey and Stern, 1998). In most ﬁrms, customer

support agents are organized both vertically and horizontally. Vertically, front-line agents try to

resolve customer problems but can seek the help of supervisors when they are unsure of how to

proceed. Customers in our data will sometimes attempt to escalate a conversation by asking to

speak to a manager. This type of request generally occurs when the customer feels the current

agent is not equipped to address their problem or becomes frustrated. Our data ﬁrm, like most

other contact centers, employs designated “escalation agents” to deal with these requests.

Horizontally, agents often represent speciﬁc departments that handle speciﬁc tasks. For example,

some may specialize in technical software issues while others specialize in account management

issues. A customer with a technical issue requiring that their account be upgraded to a premium

product would likely be transferred to a diﬀerent department.

In Figure 15 and Table 5, we consider the impact of AI model deployment on vertical and

horizontal assistance. We do not have a direct measure of whether a call is actually escalated to the

manager. Instead, we examine chat-level text data to examine requests for escalations: instances

in which a customer requests to speak to a manager or supervisor. In Column 1 of Table 5, we

ﬁnd that AI assistance generates an almost 25 percent decline in customer requests to speak to a

manager. The accompanying event study is presented in Panel A of Figure 15: we see that declines

in requests are persistent and grow over time.

In contrast, we ﬁnd mixed-to-positive evidence on the impact of access to AI on the number

of horizontal transfers to other agents. In Column 2 of Table 5, we see a positively-signed but

statistically insigniﬁcant impact on transfers. Panel B of Figure 15 suggests that transfers initially

increase but then slowly decline. Upon reviewing the text of these conversations, we see that most

transfers appear to redirect customers to a more appropriate department. Many of these transfers

occur early in the call, suggesting that transfers reﬂect a matching between a customer’s problem

and an agent’s specialty.

Finally, Appendix Figure A.8 considers how these patterns change by worker skill and tenure.

Panels A and C focus on requests to speak to a manager. We ﬁnd that these requests are particularly

reduced for agents who were less skilled or less experienced at the time of AI adoption. Panels B

and D consider call transfers. Here we ﬁnd mixed results, with positive impacts for some skill and

tenure groups and negative impacts for others, but no clear pattern.

5 Conclusion

Progress in machine learning opens up a broad set of economic possibilities. Our paper provides

the ﬁrst empirical evidence on the eﬀects of a generative AI tool in a real-world workplace. In

our setting, we ﬁnd that access to AI-generated recommendations increases worker productivity,

improves customer sentiment, and is associated with reductions in employee turnover.

We hypothesize the part of the eﬀect we document is driven by the AI system’s ability to

embody the best practices of high-skill workers in our ﬁrm. These practices may have previously

been diﬃcult to disseminate because they involve tacit knowledge. Consistent with this, we see that

AI assistance leads to substantial improvements in problem resolution and customer satisfaction for

newer- and less-skilled workers, but does not help the highest-skilled or most-experienced workers

on these measures. Analyzing the text of agent conversations, we ﬁnd suggestive evidence that AI

recommendations lead low-skill workers to communicate more like high-skill workers.

Our ﬁndings, and their limitations, point to a variety of directions for future research.

As a potential general-purpose technology, generative AI can and will be deployed in a variety of

ways, and the eﬀects we ﬁnd may not generalize across all ﬁrms and production processes (Eloundou

et al., 2023). For example, our setting has a relatively stable product and set of technical support

questions. In areas where the product or environment is changing rapidly, the relative value of AI

recommendations—trained on historical data—may be diﬀerent.

Our results do not capture potential longer-term impacts on skill demand, job design, wages, or

customer demand. For example, more eﬀective technical support could accelerate the trend towards

contact center agents taking on more complex customer responsibilities, increasing aggregate de-

mand even if agents become more productive (Berg et al., 2018; Korinek, 2022). And over the longer

term, these tools can uncover patterns and insights that may not be documented in formal channels,

changing the way workers are managed or how knowledge is shared within an organization.

Finally, our ﬁndings raise questions about whether and how workers should be compensated for

the data that they provide to AI systems. High-skill workers, in particular, play an important role

in model development but see smaller direct beneﬁts in terms of improving their own productivity.

Given the early stage of generative AI, these and other questions deserve further scrutiny.

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Figure 1: Sample AI Output

A. Sample Customer Issue

B. Sample AI-generated Suggested Response

Notes: This ﬁgure shows sample suggestions of output generated by the AI model. The suggested responses are

only visible to the agent. Workers can choose to ignore, accept or somewhat incorporate the AI suggestions into their

response to the customer.

Figure 2: Deployment Timeline

0 20 40 60 80 100

Share of Agents Onboarded

RCT

October 2020

November 2020

December 2020

January 2021

February 2021

March 2021

April 2021

May 2021

Month Onboarded

Notes: This ﬁgure shows the share of agents deployed onto the AI system over the study period. Agents are deployed

onto the AI system after a training session. The ﬁrm ran a small randomized control trial in August and September

of 2020. All data are from the ﬁrm’s internal software systems.

Figure 3: Raw Productivity Distributions, by AI Treatment

A. Resolutions Per Hour

0 .2 .4 .6

Density

0 1 2 3 4 5

Resolutions Per Hour

Pre AI Post AI

Never AI

B. Average Handle Time C. Chats Per Hour

0 .01 .02 .03 .04 .05

Density

0 20 40 60 80

Average Handle Time

Pre AI Post AI

Never AI

0 .2 .4 .6

Density

1 2 3 4 5 6

Chats Per Hour

Pre AI Post AI

Never AI

D. Resolution Rate E. Customer Satisfaction (NPS)

0 1 2 3 4

Density

0 .2 .4 .6 .8 1

Share Resolved

Pre AI Post AI

Never AI

0 .01 .02 .03 .04

Density

0 20 40 60 80 100

Customer Satisfaction

Pre AI Post AI

Never AI

Notes: This ﬁgure shows the distribution various outcome measures. We split this sample into agent-month observa-

tions for agents who eventually receive access to the AI system before deployment (“Pre AI”), after deployment (“Post

AI”), and for agent-months associated with agents who never receive access (“Never AI”). Our primary productivity

measure is “resolutions per hour,” the number of customer issues the agent is able to successfully resolve per hour.

We also provide descriptives for “average handle time,” the average length of time an agent takes to ﬁnish a chat;

“calls per hour,” the number of calls completed per hour incorporating multitasking; “resolution rate,” the share of

conversations that the agent is able to resolve successfully; and “net promoter score” (NPS), which are calculated by

randomly surveying customers after a chat and calculating the percentage of customers who would recommend an

agent minus the percentage who would not. All data come from the ﬁrm’s software systems.

Figure 4: Event Studies, Resolutions Per Hour

A. Resolutions Per Hour

-.2 0 .2 .4 .6 .8

Resolutions Per Hour

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

B. Log(Resolutions Per Hour)

-.1 0 .1 .2 .3 .4

Log(Resolutions Per Hour)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

Notes: These ﬁgures plot the coeﬃcients and 95 percent conﬁdence interval from event study regressions of AI model

deployment using the Sun and Abraham (2021) interaction weighted estimator. See text for additional details. Panel

A plots the resolutions per hour and Panel B plots the natural log of the measure. All speciﬁcations include agent

and chat year-month, location, agent tenure and company ﬁxed eﬀects. Robust standard errors are clustered at the

agent level.

Figure 5: Event Studies, Additional Outcomes

A. Log(Average Handle Time) B. Log(Chats Per Hour)

-.2 -.1 0 .1

Log(Average Handle Time)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

-.1 0 .1 .2 .3

Log(Chats Per Hour)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

C. Log(Resolution Rate) D. Log(Customer Satisfaction (NPS))

-.05 0 .05 .1 .15

Log(Share Resolved)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

-.1 -.05 0 .05 .1

Log(Customer Satisfaction)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

Notes: These ﬁgures plot the coeﬃcients and 95 percent conﬁdence interval from event study regressions of AI

model deployment using the Sun and Abraham (2021) interaction weighted estimator. See text for additional details.

Panel A plots the average handle time or the average duration of each technical support chat. Panel B plots the

number of chats an agent completes per hour, incorporating multitasking. Panel C plots the resolution rate, the

share of chats successfully resolved, and Panel D plots net promoter score, which is an average of surveyed customer

satisfaction. All speciﬁcations include agent and chat year-month, location, agent tenure and company ﬁxed eﬀects.

Robust standard errors are clustered at the agent level.

Figure 6: Heterogeneity of AI Impact, by Skill and Tenure

A. Impact of AI on Resolutions Per Hour, by Skill at Deployment

0 .1 .2 .3 .4

Log(Resolutions Per Hour)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

B. Impact of AI on Resolutions Per Hour, by Tenure at Deployment

-.1 0 .1 .2 .3 .4

Log(Resolutions Per Hour)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

Notes: These ﬁgures plot the impacts of AI model deployment on log(resolutions per hour) for diﬀerent groups of

agents. Agent skill is calculated as the agent’s trailing three month average of performance on average handle time,

call resolution, and customer satisfaction, the three metrics our ﬁrm uses to assess agent performance. Within each

month and company, agents are grouped into quintiles, with the most productive agents in quintile 5 and the least

productive in quintile 1. Pre-AI worker tenure is the number of months an agent has been employed when they

receive access to AI recommendations. All speciﬁcations include agent and chat year-month, location, and company

ﬁxed eﬀects and standard errors are clustered at the agent level.

Figure 7: Heterogeneity of AI Impact by pre-AI Worker Skill, Additional

Outcomes

A. Log(Average Handle Time) B. Log(Chats Per Hour)

-.15 -.1 -.05 0 .05

Log(Average Handle Time)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

.05 .1 .15 .2 .25

Log(Chats Per Hour)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

C. Log(Resolution Rate) D. Log(Customer Satisfaction (NPS))

-.1 -.05 0 .05 .1 .15

Log(Share Resolved)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

-.05 0 .05 .1

Log(Customer Satisfaction)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

Notes: These ﬁgures plot the impacts of AI model deployment on four measures of productivity and performance, by

pre-deployment worker skill. Agent skill is calculated as the agent’s trailing three month average of performance on

average handle time, call resolution, and customer satisfaction, the three metrics our ﬁrm uses for agent performance.

Within each month and company, agents are grouped into quintiles, with the most productive agents within each ﬁrm

in quintile 5 and the least productive in quintile 1. Panel A plots the average handle time or the average duration

of each technical support chat. Panel B graphs chats per hour, or the number of calls an agent can handle per hour.

Panel C plots the resolution rate, and Panel D plots net promoter score, an average of surveyed customer satisfaction.

All speciﬁcations include agent and chat year-month, location, and company ﬁxed eﬀects and standard errors are

clustered at the agent level.

Figure 8: Heterogeneity of AI Impact by pre-AI Worker Tenure, Additional

Outcomes

A. Log(Average Handle Time) B. Log(Chats Per Hour)

-.2 -.15 -.1 -.05 0

Log(Average Handle Time)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

0 .1 .2 .3 .4

Log(Chats Per Hour)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

C. Log(Resolution Rate) D. Log(Customer Satisfaction (NPS))

-.05 0 .05 .1 .15

Log(Share Resolved)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

-.05 0 .05 .1 .15

Customer Satisfaction, Log(NPS)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

Notes: These ﬁgures plot the impacts of AI model deployment on measures of productivity and performance by

pre-AI worker tenure, deﬁned as the number of months an agent has been employed when they receive access to the

AI model. Panel A plots the average handle time or the average duration of each technical support chat. Panel B

graphs chats per hour, or the number of calls an agent can handle per hour. Panel C plots the resolution rate, and

Panel D plots net promoter score, an average of surveyed customer satisfaction. All speciﬁcations include agent and

chat year-month, location, and company ﬁxed eﬀects and standard errors are clustered at the agent level.

Figure 9: Experience Curves by Deployment Cohort

Resolutions Per Hour, by Agent Tenure

1 2 3 4

Resolutions Per Hour

0 2 4 6 8 10

Agent Tenure, Months

Never Treated Always Treated

Treated 5-6 Mo.

Notes: This ﬁgure plot the relationship between productivity and job tenure. The red line plots the performance of

always-treated agents, those who have access to AI assistance from their ﬁrst month on the job. The blue line plots

agents who are never treated. The green line plots agents who spend their ﬁrst four months of work without the AI

assistance, and gain access to the AI model during their ﬁfth month on the job. 95th percent conﬁdence intervals are

shown.

Figure 10: Heterogeneity of AI Impact, by AI Adherence

A. Distribution of AI Adherance

0 .005 .01 .015 .02

Density

0 20 40 60 80 100

AI Percent Adherance

kernel = epanechnikov, bandwidth = 3.1231

B. Impact of AI on Resolutions Per Hour, by Initial Adherence

.05 .1 .15 .2 .25 .3

Log(Resolutions Per Hour)

Q1 (Low)

Q5 (High)

Initial Adherence at AI Deployment (%)

Notes: Panel A plots the distribution of AI adherence, averaged at the agent-month level, weighted by the log of

the number of AI recommendations for that agent-month. Panel B shows the impact of AI assistance on resolutions

by hour, by agents grouped by their initial adherence, deﬁned as the share of AI recommendations they followed in

the ﬁrst month of treatment.

Figure 11: AI Adherence over Time

A. By Agent Tenure at AI Model Deployment

25 30 35 40 45 50

AI Percent Adherance

0 1 2 3 4 5

Months Since AI Deployment

<3 Mos. 3-12 Mos.

>12 Mos

B. By Agent Skill at AI Model Deployment

25 30 35 40 45 50

AI Percent Adherance

0 1 2 3 4 5

Months Since AI Deployment

Tercile 1 (Low) Tercile 2

Tercile 3 (High)

Notes: This ﬁgure plots the share of AI suggestions followed by agents as a function of the number of months each

agent has had access to the AI model. In Panel A, the red line plots the adherence of agents with 3 to 12 months of

experience at AI model deployment, the green line plots adherence of agents with over a year of experience and the

blue line plots the adherence rates of agents with less than three months of experience when given access to the AI

model. In our sample, the average tenure of agents is nine months. Panel B plots adherence over time by tercile of

pre-deployment agent productivity: blue is the ex-ante least productive agents, red represents middle-skill workers

and green are high-skill workers. All data come from the ﬁrm’s internal software systems.

Figure 12: Textual Change

A. Within-Person Textual Change, Pre and Post AI Model Deployment

0 .1 .2 .3

Text Difference, Within Worker Pre vs. Post AI

High Skill Low Skill

B. Text Similarity Between Low-Skill and High-Skill Workers, Pre and Post AI

.5 .55 .6 .65

Text Similarity, High vs. Low Skill Workers

4/1/2020 8/1/2020 12/1/2020 4/1/2021

Chat Date

No AI Post AI

Notes: Panel A plots the average diﬀerence between an agent’s pre-AI corpus of chat messages and that same

agent’s post-AI corpus, controlling for year-month and agent tenure. The ﬁrst bar represents the average pre-post

text diﬀerence for agents in the highest quintile of pre-AI skill, as measured by a weighted index of their calls per

hour, resolution rate, and customer satisfaction score. The low-skill bar represents the same type of pre-post text

diﬀerence among the lowest skill quintile. Agent skill, or relative productivity, is deﬁned at the time of treatment.

Panel B plots the average text similarity between the top and bottom quintile of agents. The blue line plots the

similarity for never treated or pre-treatment agents, the red line plots the similarity for agents with access to the AI

model. For agents in the treatment group, we deﬁne agent skill prior to AI model deployment. Our analysis includes

controls for agent tenure.

Figure 13: Conversation Sentiment

A. Customer Sentiment, Histogram B. Agent Sentiment

0 2 4 6 8

Percent

-1 -.5 0 .5 1

Mean(Customer Sentiment)

0 20 40 60

Percent

-1 -.5 0 .5 1

Mean(Agent Sentiment)

C. Customer Sentiment, Event Study D. Agent Sentiment, Event Study

-.1 0 .1 .2 .3

Mean(Customer Sentiment)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

-.04 -.02 0 .02 .04

Mean(Agent Sentiment)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

Notes: Each panel of this ﬁgure plots the impact of AI model deployment on conversational sentiment. Panel A

shows average customer sentiments. Panel B shows average agent sentiments. Panel C plots the event study of

AI model deployment on customer sentiment and Panel D plots the corresponding estimate for agent sentiment.

Sentiment is measured using SiEBERT, a ﬁne-tuned checkpoint of a RoBERTA, an English language transformer

model. All data come from the ﬁrm’s internal software systems.

Figure 14: Impact of AI Model Deployment on Worker Attrition

A. By Tenure at AI Model Deployment

-.2 -.1 0 .1

AI X 0 Mos.

AI X 1-2 Mos.

AI X 3-6 Mos.

AI X 7-12 Mos.

AI X >12 Mos.

Agent Tenure at AI Deployment

B. By Productivity at AI Model Deployment

-.15 -.1 -.05 0

AI X Q1 (Low)

AI X Q2

AI X Q3

AI X Q4

AI X Q5 (High)

Agent Skill at AI Deployment

Notes: This ﬁgure presents the results of the impact of AI model deployment on workers’ likelihood of attrition.

Panel A graphs the eﬀects of AI assistance on attrition by agent tenure at AI model deployment. Panel B plots

the same impact by agent skill index at AI model deployment. All speciﬁcations include chat year and month ﬁxed

eﬀects, as well as agent location, company and agent tenure. All robust standard errors are clustered at the agent

level. All data come from the ﬁrm’s internal software systems.

Figure 15: Impact of AI on Chat Escalation and Transfers

A. Escalation (Requests for Manager Assistance)

-.03 -.02 -.01 0 .01

Share Req. Manager

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

B. Chat Transfers

-.02 -.01 0 .01 .02 .03

Share Transfer

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

Notes: This ﬁgure reports the coeﬃcients and 95 percent conﬁdence intervals for the event study of AI model

deployment on manager assistance and transfers or agent requests for help from other agents. Panel A graphs

requests to speak to a manager, which are initiated by the customer usually when they are unsatisﬁed or frustrated

with the interaction. Panel B plots transfers, which are usually initiated either when an agent is not responsible for

the customer’s problem or when the agent cannot solve a technical support issue and needs help from another agent.

All robust standard errors are clustered at the agent location level. All data come from the ﬁrm’s internal software

systems.

Table 1: Applicant Summary Statistics

Variable All Never Treated Treated, Pre Treated, Post

Chats 3,007,501 945,954 882,105 1,180,446

Agents 5,179 3,523 1,341 1,636

Number of Teams 133 111 80 81

Share US Agents .11 .15 .081 .072

Distinct Locations 25 25 18 17

Average Chats per Month 127 83 147 188

Average Handle Time (Min) 41 43 43 35

St. Average Handle Time (Min) 23 24 24 22

Resolution Rate .82 .78 .82 .84

Resolutions Per Hour 2.1 1.7 2 2.5

Customer Satisfaction (NPS) 79 78 80 80

Notes: This table shows conversations, agent characteristics and issue resolution rates, customer satisfaction and

average call duration. The sample in Column 1 consists of all agents in our sample. Column 2 includes control agents

who were never given access to the AI model. Column 3 and 4 present pre-and-post AI model deployment summary

statistics for treated agents who were given access to the AI model. All data come from the ﬁrm’s internal software

systems.

Table 2: Main Effects: Productivity (Resolutions per Hour)

(1) (2) (3) (4) (5) (6)

VARIABLES Res./Hr Res./Hr Res./Hr Log(Res./Hr) Log(Res./Hr) Log(Res./Hr)

Post AI X Ever Treated 0.468*** 0.371*** 0.301*** 0.221*** 0.180*** 0.138***

(0.0542) (0.0520) (0.0498) (0.0211) (0.0188) (0.0199)

Ever Treated 0.109* 0.0572*

(0.0582) (0.0316)

Observations 13,225 12,328 12,328 12,776 11,904 11,904

R-squared 0.250 0.563 0.575 0.260 0.571 0.592

Year Month FE YES YES YES YES YES YES

Location FE YES YES YES YES YES YES

Agent FE - YES YES - YES YES

Agent Tenure FE - - YES - - YES

DV Mean 2.121 2.174 2.174

Robust standard errors in parentheses

*** pă0.01, ** pă0.05, * pă0.10

Notes: This table presents the results of diﬀerence-in-diﬀerence regressions estimating the impact of AI model deployment on our main measure of productivity,

resolutions per hour, the number of technical support problems resolved by an agent per hour (res/hour). Columns 1 and 4 include agent geographic location and

year-by-month ﬁxed eﬀects. Columns 2 and 5 include agent-level ﬁxed eﬀects, and columns 3 and 6, our preferred speciﬁcation, also control for agent tenure. All

standard errors are clustered at the agent location level. All data come from the ﬁrm’s internal software systems.

Table 3: Main Effects: Additional Outcomes

(1) (2) (3) (4) (5) (6) (7) (8)

VARIABLES AHT Calls/Hr Res. Rate NPS Log(AHT) Log(Calls/Hr) Log(Res. Rate) Log(NPS)

Post AI X Ever Treated -3.750*** 0.366*** 0.0128* -0.128 -0.0851*** 0.149*** 0.00973* -0.000406

(0.476) (0.0363) (0.00717) (0.660) (0.0110) (0.0142) (0.00529) (0.00915)

Observations 21,885 21,885 12,328 12,578 21,885 21,885 11,904 12,188

R-squared 0.590 0.564 0.369 0.525 0.622 0.610 0.394 0.565

Year Month FE YES YES YES YES YES YES YES YES

Location FE YES YES YES YES YES YES YES YES

Agent FE YES YES YES YES YES YES YES YES

Agent Tenure FE YES YES YES YES YES YES YES YES

DV Mean 40.65 2.557 0.821 79.58

Robust standard errors in parentheses

*** pă0.01, ** pă0.05, * pă0.10

Notes: This table presents the results of diﬀerence-in-diﬀerence regressions estimating the impact of AI model deployment on measures of productivity and

agent performance. Post AI X Treated measures the impact of AI model deployment after deployment on treated agents for average handle time or average call

duration, calls per hour, the number of calls an agent handles per hour, resolution rate, the share of technical support problems they can resolve and net promoter

score (NPS), an estimate of customer satisfaction, and each metrics corresponding natural log equivalents. All speciﬁcations include agent ﬁxed eﬀects and chat

year and month ﬁxed eﬀects, as well as controls for agent location and agent tenure. All standard errors are clustered at the agent location level. All data come

from the ﬁrm’s internal software systems.

Table 4: Agent and Customer Sentiment

(1) (2)

VARIABLES Mean(Customer Sentiment) Mean(Agent Sentiment)

Post AI X Ever Treated 0.177*** 0.0198***

(0.0133) (0.00315)

Observations 21,218 21,218

R-squared 0.485 0.596

Year Month FE YES YES

Location FE YES YES

Agent FE YES YES

Agent Tenure FE YES YES

DV Mean 0.141 0.896

Robust standard errors in parentheses

*** pă0.01, ** pă0.05, * pă0.10

Notes: This table presents the results of diﬀerence-in-diﬀerence regressions estimating the impact of AI model

deployment on measures of conversation sentiment. All speciﬁcations include agent ﬁxed eﬀects and chat year and

month ﬁxed eﬀects, as well as agent location and agent tenure, which account for diﬀering likelihood of attrition by

agent tenure. All standard errors are clustered at the agent location level. All data come from the ﬁrm’s internal

software systems.

Table 5: Organizational Changes

(1) (2) (3)

VARIABLES Leaves this Month Request Manager Transfer

Post AI X Ever Treated -0.0868** -0.00875*** 0.00792

(0.0319) (0.00215) (0.00535)

Observations 17,902 21,839 21,839

R-squared 0.206 0.482 0.386

Year Month FE YES YES YES

Location FE YES YES YES

Agent Tenure FE YES YES YES

DV Mean 0.288 0.0377 0.0219

Agent FE YES YES

Robust standard errors in parentheses

*** pă0.01, ** pă0.05, * pă0.10

Notes: This table shows the impact of AI assistant deployment on agent attrition, customer requests for manager

help, and call transfers. The sample for Column 1 consists of all agent-months for untreated agents, and post-

treatment months for treated agents. Because exit occurs only once in our sample, Column 1 does not include agent

ﬁxed eﬀects. Columns 2 and 3 estimate our standard diﬀerence-in-diﬀerence result for our full sample and include

agent ﬁxed eﬀects and chat year and month ﬁxed eﬀects, as well as agent location and agent tenure. All standard

errors are clustered at the agent location level. All data come from the ﬁrm’s internal software systems.

Appendix Materials

Figure A.1: Sample AI Technical Suggestion

A. Sample AI-generated Technical Link

Notes: This ﬁgure shows a sample technical documentation suggestions made the by AI. Our data ﬁrm has an

extensive set of documentation for their technical support agents, known as the knowledge base, which is like an

internal company Wikipedia for product and process information. The AI will attempt to surface the most helpful

technical documentation page when triggered to do so during a customer interaction. These links are only visible to

the agent and agents must review to see if the resource is helpful. Workers can choose to read the suggested technical

documentation or ignore the recommendation.

Figure A.2: Event Studies, Resolutions Per Hour

A. Resolutions Per Hour

-.6

-.4

-.2

1.2

Resolutions Per Hour

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

Sun-Abraham de Chaisemartin-D'Haultfoeuille

Callaway-Sant'Anna Borusyak et al.

TWFE OLS

B. Log(Resolutions Per Hour)

-.2

Log(Resolutions Per Hour)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5

Months to/from AI Deployment

Sun-Abraham de Chaisemartin-D'Haultfoeuille

Callaway-Sant'Anna Borusyak et al.

TWFE OLS

Notes: This table presents the eﬀect of AI model deployment on our main productivity outcome, resolutions per

hour, using a variety of robust dynamic diﬀerence-in-diﬀerences estimators introduced in Borusyak et al. (2022),

Callaway and Sant’Anna (2021), de Chaisemartin and D’Haultfœuille (2020) and Sun and Abraham (2021) and

a standard two-way ﬁxed eﬀects regression model. All regressions include agent level, chat-year ﬁxed eﬀects and

controls for agent tenure. Standard errors are clustered at the agent level. Because of the number of post-treatment

periods and high turnover of agents in our sample, we can only estimate ﬁve months of preperiod using Borusyak et

al. (2022) and de Chaisemartin and D’Haultfœuille (2020).

Figure A.3: Heterogeneity of AI Impact, by Skill and Controlling for Tenure

A. Log(Resolutions Per Hour) B. Log(Chats Per Hour)

-.1 0 .1 .2 .3

Log(Resolutions Per Hour)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

0 .05 .1 .15 .2 .25

Log(Chats Per Hour)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

C. Log(Resolution Rate) D. Log(Customer Satisfaction (NPS))

-.1 -.05 0 .05 .1 .15

Log(Share Resolved)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

-.05 0 .05 .1

Log(Customer Satisfaction)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

Notes: These ﬁgures plot the impacts of AI model deployment on worker productivity and other outcomes. Agent

skill is calculated as the agent’s trailing three month average of performance on average handle time, call resolution,

and customer satisfaction, the three metrics our ﬁrm uses for agent performance. Within each month and company,

agents are grouped into quintiles, with the most productive agents within each ﬁrm in quintile 5 and the least

productive in quintile 1. Pre-AI worker tenure is the number of months an agent has been employed when they

receive access to AI recommendations. All speciﬁcations include agent and chat year-month, location, and company

ﬁxed eﬀects and controls for agent tenure.

Figure A.4: Heterogeneity of AI Impact, by Tenure and Controlling for Skill

A. Log(Resolutions Per Hour) B. Log(Chats Per Hour)

-.1 0 .1 .2 .3 .4

Log(Resolutions Per Hour)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

0 .1 .2 .3 .4

Log(Chats Per Hour)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

C. Log(Resolution Rate) D. Log(Customer Satisfaction (NPS))

-.05 0 .05 .1 .15

Log(Share Resolved)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

-.05 0 .05 .1 .15

Customer Satisfaction, Log(NPS)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

Notes: These ﬁgures plot the impacts of AI model deployment on measures of productivity and performance by

pre-AI worker tenure, deﬁned as the number of months an agent has been employed when they receive access to the AI

model. Panel A plots the average handle time or the average duration of each technical support chat. Panel B graphs

chats per hour, or the number of calls an agent can handle per hour. Panel C plots the resolution rate, the share of

chats successfully resolved, and Panel D plots net promoter score, an average of surveyed customer satisfaction. All

speciﬁcations include agent and chat year-month, location, pre-AI agent skill and company ﬁxed eﬀects and standard

errors are clustered at the agent location.

Figure A.5: Experience Curves by Deployment Cohort, Additional Outcomes

A. Average Handle Time B. Chats Per Hour

30 35 40 45 50

Average Handle Time

0 2 4 6 8 10

Agent Tenure, Months

Never Treated Always Treated

Treated 5-6 Mo.

1.5 2 2.5 3 3.5 4

Chats Per Hour

0 2 4 6 8 10

Agent Tenure, Months

Never Treated Always Treated

Treated 5-6 Mo.

C. Resolution Rate D. Customer Satisfaction

.7 .75 .8 .85 .9 .95

Share Resolved

0 2 4 6 8 10

Agent Tenure, Months

Never Treated Always Treated

Treated 5-6 Mo.

65 70 75 80 85

Customer Satisfaction (NPS)

0 2 4 6 8 10

Agent Tenure, Months

Never Treated Always Treated

Treated 5-6 Mo.

Notes: These ﬁgures plot the experience curves of three groups of agents over their tenure, the x-axis, against ﬁve

measures of productivity and performance. The red lines plot the performance of always-treated agents, those who

are start work in their ﬁrst month with the AI and always have access to the AI suggestions. The blue line plots

agents who are never treated. The green line plots agents who spend their ﬁrst four months of work without the

AI model, and gain access to the AI during their ﬁfth month on the job. All panels include 95th percent conﬁdence

intervals.

Figure A.6: Heterogeneity of AI Impact by Initial AI Adherence, Additional

Outcomes

A. Log(Average Handle Time) B. Log(Chats Per Hour)

-.2 -.15 -.1 -.05 0

Log(Average Handle Time)

Q1 (Low)

Q5 (High)

Initial Adherence at AI Deployment (%)

.05 .1 .15 .2 .25

Log(Chats Per Hour)

Q1 (Low)

Q5 (High)

Initial Adherence at AI Deployment (%)

C. Log(Resolution Rate) D. Log(Customer Satisfaction (NPS))

-.02 0 .02 .04 .06

Log(Share Resolved)

Q1 (Low)

Q5 (High)

Initial Adherence at AI Deployment (%)

-.04 -.02 0 .02 .04 .06

Log(Resolutions Per Hour B)

Q1 (Low)

Q5 (High)

Initial Adherence at AI Deployment (%)

Notes: These ﬁgures plot the impact of AI model deployment on additional measures of performance by quintile

of initial adherence, the share of AI recommendations followed in the ﬁrst month of treatment. Panel A plots the

average handle time or the average duration of each technical support chat. Panel B graphs chats per hour, or the

number of calls an agent can handle per hour (including working on multiple calls simultaneously). Panel C plots the

resolution rate, the share of chats successfully resolved, and Panel D plots NPS, or net promoter score, is an average

of surveyed customer satisfaction. All speciﬁcations include agent and chat year-month, location, and company ﬁxed

eﬀects and controls for agent tenure. All data come from the ﬁrm’s internal software systems.

Figure A.7: Heterogeneity in Customer Sentiment

A. By Tenure at AI Model Deployment

.1 .15 .2 .25 .3

Mean(Customer Sentiment)

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

B. By Productivity at AI Model Deployment

.05 .1 .15 .2 .25

Mean(Customer Sentiment)

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

Notes: Each panel of this ﬁgure plots the impact of AI model deployment on the mean sentiment per conversation.

Sentiment refers to the emotion or attitude expressed in the text of the customer chat and ranges from ´1 to 1

where ´1 indicates very negative sentiment and 1 indicates very positive sentiment. Panel A plots the eﬀects of AI

model deployment on customer sentiment by agent tenure when AI deployed and Panel B plots the impacts by agent

ex-ante productivity. All data come from the ﬁrm’s internal software systems. Average sentiment is measured using

SiEBERT, a ﬁne-tuned checkpoint of a RoBERTA, an english language transformer model.

Figure A.8: Escalation and Transfers, Heterogeneity by Worker Tenure and

Skill

A. Manager Assistance, by pre-AI skill B. Transfers, by pre-AI skill

-.02 -.015 -.01 -.005 0

Share Req. Manager

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

-.02 -.01 0 .01 .02

Share Transfer

Q1 (Lowest)

Q5 (Highest)

Agent Skill at AI Deployment

C. Manager Assistance, by pre-AI tenure D. Transfers, by pre-AI tenure

-.025 -.02 -.015 -.01 -.005 0

Share Req. Manager

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

-.06 -.04 -.02 0 .02 .04

Share Transfer

0 Mos.

1-2 Mos.

3-6 Mos.

7-12 Mos.

>12 Mos.

Agent Tenure at AI Deployment

Notes: Panels A and C show the eﬀects of AI on customer requests for manager assistance, by pre-AI agent skill

and in by pre-AI agent tenure. Panels B and D show the impacts on transfers by pre-AI agent skill and pre-AI agent

tenure. All robust standard errors are clustered at the agent location level. All data come from the ﬁrm’s internal

software systems.

Table A.1: Main Effects: Productivity (Log(Resolutions per Hour)), Alternative

Difference-in-Difference Estimators

Point

Estimate

Standard

Error

Lower Bound

95% Conﬁdence

Interval

Upper Bound

95% Conﬁdence

Interval

TWFE-OLS 0.137 0.014 0.108 0.165

Borusyak-Jaravel-Spiess 0.257 0.028 0.203 0.311

Callaway-Sant’Anna 0.239 0.025 0.189 0.289

DeChaisemartin-D’Haultfoeuille 0.116 0.021 0.075 0.156

Sun-Abraham 0.237 0.037 0.165 0.308

Notes: This table shows the impact of AI model deployment on the log of our main productivity outcome, resolu-

tions per hour, using robust diﬀerence-in-diﬀerences estimators introduced in Borusyak et al. (2022), Callaway and

Sant’Anna (2021), de Chaisemartin and D’Haultfœuille (2020) and Sun and Abraham (2021). All regressions include

agent level, chat-year ﬁxed eﬀects and controls for agent tenure. The standard errors are clustered at the agent level.

Discussion

>"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 percent on average, with the greatest impact on novice and low-skilled workers, and minimal impact on experienced and highly skilled workers. We provide suggestive evidence that the AI model disseminates the potentially tacit knowledge of more able workers and helps newer workers move down the experience curve. In addition, we show that AI assistance improves customer sentiment, reduces requests for managerial intervention, and improves employee retention." >"We posit that high-skill workers may have less to gain from AI assistance precisely because AI recommendations capture the potentially tacit knowledge embodied in their own behaviors. Rather, low-skill workers are more likely to improve by incorporating these behaviors by adhering to AI suggestions." > "First, AI assistance increases worker productivity, resulting in a 13.8 percent increase in the number of chats that an agent is able to successfully resolve per hour. This increase reﬂects shifts in three components of productivity: a decline in the time it takes to an agent to handle an individual chat, an increase in the number of chats that an agent is able to handle per hour (agents may handle multiple calls at once), and a small increase in the share of chats that are successfully resolved." Scaling laws can largely explain the progress since the transformer was invented. Great paper on the topic-> Scaling Laws for Neural Language Models: https://arxiv.org/pdf/2001.08361 This blog post eloquently describes the basics of transformers: https://jalammar.github.io/illustrated-transformer/ >"Progress in machine learning opens up a broad set of economic possibilities. Our paper provides the ﬁrst empirical evidence on the eﬀects of a generative AI tool in a real world workplace. In our setting, we ﬁnd that access to AI-generated recommendations increases worker productivity, improves customer sentiment, and is associated with reductions in employee turnover. >We hypothesize the part of the eﬀect we document is driven by the AI system’s ability to embody the best practices of high-skill workers in our ﬁrm. These practices may have previously been diﬃcult to disseminate because they involve tacit knowledge. Consistent with this, we see that AI assistance leads to substantial improvements in problem resolution and customer satisfaction for newer- and less-skilled workers, but does not help the highest-skilled or most-experienced workers on these measures. Analyzing the text of agent conversations, we ﬁnd suggestive evidence that AI recommendations lead low-skill workers to communicate more like high-skill workers." Generated by generative AI: >> The paper Generative AI at Work by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond explores the impact of generative AI on worker productivity using a real-world dataset from over 5,000 customer support agents. The study finds that introducing a generative AI-powered conversational assistant increased worker productivity by 14% on average, with the most significant gains (up to 34%) seen among novice and lower-skilled workers. The tool primarily helped less experienced workers by disseminating the expertise of high-performing employees, accelerating the learning curve. In addition to boosting productivity, the AI assistant improved customer satisfaction, reduced the need for managerial interventions, and increased employee retention. Interestingly, the productivity gains were minimal for highly experienced workers, suggesting that AI tools may provide more value to those still developing their skills. The study highlights the potential for generative AI to improve worker performance across various skill levels while also hinting at broader implications for how AI could reshape the labor market by enhancing, rather than replacing, human labor.

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