Learn smart - Learn online. Upto 88% off on courses for a limited time. View Courses
Error goes here
Please upload all relevant files for quick & complete assistance.
How artificial intelligence will change the future of marketing
Thomas Davenport 1&Abhijit Guha 2&Dhruv Grewal 3&Timna Br ...
How artificial intelligence will change the future of marketing
Thomas Davenport 1&Abhijit Guha 2&Dhruv Grewal 3&Timna Bressgott 4
The Author(s) 2019
In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors.
Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional
framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot.
Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the
authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the
future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be
more effective if it augments (rather than replaces) human managers.
AI is going to make our lives better in the future.
—Mark Zuckerberg, CEO, Facebook
In the future, artificial intelligence (AI) appears likely to in-
fluence marketing strategies, including business models, sales
processes, and customer service options, as well as customer
behaviors. These impending transformations might be best
understood using three illustrative cases from diverse indus-
tries (see Table1). First, in the transportation industry, driver-
less, AI-enabled cars may be just around the corner, promising
to alter both business models and customer behavior. Taxi andride-sharing businesses must evolve to avoid being marginal-
ized by AI-enabled transportation models; demand for auto-
mobile insurance (from individual customers) and breathaly-
zers (fewer people will drive, especially after drinking) will
likely diminish, whereas demand for security systems that
protect cars from being hacked will increase (Hayes2015).
Driverless vehicles could also impact the attractiveness of real
estate, because (1) driverless cars can move at faster speeds,
and so commute times will reduce, and (2) commute times
will be more productive for passengers, who can safely work
while being driven to their destination. As such, far flung
suburbs may become more attractive, vis-à-vis the case today.
Second, AI will affect sales processes in various industries.
Most salespeople still rely on a telephone call (or equivalent)
as a critical part of the sales process. In the future, salespeople
Thomas Davenport, Abhijit Guha, Dhruv Grewal and Timna Bressgott
contributed to the writing of the paper. Mark Houston served as accepting
Editor for this article.
1 Department of Technology, Operations, and Information
Management, Babson College, Babson Park, MA 02457, USA
2 Department of Marketing, Darla Moore School of Business,
University of South Carolina, Columbia, SC 29208, USA
3 Department of Marketing, Babson College, Babson
Park, MA 02457, USA
4 Department of Marketing and Supply Chain Management,
Maastricht University, Tongersestraat 53, 6211, LM
Maastricht, The Netherlands Journal of the Academy of Marketing Science
will be assisted by an AI agent that monitors tele-
conversations in real time. For example, using advanced voice
analysis capabilities, an AI agent might be able to infer from a
customer’s tone that an unmentioned issue remains a problem
and provide real-time feedback to guide the (human)
salesperson’s next approach. In this sense, AI could augment
salespersons’capabilities, but it also might trigger unintended
negative consequences, especially if customers feel uncom-
fortable about AI monitoring conversations. Also, in the fu-
ture, firms may primarily use AI bots,
cases—function as well as human salespeople, to make initial
contact with sales prospects. But the danger remains that if
customers discover that they are interacting with a bot, they
may become uncomfortable, triggering negative
Third, the business model currently used by online retailers
generally requires customers to place orders, after which the
online retailer ships the products (the shopping-then-shipping
model—Agrawal et al.2018;Gansetal.2017). With AI,
online retailers may be able to predict what customers will
want; assuming that these predictions achieve high accuracy,
retailers might transition to a shipping-then-shopping business
model. That is, retailers will use AI to identify customers’
preferences and ship items to customers without a formal or-
der, with customers having the option to return what they do
notneed(Agrawaletal.2018;Gansetal.2017). This shift
would transform retailers’marketing strategies, business
models, and customer behaviors (e.g., information search).
Businesses like Birchbox, Stitch Fix and Trendy Butler al-
ready use AI to try to predict what their customers want, with
varying levels of success.
The three use cases (above) illustrate why so many aca-
demics and practitioners anticipate that AI will change the
face of marketing strategies and customers’behaviors. In fact,
a survey by Salesforce shows that AI will be the technology
most adopted by marketers in the coming years (Columbus
2019). The necessary factors to allow AI to deliver on its
promises may be in place already; it has been stated that“this
very moment is the great inflection point of history”(Reese
2018, p. 38). Yet this argument can be challenged. First, the
technological capability required to execute the preceding ex-
amples remains inadequate. By way of an exemplar, self-
driving cars are not ready for deployment (Lowy2016),
as—amongst other things—currently self-driving cars cannot
handle bad weather conditions. Predictive analytics also need
to improve substantially before retailers can adopt shipping-
then-shopping practices that avoid substantial product returns
and the associated negative affect. Putting all this together, it
appears that marketing managers and researchers needinsights about not only the ultimate promise of AI, but also
the pathway and timelines along which AI is likely to develop.
This paper addresses the issues above, building not only from
a review of literature across marketing (and more generally,
business), psychology, sociology, computer science, and ro-
botics, but also from extensive interactions with practitioners.
Second, the preceding examples highlight mostly positive
consequences of AI, without detailing the widespread, justifi-
able concerns associated with their use. Technologists such as
Elon Musk believe that AI is“dangerous”(Metz2018). AI
might not deliver on all its promises, due to the challenges it
introduces related to data privacy, algorithmic biases, and
We argue that the marketing discipline should take a lead
role in addressing these questions, because arguably it has the
most to gain from AI. In an analysis of more than 400 AI use
cases, across 19 industries and 9 business functions,
McKinsey & Co. indicates that the greatest potential value
of AI pertains to domains related to marketing and sales
(Chui et al.2018), through impacts on marketing activities
such as next-best offers to customers (Davenport et al.
2011), programmatic buying of digital ads (Parekh2018),
and predictive lead scoring (Harding2017). The impact of
AI varies by industry; the impact of AI on marketing is highest
in industries such as consumer packaged goods, retail, bank-
ing, and travel. These industries inherently involve frequent
contact with large numbers of customers, and produce vast
amounts of customer transaction data and customer attribute
data. Further, information from external sources, such as so-
cial media or reports by data brokers, can augment these data.
Thereafter, AI can be leveraged to analyze such data and de-
liver personalized recommendations (relating to next product
to buy, optimal price etc.) in real time (Mehta et al.2018).
Yet marketing literature related to AI is relatively sparse,
prompting this effort to propose a framework that describes
both where AI stands today and how it is likely to evolve.
Marketers plan to use AI in areas like segmentation and ana-
lytics (related to marketing strategy) and messaging, person-
alization and predictive behaviors (linked to customer behav-
iors) (Columbus2019). Thus, we also propose an agenda for
future research, in which we delineate how AI may affect
marketing strategies and customer behaviors. In so doing,
we respond to mounting calls that AI be studied not only by
those in computer science, but also studied by those who can
integrate and incorporate insights from psychology, econom-
ics and other social sciences (Rahwan et al.2019;alsosee
Introduction to artif icial intelligence
Researchers propose that AI“refers to programs, algorithms,
systems and machines that demonstrate intelligence”(Shankar
1Miller (2016) outlines the difference between an AI bot and a chatbot. In
brief, chatbots rely on (relatively) simple algorithms, whereas AI bots have
greater capabilities, incorporating complex algorithms and NLP.
J. of the Acad. Mark. Sci.
2018,p.vi),is“manifested by machines that exhibit aspects of
human intelligence”(Huang and Rust2018,p.155),andin-
volves machines mimicking“intelligent human behavior”
(Syam and Sharma2018, p. 136). It relies on several key
technologies, such as machine learning, natural language pro-
cessing, rule-based expert systems, neural networks, deep
learning, physical robots, and robotic process automation
(Davenport2018). By employing these tools, AI provides a
means to“interpret external data correctly, learn from such
data, and exhibit flexible adaptation”(Kaplan and Haenlein
2019, p. 17). Another way to describe AI depends not on its
underlying technology but rather its marketing and business
applications, such as automating business processes, gaining
insights from data, or engaging customers and employees
(Davenport and Ronanki2018). We build on this latter per-
spective. A listing of this research is provided in Table2.
First, to automate business processes, AI algorithms per-
form well-defined tasks with little or no human intervention,
such as transferring data from email or call centers into
recordkeeping systems (updating customer files), replacing
lost ATM cards, implementing simple market transactions,or“reading”documents to extract key provisions using natu-
ral language processing. Second, AI can gain insights from
vast volumes of customer and transaction data, involving not
just numeric but also text, voice, image, and facial expression
data. Using AI-enabled analytics, firms then can predict what
a customer is likely to buy, anticipate credit fraud before it
happens, or deploy targeted digital advertising in real time.
For example, stylists working at Stitch Fix, a clothing and
styling service, use AI to identify which clothing styles will
best suit different customers. The underlying AI integrates
data provided by customers’expressed preferences, their
Pinterest boards, handwritten notes, similar customers’pref-
erences, and general style trends. Finally, AI can engage cus-
tomers, before and after the sale. The Conversica AI bot works
to move customer transactions along the marketing pipeline,
and the AI bot used by 1–800-Flowers provides both sales and
customer service support. AI bots offer advantages beyond
just 24/7 availability. Not only do these AI bots have lower
error rates, but also they free up human agents to deal with
more complex cases. Further, AI bot deployment can be
scaled up or down as needed, when demand ebbs or flows.
Table 1Select use cases (in the order in which they appear in the paper)
Industry or Usage Context (specific firm or AI application) Description
AI in driverless cars (e.g., Tesla) In the future, AI-enabled cars may allow for car journeys without
any driver input, with the potential to significantly impact various
industries (e.g., insurance, taxi services) and customer behaviors
(e.g., whether they still buy cars).
Online retailing AI (e.g., Birchbox) AI will enable better predictions for what customers want, which may
cause firms to move away from a shopping-then-shipping business
model and toward a shipping-then-shopping business model.
Fashion-related AI (e.g., Stitch Fix) AI applications support stylists, who curate a set of clothing items for
customers. Stitch Fix’s AI analyzes both numeric and image/other non-numeric data.
Sales AI (e.g. Conversica) AI bots can automate parts of the sales process, augmenting the capabilities
of existing sales teams. There may be backlash if customers know (upfront)
that they are chatting with an AI bot (even if the AI bot is otherwise capable)
Customer service robots (e.g., Rock’em
and Sock’em; Pepper)Robots with task-automating AI respond to relatively simple customer
service requests (e.g., making cocktails).
Emotional support AI (e.g., Replika) AI aims to provide emotional support to customers by asking meaningful
questions, offering social support, and adjusting to users’linguistic syntax.
In-car AI (e.g., Affectiva) In-car AI that analyzes driver data (e.g., facial expression) to evaluate drivers’
emotional and cognitive states.
Customer screening AI (e.g. Kanetix) AI used to identify customers who should be provided incentives to buy
insurance (and avoid those who (1) are already likely to buy and (2)
those unlikely to buy).
Business process AI (e.g., IBM Interact) AI used for multiple (simple) applications, such as customized offers
(e.g., Bank of Montreal).
Retail store AI (e.g., Café X, Lowebot,
84.51, Bossa Nova)Robots that can serve as coffee baristas, respond to simple customer service
requests in Lowe’s stores, and identifying misshelved items in grocery stores.
Security AI (e.g., Knightscope’s K5) Security robots patrol in offices or malls, equipped with superior sensing
capabilities (e.g., thermal cameras).
Spiritual support AI (e.g., BlessU-2; Xian’er) Customizable robot priest/monk offering blessings in different languages
to the user.
Companion robot AI (e.g., Harmony from Realbotix) Customizable robot companion, which promises reduced loneliness
to the user.
J. of the Acad. Mark. Sci.
As these descriptions suggest, AI offers the potential to
increase revenues and reduce costs. Revenues may increase
through improved marketing decisions (e.g., pricing, promo-
tions, product recommendations, enhanced customer engage-
ment); costs may decline due to the automation of simple
marketing tasks, customer service, and (structured) market
transactions. Furthermore, the above discussions indicate that
rather than replacing humans, firms generally are using AI to
augment their human employees’capabilities, such as when
Stitch Fix uses AI to augment its stylists’efforts to make
appropriate choices for clients (Gaudin2016). This point
aligns well with sentiments expressed by Ginni Rometty, the
CEO of IBM, who proposed that AI would not lead to a world
of man“versus”machine but rather a world of man“plus”
A framework for understanding artif icial
Building on insights from marketing (and more generally
business), social sciences (e.g., psychology, sociology), and
computer science/robotics, we propose a framework to help
customers and firms anticipate how AI is likely to evolve. We
consider three AI-related dimensions: levels of intelligence,
task type, and whether the AI is embedded in a robot.
Level of intelligence
Task automation versus context awarenessDavenport and
Kirby (2016) contrast task automation with context aware-
ness. The former involves AI applications that are standard-
ized, or rule based, such that they require consistency and the
imposition of logic (Huang and Rust2018). For example,
IBM’s Deep Blue applied standardized rules and“brute force”
algorithms to beat the best human chess player. Such AI is best
suited to contexts with clear rules and predictable outcomes,
like chess. On the cruise shipSymphony of the Seas,two
robots, Rock‘em and Sock‘em, make cocktails for customers.
Elsewhere, the robot Pepper can provide frontline greetings,
and IBM’s Watson can provide credit scoring and tax prepa-
ration assistance. Notwithstanding that these AI applications
involve fairly structured contexts, many firms struggle to im-
plement even these AI applications
2and rely on specialized
businesses like Infinia ML and Noodle, or consulting firms
like Accenture or Deloitte, to develop and set up initial AI
In contrast, context awareness continues to be developed,
and researchers in computer science are working on movingAI capabilities forward, from task automation to context
awareness (e.g., Ghahramani2015;Mnihetal.2015).
Context awareness is a form of intelligence that requires ma-
chines and algorithms to“learn how to learn”and extend
beyond their initial programming by humans. Such AI appli-
cations can address complex, idiosyncratic tasks by applying
holistic thinking and context-specific responses (Huang and
Rust2018). However, such capabilities remain distant; a 2016
survey of AI researchers indicated there was only a 50%
chance of achieving context awareness (or its equivalent) by
2050 (Müller and Bostrom2016). Building on the above
point, Reese (2018, p. 61) cautions that such AI“does not
currently exist…nor is there agreement…if it is possible.”
Nevertheless, this capability constitutes the goal of AI devel-
opments, as predicted by compelling examples from science
fiction, such as Jarvis from theIron Manmovies or Karen
fromSpider Man–Homecoming; both AI can understand
new and complex contexts and create solutions therein.
The differences between task automation and context
awareness map onto concepts of narrow versus general AI
(Baum et al.2011; Kaplan and Haenlein2019; Reese2018).
As Kaplan and Haenlein (2019) state, both narrow and general
AI may equal or outperform human performance, but narrow
AI is focused on a specific domain and cannot learn to extend
into new domains, whereas general AI can extend into new
It is important to clarify that although in this paper we
consider two levels of intelligence (task automation vs. con-
text awareness), ideally levels of intelligence are best concep-
tualized as a continuum. Some AI applications have moved
beyond task automation but still fall well short of context
awareness, such as Google’s DeepMind AlphaGo (which beat
the world’s best Go player), the AI poker player Libratus, and
4These applications represent substantial advances,
yet state-of-the-art AI stillis closer to task automation
Overview of extant researchResearch into the psychology of
automation (Longoni et al.2019), examines how customers
may respond to AI. Notwithstanding the fact that AI
may be more accurate and/ or more reliable than
humans, customers have reservations about AI, and
these reservations tend to increase as AI moves towards
context awareness. In turn, these increased reservations
negatively impact the propensity to adopt AI, propensity
to use AI, etc. A listing of such research is shown in Table2.
Moving forward, we discuss (separately) issues relating to AI
adoption and AI usage.
2Reese (2018, p. 61) cautions that this type of AI is in no way“easy AI.”3To clarify, businesses like Infinia ML etc. also provide support moving
forward, when the firm initiates more advanced AI initiatives.
4Replika (replica.ai) aims to serve as an AI friend, programmed to ask ques-
tions about you and your life that are“meaningful,”and to offer emotional
J. of the Acad. Mark. Sci.
AI adoptionCustomers appear to hold AI to a higher standard
than is normatively appropriate (Gray2017), as exemplified
by the case of driverless cars. Customers should adopt AI if its
use leads to significantly fewer accidents; instead, customers
impose higher standards and seek zero accidents from AI.
Understanding the roots of this excessive caution is important.
A preliminary hypothesis suggests that customers trust AI
less, and so hold AI to a higher standard, because they believe
that AI cannot“feel”(Gray2017).
Task characteristics also influence AI adoption. To the ex-
tent a task appears subjective, involving intuition or affect,
customers likely are even less comfortable with AI (Castelo
2019). Research confirms that customers are less willing to
use AI for tasks involving subjectivity, intuition, and affect,
because they perceive AI as lacking the affective capability or
empathy needed to perform such tasks (Castelo et al.2018).
Tasks differ in their consequences; choosing a movie is
relatively less consequential, but steering a car may involve
more consequences. Using AI for consequential tasks is per-
ceived as involving more risk, in turn reducing adoption in-
tentions. Early work has found support for this hypothesis,
more so among more conservative consumers for whom risks
are more salient (Castelo et al.2018; Castelo and Ward2016).
Finally, customer characteristics may also impact AI adop-
tion. We build from two points: (1) when outcomes are con-
sequential, this increases perceptions of risk (Bettman1973),
and (2) women perceive more risk in general (Gustafsod
1998) and take on less risk (Byrnes et al.1999). Hence, early
work has found that women (vs. men) are less likely to adopt
AI, especially when outcomes are consequential (Castelo and
Wa r d2016). Moving beyond demographics, other factors also
impact the extent of AI adoption, e.g., to the extent a task is
salient to a customer’s identity, the customer may be less like-
ly to adopt AI (Castelo2019). To elaborate, if a certain con-
sumption activity is central to a customer’s identity, then the
customer likes to take credit for consumption outcomes
(Leung et al.2018). Some customers perceive that using AI
for these consumption activities is tantamount to cheating, and
this hinders the attribution of credit post-consumption.
Therefore, if an activity is central to a customer’s identity, then
the customer may be less likely to adopt AI (for this activity).
AI usageMoving past adoption issues, we note some usage
considerations, including how AI should communicate with
customers. Customers do not associate AI applications with
autonomous goals (Kim and Duhachek2018); for example,
customers do not believe Google’s AlphaGo has the self-
driven goal to be a national Go champion. Rather, they believe
that this AI application is programmed to play the game Go.
Consistent with this perception, customers are more likely to
focus on“how”(rather than“why”) the AI application per-
forms; implying that when engaging with AI, customers will
be in a low level construal mindset. From extant research, weknow that messages are more effective when the perceived
characteristics of the message source and the contents of the
actual message match, communication from AI should be
more effective when it highlights how rather than why in its
messaging (regulatory construal fit; Lee et al.2009;Motyka
et al.2014). In line with the above, Kim and Duhachek (2018)
showed that a message from an AI application is more persua-
sive when the message is about how to use a product, rather
than why to use this product. This is because customers doubt
whether AI can“understand”the importance of engaging in
certain consumption behaviors.
Next we pivot to factors that impact the propensity of cus-
tomers to engage with AI. Examining the case of medical
decision making, Longoni et al. (2019)showthatcustomers’
reservations are due to their concerns about uniqueness ne-
glect (i.e., the AI is perceived as less able to identify and relate
with customers’unique features). Further, building from prior
work (Şimşek and Yalınçetin2010; also see Haslam et al.
2005), Longoni et al. (2019) show that these reservations are
more for customers who have higher scores on the‘personal
sense of uniqueness’scale. In other work on how customers
engage with AI, Luo et al. (2019) examined how (potential)
customers engage with AI bots. In reality, AI bots can be as
effective as trained salespersons, and 4x as effective as inex-
perienced salespersons. However, if it is disclosed that the
customer is conversing with an AI bot, purchase rates drop
by 75%. Linked to points made prior in this paper, because
customers perceive the AI bot as less empathetic, they are curt
when interacting with AI bots, and so purchase less.
Task type refers to whether the AI application analyzes num-
bers versus non-numeric data (e.g., text, voice, images, or
facial expressions). These different data types all provide in-
puts for decision making, but analyzing numbers is substan-
tially easier than analyzing other data forms. Practitioners,
such as senior managers from Infinia ML, formulate this cat-
egorization slightly differently, noting that data that can be
organized into tabular formats are significantly easier to ana-
lyze than those data that cannot. In our discussions with em-
ployees of Stitch Fix, we gained further clarity on this point.
Stitch Fix elicits data from customers using both direct ques-
tions about their preferences (which can be put in tabular
formats) and indirect elicitations from customers’Pinterest
pages and likes. Stitch Fix uses proprietary AI algorithms to
analyze the latter, non-numeric data and regards these data as
very useful, because it has learned that customers cannot al-
ways articulate their preferences on numeric scales.
The distinction in the above paragraph is critical, because
much data is non-tabular in form, and so being able to com-
prehend and analyze such data significantly enhances the im-
pact of AI. Many AI applications have started to analyze text,
J. of the Acad. Mark. Sci.
voice, image, and face data inputs. These data inputs are ini-
tially in non-numeric formats, but are often translated into
numerical formats, e.g., pixel brightness values, relating to
images. Applications that can process such data inputs in-
clude, for example (1) IPSoft, which processes words spoken
to customer agents to interpret what customers want (2)
Affectiva, which is working on in-car AI that can sense driver
emotion and fatigue and switch control to an autonomous AI,
and (3) Cloverleaf’s shelfPoint, installed on retail store
shelves, which examines customers’facial expressions to an-
alyze their emotional responses at the point of purchase.
Although currently AI’s abilities to comprehend and analyze
such non-numeric data formats remain somewhat limited, de-
veloping this ability will be critical for the full realization of
the power of AI, and computer scientists are working towards
improving AI capabilities in this regard (e.g., LeCun et al.
2015; You et al.2016).
Separate to the above, it is worth pointing out that the
ability to analyze unstructured data may be limited by legacy
infrastructures. A senior manager in Infinia ML indicated that
often data is stored in formats and structures less amenable to
AI deployment. Also, Kroger has an AI application that auto-
mates visual inspection of out-of-stock items on its grocery
shelves. In an interview with one of the authors of this paper, a
Kroger data scientist reported that the proper functioning of
Kroger’s AI application requires hardware upgrades; specifi-
cally, it needs to upgrade its cameras to higher resolution
levels if the AI application is to work properly.
AI in robots
Virtuality-reality continuumMost AI is virtual in form. For
example, Replika is available on smartphones, and Libratus
uses a digital platform. However, AI can also be embedded in
a real entity or robot form, with some elements of physical
embodiment. The extent to which a form is virtual versus
embodied reflects its position on the Milgram virtuality–
reality continuum (Milgram et al.1995). In this sense, re-
searchers and practitioners should conceive of virtual and real
forms not as distinct categories but rather as endpoints on a
continuum, within which AI entities are spread out. An AI like
Conversica is purely virtual, with no physical embodiment—
although some companies that use virtual AI do give it names.
In contrast, an AI application embedded in a robot barista
(e.g., Tipsy Robot in Las Vegas) appears somewhere on the
continuum between virtuality and reality, because it has some
physical embodiment; however, that embodiment can only
operate in a narrow range and on a specific task (making a
drink). Finally, the AI embedded in proposed multifunctional,
companion robots (that today remain under development)
would entail substantially more reality, featuring both physical
embodiment and the capacity to operate in wide range
of contexts (specifically, share physical proximity withindividuals without any protective barrier, travel with
Overview of extant researchPrior research (Table2) indicates
that using robots offer substantial advantages, especially in
cases involving customer interactions. As prior work indi-
cates, customers form more personal bonds with robots than
with AI that lack any physical embodiment. For example,
individuals enjoy interacting with a physically present robot
than with either a robot simulation (on a computer) or a robot
presented via teleconference (Wainer et al.2006). Further,
customers empathize with robots. When individuals are asked
to administer pain—via electric shocks—to a (physically pres-
ent) robot or a robot simulation, both of which go on to display
marks indicating pain after being subjected to an shocked,
individuals empathized more with the physically present robot
(Kwak et al.2013). Finally, customers interacted longer with a
robot diet coach than with either a virtually present diet coach
or a diet diary in a paper form (Kidd and Breazeal2008).
Other studies find that customers demonstrate reciprocity-
based perceptions, e.g., they express more positive percep-
tions of a care robot that asks for help and then returns this
help by offering a favor (Lammer et al.2014). In a prisoner’s
dilemma experiment, participants exhibited similar reciprocity
levels toward both robot partners and human partners
(Sandoval et al.2016), and their reciprocity towards the robot
partner increased even more if the robot provided early signs
of cooperation (vs. random behavior). Noting the benefits of
embedding AI in robots, work in robotics is examining how
best to improve not only the physical capability of robots but
also the robot–AI interface (e.g., Adami2015; Kober et al.
2013; Steels and Brooks2018). Further, to take advantage of
the preference for physical embodiment, some vendors of vir-
tual agents (or bots) try to present these agents as having a
physical form. IPsoft’s virtual agent, for example, is called
Amelia and is often represented by a lifelike avatar image
However, other research shows that customers’discomfort
with AI is accentuated when the AI application is embedded in
a robot. As robots appear more humanlike, they become more
unnerving, in line with the uncanny valley hypothesis (UVH;
5UVH arises because the appearance of robots
“prompts attributions of mind. In particular, we suggest that
machines become unnerving when people ascribe to them
experience (the capacity to feel and sense), rather than agency
(the capacity to act and do)”(Gray and Wegner2012,p.125).
Such factors may hinder AI adoption.
5Masahiro Mori wrote an influential paper arguing that making robots look
more human is beneficial, but only up to a certain point, after which such
robots elicit negative reactions. Thus, reactions become negative as robots
move. From somewhat human to human-like. Thereafter, if robots look per-
fectly human, reactions turn positive. The valley reflects these trends, as reac-
tions initially becoming more negative, then turn positive.
J. of the Acad. Mark. Sci.
Table 2Select extant research (in the order in which they appear in the paper)
Paper Domain Dimension Takeaways
Agrawal et al. (2018) BUS Artificial intelligence (AI) reduces the cost of prediction.
Gans et al. (2017)BUS
Rahwan et al. (2019) CS/R To best understand AI, bring in insights from not only
computer science, but also other disciplines
Shankar (2018)MKTGAI“refers to programs, algorithms, systems and machines
that demonstrate intelligence”(Shankar2018,p.vi),is
“manifested by machines that exhibit aspects of human
intelligence”(Huang and Rust2018, p. 155), involves
machines mimicking“intelligent human behavior”(Syam
and Sharma2018, p. 136), and provides means to“interpret
external data correctly, learn from such data, and exhibit flexible
adaptation”(Kaplan and Haenlein2019,p.17). Huang and Rust (2018)MKTG
Syam and Sharma (2018)MKTG HuangandRust(2018) - Mechanical and analytical intelligences
involve simple, rule-based tasks. Intuitive and empathetic
intelligences involve complex tasks requiring empathy, holistic
thinking and context-specific responses.
Kaplan and Haenlein (2019) MKTG Kaplan and Haenlein (2019)–Used the terms narrow versus general
AI. Narrow AI somewhat maps onto mechanical and analytical
intelligences, whereas general AI maps onto intuitive and
Davenport and Ronanki (2018) BUS LVLINT Another way to describe AI is by stating its marketing and business
outcomes, such as automating business processes, gaining insights
from data, or engaging customers and employees
Davenport and Kirby (2016) BUS LVLINT Contrasts task automation with context awareness. The former involves
AI applications that are standardized, or rule based (akin to narrow AI).
The latter is a form of intelligence that requires machines and algorithms
to‘learn how to learn’and extend beyond their initial programming
(akin to general AI).
Ghahramani (2015) CS/R LVLINT How machines can learn from experience, using probabilistic machine
Mnih et al. (2015) CS/R LVLINT How artificial agents can learn to generalize from past experience to new
situations, using reinforcement learning.
Müller and Bostrom (2016) BUS LVLINT Artificial general intelligence (AGI) is a hypothetical technology that
would be the equivalent of a human intelligence in terms of its flexibility
and capability of performing and learning a vast range of tasks (similar to
context awareness). In a survey of AI researchers, the median estimate was
for a 50% chance of achieving an AGI by 2050 and a 90% chance of
achieving one by 2075.
Reese (2018) BUS LVLINT Defines narrow versus general AI and analytical AI versus humanized AI;
both contrasts are very similar to the contrast between task automation
versus context awareness. Reese (2018) cautions that AGI does not exist,
and that there is no guarantee that it ever will.
Baum et al. (2011) SOC LVLINT
Davenport (2018) BUS LVLINT The state-of-the-art AI is closer to task automation than context awareness.
Gray (2017) PSY LVLINT Customers appear to hold AI to a higher standard than is normatively
appropriate. A preliminary hypothesis suggests that customers trust AI less,
and so hold AI to a higher standard, because they believe that AI
Castelo (2019) MKTG LVLINT To the extent a task appears subjective, involving intuition or affect, customers
likely are less comfortable with AI (Castelo2019). Customers are less
willing to use AI for tasks involving subjectivity, intuition, and affect,
because they perceive AI as lacking the affective capability or empathy
needed to perform such tasks (Castelo et al.2018). Builds from: Castelo et al. (2018)MKTG
Castelo and Ward (2016) MKTG LVLINT Using AI for consequential tasks is perceived as involving more risk, in
turn reducing adoption intentions. This is more so amongst (1) conservative
consumers, for whom risks are more salient, (2) women, who perceive
more risk in general, and take on less risk. Builds from: Bettman (1973)MKTG
Byrnes et al. (1999)PSY
J. of the Acad. Mark. Sci.
Ta b l e 2(continued)
Paper Domain Dimension Takeaways
Leung et al. (2018) MKTG LVLINT If a certain consumption activity is central to a customer’s identity,
the customer would like to take credit for consumption outcomes.
Some customers perceive that using AI for these consumption
activities is tantamount to cheating, and this hinders the attribution
of credit post-consumption. Hence if an activity is central to a
customer’s identity, then the customer may be less likely to adopt
AI for this activity.
Kim and Duhachek (2018) MKTG LVLINT Customers do not associate AI applications with autonomous goals
(Kim and Duhachek2018). In line with this perception, customers
are more likely to focus on“how”(rather than“why”) the AI application
performs; implying that when engaging with AI, customers will be in
a low level construal mindset. Because persuasion is more effective
when the perceived characteristics of the persuasion source and the
persuasion message match, communication from AI should be more
effective when it highlights how rather than why in its messaging
(regulatory construal fit; Lee et al.2009; Motyka et al.2014). AI
persuasion messages are more effective in persuading consumers to
buy the recommended product or services when the message highlights
“how”to use the product rather than“why”to use the product. These
effects are because customers doubt whether AI can understand“why”
it is important for customers to engage in certain behaviors. Builds from: Lee et al. (2009)MKTG
Motyka et al. (2014)MKTG
Longoni et al. (2019) MKTG LVLINT Examining the case of medical decision making, Longoni et al. (2019)
propose that customers’reservations are due to their concerns about
uniqueness neglect (i.e., the AI is perceived as less able to identify and
relate with customers’unique features). Further, building from prior
work (Şimşek and Yalınçetin2010; also see Haslam et al.2005), Longoni
et al. (2019) propose that these reservations would be more for customers
whohavehigherscoresonthe‘personal sense of uniqueness’scale
(Şimşek and Yalınçetin2010). Builds from:Şimşek
and Yalınçetin (2010)PSY
Haslam et al. (2005)PSY
Luo et al. (2019) MKTG LVLINT Examines how (potential) customers engage with AI bots. In reality, AI
bots can be as effective as trained salespersons, and 4 times effective
as inexperienced salespersons. However, if it is disclosed that the customer
is conversing with an AI bot, purchase rates reduce by 75%. Because
customers perceive the AI bot as less empathetic, they are curt when
interacting with AI bots, and so purchase less. Ties into themes from
Castelo et al. (2018).
LeCun et al. (2015) CS/R TSKTYPE How deep learning has improved the state-of-the-art in speech and visual
Yo u e t a l . (2016) CS/R TSKTYPE How using a new algorithm improves visual object recognition.
Milgram et al. (1995) PSY ROBOT Proposes the virtuality-reality continuum.
Wa i n e r e t a l . (2006) CS/R ROBOT Interacting with a physical robot is perceived as more enjoyable than either
interacting with a simulated robot on a computer or interacting with a
real robot presented through teleconferencing.
Kwak et al. (2013) CS/R ROBOT When asked to administer electric shocks to a (physical) robot or a simulated
robot on a computer screen, individuals empathized more with the
Kidd and Breazeal (2008) CS/R ROBOT Interactions were longer with a robot diet coach than either a virtual diet
coach or a pen-and-paper diet diary.
Lammer et al. (2014) CS/R ROBOT Individuals express reciprocity towards robots.
Adami (2015) CS/R ROBOT With suitable machine learning algorithms, robots can learn from past
Kober et al. (2013) CS/R ROBOT Reinforcement learning can work for robots embedded with suitable
machine learning algorithms.
Mori (1970) PSY ROBOT Making robots look more human is beneficial, but only up to a certain
point, after which such robots elicit negative reactions (UVH).
Gray and Wegner (2012) PSY ROBOT Machines are perceived as more unnerving when individuals ascribe
to machines the capacity to feel, rather than capacity to do.
J. of the Acad. Mark. Sci.
Moving beyond AI adoption, we pivot to how customers
interact with robots with embedded AI. Early research sug-
gests that interactions with AI-embedded robots trigger dis-
comfort (linked to the UVH) and so further trigger (negative)
compensatory behaviors, like buying of status goods, or eating
more food (Mende et al.2019). From a theory perspective,
this work not only shows the downsides of anthropomorphism
(especially in the case of robots), but also the existence of
6specifically linked to robots.
More broadly, sociologists ponder how AI (and specifically
robots with embedded AI) might transform economy and so-
ciety (Boyd and Holton2018). For example, cloud-based
technology facilitates deep learning in robots, which can learn
from human agents through repeated interactions.
Sociologists particularly note ways that robots may en-
ter multiple aspects of social life, not only in (expected)
areas such as service and transportation, but also in
domains like the arts and music.
The current state and likely evolution of AI
Short- and medium-term time horizon
In Fig.1, we combine all the above considerations to depict
the current state of AI and its likely evolution. The upper half
of Fig.1(four cells) relates to task automation and thus the
likely state of AI in the short to medium time horizon. The
lower half of Fig.1(two cells) relates to context awareness
applications that are only likely in the long term (if at all), due
to the constraints associated with the current state of AI. Notethat in the lower half of Fig.1, we do not distinguish between
numeric and non-numeric data, because context awareness–
capable AI likely will be able to handle any types of data.
The first four use cases, associated with short to medium
term developments, involve task automation (see Fig.1).
Cell 1: Controller of numerical dataThe first cell in Fig.1
reflects what AI can do very well, namely, statistical analyses
of numeric data using machine learning. A typical use case is
the application of AI to optimize prices (Antonio2018).
Pricing strategies must balance two competing concerns; that
the price is low enough to attract customers versus high
enough to enable the firm to earn sufficient profits. Firms
use AI to analyze vast amounts of numeric data (including
less intuitive predictor variables) to both set optimal prices
and then change prices in real time. For example, Kanetix
helps Canadian customers find deals on car insurance by
allowing prospective buyers to compare and evaluate policies
and rates offered by more than 50 providers. Scott Emberley,
the Business Development Director of integrate.ai, which
partnered with Kanetix to build an AI application, indicated
that the goal was identify three sets of customers (1) those
highly likely to buy, (2) those very unlikely to buy, and (3)
those in-between. Thereafter, Kanetix would direct their ad-
vertising towards these“in-between”customers, which would
provide the greatest returns, and not expend efforts on those
either very likely to buy or very unlikely to buy. With four
years of data, integrate.ai developed a machine learning model
that could identify such customers. Five months later, Kanetix
estimated 2.3 times return on its AI investment, and a more
than 20% increase in sales among previously undecided cus-
tomers. In another example, the Bank of Montreal (BMO)
uses IBM Interact to analyze customer data across all its chan-
nels and identify personalized product offerings. If a customer
6Compensatory consumption is consumption“motivated by a desire to offset
or reduce a self-discrepancy”(Mandel et al.2017,p.134).
Ta b l e 2(continued)
Paper Domain Dimension Takeaways
Mende et al. (2019) MKTG ROBOT Interactions with robots trigger discomfort (linked to UVH) and so
further trigger compensatory behaviors.
Boyd and Holton (2018) SOC ROBOT Will the combination of robotics and AI lead to an unprecedented
Pedersen et al. (2018) SOC ROBOTS Outlines the issues surrounding use of social robots in medical treatment,
care facilities, and private homes. Also, outlines ethical concerns.
André et al. (2018) MKTG LVLINT Because AI facilitates data-driven, micro-targeting marketing offerings,
customers should view such offerings favorably, because it reduces
search costs. Yet this could undermine customers’perceived autonomy,
with implications for their subsequent evaluations and choices.
Aguirre et al. (2015) MKTG LVLINT Proposes the privacy–personalization paradox, whereby individuals
balance privacy concerns against the benefits of personalized recommendations.
Wang and Kosinski (2018) PSY TSKTYPE How to use deep neural networks to identify sexual orientation, merely
by analyzing facial images
MKTGMarketing;BUSBusiness;PSYPsychology;SOCSociology;CS/ RComputer Science/ Robotics.
Dimension:LVLINTlevels of intelligence;TSKTYPEtask type;ROBOTwhetherAIinrobots
J. of the Acad. Mark. Sci.
has been exploring mortgages on BMO’s site and later calls
the contact center, IBM Interact prioritizes the list of available
mortgage offers for the contact center service agent—in effect
augmenting agents’capabilities and facilitating more relevant
Cell 2: Controller of dataEfforts to analyze non-numeric data
offer the potential to improve understanding of what cus-
tomers want, and firms’customer service. Some AI applica-
tions can analyze non-numeric data (in some cases, after con-
version to numeric data), primarily using speech and image
recognition capabilities achieved with deep learning neural
networks (Chui et al.2018). For example, Conversica AI, as
manifested in a virtual AI assistant named Angie, sends out-
bound emails to up to 30,000 leads per month, then interprets
the responses to identify the most promising leads (Power
2017). Angie engages in initial conversation with the pros-
pect, and then routes to most promising leads to a (human)
salesperson. In effect, Conversica’s AI augments
salespersons’capabilities. In a pilot test with a telecommuni-
cations company called Century Link, Angie appropriately
understood more than 95% of emails received (and sent the
rest to human agents for interpretation), and Century Link
earned a 20-fold return on its investments in Angie.
The Stitch Fix’s business model offers another example. As
we noted, Stitch Fix delivers apparel directly to customers
(Wilson et al.2016), without requiring the customers to actu-
ally engage in a formal shopping task. No Stitch Fix retaillocation exists. Instead, customers fill out style surveys, pro-
vide their physical measurements, evaluate sample styles, cre-
ate links to their Pinterest boards, and send in personal notes.
As may be expected, customers have trouble explicating their
exact style preferences using words and numbers, but their
pins and likes can be (better) indicators of their preferences.
Stitch Fix’s proprietary machine learning algorithms examine
numbers, words, and Pinterest pins, then summarize the find-
ings for the company’s fashion stylists, who in turn select
suitable clothing to send to each customer. The above example
illustrates the need to suitably balance AI input and human
input; senior managers from Stitch Fix told us that—in their
experience—their AI works best when it augments the
Noting that the AI applications in companies like
Conversica and Stitch Fix use all types of data (i.e., use nu-
meric data and non-numeric data), we term the AI applications
in this cell as reflecting“Controller of Data.”
Cell 3: Numerical data robotThis cell is similar to cell 1,
except that it incorporates AI embedded in a robotic form,
and so these AI applications can best be described as robots
that process numerical data inputs. Such robots are well suited
to retail environments with well-structured operations. At
Café X, for example, a robot barista can serve up to 120
coffees per hour (Hochman2018). Each robotic barista fea-
tures a $25,000, six-axis animatronic arm. Customers place
orders on a kiosk touchscreen (or via an app), so all inputs
currently or to
be deployed in
the short to
medium term Digital form Robot form Analyze
numbers 1 – Controller of
Business Use Case
IBM 3 – Numerical Data Robot
Business Use Case
images 2 – Controller of Data
Business Use Case
Replika 4 – Data Robot
Business Use Case
Walmart/ Bossa Nova
K5 from Knightscape
deployed in the
long term Digital form Robot form
image 5 – Data Virtuoso
Example Use Case
Jarvis 6 – Robot Expert
Example Use Case
Fig. 1AI framework
J. of the Acad. Mark. Sci.
are numeric. As in a regular coffeehouse, customers can select
various options: latte or espresso, with different amounts of
froth, and various ingredients such as organic Swedish oat
milk. The goal is not to replace baristas, but rather to augment
baristas’capabilities by taking over more routine operations.
The Cafe X robot barista augments the capabilities of the
human barista, who can then focus on providing high-
quality customer service, and also facilitating what the com-
pany calls“coffee education”(e.g., managing tastings).
Cell 4: Data robotThis cell is similar to cell 2, except that the
robotic form can process all types of data (not just numeric
data). For example, the Lowebot at Lowe’sHome
Improvement stores (Hullinger2016) can scan a product held
up by a customer (or listen to the customer speak the name of
the desired product), confirm whether the item is in stock, and
then roll along with the customer to the exact spot in the store
where he or she can find the product. This task requires com-
prehension and examination of both numeric and non-numeric
data, as well as an indoor navigation capability, which repre-
sents a significant advance over the capabilities embodied in
the Café X robot. Using the Lowebot augments the capabili-
ties of Lowe’s human sales associates, allowing focus on more
complex customer service requests.
Other retailers have similar applications. Our discussions with
senior managers at 84.51
7indicate that they are working with
Kroger to implement in-store robots that can identify misshelved
or out-of-stock items. In another example, Walmart has partnered
with Bossa Nova Robotics to deploy robots in its stores to scan
shelves. The goal appears to be to get robots to perform tasks that
repeat and are predictable, enabling (human) associates to focus
on serving customers (Avalos2018).
Finally, security robots, such as the K5 from Knightscope,
roam offices and malls at night. These robots have better sens-
ing capabilities than humans, because they incorporate ther-
mal cameras and other high-technology sensing tools. Here
again, the objective is to augment human security guards’
Long-term time horizon
For completeness, we also examine what might happen when
AI applications incorporate context awareness, as summarized
in the two cells in the lower half of Fig.1. We reiterate that
there is no indication that such developments will occur in the
short or medium term, as exemplified by the case of driverless
cars. Tesla has removed any“self-driving”labels from its
website, noting that these labels were causing confusion
(Hawkins2019). The CEO of Waymo admits that driverless
cars are unable to drive in poor weather conditions withouthuman input (Lashinsky2019). Put simply, the dream of get-
ting into a driverless car outside in one city, falling asleep, and
waking up in another city is not reality and may not be achieved
anytime soon. Even the less consequential forms of AI remain
problematic. Google’s AlphaGo Zero might have successfully
learned the complex game of Go in a short period, using adver-
sarial networks that pit two (competing) AI systems against
each other so that they can learn; yet in this case, the outcome
space was very well defined. Furthermore, all these AI systems
received significant training data. In contrast, the outcome
spaces (i.e., business domains) for most likely AI applications
are poorly defined, and relevant training data is hard to obtain.
These points reiterate the challenges of moving from task au-
tomation to context awareness. As such, the use cases we pres-
ent for the last two cells are hypothetical, and this section is
deliberately brief, reflecting that our discussion is more aspira-
tional than descriptive of any near-term reality.
Cell 5: Data virtuosoAdvanced AI could be embedded in a
digital form, as exemplified by the AI Jarvis inIron Man
movies. Jarvis has advanced data capabilities that can examine
multiple data types. Perhaps most notably, Jarvis adapts to
new contexts, beyond those for which it has been trained, such
as when it hides from the more advanced AI Ultron and finds
ways to thwart Ultron’s hacking attempts. Futurists would
have us believe that such AI will emerge in the long term,
with strong predictive abilities for customers’preferences
and high capability levels for managing customer service.
Thus, the term virtuoso seems appropriate for such AI.
Cell 6: Robot expertsAn advanced AI also could be embedded
in a robot form, such as the AI Dorian from the television
showAlmost Human. Dorian’s advanced capabilities include
facial recognition, bio scans, analyses of non-numeric stimuli
such as DNA, speed-reading, speaking multiple languages,
and taking the temperature of fluids using his finger. Like
Jarvis, Dorian can adapt to a variety of new contexts.
Futurists predict that such robot experts will emerge in the
long term to serve as companions that meet various customer
needs (e.g., in-home service, home security, medical support).
Such robots even might be able to bond emotionally with
(human) customers, and potentially replace human partners
and animal partners.
Agenda for future research
Having described AI and presented a framework to better
understand it, we pivot to outlining some important areas for
future research. These include how firms may need to change
their marketing strategy, how customers’behaviors will be
impacted, and issues relevant to policymakers. We outline
these areas in Fig.2, linking these to the cells in Fig.1.
7This consulting firm is a subsidiary of Kroger and provides retail insights to
Kroger and its partners; it has strong analytics and AI capabilities.
J. of the Acad. Mark. Sci.
AI and marketing strategy
Predictive abilityBecause AI can help firms predict what cus-
tomers will buy, using AI should lead to substantial improve-
ments in predictive ability. Contingent on levels of predictive
accuracy, firms may even substantially change their business
models, providing goods and services to customers on an on-
going basis based on data and predictions about their needs.
Multiple research opportunities thus emerge, related to differ-
ent customer purchase behaviors and marketing strategies.
One especially important research area may relate to how well
prediction AI–driven algorithms may extend to forecasting
demand for really new products (RNPs; described in Zhao
et al.2012). AI algorithms probably have good predictive
ability for incrementally new products; the open question is
whether they will have good predictive ability for RNPs. For
AI algorithms to do so would presumably require data on
RNPs that would be used in training machine learning models;
this is often not readily available. Further, when examininghow best to make predictions for RNPs, research can also
examine how best to combine AI-driven insights with human
AI is expected to play an important role in predicting not
only what customers want to buy, but also what price to
charge, and whether price promotions should be offered
(Shankar2018). Price and price promotions are important
drivers of sales (Biswas et al.2013;Guhaetal.2018), and
so are an important area of research for marketing researchers.
Thus, an important area for future research relates to how AI
can be best used to predict what prices are optimal and wheth-
er or not price promotions should be offered.
Another important research avenue pertains to allocations
of advertising resources. Much advertising focuses on devel-
oping customer awareness and driving customers’informa-
tion search. Would these advertising dollars be required
in the future, wherein firms may be able to better pre-
dict customers’preferences, and thus would not need to
advertise as much?
automationDigital form Robot form Analyze
numbers1 – Controller of Numerical
Predictive ability (MS)
AI adoption (CB)
-negative response to AI
-state and trait moderators
AI usage (CB)
-Post AI issues (CB)
-perceived loss of
-state and trait moderators
Data privacy (P)
Ethics (P)3 – Numerical Data
similar to Controller of
Numerical Data cell
Affective responses to
images2 – Controller of Data
similar to Controller of
Numerical Data cell
AI adoption for spiritual well-
being (CB)4 – Data Robot
similar to Controller of
Loss of human
awarenessDigital form Robot form
image5 – Data Virtuoso
similar to Controller of Data
cell6 – Robot Expert
similar to Data Robot
Notes: MS = marketing strategy; CB = consumer behavior; P = public
Fig. 2Research agenda for AI.
Notes: As noted in the text, the
sales AI application will be more
effective if it can process both
numeric and non-numeric data,
and hence is more related to the
Controller of Data cell. This is
more likely for more advanced
robots, and so more likely to be
relevant to robots able to handle
non-numeric data (notably voice),
and hence more related to perhaps
the Data Robot cell, but more so
to the Robot Expert cell
J. of the Acad. Mark. Sci.
Sales and AIAs we discussed with regard to Conversica, AI
may alter all stages of the sales process, from prospecting to
pre-approach to presentation to follow-up (Singh et al.2019;
Syam and Sharma2018). Thus, a wide variety of research
&Can AI analyze customer communication and other cus-
tomer information (e.g., social media posts) in ways to
devise future communications that are more persuasive
or increase engagement?
&Can AI provide real-time feedback to salespeople to help
them improve their sales pitches, based on assessments of
customers’verbal and facial responses?
&How might AI combine text and other communication
inputs (e.g., voice data), actual customer behavior, and
other information (e.g., behaviors of similar customers)
to predict repurchases? This effort demands non-numeric
data, in line with cells 2, 4, 5 and 6.
&Considering Luo et al.’s(2019) findings, how should
firms deploy AI sales bots effectively?
Answering these questions could help firms design sales to
take the most advantage of AI.
In addition, firms need to consider how they (re)organize
their sales and innovation processes. These points are not
listed in Fig.2, as they do not tie neatly into the cells shown
Sales processIn the presence of AI, how should sales be or-
ganized and what skills will salespeople need? First, how best
to structure the sales organization wherein organizational
components include both AI bots and human salespeople.
Secondly, how should the firm manage the tradeoff between
AI focusing on customers’expressed needs versus salespeople
being relatively better able to manage issues like customer
stewardship. Lastly, will salespeople be able to be trained/ to
be able to manage customers’concerns relating to AI, specif-
ically issues related to data privacy and ethics. It is clear that
sales processes will require innovation related not only to AI
technologies, but also in job design and skills (Barro and
AI innovation processBecause the impact of AI is uncertain,
firms need to figure out how best to (continually) develop AI.
In our discussions with senior managers at Stitch Fix, they
indicated that the company encourages its data scientists to
pursue projects on their own (Colson2018), such that they
continually engage in preliminary testing of new project ideas.
One Stitch Fix data scientist created a Tinder-like app called
Style Shuffle, to allow users to indicate preferences for various
clothing styles. This app not only informed stylists about cus-
tomers’preferences (the expected benefit) but also helped
match stylists with specific customers (an unexpected benefit).Clothing suggestions from stylists who“swiped”on the app
similarly to particular customers elicited more positive re-
sponses from the customers (i.e., both qualitative feedback
about the stylist and increased sales of clothes curated by that
stylist). When implementing AI, firms thus may achieve better
outcomes if they let their data scientists spend some amount of
time on unauthorized“pet projects,”a research and develop-
ment practice already in place in firms like 3 M (Shum and Lin
2007). Researching the best way to implement AI, to take
advantage of both expected and unexpected benefits, is a fruit-
ful area for research.
Modeling the evolution of AIFinally, firms need to develop
realistic expectations, because“in the short run, AI will pro-
vide evolutionary benefits; in the long run, it is likely to be
revolutionary”(Davenport2018, p. 7). That is, the benefits of
AI could be overestimated in the short term but
underestimated in the long term, a point (sometimes called
Amara’s Law) in accordance with Gartner’shypecyclemodel
of how new technologies evolve (Dedehayir and Steinert
2016; also see van Lente et al.2013;Shankar2018). This view
is popular among practitioners, according to our personal dis-
cussions and interviews with various senior managers. Will
the evolution of AI reflect this model, or will its evolution
differ and more closely map onto models that also integrate
more traditional innovation models (e.g., Roger’smodel,the
Bass model)? Research that tests which innovation model best
predicts AI evolutions will be useful.
AI and customer behavior
New technologies often alter customer behavior (e.g.,
Giebelhausen et al.2014; Groom et al.2011; Hoffman and
Novak2018;Moon2003), and we expect that AI will do so as
well. We propose three research topics, related to AI adoption,
AI usage, and post-adoption issues.
AI adoptionAs a general point, due to a wide variety of fac-
tors, customers view AI negatively, which is a barrier to adop-
tion. As noted, these negative views often stem from cus-
tomers’sense that AI is unable to feel (Castelo et al.2018;
unique about each customer (Longoni et al.2019). Also Luo
et al. (2019) suggest that customers perceive AI bots as being
less empathetic. Customers also are less likely to adopt AI in
consequential domains (Castelo et al.2018; Castelo and Ward
2016) and for tasks salient to their identity (Castelo2019;
Leung et al.2018).
Thus, an important area for future research, important from
the standpoint of both research and practice, would be to ex-
amine how best to mitigate the impact of the above. Initial
brainstorming with fellow researchers and with practitioners
suggests that positioning AI as a learning (artificial) organism,
J. of the Acad. Mark. Sci.
or else positioning the AI application as one that combines AI
and human inputs (as in Stitch Fix), may help partially miti-
gate the impact of the points above. Longoni et al. (2019)
propose that offering customers the opportunity to slightly
modify the AI may get these customers to look past unique-
ness neglect, and focus more on the benefits of personaliza-
tion. This too may be a way to mitigate the points raised prior.
The discomfort with AI is accentuated in case the AI ap-
plication is embedded in a robot. As robots become more
humanlike, then due to the UVH, customers find these robots
unnerving. Such factors may hinder AI adoption and deserve
study. An interesting moderator of this effect—worth investi-
gating—may be whether the AI form is perceived by cus-
tomers as a servant or partner; UVH effects may be stronger
if AI achieves partner status. Also deserving of study is other
ways of mitigating such effects. Early efforts in this direction
involve trying to prime empathy, by convincing cust
Enter the password to open this PDF file:
MyAssignmenthelp.com is one of the leading urgent assignment help providers in the USA. We have earned our reputation as best assignment help in multiple countries including the USA. We have designed unique fastest delivery options, which assist us to deliver immediate assignment assistance. Our teams of highly skilled qualified writers are capable of delivering fast assistances. We provide online assignment help to a wide range subjects so that whenever students face the urgent need of assignment help, they can hire our assistance within a short period.
On APP - grab it while it lasts!
*Offer eligible for first 3 orders ordered through app!
ONLINE TO HELP YOU 24X7
OR GET MONEY BACK!
OUT OF 38983 REVIEWS
Received my assignment before my deadline request, paper was well written. Highly