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Artificial Intelligence (AI) has gained traction over the past few years as the new frontier for gaining a competitive advantage. While firms have started investing heavily in AI, there is a growing disillusionment around the value that can be generated and the process through which that can be obtained. Building on this gap, we develop a conceptual framework that builds on resource orchestration theory. The framework distinguishes between the ideation of AI capabilities and the implementation of AI capabilities and present how activities related to resource orchestration theory are relevant in the context of AI deployments. We develop a set of propositions on the activities that underlie the main processes around resource orchestration of AI, and present a research design to actualize the research plan.
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Twenty-Ninth European Conference on Information Systems (ECIS 2021), Marrakesh, Morocco. 1
Research in Progress
Papagiannidis, Emmanouil, Norwegian University of Science and Technology, Trondheim,
Merete Enholm, Ida, Norwegian University of Science and Technology, Trondheim, Norway,
Mikalef, Patrick, Norwegian University of Science and Technology, Trondheim, Norway,
Krogstie, John, Norwegian University of Science and Technology, Trondheim, Norway,
Artificial Intelligence (AI) has gained traction over the past few years as the new frontier for gaining a
competitive advantage. While firms have started investing heavily in AI, there is a growing
disillusionment around the value that can be generated and the process through which that can be
obtained. Building on this gap, we develop a conceptual framework that builds on resource
orchestration theory. The framework distinguishes between the ideation of AI capabilities and the
implementation of AI capabilities and present how activities related to resource orchestration theory
are relevant in the context of AI deployments. We develop a set of propositions on the activities that
underlie the main processes around resource orchestration of AI, and present a research design to
actualize the research plan.
Keywords: Artificial Intelligence, Resource Orchestration, AI Capabilities, Conceptual Framework
1 Introduction
The surge in data availability over the past few years has enabled the emergence of Artificial
Intelligence (AI) applications that were previously not unimaginable (Ransbotham, Gerbert, Reeves,
Kiron, & Spira, 2018). AI is defined as “a system’s capability to correctly interpret external data, to
learn from such data, and to use those learnings to achieve specific goals and tasks through flexible
adaption (Makarius et al., 2020). This has signaled a vast increase of investments from organizations
in AI technologies (Ransbotham, Kiron, Gerbert, & Reeves, 2017), developing new tools, creating
new processes and trying to capitalize on new market opportunities (Duan, Edwards, & Dwivedi,
2019). Nevertheless, despite much enthusiasm about the opportunities and promises of AI in the
organizational context, many firms are facing major challenges realizing business value from their
investments. Several recent studies and reports have indicated that the vast majority of organizations
face a variety of challenges in generating business value by leveraging their AI investments (Kumar,
Rajan, Venkatesan, & Lecinski, 2019). The main reason behind these setbacks lies in the processes
that companies follow for diffusing AI investments into their operations (Davenport & Ronanki,
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2018). These early results indicate that firms lack knowledge about how to manage their AI
investments to generate value from them (Duan et al., 2019).
In fact, there are several research studies documenting the potential value-generating mechanisms of
AI for organizations, providing a plethora of examples of how AI technologies can allow
organizations to exploit the new technological innovations (Raisch & Krakowski, 2020; Wamba-
Taguimdje, Wamba, Kamdjoug, & Wanko, 2020). Yet, while there is an overabundance of papers
highlighting the potential of AI, there is a striking lack of research on the process of adopting and
deploying AI technologies in support of competitive strategies (Dwivedi et al., 2019). This has been,
and continues to be, one of the major issues' organizations face in generating value of their AI
investments (Ransbotham, Khodabandeh, Fehling, LaFountain, & Kiron, 2019). Particularly, there is a
lack of empirical studies using a theory-driven approach to understand the processes through which AI
transforms from a set of fragmented resources into an organizational capability than can be leveraged
to support strategic goals (Mikalef, Fjørtoft, & Torvatn, 2019).
The objective of this paper is to introduce some notions and ideas presented in Resource Orchestration
(RO) theory for the management of AI projects within organizations (Sirmon, Hitt, & Ireland, 2007).
RO is chosen as it provides a comprehensive framework that helps explain the different activities
through which resources are orchestrated and strategically leveraged to generate business value
(Andersén, 2019) In adopting this theoretical perspective, we develop a distinction between two
phases that characterize AI projects, the organizational ideation of AI capabilities and
the organizational implementation of AI capabilities. The former relates to the design and strategic
planning of the AI capabilities that a firm would require in order to be competitive, while the later
refers to the process of actualizing and deploying such capabilities. We therefore introduce how RO
theory can be adapted to the context of AI in organizations, and develop a set of propositions that will
guide future research. This work adds to research by providing a theory-driven approach to
understanding the process of designing and deploying AI capabilities, bridging the gap between
adoption and business value. From a practical point of view, it provides practitioners with a roadmap
of activities they need to consider when developing their implementation plans so that deployments
are in alignment with their strategic goals (Mikalef, Pateli, Batenburg, & van de Wetering, 2014).
In the next section we introduce the RO theory and describe how it contributes to understanding
strategic value generation by orchestrating and mobilizing firm resources. We then proceed to briefly
outline some recent work with regards to AI in the organizational setting. In the next section, we
introduce our conceptual framework, and present a set of propositions concerning AI resources. Next,
we describe how we plan to actualize this research. We conclude this paper with a short overview of
this work in relation to existing studies.
2 Background
2.1 Resource Orchestration Theory
Resource Orchestration (RO) theory emerged as a response to the shortcoming presented in the
Resource Based View (RBV) of the firm (Sirmon et al., 2007; Sirmon, Hitt, Ireland, & Gilbert, 2011).
Whereas the RBV argues that the firm’s resource is what drive value creation via the development of a
competitive advantage, the RO postulates that resources need to be strategically orchestrated in order
to deliver value (Sirmon et al., 2007; Sirmon et al., 2011). The theory suggests that effectively
orchestration comprises of the structuring of a film's resource portfolio, bundling the resources to build
capabilities, and leveraging those capabilities with the purpose of creating and maintaining value
(Sirmon et al., 2011). In RO theory, managers play a significant role because they are responsible for
effective resource management (Helfat et al., 2007). In addition, managers’ understanding and
initiative, in terms of coordination and mobilisation of resources, have a direct impact on the
resource utilisation (Miao, Coombs, Qian, & Sirmon, 2017). Furthermore, resource orchestration
defines different processes of structuring (i.e. acquiring, accumulating and divesting), bundling
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Twenty-Ninth European Conference on Information Systems (ECIS 2021), Marrakesh, Morocco. 3
(i.e. stabilising, enriching and pioneering) and leveraging (i.e. mobilising, coordinating and deploying)
resources to create new capabilities and exploit market opportunities (Sirmon et al., 2007).
This distinction between the processes that fall under structuring, bundling and leveraging activities is
important given that firms operate under a heterogeneous set of environmental conditions; also
referred to under the umbrella term, environmental uncertainty. Thus, the competitive pressures and
industry dynamics that firms need to cope with differ. According to Johnston, Gilmore, and Carson
(2008), there are many definitions of uncertainty, and most of them are referring to the lack of
information, knowledge, and understanding. What is more, environmental uncertainty contains aspects
of technology and market, creating potential threats to organisation's viability (Sharma & Vredenburg,
1998). In other words, since firms operate under diverse conditions of varying uncertainty, their
resource orchestration practices will differ, with those that are better suited to the context being the
ones that produce competitive performance gains.
As such, the RO perspective does not directly contradict other theories such as the RBV, but rather,
explains how resources are managed and orchestrated to enable performance gains. Resource
Orchestration Theory can therefore be seen as an extension on the Resources Based View (RBV)
(Sirmon et al., 2011). The need to extend RBV has been primarily due to the lack of indication
regarding how resources should be deployed to generate value for a firm, meaning that is unclear
which combination of resources and deployment approached would lead to competitive advantage
(Felin, Foss, & Ployhart, 2015). Additionally, the RBV largely ignores the impact of external
environmental effects, which are a central aspect RO, thus filling this gap (Sirmon et al., 2007).
2.2 AI in the organizational context
In the past decade, the primary focus of Artificial Intelligence (AI) research has been on the technical
aspects of AI (Kaplan & Haenlein, 2019). The main applications of AI to date have been on modelling
and solving specific problems. This means that the application of AI for organizational issues and for
creating new capabilities has been largely overlooked to date. Some early case studies indicate that AI
applications can improve productivity and could be also used as an aid for developing new products or
services (Crews, 2019; Mikalef & Gupta, 2021). Combining the strengths of human and AI abilities
towards decision-making can generate more value, since the cognition of humans has limitation when
addressing problems with high complexity and high informational load (Jarrahi, 2018). AI applications
on the other hand have the ability to calculate thousands of different outcomes within a matter of
seconds, and are able to uncover patterns and relationships that would could be not be easily detected
through mere human observation.
There is a growing consensus in the literature that AI can also enable automation of many manual,
repetitive and structured, or semi-structured tasks (Davenport & Ronanki, 2018). As such, the benefits
of AI in the organizational context not only extend to augmenting decision-making and the creation of
new services and products, but also to developing more efficient processes that free-up human capital
for more creative tasks (Brynjolfsson, Rock, & Syverson, 2018). Several recent papers have started
discussing the potential of AI for automating processes within various areas of application within the
firm (Acemoglu & Restrepo, 2018). These early studies showcase that AI applications can be
developed and deployed to support a range of difference strategies that organizations would pursue
(Ayoub & Payne, 2016). Nevertheless, AI resources alone cannot guarantee any such effects and
subsequently a competitive advantage (Mikalef et al., 2019). Therefore, it is crucial that firms perceive
AI-related and complementary resources as necessary but not sufficient for creating new capabilities
and ultimately achieving performance gains (Mikalef et al., 2019).
While research has made strides in identifying the relevant AI and complementary resources that are
important when initiating projects (Mikalef et al., 2019), there has been substantially less work in
understanding how such resources should be acquired, bundled and leveraged depending on the
context of operation. Studies, such as that of Baier, Jöhren, and Seebacher (2019) have investigated
the challenges in the deployment and operation of AI applications (namely, machine learning),
concluding that both technical and non-technical challenges emerge which could jeopardize the
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success of an entire project. This relates directly to the need for applying a RO perspective on AI
implementation for organizational goals. For example, a specific combination of activities within the
structuring and bundling processes could improve innovation performance, while others could be used
to target a resource advantage strategy. Such resource orchestration approaches are also highly
dependent on the external conditions of a company. This essentially means that AI deployments need
to be considered in the light of the context in which they are deployed. Adding to this, such an
approach would be able to explain how AI resources are converted into an AI capability.
3 Conceptual model of resource orchestration for AI
Resource orchestration is an overall process for structuring a company’s resources, bundling the
existing resources for the creation of new capabilities and leveraging the new capabilities with the goal
to add both business value and customer value. There are three main components, structuring,
bundling and leveraging, and each component contains three subprocesses. The environment affects
the relationships between the resources since the environment produces uncertainty, which might
create instability for a company undermining the competitive advantage of a firm over others (Carnes,
Chirico, Hitt, Huh, & Pisano, 2017).
Figure 1: Conceptual framework of resource orchestration of AI.
Similar to any type of resource, AI-related resources are also subject to such processes and are gauged
in their value depending on how well they fit the environment. In the conceptual framework we
present below we depict the main activities that relate to resource orchestration in AI. We also develop
a distinction between an organizational ideation of AI capabilities, which typically represents the
starting point of planning, and an organizational implementation of AI capabilities which corresponds
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to the actions taken to build AI capabilities. The arrows in this case represent the sequence of activities
for the organizational ideation, and the dependencies for the organizational implementation. The role
of the environment is represented as influencing the entire process, with doted lines being an
indication of feedback loops. Hence, the figure illustrates the three main processes, their subprocesses
and the flow that lead to competitive performance in an uncertain environment. The feedback loop
represents that a firm might need to start or reorchestrate its resources from a different point than other
3.1 Structuring the resource portfolio
Structuring the resource portfolio concerns the tangible and intangible resources a firm has which
dictates the potential value creation a firm could have at a given time (Li, Li, Wang, & Ma, 2017).
Such resources include the data, technological infrastructure, human skills and knowledge, and other
intangible resources such as a learning and proactive culture (Mikalef et al., 2019). This process is
essential because it provides firms with the necessary tools to build new capabilities. In the AI context,
this means that types of AI-based capabilities (such as creating NLP tools, intelligent assistants, and
forecasting) that a firm can develop depends greatly on the data, infrastructure, competencies, and
other related resources that are readily available. Structuring consists of three subprocesses, acquiring,
accumulating and divesting.
Acquiring in the case of AI can be translated as the relevant technological infrastructure, such as a
clusters with extreme high computation power, or hiring the employees with deep knowledge in AI
systems. This activity is crucial for companies that require developing and improving their AI
capabilities on demand, especially if a firm is not in a position to outsource tasks. An acquiring
decision could be costly though, and under high uncertainty environment it could cause a toil in the
financial management of the organization, so managers are responsible for putting the new resources
into good use creating a new range of responses depending on the market opportunities (Garbuio,
King, & Lovallo, 2011). For instance, acquiring an expensive dataset to complement existing data
sources may prove to be a risky endeavor, since it might not yield expected results. Hence, the
following proposition is composed:
Proposition 1: When AI development is developed internally, it is a necessity to purchase the
right resources to create value. In high uncertainty, managers should be more careful as the
cost of obtaining these resources could be expensive.
Accumulating is about the development of internal resources. The importance of accumulation lies
when firms do not have all the required resources, and the company is unable to follow market
changes. For example, the knowledge of building AI systems or managing AI projects should be
accumulated in an adequate number of employees increasing their tacit knowledge; otherwise, when
new opportunities arise, or the environment is uncertain, for instance, high-value employees leave the
company, it will be quite difficult to fill the void in time and producing gains. Hence, business
knowledge, development knowledge and the general know-how that the exit employees had are not
part of the companies tacit knowledge, which could create gaps at generating the necessary knowledge
for future development.Similarly, being able to capture all relevant data internally should be
prioritized in such activities compared to purchasing them from the factor market. Hence, the
following proposition is composed:
Proposition 2: When building AI systems, it is necessary to build tacit knowledge, especially
under high environmental uncertainty.
Divesting refers to releasing firm-controlled resources. Companies have limited resources, and it is
important to free them for other business tasks. Divesting legacy systems or infrastructure, include for
example code that is not written in preferred machine learning languages like Python or R or computer
clusters with not the right computational power, that does not enable a free flow of data is important to
generate AI capabilities. In addition, holding onto large quantities of data, usually data that are used
for building models or for forecasting, that have low value only creates complexity for technical
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employees and consume storing resources. It is important though that each resource that is divested is
carefully accounted for, as in highly uncertain conditions it may be difficult to re-acquire them or
accumulate them. This holds true not only for data and technological resources but also for employees
with specialized knowledge and skills. Hence, we suggest the following proposition:
Proposition 3: Under conditions of high uncertainty, it is important that AI resources are
divested having completely considered their importance and long-terms relevance.
3.2 Bundling resources to build capabilities
Bundling resources is the process of combining resources to create capabilities. Having a resource
does not give an advantage by itself. In the context of AI use, this means understanding in which areas
of application AI initiatives can be directed. The bundling process has three subprocesses, stabilizing,
enriching and pioneering.
Stabilising offers tiny improvements in existing capabilities. However, it contributes to value creation
by allowing the firm to maintain its competitive advantage over time. In the context of AI deployment
that could be training software developers in machine learning or training analysts how to use an
interactive interface with the AI for making new predictions. Nevertheless, if the environment has high
uncertainty stabilising might not be a valid option as the need for change could be urgent and radical.
Hence, the following proposition is composed:
Proposition 4: Maintaining an AI system allows to maintain a competitive advantage, but not
under high environmental uncertainty when change is needed.
Enriching is about extending an existing capability. This can be accomplished by adding resources and
acquiring new skills or by entering new alliances with companies that have access to the required
resources. Firms that deploy AI applications need data for building their AI models, that other
companies have access to or even have employees with deep knowledge in solving AI specific
problems. Alliances can provide vital competencies for gaining competitive advantage without taking
high risks, benefiting both parties. Still, other companies could follow similar strategies meaning that
the competitive advantage might be temporary and short-lived. Hence, the following proposition is
Proposition 5: Enriching AI technologies is necessary when it is difficult or expensive to
acquire extra resources for adding business value, especially under high environmental
Pioneering refers to the process of exploratory learning and integrating completely new resources that
were acquired from the market. This may involve integrating unrelated and radically different types of
information, in a process referred to as bisociation (Smith & Gregorio, 2017). In the context of AI
applications this might mean incorporating data sources that are radically different from those owned
or controlled by the firm, or hiring personnel with distinctive and fundamentally different skill-sets. In
uncertain environments being ablet to provide value propositions that outrival competitors. contributes
to a competitive advantage. Pioneering is a critical activity in developing such value propositions
Hence, the following proposition is composed:
Proposition 6: A pioneering AI process is necessary to build capabilities that create new
sources of value for customers. The importance of such pioneering processes is increased in
uncertain environments.
3.3 Leveraging capabilities to exploit market opportunities
Leveraging involves mobilising, coordinating and deploying processes for adding value for customers
and generating wealth for the firm (Sirmon et al., 2007). Typically, during the organizational ideation
of AI capabilities, mobilizing capabilities is the starting point where the digital business strategy is
delineated, and deploying comprises the last part of the implementation of AI capabilities.
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Mobilising intends to identify all the capabilities that a firm requires for obtaining a competitive
advantage. This is not an easy task when high uncertainty exists in the business environment. Firms
that are interested in AI technologies should identify gaps that they need to fill internally or take
advantage of market opportunities. This denotes that before starting developing AI applications, it is
critical that there is a careful consideration towards which opportunities in the environments they are
oriented towards. This places pressure on top level managers to be able to strategically foresee
developments, and have sufficient technical knowledge in order to be able to envision ways by which
AI technologies can be used creatively to address pressing issues. However, mobilizing requires
continuous adjustments in the firm’s operations and prove to be challenging to maintain in the long
run. Hence, the following proposition is composed:
Proposition 7: Identifying what should be achieved by acquiring resources for the
development of AI capability is crucial, and mobilising is a key component, but it is not a
sufficient process for maintaining a competitive advantage.
Coordinating is the process of integrating mobilised capabilities to create capability configurations.
High-level managers play a significant role as they have to coordinate individual knowledge and
capabilities of their teams, making possible integrations to be fast, efficient and smooth. In the context
of AI deployments this means that any developed AI capabilities need to be effectively coordinated
and integrated with other organizational capabilities in order to implement the leveraging strategy
effectively and create value, for instance, building AI pipelines for facilitating the efficient processing
of data and models requires a lot of effort. This means that there must be open channels of
communication and collaboration across different units in the organization in order to integrate an AI
capability with others that will allow the firm to generate value. The importance of managers here is
critical as they are responsible for using their relational capital to integrate multiple capabilities into
configurations that confer value. Hence, the following proposition is composed:
Proposition 8: High-level managers have an important impact on the coordinating process of
AI capabilities into configurations that can drive value generation.
Deploying involves the process that actively supports the leveraging strategy (Sirmon et al., 2011).
Deploying involves different leveraging strategy options such as a resource advantage, market
opportunity or entrepreneurial strategy (Sirmon et al., 2007), that uses an integrate machine leanrning
model into an existing production environment so that business decisions could be made based on
data. It entails physically using capability configurations to support a chose leveraging strategy.
Within the context of AI deployments, selecting a strategy that AI is oriented towards and deploying
solutions accordingly will have different results depending on the environmental context in which
such capability configurations are deployed. Therefore, AI uses within the organization can be varied,
and the AI capability that a firm manages to develop can be coordinated and deployed co-presently
with other capabilities in order to pursue digital business strategies. Hence, the following proposition
is composed:
Proposition 9: The deployment of AI capability configurations towards a given leveraging
strategy depends on the environmental context in which they are released.
4 Research Design
To empirically explore the propositions a mixed-method approach will be employed following the
guidelines of Venkatesh, Brown, and Bala (2013). Specifically, the study will commence with an
exploratory investigation of companies that are currently deploying AI solutions to support their
operations, and the in the second phase continue with a quantitative analysis to identify patterns of AI
resource orchestration that enable value generation in different external conditions.
4.1 First phase: Exploratory multiple case study design
The objective of this first phase is to understand how the actual processes of AI deployment
correspond to those described in the theoretical framework. To do this, we will adopt a multiple case
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study approach in order to analyze the different patterns of use within each case, as well as to be able
to perform a cross-case analysis to detect differences based on the conditions in which the
organizations operate. The outcome of this first phase will help sensitize notions, and uncover
practices that underpin each of the core activities described in the theoretical framework. In addition, it
will help understand if firms actually adopt a process as described in our framework, or if the opt for a
bottom-up approach where from the available set of resources AI capabilities are developed. This
distinction can also inform our understanding about if firms that follow an ideation to implementation
of AI capabilities approach perceive differences in the types of competitive strategies that they can
pursue, as well as the value the derive from them compared to those that directly start from an
implementation stage.
4.2 Second phase: Identification of AI resource orchestration patterns
During the second phase of the research design the objective will be to identify patterns of actions
taken by firms regarding their AI deployments that either enable or inhibit value. Specifically, we will
conduct a large-scale survey with a sample of firms that are currently deploying AI applications and
collect answers from key respondents. In order to uncover different combinations of activities towards
generating value from AI resource orchestration practices, we will apply approaches that enable
equifinality in outcomes, meaning that multiple different solutions towards a given outcome can be
extracted (Fiss, 2011). Specifically, the analysis of data can be performed using fuzzy set qualitative
comparative analysis (fsQCA) which allows the extraction of different patterns of elements that
contribute towards a specified output. Through this analysis we aspire to extract clusters of actions
around AI capability development that enable firms to achieve key performance outcomes, such as
competitive performance and innovation outputs. In this analysis the role of different environment
characteristics will be incorporated in order understand what practices around resource orchestration
are more aligned to the environment.
5 Conclusions
In this paper we have attempted to highlight the importance that resource orchestration practices have
for organizations that are attempting to leverage AI to generate business value. Building on recent
findings that highlight that many firms are facing challenges in creating value from their AI
investments, we aimed to develop a theory-driven approach that delineates the main stages from
acquiring resources, bundling them into capabilities, and leveraging the capabilities towards
competitive strategies. This work therefore provides a novel perspective on AI deployments in the
organizational setting by identifying the steps involved in developing AI capabilities and considering
the diversity of ways in which this can be achieved. In doing so, we take into account the role of the
environment. In doing so we document how these activities can be used for digital strategy
formulation, and for digital strategy execution. Concluding, we present a two-phase study design in
order to explore the previously described framework, validate it, and empirically identify
configurations of activities that enable value generation under different environmental conditions.
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... While investing in technological infrastructure for AI may be an important part, organizations hoping to use AI in core operations must be able to govern the necessary resources and have thorough practices and mechanisms for orchestrating and following up on projects from ideation to completion (Papagiannidis et al., 2021). In addition, AI applications require several phases of maturation, and are subject to continuous improvement and development. ...
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Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizations are increasingly turning to AI in order to gain business value following a deluge of data and a strong increase in computational capacity. Nevertheless, organizations are still struggling to adopt and leverage AI in their operations. The lack of a coherent understanding of how AI technologies create business value, and what type of business value is expected, therefore necessitates a holistic understanding. This study provides a systematic literature review that attempts to explain how organizations can leverage AI technologies in their operations and elucidate the value-generating mechanisms. Our analysis synthesizes the current literature and highlights: (1) the key enablers and inhibitors of AI adoption and use; (2) the typologies of AI use in the organizational setting; and (3) the first- and second-order effects of AI. The paper concludes with an identification of the gaps in the literature and develops a research agenda that identifies areas that need to be addressed by future studies.
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Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines the relationship between an AI capability and organizational creativity and performance. Findings empirically support the suggested theoretical framework and corresponding instrument and provide evidence that an AI capability results in increased organizational creativity and performance.
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Purpose: The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (i) analysis of AI and AI concepts/technologies; (ii) in-depth exploration of case studies from a great number of industrial sectors; (iii) data collection from the databases (websites) of AI-based solution providers; and (iv) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations. Design/methodology/approach: This study has called on the theory on IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase. Findings: AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can therefore enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes. Implications: AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared towards a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations and, at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships, and scalable infrastructure. Originality/Value: This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 cases studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.
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Taking three recent business books on artificial intelligence (AI) as a starting point, we explore the automation and augmentation concepts in the management domain. Whereas automation implies that machines take over a human task, augmentation means that humans collaborate closely with machines to perform a task. Taking a normative stance, the three books advise organizations to prioritize augmentation, which they relate to superior performance. Using a more comprehensive paradox theory perspective, we argue that, in the management domain, augmentation cannot be neatly separated from automation. These dual AI applications are interdependent across time and space, creating a paradoxical tension. Over-emphasizing either augmentation or automation fuels reinforcing cycles with negative organizational and societal outcomes. However, if organizations adopt a broader perspective comprising both automation and augmentation, they could deal with the tension and achieve complementarities that benefit business and society. Drawing on our insights, we conclude that management scholars need to be involved in research on the use of AI in organizations. We also argue that a substantial change is required in how AI research is currently conducted in order to develop meaningful theory and to provide practice with sound advice.
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Despite the claim that Artificial Intelligence (AI) can revolutionize the way private and public organizations do business, to date organizations still face a number of obstacles in leveraging such technologies and realizing performance gains. Past studies in other novel information technologies argue that organizations must develop a capability of effectively orchestrating and deploying necessary complementary resources. We contend that if organizations aim to realize any substantial performance gains from their AI investments, they must develop and promote an AI Capability. This paper theoretically develops the concept of an AI capability and presents the main dimensions that comprise it. To do so, we ground this concept in the resource-based view of the firm and by surveying the latest literature on AI, we identify the constituent components that jointly comprise it.
Conference Paper
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Machine learning has recently emerged as a powerful technique to increase operational efficiency or to develop new value propositions. However, the translation of a prediction algorithm into an operationally usable machine learning model is a time-consuming and in various ways challenging task. In this work, we target to systematically elicit the challenges in deployment and operation to enable broader practical dissemination of machine learning applications. To this end, we first identify relevant challenges with a structured literature analysis. Subsequently, we conduct an interview study with machine learning practitioners across various industries, perform a qualitative content analysis, and identify challenges organized along three distinct categories as well as six overarching clusters. Eventually, results from both literature and interviews are evaluated with a comparative analysis. Key issues identified include automated strategies for data drift detection and handling, standardization of machine learning infrastructure, and appropriate communication and expectation management.
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Firm-specific human capital (HC) is widely recognised as the most important resource for superior firm performance. Contemporary literature on the resource-based view (RBV) and resource orchestration has stressed the importance of organising resources, such as firm-specific HC, in order to fully exploit them. However, companies with idiosyncratic resources cannot rely on established resource exploitation practices, making the exploitation of firm-specific HC a complex issue. Nevertheless, few studies have empirically examined how to orchestrate firm-specific HC. Therefore, the aim of this study is to examine how resource orchestration—operationalised as collaborative human resource management (CHRM) and entrepreneurial orientation (EO) both individually and combined—moderates the relationship between firm-specific HC and firm performance. Based on a sample of 151 Swedish manufacturing firms, the findings demonstrate that CHRM and EO do not independently influence the relationship between firm-specific HC and performance. However, firms with firm-specific HC benefit from either being highly entrepreneurial and relying on CHRM or being non-entrepreneurial and not focusing on CHRM; they perform worst if they are entrepreneurial without using CHRM. Whereas previous RBV-studies on resource exploitation have mainly stressed that HC has to be exploited, this study contributes to the RBV by examining how firm-specific HC should be exploited.
Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
This article explores the role of artificial intelligence (AI) in aiding personalized engagement marketing—an approach to create, communicate, and deliver personalized offerings to customers. It proposes that consumers are ready for a new journey in which AI is a tool for endless options and information that are narrowed and curated in a personalized way. It also provides predictions for managers regarding the AI-driven environment on branding and customer management practices in both developed and developing countries.
Overview: Machine learning applications in business that return forecasts or predictions of future market or consumer behavior must pay attention to nontechnical aspects of how those forecasts are created and used by leaders. Machine learning projects can generate better forecasts that have greater effect by embracing key methods developed through almost 50 years of corporate foresight practice to improve the adoption and use of forecasts in organizations.