Factors Influencing the AI Adoption in Organizations

Abstract

Adoption of Artificial Intelligence (AI) in organizations is getting popular due to the impact it is creating in business and society. While AI as a technology has made progress due to advances in various research areas, there is limited study on the adoption of AI in organizations. This article intends to develop a model for identifying the factors influencing AI adoption in organizations. We developed the model using Diffusion of Innovation and Technology-Organization-Environment theories, adopted from the field of Information Systems. In this article, we provide an analysis of application of theories toward AI adoption and provide a model at organization level. We collected data by designing a survey on 7-point Likert scale to validate our proposed model using regression-based approach and structural equation modelling. The results suggest that the change capability of an organization and leadership positively impact the AI adoption in organizations. Further, our study reflects that AI readiness by the organizations and AI adoption by trading partners of an organization also impact AI adoption positively.

Keywords

AI Adoption TOE DoI Trading Partner AI Readiness

Introduction

Technology adoption is a common area of study in the IS area. It is the study of variables related to adoption behaviour of technology at Individual, Group, and Organizational levels.¹ In organizations, technology adoption has been studied as a part of IT business value² and innovation techniques that enable organizations to improve organizational performance and succeed in the business environment. Study of technology as a part of innovation in organization mention two types of research, studies in the context of the industry and studies in the organizational sub-unit level.³

When an organization starts adopting any technology, there are various factors that determine the success or failure of the technology adoption. These factors vary on the type of technology, nature of the organization doing the adoption, and various other aspects.⁴ In recent times, one of the technologies that has become popular is Artificial Intelligence (AI). Be it individuals using mobile phones or using Google search engine⁵ or organizations optimizing their operations,⁶ there is some component of AI. Due to this, as AI becomes a broad technology having impact across multiple fields, there are multiple definitions of AI depending on the context.⁷ From an organizational context, AI is defined as the capability to recognize, understand, derive insights, and learn from the data to meet the strategic goals.⁸ Organizations are working towards capturing business value in various ways using AI. A Gartner study has found that the count of organizations adopting AI has increased by 270% since 2015 and grown by three times in 2019.⁹ AI is expected to change not only the way organizations work but also the business models of organizations.¹⁰ Still, as a technology while AI has made advances, there is a lack of understanding in terms of why organizations should use AI.^5,11–13 Also the nature of AI is unique compared to earlier technologies that have been used by organizations.⁸

One of the uniqueness of AI models is the need for data for training. AI models need to be trained with specific data related to be use case or business problem. This data are called labelling data.¹⁴ Usual AI models are like a small child who does not know how to read or write. The models are trained using historical data comprising of decision parameters and the final decision taken. Once the AI model is trained with adequate variety of data, it becomes ready to be deployed for real-world applications.

Another aspect of AI is change management. By its very nature, AI replicates human thinking and need to work closely with humans.¹⁵ Humans need to adopt to new way of working with solutions that use AI technology. This requires changes across the entire organization. As a result, does organizational change management functionality assist in AI adoption is important.

This makes it important to study the factors of AI adoption from the lens of an organizational implementation. Past studies have focused on technology adoption such as adoption of internet, open source, mobile, and cloud.¹ There have also been studies in adoption of emerging technology as a whole.⁴ But there are very few studies of AI as a technology that is adopted in organizations. So, this article tries to fix this gap by identifying what are the factors that determine AI adoption in the context of organizations.

Research Question

Due to advances in hardware technology, it is now possible to process high amount of data in a short span of time. This is helping AI to execute complex activities such as natural language processing, image processing, building voicebots and chatbots, and using deep learning algorithms.¹⁶ It has led to development of expert systems and AI-based solutions that facilitate decision-making, increase automation and improve customer experience. AI has become instrumental in bringing about transformation in various industries such as manufacturing, legal, information broadcasting,¹⁷ and banking. It is now becoming integral part of any new product.¹⁸

While lot of advances have been made in AI from a technology perspective, there has been very less focus on application of AI in management studies and AI adoption in business environment.¹⁹ In the organizational context, in each industry, there are various use cases needing application of AI.²⁰ The applicability of AI varies across industries and business functions that are common across any organization. However, for implementing these use cases there are various drivers and barriers.²¹ Organizations need to understand and use the drivers to overcome the barriers for successful AI implementation. In this process, various factors across different dimensions play a role. Municipalities in Germany are adopting AI and factors of adoption have been direct and indirect technology benefits, compatibility with existing technology, innovativeness, strategic alignment, financial cost, availability of technical competence, and pressure from government, industry, and society.²² For organizations ready for AI implementation, the AI readiness is defined by variables organization culture and employee, management of technology artefacts in the organization, the leadership support for AI projects and project governance, organization strategy, support of AI-specific infrastructure, information management, and knowledge dissemination and security of AI implementations.²³ For organizations planning to leverage AI for competitive advantage, support from leadership, clarity on using AI technology as differentiator, availability of technical resources, culture in the organization, and availability of use cases where AI can be applied were found to be important.²⁴ Machine learning, a sub-field of AI, was studied from adoption perspective in organization in USA and Germany and organization size and structure were found to be an adoption factors other than leadership, availability of resources, compatibility of machine learning technology with existing technology, advantage over existing processes and complexity.²⁵ In Australian organizations, it was found that they adopted AI only if it was giving them relative advantage, had compatibility with existing technology, generated value and had top management support.²⁶ So, given the variety of context-specific factors, the research question we intend to explore in this article is:

RQ: What are the factors of AI adoption in an organization?

Model Design

Adoption of innovation technologies like AI is studied as innovation dissemination in organization studies.²⁷ The Diffusion of Innovation (DoI) theory describes the characteristics of innovation as perceived attributes of innovations, type of innovation-decision, communication channels, nature of the social system, and extent of change agents’ promotion effects.²⁸ It defines relative advantage, compatibility, complexity, trialability, and observability as perceived attributes of innovation.²⁹ Relative advantage is the extent to which the innovation is expected to improve the current process in the organization. Compatibility is the level of overlap the innovation has with existing infrastructure, organizational culture or values. Complexity is the magnitude of the extent to which the innovation is viewed as difficult to grasp and implement.³⁰ Trialability is the easiness of using and testing the innovation. Observability captures the level of perception of the innovation technique. The perceived attributes of innovation is presented in Figure 1.²⁸

Figure 1.

Source: Rogers (1995).

Out of these, in the context of adoption of innovation in organizations, the attributes of relative advantage, compatibility, and complexity were found to be relevant.³¹ These attributes have been found applicable for AI adoption in organizations also. For this study, compatibility and relative advantage are the attributes being studied since the assumption is that AI adoption will have some level of complexity associated.

Another model used in the context of technology adoption in organizations is the Technology-Organization-Environment (TOE) Framework.³² The framework explains the role of context in the process of adoption of innovative technologies. It describes three types of contexts, the technological context, which is used for innovation, the organizational context where the innovation adoption is taking place, and the environmental context of the organization implementing the innovation. Organizational context is the attributes of the organization such as size of the firm, structure like centralized or decentralized, managerial structure complexity, and other aspects. Technology context is the organization’s technology capability used internally and external technical capabilities. Environmental context is ecosystem in which the organization operates such as the industry, resources, government regulations, and competitors. The context of technological innovation³² is given in Figure 2.

Figure 2.

Source: Tornatzky et al. (1990).

The framework provides the flexibility for the researcher by not listing the variables.³³ Due to this adaptability and applicability of the framework,³⁴ it finds a good fit in AI adoption.

Model Development and Hypotheses Formulation

The model proposed uses the attributes from the DoI theory and TOE framework.

Technological Context

The technology context is the preparedness of the organization to adopt latest technology.³⁵ It consists of the portfolio of technology solutions, technical innovation capability, and technical know-how in the organization. AI technology is being used by organizations for various purposes such as anomaly detection in manufacturing, customized recommending of products, and flagging suspicious transactions.³⁶ Subsequently, for an organization, compatibility of the AI technology with its existing infrastructure, relative advantage of implementing AI solution, and readiness to implement AI define the technology context.

Compatibility

Compatibility is the ability to support AI technology by the organization’s existing IT capability.³⁷ Organizations have the technique of deploying their technology solutions either on premises, on cloud infrastructure, or using a combined approach.³⁸ Infrastructure needed for AI solution are libraries supporting AI algorithms, data processing capabilities, and Graphical Processing Units (GPUs) for training complex algorithms.³⁹ Organizations prefer hosting their IT solution on premise to reduce risk.⁴⁰ On the other hand, cloud technology like serverless computing⁴¹ is enabling organizations to build solutions faster. As a result, organizations will prefer AI solutions and platforms that are compatible with their existing infrastructure. An organization that has its IT backbone on its premises will want the AI technology to be compatible with on premise technology. Similarly, an organization that is having its infrastructure on cloud will prefer AI technology to support cloud infrastructure.

Business case is another dimension of compatibility in AI adoption in organizations. AI technology needs to justify how it is better than current technology in terms of return on investment. Business case identifies the business problem that AI is required to solve and the process in which AI will make the process efficient.⁴²

H1: Compatibility has a positive impact on AI adoption in organizations.

Relative Advantage

Organization will adopt a technology only if the technology provides advantage over existing process or product in the organization.³⁰ This approach is the relative advantage of the technology adoption. Similarly, when an organization is about to implement AI, it is critical for it to assess the advantage of the AI solution over the existing process or solution. This is the relative advantage of the AI adoption. While any technology intervention has the capability to do tasks repeatedly without any errors and for any duration of time, AI helps in taking a decision like a human in case of uncertainty.⁴³ AI also enables organizations consume and process high amount of data that are humanly not possible to analyze and make data-driven decisions.⁴⁴ As a result, AI adoption gives organizations relative advantage due to the automation functionality.

H2: Relative advantage has a positive impact on AI adoption in organizations.

AI Readiness

AI readiness is defined as the level of preparedness organizations have for AI-related changes and identified it as a key part of AI implementation in organizations.⁴⁵ Organizations adopting AI need to be ready in various aspects such as human resources, data readiness, financial support, and support of technology.⁴⁶ Human resources needed are AI technical experts, process experts, and consultants who understand AI. In order to design and develop AI solutions, technical experts are needed while process experts are required to provide assessment on the process automation which AI technology can enable. Consultants are needed to work with domain and process experts to understand the business requirements, assess the applicability of AI, and convert the relevant requirements into technical requirements. Data readiness is to ensure that the right data are available for the AI algorithms to consume while technology readiness is the availability of technology unique to AI.

H3: AI readiness has a positive impact on AI adoption in organizations.

Organizational Context

An organization has processes and structures to support adoption and innovation. It is a combination of formal and informal processes and activities. In a formal set-up, organizations define the key units and teams that will do a specific task while informal interactions and behavioural aspects assist in performing the task successfully. For an organization with the goal to adopt AI, the leadership vision and having prior experience in change management act as key factors.

Leadership Vision

IT adoption and implementation are highly dependent on the leaders of the organization where IT is being implemented.⁴⁷ The leader’s role in technology adoption is in the form of defining the vision and how technology can play a role in it,⁴⁸ allocating the budget needed and providing the resources needed. For successful AI adoption, the leaders of the organization need to understand AI as a technology and be aware of the value it can bring while acknowledging the associated risks. Since AI as a technology is still nascent, many AI pilot projects fail.⁸ In such situations, the leaders who have the larger vision of the strategic role of AI that it will play in the organization need to support AI initiatives during such initial setbacks.

H4: Leadership vision of AI has a positive impact on AI adoption in organizations.

Change Management Capability

AI as a technology changes the way work is done, both at the employee level⁴⁹ and at process level.⁵⁰ An AI implementation changes how processes are executed by either performing complete automation³⁶ of tasks that were performed manually or augmenting humans.⁵¹ So, for any AI adoption, organizations need to be ready for change. A survey of 500 senior IT managers indicated that a change in the IT and company culture was a challenge for AI business cases while 40% of respondents of a large-scale survey cited company culture as resistance to AI implantation.⁸ An organization that has earlier executed similar change will be aware of the pitfalls and challenges such a change will bring. As a result, organizations that have experience in successful change implementation are more likely to succeed in deploying AI.

H5: An organization’s change management capability has a positive impact on AI adoption in organizations.

Environmental Context

The environment in which an organization conducts its business usually determines lots of its activities. It has also been found that e-commerce adoption is determined by global environmental forces intermediated by environment and policy at national level.⁵² At global level, competition is a factor while from an organization perspective, the trading partner is considered as part of the business environment. So, for AI adoption, the level of competitiveness and partner ecosystem of the industry are studied as factors that determine AI adoption success.

Competitive Pressure

Competitive pressure is the pressure applied by the competitors of an industry on an organization. Organizations can leverage innovation to outdo competitors, impact the industry structure and change the competitive environment.⁵³ Multiple studies have indicated the impact of competition on technology adoption.⁵⁴ The same applies in the case of AI adoption. AI capability such as natural language processing and natural language generation are critical components of chatbots and voicebots. When an organization implements chatbots or voicebots for faster customer service, customers of competing organizations will demand similar service. In such instances, competitive pressure leads organization to adopt AI.

H6: The competitive pressure on an organization has a positive impact on AI adoption in organizations.

Trading Partners

IT Business Value model has been defined as comprising of the focal firm and trading partners.² A partner like vendor, supplier, or trader who trades with the organization is referred to as Trading Partner.⁵⁵ Partnership help organizations acquire assets that would have been otherwise difficult to obtain, share critical machinery, develop Intellectual Property, or generate organizational knowledge.⁵⁶ Organizations get into partnership with trading partners depending on their strategic goals. Suppliers and buyers use partnership to improve their coordination.⁵⁷ Trading partners are also developing collaborations across the value chain that are difficult to replicate and get competitive advantage.⁵⁸ AI impacts trading partners in terms of availability of data for processing for organizations and process automation.

H7: Trading partners have a positive impact on AI adoption in organizations.

Figure 3.

Source: The authors.

Study Details

In order to test the proposed model, a survey instrument was designed. The survey measures were a mix of adapted items from instruments validated previously and non-validated items that were relevant to the survey. Data were taken from employees of various organizations across India, UK, USA, and Canada and analyzed for model applicability.

Instrument Development

There were seven latent factors in the form of compatibility, relative advantage, AI readiness, business process change capability, leadership vision, competitive pressure, and trading partner as part of instrument that were tested. The questionnaire was designed using items from technology adoption, innovation diffusion, and technology implementation in organizations with modifications to accommodate AI adoption-related questions. The compatibility was tested using the scale by Jadhav¹⁹ and Chen.²⁹ Compatibility tested the respondent’s feedback on compatibility of AI-based technology and platforms with organization’s existing infrastructure. Relative advantage is tested by asking respondents on ability of AI to solve existing business problems and comparing AI with existing processes requiring manual intervention. The scale was modified from Chen.²⁹ AI readiness was tested by assessing readiness of the organizations in terms of AI resources, processes, and technology. The knowledge of AI of leaders and their vision of using AI for organizational strategy was part of the leadership questions. It was adapted from Yoon.⁵⁹ Change capability checked the organization’s prior experience in implementing change in the organization. The scale from Mikalef and Gupta⁸ was leveraged. Competitive pressure assessed the level of competition in the industry of the organization and used scale from Jadhav¹⁹ and Yoon.⁵⁹ Trading partner relationship was tested using the trading partner’s AI understanding and integration with trading partner’s ecosystem. The scale was adapted from Yoon.⁵⁹ The organization’s intention to adopt AI was tested by understanding the industry outlook towards AI. The scale used was from Jadhav¹⁹ and Yoon.⁵⁹ We created a seven-point LIKERT questionnaire, having seven scales, starting from 1 (strongly disagree) to 7 (strongly agree) to measure all the reflective indicators. There was a total of 43 items to collect AI adoption data. Since the nature of the research needed respondents to have exposure to technology and business both, the questionnaire was sent only to professionals having more than five years of experience or having a master’s degree and four years of experience. The survey was published using Google Forms.

Data Analysis

Ninety employees known to the authors were shared the survey link using their email ids and WhatsApp numbers. A total of 80 responses were received over a period of approximately one month. The years of experience of respondents varied from five years to 25 years. The industry of the participants varied with professionals from Technology and BFSI comprising the major respondents.

The information on the latent constructs was collected using 32 indicators. For convergent validity, constructs having the Average Variant Extracted (AVE) greater than or equal to 0.5 were only considered.⁶⁰ The construct reliability was kept at a value greater than 0.6 for Cronbach’s alpha and ρa.⁶⁰ Details of the construct reliability and validity are provided in Table 2.

Table 1.

Industry Break Up of Respondents

Industry	%
Automotive	2.50
BFSI	31.25
Engineering and construction	1.25
Healthcare	2.50
Manufacturing	2.50
Professional services	3.75
Retail	1.25
Technology	46.25
Telecommunication	8.75
Total	100.00

Source: The authors.

Table 3 shows the indicator loadings. Indicator loadings greater than 0.7 are considered except in few cases where the AVE has been found to be 0.5 or more.⁶⁰

The adjusted R2 is calculated to be 0.636. For measuring the discriminant validity, we checked that the heterotrait–monotrait ratio (HTMT) was below 0.9 for conceptually similar constructs and less than 0.85 for different constructs.⁶⁰ Table 4 has the HTMT details.

Table 2.

Construct Reliability and Validity

Construct	AVE	Composite Reliability	Cronbach’s Alpha	ρa
AI adoption	0.500	0.791	0.649	0.671
AI readiness	0.630	0.895	0.855	0.861
Change capability	0.739	0.893	0.816	0.871
Leadership	0.591	0.852	0.791	0.833
Trading partner	0.627	0.832	0.691	0.694

Source: The authors.

Results and Discussion

Considering the various criteria of PLS SEM, four factors that emerged as significant were AI readiness, trading partners, change management capability, and leadership. The structural model, as per the above analysis, is shown in Figure 4.

Table 3.

Item Loading for Indicators of Latent Constructs

Indicator	AI Adoption	AI Readiness	Change Capability	Leadership	Trading Partner
I would consider adopting AI in my project or line of business	0.814
I am aware of AI implementation in my industry	0.718
AI has created an impact on industry	0.662
My industry has a positive outlook towards AI	0.586
My organization has resources for building AI applications		0.834
My organization provides timely resources for AI deployment		0.83
My organization has consultants who understand AI and the industry		0.799
My organization has resources for model monitoring and feedback		0.769
My organization has centrally managed AI practice		0.732
My organization has business function where prior business process change done			0.942
My organization has business functions that successfully switched to using new technology products from old ones			0.936
My organization has successfully implemented process change			0.674
Leaders in my organization are ready to accept risks related to AI implementation				0.800
Leaders in my organization have vision of using IT capability as a strategic competence				0.773
Leaders in my organization have vision to use AI technology				0.764
Leaders believe that adopting AI makes our organization remain competitive in the industry				0.736
I am aware of the output generated by applications being used by trading partners					0.871
I am aware of applications using input from trading partners					0.838
I am aware of applications that connect our organization’s IT systems to trading partners					0.648

Source: The authors.

Table 4.

Heterotrait–Monotrait Ratio

	AI Adoption	AI Readiness	Change Capability	Leadership
AI readiness	0.784
Change capability	0.813	0.521
Leadership	0.853	0.601	0.560
Trading partner	0.903	0.505	0.545	0.792

Source: The authors.

AI readiness is positively affecting AI adoption in an organization, supporting H3. When organizations start their AI adoption journey, their success depends on factors such as resourcing available to support AI technology, presence of business cases for AI adoption, and presence of consultants who can identify the processes where AI can be implemented. Ensuring resources (human and technology), data, and financial fund availability for AI implementation assists organizations in AI adoption. In technology, AI-specific hardware and software are needed in the organization. Machine learning operations (MLOps) is the process of managing machine learning models such as model development, training, deployment, and recalibrating.⁶¹ Organizations should have the underlying infrastructure and software to support MLOps. Software commonly used for AI solution development is the cognitive services provided by various open source platforms or cloud service vendors.⁶² Product partners bring into the organization the industry-specific products having AI functionalities. Organizations having the resources ready are able to adopt AI promptly.

Figure 4.

Source: The authors.

Leadership vision of AI is positively affecting AI adoption in organization, supporting H4. The leaders of the organization of any organization are critical for AI implementation. Leaders who know the business value of Information Technology and its strategic value will explore AI as a value creator. Such leaders will have the vision to use AI within the organization as a tactical solution and as a differentiator outside the organization. When organizations start their AI journey, there is a risk of failure. Leaders who are ready to take the risk enable AI in the organization. These leaders also understand the need for AI consultants who understand the domain and AI as a technology and enable them.

Organization’s change management capability is positively affecting AI adoption in organization, supporting H5. AI adoption has high amount of change involved. AI as a technology is unique from earlier technology implementations because it replicates human thinking. So due to this automation capability of human cognition, lot of work which was not earlier impacted by technology is now getting affected. As a result, AI adoption is getting impacted across various divisions of the organization. This leads to impact on multiple units of the organization. An example is invoice processing in organization. Traditionally, an invoice would be submitted to an organization by a supplier which would be manually checked by the operations department. Once the invoice is reviewed for the key sections, it will go to the procurement department. As soon as the procurement division approves, it goes to finance and finally gets paid. Now, AI is used to read the invoice and extract information. Once information is extracted, AI will decide if the invoice can be auto-approved or need manual review. Once approved, it will automatically get paid. Since multiple units are getting involved, organizations that have prior experience in managing change across the entire organization are more likely to be successful in adopting AI.

Trading partner is positively affecting AI adoption in organization, supporting H7. Automation of data exchange with trading partners is enabling organizations to adopt AI. Data like IoT feed from suppliers help organizations use AI to forecast the inventory and plan the supply chain activity.⁶³ A trading partner’s capability to consume the output from AI systems of the organizations enables automation and faster execution of processes. For instance, when AI is able to identify the need for inventory replenishment, the trading partner’s ability to support automated request creation for order replenishment helps organizations create end-to-end automation. Email automation, chatbots, and voicebots enable trading partners to get real-time information about status of account receivables from organizations. Thus, the ability of an organization’s trading partner to generate data that can be processed by the organization and consume the AI-related output plays a role in AI adoption.

Conclusion, Limitations, and Future Research

The study surveyed employees working in the area of AI to identify factors relevant for AI adoption in organization and develop an AI adoption framework. The results indicate that the factors of AI adoption fit into the TOE framework. By adding the variable compatibility of DoI theory, the TOE framework is enriched to be applied to organizations which are planning to implement AI. The model classified the four factors into the framework of Technology, Organization, and Environment components. The research is expected to contribute to the AI adoption-related theory and practice. For practitioners, the framework will assist the leadership team of organizations starting the AI journey on the key factors to consider. Managers managing AI projects need to be aware of the factors like AI readiness and change management activities during the implementing phase. Theoretical contribution comprises the framework for AI adoption using TOE and DoI theory. While TOE and DoI have been proposed for AI adoption,⁴² this study uses a survey-based approach to identify the interplay of the factors of AI readiness, change management, leadership vision, and trading partners. These factors have been identified earlier too in different contexts; this study strengthens their importance in AI adoption in organizations.

Due to the limited sample size, the factors identified have been limited. The sample is skewed with majority of respondents from the technology industry. Also, the survey covers limited countries due to which country-specific factors are not reflected. The study does not cover organizational factors like size or culture like trust in AI, managerial capability, and other factors. Such factors may have moderating effect on the model and the relationships amongst the factors.

Using a larger sample covering more countries and industries can assist identify more relevant factors. While the study has used TOE and DoI theories, other theories applicable at the organizational level can be explored for AI adoption. RBT can be used to identify the AI-related resources of a firm.⁸ A cross-country and cross-industry study can help identify new factors or make the current factors universal.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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