Artificial intelligence is part of our day-to-day lives and has become an integral part of different products, applications, and services. AI has outperformed conventional solutions in many business areas, such as Manufacturing, Healthcare, Transportation, Banking, and Retail, which has also helped increase the use of AI methods in these areas (Bharati et al., 2024). AI is also transforming functional areas like Marketing, Finance, Operations, Human Resources management, etc. (Haleem et al., 2022; Routray, 2024).  However, there are concerns around AI that are posing challenges to the adoption. While AI adoption unlocks new value for organisations, at the same time, it also introduces new risks (Alzubaidi et al., 2023; Zhou, 2024). To realise the benefits and improve the adoption of AI, organisations should assess and mitigate those risks by incorporating principles that add trust in each stage of AI development (Mukherjee, 2024) and operations. As companies navigate the intricate path of integrating AI technologies in their business processes, understanding the theoretical underpinnings of this adoption process becomes crucial for successful implementation.

The adoption of AI is influenced by a multitude of factors, including technological barriers, ethical concerns, and organisational resistance. These challenges highlight the need for a structured approach to understanding how and why organisations choose to adopt AI. This is where theoretical frameworks play a pivotal role. Frameworks such as the Technology Acceptance Model (TAM), the Diffusion of Innovations (DOI), and the Theory of Planned Behavior (TPB) provide valuable insights into the dynamics of AI adoption. These theories offer lenses through which can analyse the decision-making processes, behavioural intentions, and innovation diffusion that are integral to AI integration (Ajzen, 1991; Davis, 1989; Rogers, 2003)

Despite the abundance of theories, there exists a gap in synthesising these frameworks to provide a comprehensive understanding of AI adoption. Previous studies have often focused on specific aspects of AI adoption, yet there remains a need for a holistic analysis that integrates multiple theoretical perspectives. The significance of understanding AI adoption lies in its potential to unlock competitive advantages, drive innovation, and enhance operational efficiency. However, without a robust theoretical foundation, organisations may encounter difficulties in effectively implementing AI technologies.

Objective

The objective of this Systematic Literature Review (SLR) is to analyse both the challenges and opportunities inherent in AI adoption at the organisational level, aiming to promote a deeper understanding and facilitate broader integration of these frameworks and theories in industry and academia.

Table 1. Research Questions & Motivation.

In this paper, the authors explore how various theoretical frameworks can be applied and combined to provide a deeper understanding of the AI adoption process. Ultimately, the paper provides a comprehensive view of diverse factors that impact AI adoption in an organisational context, offering valuable insights for future research and practical implementations.

To create a sound information base for both researchers and practitioners on the topic of AI adoption, the authors followed the systematic approach of an SLR. Our SLR aims to select, analyse, and synthesise findings from the existing literature on AI adoption. This systematic literature review was conducted following established guidelines (Kitchenham & Charters, 2007; Moher et al., 2009) to ensure comprehensive coverage and reproducibility. The review involved a structured search across several academic databases, including Scopus, arXiv, Springer, IEEE Access, ACM Digital Library, Frontiers in Robotics and AI, Applied Sciences, and Google Scholar. The AI adoption-specific searches were restricted to peer-reviewed articles written in English and searched across classical adoption theories, AI adoption-specific theories and frameworks, and Industry frameworks. 

Selection Criteria

The selection process incorporated both inclusion and exclusion criteria to refine the pool of literature. Articles were included if they met the following requirements in the table below

Table 2. Study selection criteria.

The systematic literature review (SLR) process for identifying AI adoption theories and frameworks follows a structured and rigorous methodology.    The methodology is based on PRISMA statement (Page et al., 2021) which suggests to selection process using a flow diagram as depicted in Fig.1. The approach can be broken down into three main phases: Identification, Screening, and Inclusion.

In the identification phase, a comprehensive search for relevant literature across multiple databases and sources was done. A total of 54,601 records were initially identified. To ensure the quality and relevance of the data, irrelevant or low-quality records were removed. 34,291 records were excluded due to a lack of sufficient citations. 3,458 preprints were removed to focus on peer-reviewed and finalized studies. And 462 records were excluded to maintain the integrity of the review. After this filtering, 16,390 records remained for further evaluation.

The screening phase involved a detailed review of the remaining records to assess their relevance to the research topic. 34,291 records were excluded based on their titles, as they were unrelated to AI adoption theories or frameworks. 3,458 records were excluded after abstract screening, as they focused on unrelated topics such as model learning frameworks. Advanced methods like Symantec search and Hybrid search were used to exclude some of the records.  This process narrowed the pool to 324 reports deemed potentially relevant. Of the 324 reports, 27 could not be retrieved, leaving 297 records for full-text screening.

Fig. 1. Flow chart showing AI adoption theories and frameworks for study selection

A further 262 reports were excluded after a detailed review, as they did not meet the inclusion criteria (e.g., lack of focus on organisational AI adoption frameworks or insufficient methodological rigour). In the final phase, 35 theories and frameworks were identified and included in the review. These represent the most relevant and high-quality contributions to the understanding of AI adoption at the organisational level.

The programmatic approach used in the initial search process minimises the risk of researcher bias in the selection of studies. While authors recognise the possibility that some relevant manuscripts may not have been captured by the automated process, the extensive number of studies identified through the broad search queries provides a robust foundation for this systematic literature review (SLR).

In this section, authors thoroughly analyse the studies included in this review and discuss the considerations and gaps. We will also talk about the factors various frameworks considered for adoption. This section will try to address the research question discussed in Table 1.

How do different frameworks and theories address the challenges and opportunities associated with AI adoption? (RQ1)

To address this research question, authors have analysed various classical organisational theories, AI-specific frameworks and industry frameworks.

Classical Organisational Theories

Various classical organisational theories can be utilised to explain the adoption of AI in organisations. The Organisational Learning Theory(Argyris & Schön, 1978) emphasizes the importance of fostering a culture of continuous learning within organisations. This theory highlights the need for organisations to adapt to changes by learning from past experiences and challenging existing assumptions. It underscores the role of double-loop learning in enabling organisations to embrace innovation and effectively integrate technologies like AI. The Technology Acceptance Model (TAM) (Davis, 1989) and the Theory of Planned Behavior (TPB) (Ajzen, 1991) focus on individual-level acceptance of technology. AI-TAM (Baroni et al., 2022), which is an extension of TAM for AI, identifies perceived usefulness and ease of use as critical factors influencing AI adoption, making it essential to design AI systems that are intuitive and beneficial to end-users. TPB, on the other hand, explains how employees' attitudes, peer influence, and confidence in their ability to use AI impact their willingness to adopt it. These theories are particularly relevant for addressing resistance to AI adoption and ensuring user-level acceptance.

At the organisational level, the Technology-Organisation-Environment (TOE) Framework (Tornatzky, 1990) provides a holistic view by considering technological readiness, organisational resources, and external pressures such as competition and regulations. Similarly, the Resource-Based View (RBV) (Barney, 1991) emphasizes leveraging organisational resources, such as financial, human, and technological capabilities, to gain a competitive advantage through AI adoption. These frameworks highlight the importance of aligning AI initiatives with organisational strategies and ensuring the availability of resources for successful implementation. The Strategic Alignment Model (SAM) (Henderson & Venkatraman, 1994) and Institutional Theory (Scott, 2001) focus on aligning AI strategies with business objectives and addressing external pressures, respectively. SAM underscores the need for strategic alignment between IT and business goals to maximise the value of AI adoption. Institutional Theory highlights the role of regulatory requirements, industry standards, and societal expectations in driving AI adoption, making it essential for organisations to navigate external pressures effectively.

Finally, theories like the Diffusion of Innovations Theory (Rogers, 2003) and the Technology Readiness Index (TRI) (Parasuraman, 2000) provide insights into the spread of AI technologies and the readiness of organisations to adopt them. These theories emphasise the importance of factors such as relative advantage, compatibility, and organisational readiness in facilitating AI adoption. Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) integrates multiple theories, including social influence and facilitating conditions. Together, these frameworks and theories offer a comprehensive understanding of the multifaceted nature of AI adoption, addressing individual, organisational, and environmental factors.

Table 3. Classical Organisational Theories Analysis

AI Specific Frameworks

Classical organisational theories explain the general adoption of technologies within organisations; however, they often fail to explain AI-specific aspects such as Ethics, Explainability and other AI-related factors. To understand these, the authors have analysed AI-specific frameworks.     

 The AI frameworks collectively address critical aspects of artificial intelligence (AI) development, adoption, and governance, emphasising ethics, safety, transparency, and societal impact. Frameworks like the Ethics of Artificial Intelligence (Bostrom & Yudkowsky, 2014) and the Beneficial AI Framework (Russell et al., 2015) focus on aligning AI systems with human values and ensuring their long-term benefits while mitigating existential risks. Similarly, the Value Alignment Framework (Gabriel, 2020) and the Ethical AI Framework(Vesnic-Alujevic et al., 2020) provide guidelines for embedding ethical principles, fairness, and accountability into AI systems, ensuring they respect human rights and societal norms.

Table 4. AI-Specific Theories & Frameworks analysis

On the technical side, frameworks like Explainable AI (XAI)(Gunning et al., 2019) Framework and Causal AI Framework(Sgaier et al., 2020) emphasize improving AI interpretability and decision-making by incorporating transparency and causal reasoning. The AI Safety Framework(Amodei et al., 2016) and Risk Management Frameworks focus on ensuring the robustness, reliability, and security of AI systems, particularly in high-stakes applications. Additionally, frameworks like the AI Transparency Framework and the AI Fairness Framework(Barocas et al., 2023) address issues of bias, fairness, and accountability, aiming to build trust and societal acceptance of AI technologies.

From a societal perspective, frameworks such as the AI for Social Good Framework(Floridi et al., 2020) and the Human-Centred AI Framework(Shneiderman, 2020) advocate for leveraging AI to address global challenges and prioritise human needs. The Human-in-the-Loop (HITL) Framework(Mosqueira-Rey et al., 2023) emphasizes the importance of human oversight in AI decision-making, ensuring accountability and reliability. Finally, practical frameworks like the TOP Framework-Based Checklist(Tursunbayeva & Chalutz-Ben Gal, 2024) and Davenport's AI Value Realisation(Davenport, 2018) focus on guiding organisations in adopting AI effectively, aligning AI initiatives with business goals, and maximising their value. Together, these frameworks provide a comprehensive roadmap for ethical, safe, and impactful AI development and adoption.

Industry-Specific Frameworks

While classical organisational theories and AI-focused adoption frameworks provide good insights into AI adoption, they often miss practical implementation nuances. To address some of those challenges, AI pioneers such as Google, Microsoft and others introduced frameworks for AI adoption. These frameworks provide comprehensive guidance for organisations to integrate AI effectively. They emphasise key aspects such as strategy alignment, ethical AI practices, data readiness, governance, and scalability.

Table 5. Industry Theories & Frameworks analysis

What are the critical factors identified in existing frameworks and theories that influence AI adoption at the organisational level? (RQ2)

Based on the above analysis, authors have identified the factors that are impacting AI Adoption. Based on the nature of those factors, they are divided into Technical, Organisational, Psychological, Environmental, Social, Legal and Ethical factors.

Fig. 2. Factors that contributed to the AI adoption the table below shows individual parameters within these factors

Table 6. Factors and Parameters impacting AI adoption

While each framework focuses on one or other aspect, there is a greater need to create a comprehensive framework with these factors

The overarching goal of this systematic literature review (SLR) is to provide comprehensive insights into the adoption of Artificial Intelligence (AI) at the organisational level by analysing existing frameworks and theories. This study involves an extensive search of the scientific literature using well-defined search terms and research questions, as outlined in the methodology section. While AI offers transformative potential, its successful implementation demands a strategic alignment of organisational resources, technological infrastructure, and external environmental factors.

The reviewed frameworks highlight the multifaceted nature of AI adoption. These frameworks emphasise critical factors such as organisational readiness, regulatory compliance, and the alignment of AI initiatives with business objectives. However, the adoption process is further complicated by the need for a collaborative culture, cross-functional expertise, and robust governance mechanisms. Additionally, the rapid evolution of AI technologies, including generative AI and edge computing, introduces new challenges related to scalability, ethical considerations, and data management.

This review also underscores the importance of addressing barriers such as resistance to change, lack of technical expertise, and resource constraints. The rise of emerging paradigms such as Generative AI, cloud computing, edge AI and compound AI technologies necessitates innovative solutions to support distributed and heterogeneous AI ecosystems. It highlights the need for organisations to adopt a holistic approach that integrates technological advancements with organisational strategies and external environmental factors.

 As for future work, this study identifies gaps in the existing frameworks, particularly in addressing user behaviour for AI adoption in organisational context, impact of reliable Model operations, Model Quality Assurance (for ex. Hallucinations etc. in the context of Large Langue Models) and societal implications of AI, especially with the emergence of new paradigms such as generative AI and agentic AI. Generative AI, with its ability to create content, designs, and solutions autonomously, raises critical questions about intellectual property, ethical use, and the potential for misuse, requiring frameworks to incorporate robust model validation, governance, and reliable model operations that are specific to these scenarios. Similarly, Agentic AI, which operates with a degree of autonomy and decision-making capability, demands a re-evaluation of trust, control, and human oversight in AI systems, as well as strategies to ensure alignment with human values and organisational goals.

Future studies should focus on developing adaptive AI adoption frameworks that integrate principles of explainability, fairness, and safety tailored to these advanced AI paradigms. Research is needed to explore how organisations can balance innovation with ethical considerations, address biases in generative outputs, and ensure agentic systems remain corrigible and aligned with human intent. Additionally, frameworks must evolve to include guidelines for managing the societal and economic impacts of these technologies, such as workforce transformation, regulatory compliance, and equitable access. By addressing these emerging challenges, future studies can ensure that AI adoption frameworks remain relevant and effective in guiding organisations through the complexities of Generative and Agentic AI.

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