GenAI Adoption: Trends & Challenges

• Generative AI (GenAI) adoption is the integration of advanced machine learning models, like large language models, into various sectors, emphasizing compatibility and perceived usefulness.
• Empirical studies using methods such as PLS-SEM and regression analysis reveal that compatibility with existing workflows is crucial for measurable productivity gains in GenAI integration.
• Implementation challenges include mitigating risks such as hallucinations, ethical concerns, and skill shifts while ensuring effective human oversight and continuous evaluation.

Generative AI (GenAI) adoption refers to the integration, acceptance, and diffusion of machine learning systems—particularly LLMs and other generative models—across industries, domains, organizations, and educational environments. Rapid advances in GenAI capabilities over the period 2022–2025 have led to its deployment in software engineering, education, business, science, finance, and beyond, fundamentally transforming workflows, skill requirements, organizational structures, and regulatory approaches. The complexity of adoption is shaped by compatibility with existing practices, perceptions of usefulness, ethical and societal risks, and an evolving body of theoretical models and empirical studies.

1. Determinants and Models of GenAI Adoption

Adoption patterns of GenAI diverge markedly from traditional information technology models, emphasizing compatibility with established workflows as the dominant factor, particularly in technical domains such as software engineering (Russo, 2023). The Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) theory are frequently applied to paper GenAI adoption among individuals and organizations, focusing on perceived usefulness (PU), perceived ease of use (PEOU), compatibility, complexity, and social factors (Ghimire et al., 29 Mar 2024, Russo, 2023). However, findings consistently indicate that compatibility—alignment of GenAI with current technical, organizational, or pedagogical processes—mediates perceived benefits and intentions to use, superseding direct effects of usefulness or social influence in early stages of diffusion.

Theoretical integrations, such as the Human-AI Collaboration and Adaptation Framework (HACAF), formalize these interdependencies:

$$IU = \beta_1 \cdot CF + \beta_2 \cdot SF + \beta_3 \cdot PEF + \epsilon$$

$CF = \beta_4 \cdot PT + \epsilon_1$

$SF = \beta_5 \cdot PT + \epsilon_2$

where $IU$ is intention to use, $CF$ denotes compatibility factors, $SF$ social factors, $PT$ perceptions of the technology, and $PEF$ personal and environmental factors. Empirical studies employing Partial Least Squares Structural Equation Modeling (PLS-SEM) and mixed-methods (e.g., Gioia methodology) quantitatively validate these frameworks, revealing that direct influences of PU/PEOU on usage intention are significantly weaker than indirect compatibility-mediated paths (Russo, 2023).

In educational contexts, regression analyses confirm that perceived usefulness dominates (coefficient = 0.678, $p = 7.2 \times 10^{-13}$), with ease of use playing a minor but significant role (coefficient = 0.227, $p = 0.026$), jointly explaining a substantial portion of variance in acceptance (Ghimire et al., 29 Mar 2024).

2. Sector-Specific Adoption Patterns

Adoption patterns exhibit sectoral specificity:

• Software Engineering: Early adoption is niche-focused, emphasizing seamless integration into existing development environments for tasks such as code drafting and debugging (Russo, 2023). In software architecture, GenAI is predominantly used for architectural decision support and reconstruction, with OpenAI GPT models and prompting techniques such as few-shot learning and retrieval-augmented generation (RAG) dominating (Esposito et al., 17 Mar 2025). However, rigorous validation and the lack of architecture-specific datasets remain obstacles.
• Business Leadership: Global surveys reveal widespread adoption among executives and business leaders, driven by productivity and innovation benefits (cited by ~90% of respondents) but tempered by significant concerns about misuse (82%) and privacy (67%) (Davis et al., 6 Apr 2024).
• Education: Adoption by educators and students is driven by perceived enhancement of learning outcomes and efficiency, but concerns about over-dependence, academic integrity, and skill degradation—such as the "Junior-Year Wall"—appear consistently (Dickey et al., 2023, Smith et al., 17 Nov 2024). University-level adoption is often fragmented without clear frameworks, stimulating the development of stepwise institutional models (e.g., the "4E Framework") and objective evaluation matrices (Shailendra et al., 21 Jul 2024).
• Financial Institutions: GenAI is leveraged for customer engagement (via chatbots and personalized advisory), workflow automation (compliance, fraud detection), and internal productivity gains, with regulatory approaches rapidly evolving to address cybersecurity, bias, and transparency (Saha et al., 30 Apr 2025).

3. Challenges: Risks, Skills, and Ethical Implications

GenAI adoption is accompanied by a spectrum of challenges:

• Model Reliability: Hallucinations (plausible yet incorrect outputs) are a major barrier in knowledge- and safety-sensitive domains. Theoretical models of organizational adoption show that hallucination rates must stay below a critical threshold to justify deployment (Xu et al., 31 May 2025).
• Skill Shifts: Job market analyses reveal that GenAI adoption induces higher demand for cognitive (problem-solving, critical thinking) skills (+36.7%) and, over time, increases the need for social/collaborative skills, while reducing reliance on more automatable, routine capacities (e.g., customer service, self-management) (Gulati et al., 12 Mar 2025). In organizational settings, "deskilling" phenomena are observed, as GenAI augmentation allows firms to lower workforce knowledge requirements while preserving operational effectiveness.
• Ethical and Governance Issues: The risk landscape includes data leakage, privacy breaches, adversarial attacks (prompt injection, data poisoning), bias propagation, intellectual property confounds, and regulatory non-compliance (Eacersall et al., 11 Dec 2024, Saha et al., 30 Apr 2025). Frameworks such as ETHICAL (Examine, Think, Harness, Indicate, Critically engage, Access secure, Look at agreements) provide multi-pronged guidance on responsible GenAI adoption in research environments (Eacersall et al., 11 Dec 2024).
• Societal and Emotional Reception: Sentiment analysis and behavioral models (Gartner Hype Cycle, Kübler-Ross Change Curve) demonstrate that adoption proceeds through phases of inflated expectations, disillusionment, and gradual plateauing, paralleled by emotional stages from shock and denial to acceptance and integration (Truong, 25 Apr 2025).

4. Methodologies and Evaluation Frameworks

Adoption research employs convergent mixed-methods combining:

• Qualitative Explorations: Structured interviews, Gioia methodology, and open-ended coding for thematic analysis (e.g., "task-specific efficiency improvements," "error identification in GenAI outputs") (Russo, 2023, Chen et al., 24 Jul 2025).
• Quantitative Survey and Modelling: Use of PLS-SEM, regression analysis, and importance-performance matrix analysis to quantify latent constructs (e.g., system/output quality, functional value, goal maintenance) and their importance for trust and behavioral intention (Choudhuri et al., 23 May 2025).
• Objective Metrics: Evaluation matrices (e.g., AVM in education) aggregate multi-dimensional indicators (awareness, readiness, ethics, ICT adoption, access equality) with the potential for iterative improvement (Shailendra et al., 21 Jul 2024).
• Sentiment and Emotion Analytics: VADER and EmoRoBERTa models for sentiment/emotion trajectory analysis in large-scale social data (Truong, 25 Apr 2025).

5. Practical Implementation Considerations

Effective GenAI adoption depends on:

• Design and Integration: Tools with high compatibility and minimal workflow disruption achieve the greatest early adoption in technical disciplines (Russo, 2023, Yu, 25 Apr 2025). Immersive development environments and iterative prompt refinement improve coding productivity but must be supplemented by quality assurance mechanisms.
• Human-in-the-Loop and Oversight: Human validation remains indispensable, especially in domains where hallucinations or model-induced errors are costly (Xu et al., 31 May 2025). Organizational models show human validation can expand managerial spans of control if implemented efficiently.
• Ethics, Transparency, and Explainability: Adoption frameworks urge the explicit documentation of AI use, bias control, auditability, explainability (using tools like SHAP, LIME), and incident response planning (Eacersall et al., 11 Dec 2024, Saha et al., 30 Apr 2025).
• Skill and Curriculum Development: Curricula should address technical proficiency, critical thinking, prompt engineering, bias awareness, and ethical reasoning. Both project-based, hands-on methodologies and institutional policy updates are necessary for durable adoption (Krause et al., 28 Apr 2025, Shailendra et al., 21 Jul 2024).
• Stakeholder Alignment and Feedback Loops: Stepwise frameworks (such as 4E: Embrace, Enable, Experiment, Exploit) structure campus-wide adoption, delineating clear roles for management, faculty, ICT staff, students, and regulators, and embed continuous feedback via evaluation matrices (Shailendra et al., 21 Jul 2024).

6. Future Directions and Open Research Areas

Rapid GenAI diffusion is expected to persist, with advances in autonomous agent architectures, interactive and adaptive learning companions, and domain-specific fine-tuned models. However, successful large-scale and sustainable adoption will hinge on resolving open questions including:

• Optimal methods for reducing hallucinations and establishing effective human-AI collaboration workflows (Xu et al., 31 May 2025).
• Cross-domain generalization of evaluation metrics and benchmarks, particularly in architecture, science, and education (Esposito et al., 17 Mar 2025, Ding et al., 30 Dec 2024).
• The role of regulation at national and international levels as data privacy, fairness, and energy consumption concerns become more acute (Saha et al., 30 Apr 2025, Eacersall et al., 11 Dec 2024).
• Balancing deskilling and upskilling dynamics within organizations and their attendant societal impacts (Gulati et al., 12 Mar 2025, Xu et al., 31 May 2025).
• Resolving disparities in adoption and benefit across regions, demographics, and industries, with attention to inclusion, equity, and accessibility (Davis et al., 6 Apr 2024, Krause et al., 28 Apr 2025).

7. Summary Table: Principal Determinants of GenAI Adoption (select findings)

Sector/Domain	Dominant Driver	Major Risk/Barrier
Software Engineering	Workflow compatibility	Validation of outputs
Higher Education	Perceived usefulness (PU)	Skill degradation, fairness
Business Leadership	Productivity gains	Misuse, privacy, regulation
Finance	Compliance/automation	Adversarial attacks, bias
Scientific Research	Automated processes	Data quality, collaboration

In conclusion, GenAI adoption manifests as a multi-level, multi-factor process, strongly shaped by compatibility with extant practices, nuanced by individual and organizational perceptions, and modulated by emergent ethical, legal, and skill-related considerations. The multidimensional theoretical and empirical foundation synthesized in recent literature provides a robust roadmap for navigating adoption trajectories and developing evaluation and governance strategies suited to the unique challenges and opportunities posed by generative AI.