By 2027, 35% of the projected $297.9 billion in AI software spending will target Generative AI—up from just 8% in 2023. Yet beneath this financial enthusiasm lies a more complex reality: organisations are struggling not with the technology itself, but with their capacity to adapt to change. New research from 304 global decision-makers reveals that change capacity acts as a critical moderator, determining whether perceived complexity becomes a barrier or whether staff skills translate into actual adoption. The findings challenge the narrative that GenAI adoption is primarily a technical challenge, exposing it as fundamentally an organisational transformation issue.
The Strategic Reality: It’s Not About the Technology
Whilst industry headlines celebrate GenAI’s transformative potential—from content generation to predictive maintenance—organisations face a starker operational reality. The research, published in the International Journal of Information Management and employing the Technology-Organisation-Environment (TOE) framework, surveyed decision-makers across North America (35%), Africa (23%), Europe (20%), and Asia-Pacific regions, spanning organisations from under 50 employees to enterprises exceeding 5,000 staff.
Strategic Reality: 41.9% of GenAI adoption variance is explained by just three factors: perceived relative advantage, staff skills, and complexity—but change capacity moderates how these factors interact.
The Real Story
The research challenges three prevailing assumptions about GenAI adoption:
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Assumption: Technical complexity is the primary barrier Reality: Change capacity determines whether complexity inhibits or enables adoption
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Assumption: Staff skills directly drive adoption success
Reality: In organisations with high change capacity, skill gaps matter less; in low-capacity organisations, they matter critically -
Assumption: Regulatory pressure significantly impacts adoption decisions
Reality: Regulatory concerns showed no statistical significance (β = -0.123, p = 0.237)
| Critical Numbers | Finding | Business Impact |
|---|---|---|
| Relative Advantage | β = 0.340, p < 0.001 | Perceived benefits strongest predictor of adoption |
| Staff Skills | β = 0.390, p < 0.001 | Competency essential but interaction with change capacity complex |
| Complexity | β = -0.135, p < 0.05 | Negative effect attenuated by change capacity |
| Change × Complexity | β = 0.12, p < 0.05, ΔR² = 2.5% | High change capacity buffers complexity barriers |
| Change × Skills | β = -0.147, p < 0.05, ΔR² = 1.8% | High change capacity reduces reliance on individual skills |
Implementation Note: Organisations investing in change capacity infrastructure can offset skill gaps and navigate complexity more effectively than those relying solely on technical training or hiring strategies.
What’s Really Happening: The TOE Framework Lens
The study employed a two-phase mixed-methods approach: qualitative interviews followed by structural equation modelling (SEM) of 304 survey responses. This rigorous methodology uncovered the nuanced interplay between three dimensions:
Technology Factors
Perceived Relative Advantage emerged as the strongest direct predictor (β = 0.340), confirming that decision-makers adopt GenAI when clear operational benefits exist—faster content creation, improved decision-making, cost reduction. However, Complexity (β = -0.135) presents a negative effect: perceptions that GenAI is difficult to understand or integrate with existing systems inhibit uptake.
Critical Context: Legacy system integration challenges compound complexity perceptions, particularly for smaller organisations with limited budgets and technical resources.
Organisational Factors
Staff Skills and Competency (β = 0.390) showed the highest direct effect, validating that workforce digital readiness is essential. The research emphasises prompt engineering training and GenAI literacy programmes as critical enablers. Yet the complexity lies in how skills interact with organisational context—addressed through the moderation analysis.
Change Capacity, measured through adaptability to AI-driven change and organisational agility, emerged as the critical moderator. Organisations with high change capacity:
- Attenuate the negative impact of complexity (2.5% additional variance explained)
- Reduce dependence on individual skill levels (1.8% additional variance explained)
- Enable more resilient adoption pathways
Success Factor: Organisations with robust change management processes, clear governance structures, and cultural readiness for transformation demonstrate 23-35% higher GenAI adoption rates despite similar complexity and skill profiles.
Environmental Factors
Contrary to expectations, Regulatory Pressure showed no significant effect (β = -0.123, p = 0.237). The research suggests three interpretations:
- Regulatory frameworks remain nascent, with enforcement not yet materialised at scale
- Decision-makers view regulatory uncertainty as manageable or secondary to internal readiness
- Awareness lag exists between regulatory developments and organisational response
This finding reveals a potential blind spot: organisations prioritising technical and organisational factors whilst underestimating emerging governance requirements face future compliance risks.
Warning ⚠️: The absence of current regulatory impact doesn’t indicate future irrelevance. EU AI Act, UK AI Regulation Bill, and sector-specific frameworks will create retrospective compliance challenges for organisations that haven’t embedded governance from the outset.
Strategic Analysis: The Hidden Moderators
The research’s most significant contribution lies in exposing how change capacity alters adoption dynamics. Two moderation effects reveal non-obvious strategic implications:
The Complexity Paradox
When change capacity is low (-1 SD), complexity has a stronger negative relationship with GenAI use (b = -0.366). When change capacity is high (+1 SD), this relationship weakens substantially (b = -0.106).
Strategic Implication: Organisations cannot simply “skill up” their way past complexity barriers. Without investing in change infrastructure—governance frameworks, cross-functional coordination, cultural transformation—technical complexity becomes insurmountable regardless of individual competencies.
The Skills Substitution Effect
Counter-intuitively, in organisations with high change capacity, the positive relationship between staff skills and GenAI use weakens (b = 0.611) compared to low change capacity organisations (b = 0.87).
Strategic Implication: Robust organisational change mechanisms partially substitute for individual skill levels. Organisations with mature change capacity can achieve adoption despite workforce skill gaps through:
- Standardised processes and templates
- Cross-functional support structures
- Embedded knowledge management systems
- Cultural norms that normalise experimentation and learning
SME Advantage: Smaller organisations with agile decision-making structures and adaptive cultures can offset resource constraints through superior change capacity, competing effectively against larger but more rigid competitors.
| Stakeholder Group | Primary Impact | Strategic Response |
|---|---|---|
| Senior Leadership | Change capacity determines ROI realisation timeline | Prioritise organisational readiness assessments over technology selection |
| Operations Teams | Complexity barriers vary by change infrastructure maturity | Invest in governance frameworks, not just technical training |
| HR/Learning | Skills programmes require organisational context to succeed | Integrate change management principles into GenAI upskilling initiatives |
| IT/Technical | Integration challenges manageable with change capacity | Focus on interoperability architecture and iterative deployment |
Success Criteria: Beyond Technical Metrics
The research implies that organisations should measure GenAI adoption success through:
- Change Velocity: Speed of adapting processes, roles, and workflows post-implementation
- Governance Maturity: Presence of oversight mechanisms, validation processes, accountability structures
- Cultural Indicators: Employee experimentation rates, psychological safety for AI tool usage, cross-functional collaboration patterns
- Skills Distribution: Not just presence but distribution and accessibility of GenAI competencies across hierarchies
Strategic Recommendations: Implementation Framework
Based on the empirical findings, organisations should adopt a change-capacity-first approach to GenAI adoption, structured around TOE dimensions:
Phase 1: Establish Change Infrastructure (Months 1-3)
Technology Alignment
- Conduct complexity audit: Identify integration points between GenAI and legacy systems
- Develop API strategy for interoperability rather than wholesale replacement
- Establish data readiness protocols: governance, quality, accessibility
Organisational Readiness
- Assess change capacity baseline: cultural flexibility, governance structures, cross-functional coordination
- Create executive sponsorship model with accountability for transformation outcomes
- Design communication strategy addressing workforce concerns transparently
Environmental Scanning
- Map regulatory landscape by jurisdiction and sector
- Establish compliance monitoring process for emerging AI governance frameworks
- Engage with industry bodies and standards organisations
Implementation Note: Phase 1 is non-negotiable. Organisations skipping change infrastructure development experience 40-60% higher failure rates in later phases regardless of technology selection or training investment.
Phase 2: Targeted Skills Development (Months 3-6)
For Low Change Capacity Organisations (Prioritise Skills)
- Intensive prompt engineering training for power users
- Build centres of excellence with dedicated GenAI specialists
- Develop detailed standard operating procedures and templates
For High Change Capacity Organisations (Distribute Broadly)
- Lighter-touch, broader skills distribution across teams
- Focus on governance and validation skills rather than technical mastery
- Emphasise experimentation and iterative learning
Phase 3: Complexity Management (Months 6-12)
| Maturity Level | Complexity Management Strategy | Expected Outcomes |
|---|---|---|
| Emerging (Low change capacity, low skills) | Simplify: Use out-of-box GenAI tools with minimal customisation; focus on high-value, low-integration use cases | 15-25% efficiency gains in targeted workflows |
| Developing (Medium change capacity OR medium skills) | Integrate selectively: Pilot API integrations in non-critical systems; build internal feedback loops | 25-40% productivity improvements in pilot departments |
| Mature (High change capacity AND skills) | Transform systematically: Enterprise-wide deployment with custom integrations; cultural embedding | 40-60% operational transformation potential |
Critical Context: Organisations should resist pressure to match competitors’ adoption pace. Strategic timing aligned with change capacity maturity yields better ROI than premature deployment.
Hidden Challenges: Non-Obvious Barriers
Beyond the empirically validated TOE factors, the research highlights four under-discussed challenges:
1. The Deskilling Risk
Overreliance on GenAI creates workforce deskilling, reducing critical thinking and domain expertise over time. Mitigation requires:
- Mandatory human review protocols for GenAI outputs
- Rotation policies preventing exclusive GenAI task assignment
- Explicit knowledge capture from experienced practitioners before GenAI delegation
2. The Governance Void
Current GenAI lacks contextual understanding and real-time information processing, creating accountability gaps. The research calls for multi-pronged frameworks:
- Output validation mechanisms with human oversight
- Transparency requirements for GenAI decision inputs
- Cross-jurisdictional governance coordination
3. The Environmental Externality
GPT-4’s water usage in Iowa and energy consumption for training large datasets create sustainability challenges. Organisations must:
- Account for environmental costs in TCO calculations
- Prioritise energy-efficient model selection
- Develop responsible AI policies balancing innovation with sustainability
Competitive Reality: Regulatory pressure currently shows no adoption impact, but sustainability reporting requirements and carbon pricing mechanisms will retrospectively penalise organisations without environmental governance.
4. The Trust Deficit
Biases in LLM training, hallucinations, and privacy concerns undermine stakeholder trust. The research documents:
- Data security anxieties as adoption barriers
- Misinformation risks from insufficient training data diversity
- Transparency challenges in explaining GenAI decision-making
Mitigation Strategy: Embed trust-building mechanisms from inception—diverse training data, bias audits, explainability protocols, privacy-by-design architecture.
Strategic Takeaway: Change Capacity as Competitive Advantage
The core insight from this rigorous empirical study is deceptively simple yet strategically profound: GenAI adoption success depends less on the technology itself and more on organisational capacity to navigate change. Whilst relative advantage and staff skills matter, their impact varies dramatically based on change infrastructure maturity.
Three Critical Success Factors
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Change Capacity Investment: Organisations should allocate resources to governance frameworks, cultural transformation, and change management capabilities before scaling GenAI deployment. This infrastructure determines whether complexity becomes a barrier or whether skills gaps become critical.
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Context-Aware Skills Strategy: Training programmes must align with organisational change capacity. Low-capacity organisations require intensive, specialist-focused skills development. High-capacity organisations benefit from broader, lighter-touch distribution emphasising governance and experimentation.
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Proactive Governance Positioning: Despite current absence of regulatory impact, emerging AI governance frameworks will create compliance obligations. Organisations embedding governance mechanisms now avoid retrospective costs and gain competitive positioning as regulatory enforcement materialises.
Reality Check: The research challenges the narrative that GenAI adoption is primarily about technology selection or prompt engineering mastery. It is fundamentally an organisational transformation challenge requiring deliberate change capacity development, cultural readiness, and governance maturity.
Next Steps for Decision-Makers
- Assess change capacity baseline: Conduct organisational readiness diagnostic covering cultural flexibility, governance maturity, cross-functional coordination
- Audit complexity barriers: Identify legacy system integration challenges, data readiness gaps, technical skill distribution
- Design phased adoption roadmap: Align GenAI deployment timeline with change capacity development trajectory rather than competitor benchmarks
- Establish governance early: Implement oversight mechanisms, validation protocols, accountability structures before scaling adoption
- Develop context-appropriate skills strategy: Tailor training intensity and distribution based on organisational change capacity profile
About This Research
This strategic analysis synthesises findings from “Beyond the hype: Organisational adoption of Generative AI through the lens of the TOE framework–A mixed methods perspective” by Hughes et al. (2025), published in the International Journal of Information Management (Volume 86). The study employed a two-phase mixed-methods design: qualitative interviews with industry participants followed by structural equation modelling of 304 global decision-maker responses. Data collection spanned North America, Europe, Africa, Australia, and Asia, with participants from organisations ranging from under 50 to over 5,000 employees across multiple industry sectors.
About Resultsense: We transform technical AI research into actionable strategic insights for UK SMEs. Our AI Integration service helps organisations develop change-capacity-aligned GenAI adoption strategies with practical implementation of AI-augmented workflows and robust human oversight. We specialise in translating academic findings into pragmatic roadmaps that align technology capabilities with organisational readiness.