Enterprise AI Analysis
Enablers and barriers to AI adoption: evidence from the heavy machinery industry
Authors: Alena Valtonen, Kirsi Kokkonen, Minna Saunila, Francesco Verdoja, Grzegorz Orzechowski, Emil Kurvinen, Päivi Aaltonen & Jussi Salakka
Publication Date: 24 February 2026
Journal: Discover Artificial Intelligence (2026)
DOI: https://doi.org/10.1007/s44163-026-01038-0
Artificial intelligence (AI) holds significant potential for heavy machinery manufacturing, yet adoption in this safety-critical and highly customized industry remains limited and insufficiently understood. This study examines enablers and barriers to AI adoption through a multiple-case study of heavy machinery manufacturers, analyzing dynamics across external, organizational, and individual levels. The findings show that AI adoption is shaped less by technological maturity than by safety requirements, regulatory complexity, organizational capabilities, and human expertise. Safety emerges as a central lens guiding adoption decisions across all levels. Simulation plays a key enabling role by supporting safe development, validation, training, and coordination, while reducing uncertainty about AI reliability. Adoption follows a hybrid and incremental logic, with firms retaining humans in the loop and expanding AI-supported decision-making as reliability and confidence increase. Organizational orchestration capability is critical for aligning technological possibilities with regulatory, organizational, and human constraints. By focusing on heavy machinery manufacturing, this study extends AI adoption research to an underexplored industrial context and clarifies how enablers and barriers shape AI adoption pathways.
Keywords: artificial intelligence; AI adoption; heavy machinery manufacturing; digital transformation; multiple case study
Executive Impact: Key Findings for Leadership
This study delves into AI adoption within the heavy machinery industry, a sector characterized by high costs, safety-critical operations, and extensive customization. Unlike lighter manufacturing, AI adoption here is driven not just by technology, but crucially by stringent safety regulations, complex operational environments, and the need for specialized human expertise. Key findings highlight that simulation is a pivotal enabler, facilitating safe development and validation, while organizational orchestration is essential for harmonizing technological opportunities with regulatory and human constraints. The industry adopts AI incrementally, favoring hybrid human-machine systems due to labor shortages and the need for robust reliability in hazardous settings.
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Annual Industry Growth (Predicted annual growth of the global AI market in industrial machinery)
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Current Market Value (Global AI-enabled predictive maintenance market for heavy equipment in 2024)
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Projected Market Growth (Anticipated exponential growth by 2035)
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Firms using AI in EU (Percentage of EU enterprises using AI technologies in 2023)
Key Challenges Summary: AI adoption in heavy machinery is hindered by several interconnected factors: legal ambiguities around liability for AI-driven decisions, the extreme environmental complexity of operating sites (e.g., mines, forests), fragmented and evolving regulatory standards across diverse applications and regions, and significant cost-benefit concerns for highly customized solutions. Internally, companies face challenges with AI reliability and explainability, leading to low trust, and a shortage of cross-disciplinary developer skills compounded by 'siloed thinking' within engineering departments. Operator skill gaps and availability issues further complicate deployment, emphasizing the need for robust human-AI collaboration.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Analysis of macro-level conditions influencing AI adoption in heavy machinery, including regulatory, market, and environmental aspects.
| Feature | Heavy Machinery Industry | Light Industries / Data-Intensive |
|---|---|---|
| Environment | High-cost, safety-critical, highly customized, harsh operating environments (mines, forests) | Standardized, less critical, often controlled environments (e-commerce, typical manufacturing) |
| Key Drivers | Safety requirements, regulatory complexity, organizational capabilities, human expertise | Technological maturity, data availability, process efficiency |
| Adoption Logic | Hybrid (human-machine collaboration), incremental expansion | Full automation, rapid scaling |
| Data Characteristics | Limited comparability due to customization, heterogeneous systems | Abundant, standardized, easily integrated |
| Regulatory Impact | Strict, fragmented, requires recertification, high liability concerns | Less stringent, more standardized |
Examination of internal company strengths and weaknesses, such as financial resources, simulation capabilities, and R&D integration, affecting AI adoption.
Simulation's Role in a Finnish OEM
One Finnish Original Equipment Manufacturer (OEM) highlighted simulation as indispensable for AI adoption. They explained, "You cannot test the software using physical machines. So, it's one driver toward the simulation. You don't have any options anymore." This allows them to virtually test machine configurations, significantly reducing reliance on costly physical prototypes and shortening innovation cycles. Crucially, simulation also contributes to safety by providing a controlled environment for operator training and enabling collision prediction, building confidence in AI reliability before real-world deployment.
Focus on the role of human expertise, skill gaps among developers and operators, and cultural aspects influencing AI integration.
Discussion of the broader strategic insights derived from the study, including safety as a central logic, incremental adoption, and the role of organizational orchestration.
Safety First
Overarching principle for AI adoption in heavy machinery
AI Adoption Pathway in Heavy Machinery
Identify Safety-Critical Task
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Simulation-Based Design & Testing
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Incremental Deployment (Human-in-Loop)
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Operator Training & Acceptance
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Regulatory Compliance & Certification
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Full Integration & Monitoring
Calculate Your Potential AI ROI
Estimate the financial and operational benefits of AI adoption for your enterprise by adjusting key variables. Understand the impact before you invest.
Estimated Annual Cost Savings
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Total Annual Hours Reclaimed
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Your AI Implementation Roadmap
A strategic outline for integrating AI, based on best practices from leading heavy machinery manufacturers.
Phase 1: Needs Assessment & Pilot Definition
Identify critical safety-related processes and high-value AI applications. Define clear KPIs and scope for pilot projects, focusing on human-AI collaboration rather than full automation.
Phase 2: Simulation & Validation
Utilize advanced simulation tools to design, test, and validate AI models in virtual environments. Iteratively refine AI performance and integrate human feedback to ensure reliability under varying conditions.
Phase 3: Cross-Functional Team Building
Foster cross-disciplinary expertise by promoting collaboration between mechanical, control, and AI engineers. Address skill gaps through targeted training and strategic partnerships with external AI providers.
Phase 4: Regulatory Alignment & Certification
Engage with regulatory bodies and industry consortia to clarify liability frameworks and compliance pathways. Prepare for necessary certifications, leveraging simulation data to expedite approval processes.
Phase 5: Incremental Deployment & Monitoring
Deploy AI solutions incrementally in real-world settings, starting with less safety-critical functions. Establish robust monitoring systems and maintain human oversight, expanding AI autonomy as reliability and confidence grow.
Phase 6: Continuous Learning & Optimization
Regularly collect operational data to refine AI models and improve performance. Implement feedback loops from operators and maintenance teams to drive continuous improvement and adaptation to evolving market and regulatory demands.
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