What’s Really Holding Back AI Adoption in Manufacturing and Supply Chains? (Part 1 — Organizational & Cultural Barriers)

This article is part of a 3-part series exploring the key barriers preventing successful AI adoption in manufacturing and supply chains, based on recent research and industry insights. In Part 1, I focus on organizational and cultural barriers.

AI has emerged as a transformative force in manufacturing and supply chain management, promising enhanced efficiency, predictive capabilities, and strategic agility. However, integrating AI into strategic decision-making processes is not without its challenges. Over the past few months, I've engaged in enlightening conversations with students, fellow researchers, and industry professionals about the adoption of AI in Switzerland, one of the countries I proudly call home. Switzerland has consistently ranked as the world's most innovative economy, securing the top position in the Global Innovation Index for the 14th consecutive year (Switzerland Global Enterprise, 2024), thanks to their robust institutional frameworks, high R&D investments, and a strong emphasis on knowledge and technology outputs. 

Given this impressive track record, one might assume that Swiss companies are at the forefront of AI adoption. However, my discussions revealed a more nuanced reality. Despite the country's innovation prowess, many organizations face significant hurdles in integrating AI into their strategic decision-making processes. This paradox piqued my interest, prompting a deeper dive into the research.

Through this exploration, I identified three primary categories of challenges hindering AI adoption, not only in Switzerland but globally:

1. Organizational and Cultural Barriers

2. Technical and Data Challenges

3. Ethical and Governance Concerns

This article marks the beginning of a three-part series aimed at shedding light on these challenges and provide actionable insights for organizations striving to harness AI's full potential in end-to-end supply chain operations.

The Organizational Hurdles

Organizational hurdles refer to internal structural and strategic barriers that limit a company’s ability to successfully adopt and scale AI across its operations. The main organizational challenges identified in the literature (Kar et al. 2022; Stone et al. 2020) include: 

1. Lack of a Clear AI Strategy: Many organizations start AI initiatives without a clear strategy that links them to overall business goals. This misalignment can lead to fragmented efforts and underwhelming results. For example, some companies invest in isolated AI pilots, like a chatbot for customer service or predictive maintenance for machines, without connecting them to broader business objectives. As a result, these projects stay stuck in the testing phase and never scale across the organization.

2. Leadership Commitment: Executive buy-in is crucial for driving AI adoption. Without strong leadership support, AI projects may lack the necessary resources and prioritization . In many cases, AI initiatives are driven only by IT or innovation teams, while senior leadership remains disengaged. Without clear direction from executives, budgets are limited, priorities shift, and employees don’t see AI as a strategic priority, slowing or stopping adoption efforts.

3. Siloed Departments: Organizational silos can hinder the flow of information and collaboration necessary for AI projects. For example, the supply chain team might use AI to forecast product demand, while the marketing team independently runs campaigns or promotions without sharing this information. Without coordination between these functions, sudden changes in demand driven by marketing are not captured in supply chain planning, leading to stockouts or excess inventory.

The Cultural Challenges

Cultural hurdles are people-related challenges rooted in mindset, behavior, and trust that influence how individuals and teams engage with AI technologies. The most impactful cultural challenges identified in the literature (Kar et al. 2022; Stone et al. 2020), are:

1. Resistance to Change: Employees often fear that AI will automate their roles, making their skills obsolete. For instance, in manufacturing, warehouse operators might resist the introduction of AI-driven inventory management systems that optimize stock levels and automate reordering. If workers believe the AI system will lead to staff reductions or reduce their autonomy in decision-making, they may push back, avoid using the system properly, or even undermine its deployment. 

2. Skill Gaps: AI adoption requires employees with new skills, not just technical roles like data scientists, but also operational staff who understand how to interact with AI tools. For example, a factory implementing predictive maintenance powered by AI sensors needs maintenance teams to interpret AI alerts, understand anomaly detection outputs, and decide on the appropriate intervention. If workers lack data literacy or do not understand how AI models operate, the value of the technology is limited. Similarly, supply chain managers need to grasp the basics of how AI-driven demand forecasting works, not to program it, but to trust and correctly apply its output.

3. Trust in AI Systems: Even when AI systems are technically accurate, adoption often stalls if decision-makers don’t trust their recommendations. This is especially true when users face the “black-box problem”, where there is a lack of transparency into how AI arrived at its decision. In supply chain operations, this trust gap is further amplified by algorithm aversion, which is a well-documented tendency for people to prefer human judgment over algorithms, especially after witnessing a single error. 

Strategies for Overcoming These Barriers

Literature suggests that successfully overcoming organizational and cultural barriers to AI adoption requires a combination of strategic clarity, leadership commitment, cross-functional integration, workforce development, and transparent AI systems (Kaggwa et al., 2024; Kar et al., 2022; Stone et al., 2020). In manufacturing and supply chains, these strategies must move beyond theory to be anchored in practical action.

• Develop a Comprehensive AI Strategy: Organizations that succeed with AI do not treat it as an isolated technology project, they build an enterprise-wide strategy that links AI use cases to business objectives. For example, a manufacturing company might develop an AI strategy that prioritizes predictive maintenance to reduce downtime, AI-driven demand forecasting for supply chain resilience, and computer vision for quality control. Crucially, this strategy should outline how these AI initiatives will scale across the organization, not stay confined to pilot projects.

• Foster Executive Sponsorship: Leaders should actively support AI projects, allocate resources, and communicate the importance of AI in achieving strategic goals. In practice, this could look like a Chief Supply Chain Officer personally sponsoring an AI-driven network optimization project, ensuring cross-functional involvement from procurement, logistics, and operations teams, and setting measurable business targets linked to AI performance. Without this level of sponsorship, AI projects often remain isolated in IT departments, disconnected from operational strategy.

• Promote Cross-Functional Collaboration: Break down silos by encouraging collaboration between departments, facilitating the sharing of data and insights. For example, a supply chain organization introducing an AI-powered inventory optimization tool should involve both warehouse managers and demand planners in its design and testing. This ensures the AI recommendations align with operational realities, such as storage constraints or supplier limitations.

• Invest in Training and Development: Workforce capability gaps are a consistent barrier across industries. Companies should not assume AI expertise will only reside in the data science team, instead, AI literacy should be built across operational roles. In manufacturing, this could involve training maintenance staff on how to interpret AI-generated anomaly alerts or providing supply chain managers with basic training on how machine learning models forecast demand variability. Such training demystifies AI, reduces fear of job loss, and empowers employees to work effectively with AI systems.

• Implement Transparent AI Systems: Use AI models that provide clear explanations for their decisions, enhancing trust among users. For instance, in a logistics company using AI to suggest delivery routes, the system should provide a rationale such as "Route optimized for minimal delay due to real-time traffic conditions and lower fuel costs." Providing such explanations builds user confidence, reduces resistance, and enhances decision-making collaboration between humans and machines.

Adopting AI in strategic decision-making is not just a technological challenge, it is fundamentally an organizational and cultural transformation. Without a clear strategy, committed leadership, collaboration across functions, investment in people, and trustworthy AI systems, even the most advanced technologies are unlikely to deliver their full potential. For manufacturing and supply chain leaders, overcoming these barriers is a critical first step in building AI-driven organizations that are resilient, adaptive, and competitive.

In the next article of this series, I will explore another set of challenges: the technical and data-related obstacles that organizations face when implementing AI. From fragmented data landscapes to legacy systems and scalability issues, understanding and addressing these challenges is essential for moving AI initiatives from pilots to enterprise-wide adoption.

Stay tuned.

References:

Kaggwa, S., Eleogu, T. F., Okonkwo, F., Farayola, O. A., Uwaoma, P. U., & Akinoso, A. (2024). AI in Decision Making: Transforming Business Strategies. International Journal of Research and Scientific Innovation, 10(12), 423–432. https://doi.org/10.51244/IJRSI.2023.1012032

Kar, S., Kar, A. K., & Gupta, M. P. (2022). Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective. Intelligent Systems in Accounting, Finance and Management, 28(4), 217–238. https://doi.org/10.1002/isaf.1503

Stone, M., Woodcock, N., Machtynger, J., & Heppel, M. (2020). Artificial Intelligence in Strategic Marketing Decision Making: A Review. The Bottom Line, 33(3), 205–214. https://doi.org/10.1108/BL-03-2020-0022

Switzerland Global Enterprise. (2024). Switzerland continues to lead global innovation for the 14th consecutive year. Greater Geneva Bern area (GGBa). Retrieved from https://ggba.swiss/en/switzerland-continues-to-lead-global-innovation-for-the-14th-consecutive-year/