AI ecosystem bottlenecks: Hardware, data and skills at the center of strain

Artificial intelligence has become the backbone of digital transformation, yet its global ecosystem remains fragmented, uneven, and vulnerable to bottlenecks across supply and delivery. A new study published in Technologies offers a multi-layered framework for understanding how value is created and transferred in the AI sector.

The article, “AI Ecosystem and Value Chain: A Multi-Layered Framework for Analyzing Supply, Value Creation, and Delivery Mechanisms”, sets out a comprehensive model of the AI value chain, covering hardware, data, foundational models, advanced AI capabilities, and delivery mechanisms. The authors argue that AI should be analyzed as a socio-technical system, where technical innovation is inseparable from governance, skills, and policy environments.

Where do bottlenecks exist in the AI value chain?

The study maps five key layers of the AI value chain. At the foundation is hardware, where compute infrastructure and semiconductor production drive global capacity. However, this layer is constrained by supply concentration and high costs, leaving many countries dependent on limited sources of advanced chips.

The data management layer emerges as a critical gatekeeper. Data quality, curation, and governance directly affect the trustworthiness of AI applications. Despite progress, the sector struggles with data scale, metadata, and compliance frameworks, creating friction in how AI systems are trained and audited.

Above this lies foundational AI, where large-scale models transform curated data into adaptable systems. These models, while powerful, are resource-intensive and carry risks of bias and limited explainability. The next layer, advanced AI capabilities, applies these models to industry tasks such as healthcare, finance, and logistics. Here, the challenge is integration, as ethical risks and inherited biases from upstream processes continue to undermine trust.

Finally, the delivery layer, the last mile of AI adoption, focuses on embedding solutions into products, services, and workflows. The authors identify this stage as the most visible to end users but also the most vulnerable to usability, transparency, and trust deficits. Failures at this layer can erode the value created across the entire chain.

How does human capital shape AI’s global development?

The framework highlights that technical bottlenecks only tell part of the story. The study’s most striking conclusion is that human capital readiness, the skills, leadership, and adaptive capacity of the workforce, is decisive in determining whether AI delivers value in practice.

Even with access to advanced hardware and datasets, countries and organizations without strong managerial competence and digitally literate workforces fail to maximize AI’s potential. The authors point out that education systems, training programs, and industry–academia collaboration are vital to closing these gaps. Without such investment, AI adoption risks stalling in the delivery stage, where human trust and integration matter most.

The study also maps the diverse range of stakeholders that shape the ecosystem, from chip manufacturers and cloud providers to regulators, policymakers, and end users. Their interactions determine whether AI systems are deployed ethically, equitably, and sustainably. For many developing economies, this stakeholder complexity presents a challenge but also an opportunity to leapfrog by focusing on delivery tailored to local needs rather than replicating every upstream layer.

The paper draws particular attention to the Philippines as a case illustration. Positioned at the crossroads of global AI knowledge flows, the country is influenced by developments in Europe, the Americas, Asia, and the Middle East. The authors suggest that this positioning allows smaller economies to selectively adopt foundational knowledge while prioritizing delivery models suited to domestic priorities such as healthcare, agriculture, and governance.

What policy and strategic steps are needed?

The research stresses that the AI ecosystem cannot be treated as purely technical. It is deeply embedded in a socio-technical enabling environment of governance, standards, and institutions. Effective strategy must therefore align technology adoption with robust policies, ethical frameworks, and investment in human capacity.

The authors recommend that governments and enterprises focus on several areas. First, investing in human capital is paramount. Digital literacy programs, adaptive leadership development, and workforce training must be prioritized to bridge the socio-technical gap. Second, policy levers should be used to coordinate standards, compliance, and incentives across government, academia, and industry, reducing fragmentation in AI adoption.

Third, investment in the last mile of delivery is critical. AI’s impact depends not just on cutting-edge models but on whether end users can trust, understand, and integrate solutions into daily work. Enhancing usability, transparency, and accountability in delivery channels ensures that upstream gains translate into tangible outcomes.

Furthermore, strategies must anticipate upstream constraints such as hardware dependencies, energy demands, and data governance requirements. Without planning, these bottlenecks could derail national AI strategies.