AI is reaching a pivotal moment in engineering and R&D – but in the semiconductor industry, this shift comes with a unique level of complexity.

Engineering spans design, simulation, manufacturing, and lifecycle optimization, within environments where process integrity, and IP protection are non-negotiable. Decision-making is tightly coupled across domains, and even small deviations can directly impact uptime, cost, and time-to-market.

In this context, scaling AI is particularly challenging. It is not about capability, but about applying AI in a way that fits the rigor and constraints of semiconductor environments.

Key challenges include:

  • Fragmented AI across the value chain, from design to fab operations
  • Highly sensitive IP environments, limiting data access and deployment models
  • Heterogeneous, disconnected data landscapes across tools, fabs, and partners

As a result, AI often remains confined to pilots—delivering local optimization, but failing to translate into sustained, enterprise-wide value.

This challenge is especially visible in semiconductors, but not unique to it. According to the Capgemini Research Institute – Engineering & R&D Pulse 2026, while leaders expect AI to be transformative within the next three years, most organizations remain stuck in pilots that do not scale.

The core issue is not AI capability, but how AI is conceptualized, governed, and embedded across engineering organizations.

Today, AI is still largely treated like traditional software—applied to isolated use cases and implemented in silos, disconnected from core engineering systems and data foundations. This limits impact and undermines both trust and scalability.

To move forward, AI must be treated as a utility—secure, reliable, and accessible by default, and embedded across engineering systems, processes, and ways of working.

This is enabled through Augmented Engineering: a hybrid model combining human expertise and AI, designed for the complexity of engineering environments such as semiconductors. Supported by the Resonance AI Framework, it provides the foundations for scalable AI capabilities, governance, and effective human–AI collaboration.

For semiconductor companies, this shift is critical—moving from fragmented experimentation to a structured, enterprise-wide approach that can deliver AI value at scale.

Why AI pilots succeed, but AI transformation fails at scale

Explore our point of view to learn how to move beyond pilots and unlock AI at scale.

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ER&D pulse