Breaking The Legacy Trap: How Semiconductor Executives Can Accelerate AI Adoption And Transform IT Applications At The Same Time

The semiconductor industry is facing a strategic paradox. AI has rapidly moved from experimental technology to a competitive necessity promising faster yield improvement, smarter supply chain decisions, and autonomous factory operations. Yet the very systems that semiconductor manufacturers depend on to run their fabs, manage their supply chains, and serve their customers were built for a different era. Replacing them overnight is not an option. Leaving them untouched is not an option either.

This is the legacy trap, and breaking out of it is now one of the defining CIO challenges in the industry.

The budget locked in the past

The numbers tell a sobering story. According to research by Gartner and Kearney, companies on average spend more than 70% of their IT budget simply maintaining existing systems. That leaves less than 30% for the innovation and digital transformation investments executives know they need to make. In semiconductor manufacturing, an industry defined by relentless technological advancement is allowing the majority of a technology budget to be consumed by yesterday’s infrastructure is an untenable competitive position.

The problem compounds over time. Without a deliberate modernization strategy, legacy costs don’t stabilize but grow. Every new initiative added on top of aging systems accumulates integration debt. Data remains siloed across departments, locked in incompatible repositories. Transformation becomes an increasingly risky, increasingly expensive proposition. The 70% problem becomes permanent.

Why AI demands a data foundation first

Executives across the semiconductor industry have reached a remarkably consistent conclusion: AI adoption is primarily a data problem, not an algorithm problem. Tom Caulfield, Chairman of GlobalFoundries, at the recent PDF Solutions Users Conference, put it plainly: no company struggling to implement AI is failing because its algorithms don’t work. The problem is almost universally that data isn’t managed in a way that allows algorithms to be used effectively.

This is a critical insight for semiconductor CIOs to internalize. The instinct to chase AI applications, yield prediction models, defect classification engines, and demand forecasting tools is understandable. But deploying AI on top of fragmented, siloed, inconsistently formatted data produces fragmented, unreliable results. The lesson, as Caulfield framed it, is to prioritize getting data collected correctly and structured properly before worrying about the applications built on top of it.

In practice, this means that semiconductor manufacturers cannot treat AI adoption and data infrastructure modernization as separate workstreams. They are the same workstream.

The platform imperative

The good news is that there is a proven path forward that doesn’t require choosing between operational continuity and transformation. A platform-based approach allows manufacturers to modernize incrementally: wrapping and integrating legacy systems rather than ripping them out, while simultaneously building the data foundation that AI requires.

The logic is straightforward. A unified platform bridges old and new systems, harmonizing data from legacy repositories into a modern analytics layer without requiring full migration. Shared infrastructure, reusable APIs, common security frameworks, and centralized monitoring reduce operating costs over time, gradually freeing budget from “run the business” activities toward genuine innovation. New initiatives can leverage pre-built capabilities rather than rebuilding foundational components from scratch.

The contrast with the alternative is stark. Without a platform, every new initiative adds integration complexity. Transformation remains a high-risk, “big-bang” proposition. With a platform, transformation becomes incremental and manageable, and the economics steadily improve as legacy costs stabilize and decline.

What this looks like in practice

As Intel Foundry’s Aziz Safa noted at a recent event, building a modern autonomous manufacturing platform requires three foundational pillars: a unified data infrastructure capable of handling vast amounts of high-quality data, sufficient computing power, and a highly skilled team of data scientists, ML engineers, and software developers. Legacy architectures make all three harder to achieve. But for Intel Foundry, the starting point was a familiar picture: siloed departments (engineering, product costing, product management, manufacturing, supply chain, and finance) each running disparate legacy systems with separate databases and offline analysis. Data could not easily flow between these groups, and providing visibility to foundry customers was a manual, fragmented process.

The target state replaces this fragmentation with a centrally integrated analytics platform deployed across multiple sites. Manufacturing data from across the fab (work in progress or WIP tracking, sort and electrical test, process control, classification and final test, defect inspection) flows into a unified central database with real-time analytics and standardized user access. The platform becomes the connective tissue linking all these data sources, eliminating the silos while keeping core operations running throughout the transition.

As Syed Baquar, Principal Engineer and Director of Data & Analytics at Intel Foundry, described it, the key requirements were a single unified user interface, integrated data and analytics, and systems that are modern, flexible, scalable, and robust. The goal is not sophisticated technology for its own sake, it is giving people frictionless access to the data they need to make decisions.

From systems of record to systems of action

Platform integration does more than consolidate data. It lays the groundwork for a fundamental shift in how semiconductor manufacturers operate: moving from systems of record to systems of action.

Traditional enterprise applications, such as ERP, MES, PLM, and SCM, are excellent at capturing what has happened. They record transactions, store data, and support human analysis. But in an industry where process windows are measured in nanometers and yield excursions can be costly, the ability to automatically act on data as it is generated, across complex, multi-system processes that span internal operations and external partners, is a qualitative leap in capability.

Automated orchestration between systems creates the infrastructure on which agentic AI can eventually operate: AI that doesn’t just surface insights but acts within defined parameters across the full manufacturing network. For semiconductor manufacturers using AI driven orchestration to manage outsourced assembly and test partners, coordinating with EDA and supply chain systems, and serving customers who increasingly demand real-time visibility into their production status, is not a distant aspiration. It is a near-term competitive differentiator.

The case for a purpose-built approach

Semiconductor manufacturing is not a generic industry. Its data volumes are immense. Its data semantics — the relationships between process steps, equipment parameters, lot genealogy, yield metrics, and test results — are highly specialized. Its integration requirements span a complex ecosystem of EDA tools, MES systems, ERP platforms, and external OSAT partners.

Generic data platforms can provide horizontal infrastructure capabilities, but lack the semiconductor-specific data models, native manufacturing system integrations, and domain-specific use cases like NPI, yield analysis, test analytics, supply chain synchronization that translate data consolidation into operational value quickly. For semiconductor manufacturers, this distinction matters significantly for both time-to-value and total cost of implementation.

A call to action for semiconductor executives

The window for deliberate, controlled modernization is narrowing. Manufacturers who delay will find themselves attempting to compete with AI-enabled operations while still burdened by the full weight of legacy infrastructure costs and facing transformation under competitive pressure rather than on their own terms.

The path forward is not a choice between protecting current operations and investing in future capabilities. A platform-led approach enables both simultaneously. It reduces modernization risk through incremental change rather than disruptive replacement. It unifies data across old and new systems, unlocking AI and analytics value without requiring a clean-slate migration. It builds the governance, security, and scalability foundations that enterprise AI deployment demands.

The semiconductor manufacturers who will lead the next decade are making these architectural decisions now, not because the technology is mature enough to remove all uncertainty, but because they understand that data infrastructure is a long-cycle investment, and that the time to build the foundation is before you need it.