Why European manufacturing leaders must pivot from legacy systems to AI-integrated software platforms in 2026

The European industrial core, spanning automotive, chemicals, industrial automation, aerospace, medtech and semiconductors, is at a critical inflection point. The simultaneous pressures of geopolitical volatility, complex regulatory shifts (MDR, EU AI Act) and the global race for software-defined products mandate a complete internal IT overhaul. Decades of reliance on monolithic systems (including mainframes and fragmented ERPs) are now one of the greatest barriers to resilience, growth and compliance.

The return on investment (ROI) for achieving digital leadership isn’t just cost reduction. It’s the accelerated delivery of monetizable software and the mitigation of catastrophic regulatory risk.

The legacy system integration crisis in European manufacturing

The imperative for change is clear. But for most organizations, their pursuit of digital transformation is stymied by systemic technical debt. Across major industrial sectors numerous technical problems converge, with severe detrimental impacts:

Digital transformation blueprint: Four strategic investment pillars

The solution isn’t a single tool, but a systemic transformation of the organization's technology operating model to an AI driven one. Thoughtworks’ approach to this transformation focuses on building internal competency and delivering incremental, measurable value across four pillars.

Pillar A: The right target operating model (AI readiness) 

The journey toward AI readiness begins with a clear roadmap from concept to execution that lays out exactly how AI will be governed.

To build the foundation of an AI ready operating model, organizations must:

1. Identify where and how to apply AI solutions for maximum business impact.
2. Connect those decisions to the broader organizational vision, commercial strategy and fit to company culture.
3. Balance risk management with the desired pace of innovation.
4. Create an overall AI governance structure and guardrails for the organization.
5. Build workforce AI literacy to take maximum advantage of AI technologies.

Pillar B: Enterprise platform modernization (de-risking the core)

The migration of legacy systems across the PERA model (Purdue Enterprise Reference Architecture) must be treated as a strategic business initiative, not just an IT project.

Investment area: Mainframe decoupling and cloud-native platforms

• Methodology: We leverage the strangler fig pattern to incrementally decompose core monolithic applications, isolating business logic and safely rewriting it to modular microservices hosted on a standardized cloud platform. This allows for continuous development alongside the legacy system.
• ROI example (cost reduction): European enterprises running COBOL mainframes often face operating expenses (OpEx) of $1,000–$2,000 per MIPS annually. Migrating legacy workloads to the cloud are seeing returns on investment of up to 362% while mitigating the risk of the aging COBOL talent shortage.

Investment area: AI-enabled legacy modernization

• Methodology: Our partnership with Mechanical Orchard integrates specialized tools that employ GenAI agents to analyze legacy code (like COBOL or PL/I) and monolithic architectures. Unlike automated tools that only translate code (creating unmaintainable code), our approach focuses on understanding business logic and dependencies. This allows us to rapidly generate high-fidelity test suites and perform targeted, intelligent refactoring of critical components to modern, cloud-compatible languages. This accelerates the decomposition of monoliths using the strangler fig pattern safely and with greater precision than manual analysis.
  
• ROI example (shorter migration timeline): The ROI expected is a significant 30-50% reduction in the migration timeline compared to traditional methods, drastically lowering the total cost of ownership (TCO) and enabling clients to unlock critical business logic for new cloud-native features and innovation much faster.

Pillar C: Data modernization for traceability and AI

We transform data from a burdensome liability into a core asset by implementing the data mesh paradigm.

Investment area: Data mesh architecture for digital thread

• Methodology: We help clients shift data ownership to domain teams, defining data (e.g., "supplier quality records," "engine sensor telemetry") as data products that are discoverable, governed and consumable via APIs.
• Use case example (aerospace): For a global aerospace manufacturer, Thoughtworks helped implement a data mesh solution for engine maintenance data. This allows maintenance teams to access real-time engine health data alongside historical maintenance logs, enabling proactive maintenance. The result is minimized unplanned downtime, the single greatest cost driver for airlines.

Pillar D: Software engineering excellence (velocity and compliance)

We embed engineering rigor into the development pipeline, accelerating software delivery while automating compliance evidence.

Investment area: Vehicle DevOps and CD4ML (continuous delivery for machine learning)

• Methodology: We established a cloud-to-vehicle software factory that uses advanced automation and virtualization.
  • SDV velocity: We establish CI/CD pipelines that automate the testing and deployment of software to virtual ECUs (vECUs). This allows thousands of integration tests to be run digitally before physical hardware is available, addressing the SDV challenge of complex heterogeneous architectures.
  • MedTech Compliance (SaMD): We build regulated MLOps platforms that automatically generate the audit trails and documentation at every stage of the model lifecycle (data drift, re-training, deployment). This is critical for scaling SaMD products rapidly. 

Achieving Industry 5.0 ROI: Resilience, sustainability, and human-centricity

The combination of platforms and agile delivery allows European manufacturers to achieve the goals of Industry 5.0:

By viewing the digital transformation not as a cost center, but as the engine for achieving competitive velocity and sustainable compliance, European manufacturing leaders can secure their dominance in the post-digital era. The time for large-scale, strategic IT investment is now.

Thoughtworks positions itself not as a system integrator, but as a strategic partner, utilizing platform engineering, data modernization and AI-First Software Delivery to help these leaders achieve Industry 4.0 (digital efficiency) and Industry 5.0 (sustainability and human-centricity) standards.