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From monolith to agentic AI readiness

A practical path for manufacturers to introduce AI-powered efficiency that scales
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Engineering excellence must become digital advantage

German manufacturing built its global position through engineering excellence. But that engineering strength hasn't translated into matching digital maturity. Closing that gap is increasingly important as manufacturers pursue connected services, software- and AI-defined products and greater efficiency. 

Three gaps explain most of the shortfall

Legacy dependencies

Decades-old core systems, embedded code and tightly interwoven ABAP/SAP landscapes make even small changes slow and costly, and make it hard for agents to reach capabilities through standard interfaces.

Fragmented data

Gaps in data literacy, quality, ownership and governance create silos. Batch-heavy platforms and years of accumulated integration logic leave teams unable to deliver reusable data products fast enough for new AI use cases.

Workforce gaps

Decades of maintaining legacy applications produced experts in a single system, not a broad AI-ready workforce. Without a deliberate upskilling model, AI adoption stays confined to a few specialists instead of scaling across the business.

From monolith to agentic AI readiness lays out a three-stage path to close these gaps

Stage one

Modernize with minimal risk using the Strangler Fig pattern and AI-assisted reverse engineering, so production stays live while legacy systems change underneath it.

Stage two

Build trusted data foundations with the Data Product Workbench, cutting data-product delivery time by 60% to 80%.

Stage three

Decoupled capabilities and curated data products let agents move from isolated pilots into governed, operational use.

The success factor across all three stages: Organizational readiness 

 

Legacy experts need new tools, not a replacement. The whole workforce needs a path into AI, not just a handful of specialists.

 

Thoughtworks' enablement model runs 70% through real delivery work, 20% through peer communities, 10% through training, with consultants and client teams working as one team until the client can run independently.

 

Want the detail first? Download the full solution brief. Ready to talk it through? Book your AI readiness session below.

Download solution brief

Where we've already closed the gaps

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Start with a working session, not a program

Book a complimentary 60-minute AI readiness session. Thoughtworks modernization and data experts examine your current AI readiness and recommend a practical way forward, no obligation.

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FAQs

  • That's why we stabilize what runs today and modernize one capability at a time using the Strangler Fig pattern. Production stays live throughout.

  • Correct. We build the business case alongside the architecture, not after it, so the investment maps to a named outcome from day one.

  • So does most of the industry. 84% see measurable value, but only 20% of use cases scale. The difference is the data foundation and engineering discipline underneath the pilot.

  • No. The first step is a 60-minute working session. The first delivery slice is scoped to prove value early.