On November 6th, CUBE 2025 gathered leaders from across automotive, defense, aerospace, and industrial machinery to answer a defining question for the decade ahead: How do we make engineering AI-ready, not someday, but now?
Hosted by SPREAD at our annual “AI Ready for Engineers” event, CUBE 2025 brought together executives and engineering innovators from Volkswagen, BMW, Alstom, MBDA, Audi, Accenture, Siemens, and more.
Across customer keynotes, SPREAD product deep dives, and a cross-industry panel, a unified theme emerged:
No matter the industry or lifecycle stage, R&D, production, or aftermarket, engineering teams face the same structural challenges. And they require the same foundation to solve them: contextualized, trustworthy data.
CUBE 2025 opened with a clear message from SPREAD’s leadership: engineering is entering a phase of structural transformation, not incremental change. In the keynote, Co-Founders Robert Göbel and Philipp Noll and CPO Shane Connelly described a shift that echoes across every engineering-heavy industry, the movement from connected systems to intelligent systems.
For years, organizations have invested in stitching together PLM, ALM, ERP, simulation environments, and test systems. Connectivity solved only part of the problem. Data still lived in isolation, engineers still spent hours searching, validating, and cross-checking information, and AI initiatives remained stuck because the underlying signals carried no shared meaning.
Philipp captured the turning point:
This idea, that engineering knowledge must be computable and contextual before AI can deliver value, became the backbone of CUBE 2025. It reframes the role of AI entirely: not a chatbot, not an isolated analytics tool, but an intelligence layer that sits across engineering processes, giving teams a shared and explainable understanding of their products.
Shane expanded on this by positioning AI as something that must mirror human engineering reasoning.
His demonstration illustrated how SPREAD’s engineering intelligence architecture applies retrieval-augmented reasoning, semantic graphs, and explainability to everyday engineering tasks, requirement checks, change analysis, error triage, risk propagation, and system navigation. Instead of generating guesses, the system traces the logic behind suggestions, exposing the lineage and dependencies behind every decision.
This shift, from raw data to contextualized knowledge, is what makes engineering AI-ready. It transforms data into something engineers can trust, validate, and build upon. It turns product complexity into a navigable landscape. And it lays the foundation for agentic systems that can participate meaningfully in engineering work, rather than sit outside it.
By the end of the keynote, one thing was clear:
AI will accelerate engineering only when it is grounded in context, when it understands how a product works, why decisions are made, and what depends on what.
CUBE 2025 began not with hype, but with a blueprint for how engineering intelligence becomes the structural fabric of the next decade of product development.
The customer keynotes at CUBE 2025 focused not on solutions, but on the real challenges slowing engineering across industries.
Volkswagen’s Dr. Peter Oel, VP Integration, Verification & Validation MQB&SDV, described the challenge created by modern software-defined vehicles: massive volumes of test, requirements, and diagnostics data spread across disconnected systems that make it difficult to identify root causes quickly. Engineering clarity becomes almost impossible without a unified view.
Frank Nürnberg, Tenders and Projects Director Traxx at Alstom, outlined a unique but universal challenge: locomotives that must comply with up to 14 national signaling systems, each with overlapping, conflicting requirements. The result is slow certification cycles, inconsistent documentation, and constant rework.
In the defense vertical, Dr. Ulrich Nuding, Engineering Director Germany at MBDA, explained how strict “National Eyes Only” environments create unavoidable data silos.
Yet engineering teams must move faster than ever, with zero room for error and full traceability across requirements, CAD, simulation, and documentation. His remark captured the urgency:
Moderated by Alexander Matthey, the panel brought together Dr. Christof Horn (Accenture IX), Stefan Tolle (SPREAD; ex-MANN+HUMMEL, ex-Bosch), and Dr. Ulrich Nuding (MBDA).
Despite representing different industries, their conclusions aligned around a single truth:
The pressures shaping engineering are the same everywhere.
They discussed the need to accelerate development cycles without losing compliance, the burden of fragmented requirements and documentation, and the rising necessity of explainable AI that engineers can trust in daily work.
Dr. Horn summarized the heart of the challenge: “Context beats volume.”
The panel emphasized that scaling AI requires not more data but better-organized, meaningful data supported by trust, governance, and cross-functional collaboration.
As mentioned by Robert Göbel during the event's opening remarks:
CUBE 2025 showed the roadmap for the future of engineering. AI-ready engineering is no longer aspirational, it is emerging now, driven by teams who understand that context, trust, and intelligence are the new levers of industrial innovation.