How Variosystems Intelligence uses AI for operational decision-making

With the launch of Variosystems Intelligence (Vİ), Variosystems has introduced a new platform for managing electronic components. The goal is to more tightly connect data and processes across the supply chain and to embed AI into operational decision-making step by step—while maintaining a strong commitment to data sovereignty and regulatory security.

The electronics industry is facing the challenge of managing increasingly complex supply chains: volatile markets, shorter product life cycles, rising compliance requirements and growing pressure to digitize. In this environment, transparency alone is no longer enough. What’s needed are systems that turn information into reliable decisions and provide real operational confidence.

In this Interview, André Bättig, CIO and Head of IT & Business Excellence at Variosystems, explains how our company is putting this ambition into practice, the role controlled data and architectural decisions play in the process and why AI only delivers value when it is embedded in clearly defined processes.

André: Significantly. They force us to take AI seriously and think holistically instead of stopping at isolated tools. A holistic approach obliges us to bring data and processes together in a structured manner and to clearly define responsibilities. Once decisions are automated or supported by systems, the fundamentals must be right—otherwise, we end up accelerating errors rather than creating value.

André: It is, but that’s exactly what we need in a global supply chain with thousands of components. Simply having visibility at inventory levels, deliveries and so on isn’t enough. We need to truly be in control of information: consistent data models, clear ownership and systems that work together seamlessly.

André: Exactly. With Variosystems Intelligence—Vİ for short—we enable structured and reliable management of all components. We don’t just track availability or pricing; we continuously monitor obsolescence, compliance, and risk as well. The key point is that when something changes, we can respond in a targeted way, for example, through engineering measures or alternative sourcing strategies.

André: Correct. A dashboard tells you that a problem exists. Control means turning that insight into a decision and doing so in time.

André: Because AI works without compromise. It processes exactly what you feed into it. If data is inconsistent or incomplete, it will automatically produce incorrect conclusions. In a supply chain, that can have very real consequences. For us, control doesn’t mean bureaucracy, it means reliability and scalability.

André: To me, data sovereignty is first and foremost a business capability. It determines how manageable a business model really is. If I don’t clearly know where my data resides, how it’s created, how it’s used and who is accountable for it, I lose control over key decisions.

Especially in a supply chain that operates across multiple countries, partners, and regulatory frameworks, this transparency is essential. It’s not only about security, it’s also about speed, scalability, and customer trust.

Data sovereignty enables us to build new digital services without having to reinvent the architecture every time or shift risks elsewhere. It ensures that the core remains stable while allowing us to respond flexibly and quickly to change at the edges.

André: In the long run, they make innovation possible in the first place. When architecture and governance are solid, new digital services can be scaled reliably. Without that foundation, you may gain speed, but you lose control.

André: Vİ is just at the starting point. We’re gradually integrating additional AI-supported processes—from cost calculation and component management to lifecycle services. But always in a controlled and integrated way. The goal isn’t maximum automation; it’s maximum operational confidence.

André: AI doesn’t create value through speed alone, but through responsible control. Data sovereignty, clear processes and resilient architecture don’t end in themselves. They’re the prerequisites for making digital intelligence truly effective in business.