Digital Sovereignty and Strategic Build vs Buy
Antec Systems CTO Alexander van der Steg discusses the strategic advantages of maintaining a small, high-performing team and rejecting hyperscaler dependency. The analysis covers the nuances of digital sovereignty, the build-versus-buy decision framework, and the critical importance of data readiness before AI implementation.
Strategic Autonomy in a Volatile Tech Landscape
In an era dominated by hyperscaler dependency and rapid AI adoption, Antec Systems CTO Alexander van der Steg presents a counter-intuitive strategy: deliberate smallness and technological self-sufficiency. By maintaining a team size well below the 100-employee threshold, the company preserves high revenue per head, enabling competitive compensation and a culture of direct, low-overhead communication. This structural choice is not ideological but a calculated move to maintain agility and quality in a niche IT asset management market.
The Build vs. Buy Dichotomy
The core of this strategy is a rigorous build-versus-buy framework. Antec Systems builds its core product, Inventory 360, and internal infrastructure, such as its CRM-like system Jarvis, to ensure maximum control over data and processes. However, it avoids the trap of building everything, opting to buy non-core services like ticketing systems. This hybrid approach balances the need for sovereignty with the efficiency of leveraging existing market solutions. The decision to build is driven by the desire to avoid vendor lock-in and to retain the ability to pivot technology stacks without external constraints.
Digital Sovereignty as Risk Management
Van der Steg reframes digital sovereignty not as a political stance but as a fundamental risk management tool. He argues that relying on US-based hyperscalers exposes companies to legal risks, such as the Cloud Act, and geopolitical instability. By hosting data in autonomous data centers and controlling the entire supply chain, companies can make free technological decisions without external pressure. This autonomy also provides a buffer against price hikes from major cloud providers, offering long-term cost stability.
AI Implementation and Data Prerequisites
A critical insight for CTOs is that AI is not a standalone solution but a layer atop existing processes. Van der Steg emphasizes that most companies lack the data quality and automated processes required to leverage AI effectively. Before investing in AI, organizations must address their digital transformation backlog, ensuring that data is clean, accessible, and structured. AI should be adopted iteratively, focusing on use cases that deliver immediate business value, rather than chasing every new model or tool.
Conclusion
The Antec Systems model demonstrates that in a complex tech environment, control and clarity are more valuable than speed and scale. By prioritizing data readiness, maintaining strategic autonomy, and carefully balancing build and buy decisions, companies can navigate the AI era with resilience and competitive advantage.
Key insights
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Maintaining a small team size below 100 employees allows for higher revenue per head and better talent retention through competitive compensation and direct communication structures.
Impact: Enables higher margins and faster decision-making, reducing the overhead and communication friction typical of larger organizations.
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Digital sovereignty is primarily a risk management strategy that mitigates exposure to geopolitical risks, legal mandates, and hyperscaler price volatility by controlling the entire tech stack.
Impact: Provides long-term cost stability and operational independence, protecting the business from external shocks and regulatory changes.
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The build-versus-buy decision should be based on core value proposition; build critical infrastructure to ensure control, but buy non-differentiating services to avoid unnecessary complexity.
Impact: Optimizes resource allocation by focusing internal engineering efforts on areas that provide competitive advantage while leveraging market solutions for peripheral needs.
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AI implementation is ineffective without prior data readiness; organizations must first ensure high data quality and automated processes before deploying AI tools.
Impact: Prevents wasted investment in AI tools that cannot deliver value due to poor underlying data infrastructure, ensuring higher ROI on AI initiatives.
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Legacy code should be treated as an active liability with defined end dates, prioritized for refactoring based on security and business relevance rather than being left indefinitely.
Impact: Reduces security risks and maintenance costs by systematically addressing outdated systems, preventing technical debt from becoming a critical vulnerability.
Action items
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Audit your current tech stack to identify core components that require in-house development for sovereignty and peripheral services that can be purchased from third parties.
Impact: Clarifies the build-versus-buy strategy, ensuring that engineering resources are focused on differentiating capabilities while reducing overhead in non-core areas.
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Assess your data quality and process automation levels before initiating any AI projects; prioritize data hygiene and process standardization as a prerequisite.
Impact: Ensures that AI investments are built on a solid foundation, increasing the likelihood of successful implementation and measurable business impact.
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Define clear end dates and refactoring priorities for all legacy systems, focusing on those with high security or business relevance.
Impact: Mitigates security risks and reduces long-term maintenance costs by systematically addressing technical debt rather than allowing it to accumulate.
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Evaluate your dependency on hyperscalers and assess the risks associated with legal mandates and price volatility; consider alternative hosting or infrastructure options for critical data.
Impact: Enhances digital sovereignty and reduces exposure to external risks, providing greater control over data and operational continuity.
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Adopt an iterative approach to AI adoption, starting with specific use cases that deliver immediate business value, and scale based on proven results.
Impact: Avoids the pitfalls of parallel AI experimentation and ensures that each implementation contributes to business goals, maximizing ROI and minimizing disruption.
Quotes
“Ich glaube, wir gehen den schweren Weg, fast nichts einzukaufen.”
“Digitale Souveränität minimiert Risiken.”
“KI ist nur ein Treiberthema für die sowieso schon verschleppte Digitalisierung und Transformationsdiskussion.”